For [80], Industry 5.0 includes two visions: one is related to the interaction between humans and robots and the other is approaching issues related to the bioeconomy, which is pretty much related to the sustainability issues. For instance, Ref. [67] emphasize that Industry 5.0 may generate huge and positive impacts in terms of sustainability in production systems (e.g., algae production). Yet, Ref. [59] affirm that Society 5.0 becomes an obligatory practice to get stability in terms of sustainable economic due to the advent of Industry 4.0. The main idea of the concepts of Industry 5.0 and Society 5.0 is developing from digital manufacturing to digital society. Social orientation and technical innovations from Industry 4.0 were the basis for the concept of Industry 5.0, being focused on more sustainable development. Industry 5.0 and Society 5.0 have as their main aim digital technologies for the development of society [65].

#### 3.1.4. Construct 4—Transition Issues

For this fourth construct, only two words were grouped: challenge and implementation. It is related to the challenges and implementation aspects that must be addressed

for Industry 5.0 to become a reality. The transition from a fully technological to a balanced human-centric perspective is being considered as one of the main challenges and the Industry 4.0 paradigm to be overcome.

In that sense, Ref. [44] emphasize that it is challenging to create an Industry 5.0 by incorporating the disruptive technologies from Industry 4. 0. Ref. [80] state that although there are few visions about what Industry 5.0 means, some futurists are fostering this theme. One emerging theme is human–robot co-working and its implications from the organizational and human relations side. According to these authors, psychological, social, ethical, learning, legal, and regulatory issues will play some of the most important roles to properly guide and regulate the relations between humans and robots. In the same sense, Ref. [73] emphasize that some areas need to be addressed with regard to Industry 5.0 implementation, including education and skills, working environment, the relationship between productivity and wages, technologies and human redundancies, optimum products, sustainability, governance, and ethics. Ref. [56] argue that the implementation of strategies for Industry 5.0 often depends on a series of factors for which sharing is necessary, such as any territorial support in growth policies. Institutions, entrepreneurs, and managers should take into consideration these differences and plan interventions reflecting the real conditions of their contexts. Ref. [40] propose to consider four main elements in designing Industry 5.0- s strategy: organization, people, technology, and tasks. Ref. [73] emphasize that some areas that need to be addressed with regard to Industry 5.0 trend are education and skills, working environment, the relationship between productivity and wages, technologies versus human redundancies, optimum products, sustainability, governance, and ethics.

#### *3.2. Alignment with the Supply Chain Context*

In the current context, Supply Chain 4.0 has been reasonably discussed by the academic and practical audience. Some researchers have proposed some frameworks for the development of a Supply Chain 4.0 strategy. Among these stand out the models proposed by [24–31]. In their majority, these proposals consider similar aspects which have been extracted from the literature review about Industry 5.0 herein presented and discussed in Section 3.1. Supply Chain 4.0 also takes into consideration aspects such as its own strategy, a set of disruptive technologies, required capabilities and other criteria such as challenges to properly implement these disruptive technologies, and implications in terms of the performance of the supply chain processes.

In order to follow the structured rationale of this article, the alignment between Supply Chain 5.0 and Supply Chain 4.0 will be presented according to the four (4) constructs extracted from the systematic literature review, as presented and discussed in Section 3.1.

Concerning the construct **Industry Strategy**, while the literature considers the Supply Chain 4.0 concept as having a highly technological environment focus, Supply Chain 5.0 keeps this technological aspect, but also considers a balanced human–technological environment, mainly allowed by cobots (collaborative robots). Supply Chain 4.0 also aims to have a mass customization advantage besides enabling greater performance from the supply chain's processes in terms of transparency, responsiveness, flexibility, waste reductions, and efficiency. Supply Chain 5.0 seeks to keep those performance improvements, but also add more value by pursuing a mass personalization of products and services.

In respect to the construct **Innovation and Technologies**, Supply Chain 4.0 is formed by technologies such as IoT, big data analytics, 3D printing, cloud computing, robotics, blockchain, augmented reality, and artificial intelligence. These technologies remain in Supply Chain 5.0, and artificial intelligence is enhanced. Indeed, these Industry 4.0 technologies are the scaffolding for the implementation of Supply Chain 5.0; however, new technological advancements are added as well, including collaborative robots (cobots), multi-agent systems and technologies, digital ecosystems, complex adaptative systems, 4D printing, 5D printing, 3D scanning, holography, intelligent autonomous systems, evergetics, and machine cognition. Additionally, the approach of innovation ecosystems is going to play a crucial role in this upcoming technological transformation.

In the construct of **Society and Sustainability**, while in Supply Chain 4.0 the society is more passive, being smoothly impacted by Industry 4.0- s technologies, in the Supply Chain 5.0 approach society is an active and target element. In this new industrial revolution, one of the main aims is to create a super and digital smart society. In this new concept, the focus goes beyond the organization's thresholds and embraces the supply chain's linked society. Additionally, in Supply Chain 5.0, sustainable development becomes one of the main targets, much more than only being impacted by technologies, as it is being approached in the current context of Supply Chain 4.0. This interaction between Supply Chain 5.0- s technologies and approach and smart society must be designed to create a most advanced sustainable environment in both organizations and societies.

Regarding the **Transition Issues** construct, authors who have discussed Supply Chain 4.0 have in general considered as challenges and implementation issues aspects such as coordination and leadership support, digital infrastructure, strategic alignment, and people skills and training. In the Supply Chain 5.0 concept these issues remain; however, they become more complex and comprehensive, by including aspects such as psychological issues, workers' safety, social, ethical, learning, and legal and regulatory issues. Lastly, the main challenge is the paradigm transition which involves a change from a fully technological to a balanced human-centric perspective.

Based on the presented alignment, Figure 6 shows the framework with a vision of the Supply Chain 5.0 concept and structure.

**Figure 6.** Supply Chain 5.0 framework and concept.

Therefore, based on Figure 6, Supply Chain 5.0 involves an industry strategy that pursues a balanced human–technological environment and a sustainable and smart society. This strategy is supported by technologies and innovation that include Industry 4.0- s technologies and other emergent technologies as well as an innovation ecosystem. A Supply Chain 5.0 strategy also has some transition issues related to Industry 4.0- s paradigms, Industry 4.0- s capabilities, and other issues such as psychological, workers' safety, social, ethical, legal, and regulatory. As the main purpose, in terms of social and sustainable

aspects, Supply Chain 5.0 aims to allow a more sustainable, smart society. It also creates a mass personalization in terms of products and services of the supply chains.

#### **4. Conclusions**

Industry 5.0 is still a visionary concept which aims to include the human, social, and sustainability aspects amid the current and highly focused technological scope of Industry 4.0. Although the literature is still scarce, there is a growing trend towards Industry 5.0 discussions by the academic and practical audience. Aiming to contribute to these discussions, this paper has presented the relationship between Industry 5.0 and supply chains, having as its basis a systematic literature review.

In the systematic literature review, it was possible to identify forty-one (41) articles related to the subject herein proposed. By the analysis of these articles through VOSviewer software, nineteen (19) words were clustered forming the four (4) main constructs conceptualized and discussed: **Industry Strategy**, **Innovation and Technologies**, **Society and Sustainability**, and **Transition Issues**. This answered the **RQ1** of this research, as described in the Introduction of this article.

To answer **RQ2** of this research, an alignment between Industry 5.0- s constructs and the already available understanding of Supply Chain 4.0 was proposed. Although it is possible to identify some similarities between Supply Chain 4.0 and Supply Chain 5.0, the last adds more things in the four (4) constructs considered. Supply Chain 5.0 aims at mass personalization, adds revolutionary technologies, enables a super smart and sustainable society, and faces some transition challenges in its implementation, mainly linked to the paradigms established by the ongoing Industry 4.0 wave.

#### *4.1. Practical and Theoretical Implications*

The practical implications of this work are relevant since organizations' leadership, policymakers, and other practitioners involved in supply chains, and mainly those currently working with Industry 4.0 programs, can benefit from this research in having clear guidance regarding the dimensions needed to structurally design and implement Industry 5.0- s initiatives. Moreover, it can encourage practitioners to think about the benefits of Industry 5.0 in supply chains and their role with the aim to pursue a more sustainable and smart society. As identified in the literature review findings, Industry 5.0 may enhance strategic outcomes of supply chains by allowing mass personalization in products and services.

As theoretical implications, this article brings a novel contribution, being a starting point related to the relationship between Industry 5.0 and supply chains. This article also encourages further research in the area of supply chain and operations management in the context of this upcoming industrial revolution, by providing a clear set of constructs which form Supply Chain 5.0. The set of listed topics in Table 4 can also foster the development of new research programs linked to Industry 5.0 in supply chains. New theoretical and empirical studies may be deployed from the research. Surveys, case studies, and action research may support the validation of the constructs presented in this article as well as the addition of new ones. These new developments of research are crucial to a deeper understanding of the Industry 5.0 phenomenon in supply chains. Additionally, future reviews of the literature may also be of benefit to the subject of Supply Chain 5.0.

**Table 4.** Research agenda.

#### *4.2. Limitations and Research Agenda*

Although this paper presents relevant insights, further research is required to overcome the limitations related to the validation of the constructs herein proposed. As the literature is still scarce and incipient, more constructs may be added in the future as the understanding of Industry 5.0 evolves.

Therefore, there is a fruitful field of research to be explored in both empirical and theoretical sides about the future supply chains in the context of Industry 5.0. With the purpose to help in the guidance of these future studies, this article also proposes a research agenda which is not limited to some research questions based on the four (4) constructs identified in this study. This closes the article and answers **RQ3**, which was presented in the Introduction section.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Conflicts of Interest:** The author declares no conflict of interest.

#### **References**

## *Article* **Evaluating the Status of SMEs in Jordan with Respect to Industry 4.0: A Pilot Study**

**Maram I. Shqair \* and Safwan A. Altarazi**

Industrial Engineering Department, German Jordanian University, Amman 11180, Jordan

**\*** Correspondence<EMAIL_ADDRESS>
**Abstract:** *Background:* Industry 4.0 is a burgeoning research area that has been addressed by many research entities. However, the literature shows that the industrial sector lacks the awareness and knowledge needed to comply with Industry 4.0 implications, particularly in developing countries. *Methods:* This study evaluates the status of small and medium enterprises (SMEs) in Jordan concerning Industry 4.0. Four criteria are assessed, including Industry 4.0 readiness, maturity, drivers, and barriers. Samples of SME respondents and Industry 4.0 experts are surveyed using an online questionnaire. *Results:* The results show that SMEs in Jordan are not mature enough nor ready to apply Industry 4.0. For the readiness dimension, SME respondents and experts agreed that the Jordanian SMEs' status is between having initiatives in the pilot phase or implementing concepts to low degrees, except for autonomous workpiece and smart product aspects, in which Jordanian SMEs are behind due to financial and technological reasons. It was found that none of the Industry 4.0 investigated technologies have reached maturity levels. Customer requirements, cost reduction, competitors' practice, productivity improvement, and quality improvement are found to be the major influencing drivers for Industry 4.0, while a lack of awareness and knowledge is found to be the crucial barrier hampering Industry 4.0 implementation. *Conclusions:* Jordan needs country-scale initiatives for the implementation of groundbreaking Industry 4.0 development, incorporating government agencies, industrial parties, and experts, relying on Industry 4.0's readiness and practice status as a starting point, and considering the influential drivers and barriers to steer the development process.

**Keywords:** developing countries; digital transformation; Industry 4.0; drivers; barriers; readiness; maturity models; small and medium enterprises

#### **1. Introduction**

Since the 1800s, humanity has experienced three industrial revolutions, each powered by new technology: the use of steam power and mechanization of production (Industry 1.0), the discovery of electricity and assembly line production (Industry 2.0), and partial automation using memory-programmable controls and computers (Industry 3.0). Recently, the concept of digital transformation of the industry (known as the fourth industrial revolution or Industry 4.0) has begun to change the world of manufacturing in parallel with its economic environment.

The term Industry 4.0 was invented in 2011 in Germany as an initiative of the German federal government to strengthen the competitiveness of the German manufacturing industry [1,2]. There are many definitions for Industry 4.0. For example, Kagermann et al. [3] defined it as the involvement of the technical integration of cyber–physical systems in manufacturing and logistics and the use of the internet of things and services in industrial processes. Trappey et al. [4] defined it as a general concept of manufacturing, enabling the elements of tactical intelligence using techniques and technologies such as the internet of things, cloud computing, and big data. Industry 4.0 technologies are applied to enable companies to have flexibility in manufacturing processes with real-time data analysis. This implies developing embedded systems and enhancing strategic and

**Citation:** Shqair, M.I.; Altarazi, S.A. Evaluating the Status of SMEs in Jordan with Respect to Industry 4.0: A Pilot Study. *Logistics* **2022**, *6*, 69. https://doi.org/10.3390/ logistics6040069

Academic Editors: Xue-Ming Yuan and Anrong Xue

Received: 25 July 2022 Accepted: 22 August 2022 Published: 28 September 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

operational decision-making processes [5]. Nine technologies have been considered as Industry 4.0 application pillars: big data and analytics, autonomous robots, simulation, horizontal and vertical integration, the industrial internet of things (IIoT), cybersecurity, the cloud, additive manufacturing, and augmented reality [6]. Such technologies could improve information transmission throughout the system, which enables more control over operations and adaptability to stochastic environments [7].

Small and medium enterprises (SMEs) are engines of sustainable economic growth worldwide. Industry 4.0 application to SMEs is not only a new research field, but also it has a limited implementation even in developed countries due to a lack of awareness and knowledge [7–11]. This study aims to investigate the status of SMEs in Jordan concerning their Industry 4.0 readiness, maturity, drivers, and barriers. It aims to diagnose how ready the Jordanian industry is to absorb and apply Industry 4.0 concepts and technologies. Additionally, it aims to point out the weaknesses and improvement points that should be taken into consideration regarding applying Industry 4.0 technologies in Jordan.

The rest of this paper is organized as follows: Section 2 describes the industrial sector in Jordan. The literature review is presented in Section 3. The methodology is explained in Section 4. The key findings are demonstrated and discussed in Section 5. Finally, the conclusions, study implications, and suggestions for future research are outlined in Section 6.

#### **2. Industrial Sector in Jordan**

This section describes two aspects of the industrial sector in Jordan: the technological development process and the SMEs' status.

#### *2.1. Technological Development*

It is well known that the adoption of advanced technologies is more challenging in developing countries due to several reasons such as culture, level of education, limited resources, and political instability. Consequently, companies in developing countries are behind their counterparts in developed countries concerning technology adoption [5]. This technological gap is quantified by a measure called the "growth competitiveness index", which accounts for the competitiveness in several aspects such as institutional framework and infrastructure, health and education, the size of the market, the environment at the macroeconomic level, development of capital markets, availability of technology, and business development and creativity [12]. The first appearance of Jordan in this index, which is tabulated by the World Economic Forum, occurred in 1996 [13]. The annual survey of the World Economic Forum, in 2014, revealed that Jordan progressed forward by four degrees in indicators of competitiveness to be ranked 64th globally and eighth among Arab countries. The report also showed the relative improvement in the economic indicators of Jordan in terms of reducing the budget deficit and the development of the financial market and education sectors [13]. In general, it can be seen that Jordan is achieving progress according to several growth competitiveness index indicators, which makes it a promising environment for Industry 4.0 development. Jordan is also a good candidate for this study due to its vital geographical position and political stability. Moreover, the Jordanian government, in spite of its limited resources, is making tremendous efforts to support and facilitate SMEs' economic growth.

Several developed countries have formulated national strategies and programs for incentivizing Industry 4.0 technologies such as the "High-Tech Strategy 2020" program in Germany, the "Advanced Manufacturing Partnership" program in the USA, and the "Made in China 2025" program in China [3,14,15]. Jordan, as a developing economy, still does not have a roadmap for Industry 4.0 adoption and implementation; however, the government of Jordan has made a lot of effort to start up country-based initiatives to facilitate digitalization and technological development, including:

#### *2.2. SMEs' Status*

Jordan is an upper-middle income country that depends almost completely on SMEs as the economy driver. For instance, in 2021, SMEs accounted for 95% of the business market in Jordan [19]. Moreover, SMEs represent roughly 95% of all registered companies, contribute 50 percent or more to GDP, and provide employment to an estimated 60 percent of the Jordanian workforce [20]. These are significant indicators of the importance of SMEs to the Jordanian economy and how crucial their involvement in any economic development plan is. In Europe, an SME is defined as a firm that has fewer than 250 employees with a total turnover that does not exceed EUR 50 million [21], while in Jordan the numbers are much smaller, where SMEs are defined as firms employing fewer than 100 employees with a total turnover that does not exceed JOD 3 million (JOD 1 JD equals USD 1.41) [17]. According to the Ministry of Industry (Trade and Supply Act No. 10 of 2005), a small enterprise in Jordan is defined as any individual company or enterprise with the primary purpose of industry, where its capital is less than JOD 30,000, and it has fewer than 10 employees registered in the Social Security Administration. Micro businesses comprise 89% of the total enterprises in Jordan, while 9% are small enterprises and the remaining 2% are medium and large enterprises [22]. Figure 1 depicts the sectoral distribution of SMEs in Jordan. It can be seen in Figure 1 that the largest share of SMEs in Jordan is in the commercial establishment category (35%), followed by the service provider category (23%), whereas a significant share is in industrial production (20%), and the rest is dedicated to tourism, construction, transport, and the finance sectors (9%, 8%, 4%, and 1%, respectively) [22].

**Figure 1.** Sectoral distribution of SMEs in Jordan [22].

Jordan began promoting SMEs in the early 1970s through a five-year economic development plan (1976–1980), which enabled SMEs to provide alternatives for imported

goods and products. After that, several corporations, societies, and socio-economic production programs were created, focusing on the development of SMEs [23]. SMEs have different characteristics to large enterprises that should be taken into consideration when investigating Industry 4.0 application; firstly, they have fewer resources and less experience in managing new technologies [24,25]. Secondly, they strongly involve top management in all the company's decisions. Thirdly, they are more operation-focused on the expense of strategic activities [26,27]. SMEs in Jordan are facing many challenges that could hinder their technological development, including financial, exports, fees and procedures, and decentralization challenges. Regarding the financial constraints, a lack of different forms of financial funding is a traditional constraint preventing SMEs from growth and expansion [28]. For instance, the share of credit allocated to SMEs in Jordan is small and shrinking too, declining from 11% to only 8.5% of total credit available to the private sector in 2016 [29]. Concerning exports, most SMEs are not export-oriented because they focus on the production of traditional, low-value-added products or services with modest quality [30]. Regarding fees and procedure limitations, the cost of conducting business is considered high for several SMEs in Jordan. The tax system in Jordan and the newly produced law, which forces companies with a capital of JOD 20,000 to appoint legal counsel, have both led to additional costs. Moreover, SMEs that create patents may not be able to protect their intellectual property due to the high cost of intellectual property registration in Jordan [31]. Decentralization is an obstacle facing SMEs in many countries, including Jordan. The companies and their commercial activities are agglomerated in the capital city, where a high-income population resides. Meanwhile, outside Amman, many cities suffer from underdevelopment, which implies high unemployment rates and poverty. SME establishment outside the capital needs encouragement via tax incentives and the facilitating of policies [31].

#### **3. Literature Review**

There is a scarcity of published research regarding the application of Industry 4.0 to SMEs [7]. One of the reasons could be the lack of awareness of how important it is to apply Industry 4.0 technologies, and how they could be harmonized with the companies' business processes [8]. According to several surveys, leaders of many enterprises have not heard about Industry 4.0. On the other hand, a large portion of them is aware of the Industry 4.0 basic concept, but they lack the knowledge related to introducing this new concept and how to be compliant with it [8]. It has also been found that many academic Industry 4.0 readiness models are not even known for the industrial sector [9]. For instance, a survey, conducted among 1000 medium-sized companies in Germany, revealed that in 25% of the surveyed companies, the topic of digitalization is not yet relevant [10]. In another recent survey, conducted by the consulting firm Deloitte in 19 countries, the results revealed that only 14% of chief executive officers were sure that their organizations are ready and capable to incorporate the changes accompanied by Industry 4.0 [11].

It is important to realize that the digital transformation process is an overall business strategy, with multi-components, which involves the management's commitment to adopt technological and organizational changes. Such a major strategic decision requires a prerequisite of enterprises' assessment of their status and readiness to apply Industry 4.0 technologies before investing resources [8,32]. Maturity models are one of the most used tools to evaluate the status of the company, concerning industrial revolutions, and assess its ability to apply Industry 4.0 technologies [32]. In the last few years, there has been a rapid increase in the number of Industry 4.0 readiness models [33]. However, a recent literature review revealed that there is a lack of empirical research on the application of Industry 4.0 technologies in SMEs [7]. Another study showed that larger companies are more ready to apply Industry 4.0 technologies than SMEs, which means that SMEs could face more barriers while trying to adopt and implement Industry 4.0 concepts, but this cannot be proved since little is currently known about implementing Industry 4.0 in this category [2,34,35].

Regarding maturity models, Mittal et al. [36] reviewed the commonly available models for Industry 4.0. Fifteen maturity models were presented and compared in terms of their methodology, focus, dimensions, and gaps. In terms of dimensions, the most used are strategy and organization, technology, IT, smart factory, smart products, data utilization, and employees. Other models included extra dimensions such as security policies [37] and customers [32].

IMPULS—Industrie 4.0 Readiness is one of the most well-known Industry 4.0 readiness models, which was developed by IW Consult (a subsidiary of the Cologne Institute for Economic Research) and the Institute for Industrial Management (FIR) at RWTH Aachen University [36]. The assessment process in this model relies on six pillars including strategy and organization, smart factory, smart operations, smart products, data-driven services, and employees. According to IMPULS, companies are classified into three categories, with six levels, based on their level of readiness to apply Industry 4.0. The first category is the newcomers, which includes two levels: the outsider and beginner levels. The learners' category is the second, which comprises the intermediate level. The third category is the leaders, which encompasses experienced, experts, and top performers [38]. Another well-known model is the connected enterprise maturity model in which Industry 4.0 is implemented in five stage processes; assessment, secure and upgraded networks and control, defined and organized working data capital, analytics, and collaboration [37]. A third important model for manufacturing enterprises' maturity is the one developed by Schumacher et al. [32]. In this model, nine dimensions are defined, and sixty-two items assigned to them. The dimensions are products, customers, operations, technology, strategy, leadership, governance, culture, and people.

Stentoft et al. [35] investigated the relationship between Industry 4.0 drivers and barriers to Industry 4.0 readiness and actual practice. They analyzed 308 Danish SMEs, from different manufacturing sectors, to examine how managers' perception of Industry 4.0 drivers and barriers could affect their companies' readiness and practice of Industry 4.0 technologies. They measured a relatively low degree of Industry 4.0 readiness and practice among the studied SMEs. They also found, through testing different hypotheses, that Industry 4.0 drivers have a positive impact on Industry 4.0 readiness and practice. Raj et al. [11] focused their study on the barriers to Industry 4.0 implementation, in the manufacturing sector, for both developed and developing economies. Among the 15 barriers they investigated, they found that the lack of a digital strategy alongside resource scarcity is the prominent barrier in both economies. They also found that improving standards and government regulation could enhance the adoption of Industry 4.0 technologies in developing countries, while in developed countries, technological infrastructure would be essential to promote Industry 4.0 adoption and implementation.

Several initiatives, in different countries, have tried to assess the readiness forIndustry 4.0 technologies' application. One experience is from Hungarian industry where a hybrid model was used, relying on three different resources; the IMPULS—Industrie 4.0 Readiness model, the Hungarian Industry 4.0 National Technology Platform suggestions, and the authors' contribution based on in-depth expert interviews [39]. A study based on the Czech experience was simpler; it focused on four main aspects including Industry 4.0 awareness, barriers, drivers, and strategy [40]. Another country-based attempt to assess Industry 4.0 readiness was conducted in Malaysia and relied on seven key areas: market pressure, risk taking, knowledge, management support, competencies, motivation, and freedom [41]. A specific study for manufacturing companies was conducted in Serbia to evaluate the role of advanced digital technologies (e.g., ERP as a backbone of vertical integration) in the context of Industry 4.0 [42]. To the best of the researchers' knowledge, this study is the first to investigate the status of SMEs in Jordan concerning Industry 4.0.

#### **4. Methodology**

The current research surveys the awareness and application of Industry 4.0 concepts and technologies in Jordanian SMEs. The main research question in this survey is: What is

the situation of Jordanian SMEs concerning Industry 4.0 readiness, maturity, drivers, and barriers? To answer this question, an online questionnaire was created. Questionnaires are the typical quantitative technique to adopt in such exploration studies [32,39,41,43]. The study explores the views of two different categories; the first one is the SME respondents, while the other category represents local experts in Industry 4.0, including industry consultants, legislation bodies, and academics. SMEs' responses reflect the real situation of Industry 4.0 on the ground, which is, in general, profit-oriented and focused on short-term deliverables. Conversely, the long-term vision, theoretical background, and enlightenments, revealed in experts' opinions, could be utilized in strategizing and planning schema for Industry 4.0 implementation at a country scale. Data collection was carried out by completing the online questionnaire. Overall, 90 companies and 35 Industry 4.0 experts were addressed and 24 of them fully answered the entire questionnaire, which made for a 19.2% response rate. These modest values are justified for such kinds of surveys due to the following practical and theoretical reasons; practically, purpose sampling technique was used as a pilot study to obtain representative responses. The questionnaire was only sent to respondents for whom Industry 4.0 concepts are relevant. Otherwise, the responses would have underestimated the status of Industry 4.0 due to misunderstanding and lack of knowledge. This technique is also used in other country-based initiatives to evaluate Industry 4.0 status [40,41]. Theoretically, the sample size was within the acceptable range for pilot studies mentioned in the literature. For instance, Saunders and Tosey [44] recommend a minimum of 10 respondents, Aaker and Keppel [45] recommend 15 to 25 respondents, Taylor et al. [46] recommend 15 to 30 respondents, Johanson and Brooks [47] recommend that 30 respondents' sample is a reasonable count for a pilot study. Respondents who answered the questions consisted mainly of practitioners from industry (37.5%) and experts (62.5%). According to Amman Chamber of Industry (ACI), the industrial companies in Jordan are categorized under one of the following ten categories: chemical and cosmetics; engineering, electrical, and IT; leather and garments; mining; printing, packaging, paper, cartoon, and stationeries; therapeutic and pharmaceutical; construction; food, supplies, agricultural, and livestock; wooden and furniture; and plastic and rubber [48]. In this study, the ACI classification is considered to group SMEs. Figure 2 depicts the breakdown of respondents by categories. As can be seen in Figure 2, most of the SME respondents (45%) were from engineering, electrical, and IT industries. The second largest category was from food, supplies, agricultural, and livestock industries (22%), followed by chemicals and cosmetics (11%). The category "others" included areas such as transport, tourism, and finance sectors. Industry 4.0 experts' sample consisted mainly of academics (73%). The "other" category includes Industry 4.0 consultants, legislation bodies, etc.

Table 1 depicts the four criteria surveyed in this study along with brief descriptions, number of related survey questions, questions' type, and associated literature. Those criteria are mainly based on the IMPULS model and partially on Stentoft et al.'s [35] study, with some modifications to make them compatible and applicable in a developing country such as Jordan. The first criterion is about how the SMEs in Jordan are able to exploit Industry 4.0 principles (Industry 4.0 readiness). It includes six items: strategy, plans, smart equipment, digital data collection, autonomous workpiece, and smart product. The second criterion is related to the degree the Jordanian SMEs apply the nine technologies of Industry 4.0. The third criterion measures the motivations behind Industry 4.0 application. Seven different drivers are investigated which are: customer requirements, competitors' practice of Industry 4.0, cost reduction, legal requirements, productivity improvement, quality improvement, and flexibility improvement. The last criterion measures the obstacles preventing the application of Industry 4.0 technologies. Seven different barriers are also investigated which are: lack of knowledge (know-how), lack of standards, lack of data protection, lack of qualified workforce, lack of awareness of the importance of Industry 4.0, lack of financial resources, and lack of governmental support. The first two criteria are measured using a 5-point rating scale to capture the intensity of respondents' opinions about the questionnaire items. The five scales used are: 1—Don't have, 2—Have only in the

test and pilot phase, 3—Have to a low degree, 4—Have to a moderate degree, 5—Have to a high degree. The third and fourth criteria are measured using multi-response questions that allow the respondents to choose from none to all the drivers and barriers given in the designed questions.

**Figure 2.** Responses' breakdown by categories: (**a**) SME respondents; (**b**) Industry 4.0 experts.

The questionnaire used in this study was developed in a multi-stage process. Firstly, a more detailed and technical questionnaire was developed based on the IMPULS maturity model. Next, the developed questionnaire was evaluated by two Industry 4.0 experts to check if it would be applicable in the Jordanian market. The feedback received from the experts indicated that the first version of the questionnaire was too technical and advanced to be suitable for the modest initiatives of Industry 4.0 in Jordan. Finally, a simplified and generalized version of the questionnaire was constructed and used incorporating suggestions given by the two experts.

Regarding the theoretical contribution of this study, the new dimension, which is not addressed in the literature, is the alignment between the theoretical aspect, represented in the experts' opinions, and the practical aspect, which reflects the situation on the ground. Accordingly, the findings of this study add to the literature of Industry 4.0. As revealed in this study, Industry 4.0 experts in Jordan have a bird's eye view with accurate judgment for the status of Industry 4.0. This holistic view and experience should be exploited by the Jordanian government to steer the SMEs toward smart products and operations' transition, using the available resources and technologies. The other theoretical outcome of this study is the hybrid model, which was specifically built for the Jordanian case. In this modified model, Industry 4.0 major dimensions (readiness, maturity, drivers, and barriers) have been addressed but in a simplified schema to be compatible with the modest initiatives in Jordan. However, this model can be exported and applied in similar developing countries, especially in the Middle East region.

#### **5. Results and Discussion**

This section presents the findings of the Industry 4.0 criteria measures for SMEs in Jordan. Table 2 shows the mean of each measure for both respondents' categories (SME respondents and Industry 4.0 experts). The general trend in the results of both categories is that the Jordanian SMEs are not mature enough nor ready for Industry 4.0 implementation, with most mean values between two and three (mostly between a pilot level and a low level). This result is expected in a developing country with limited resources such as Jordan. In the absence of thorough governmental efforts to spread a digitalization culture among the industrial society, Industry 4.0 initiatives are launched by individuals who are not fully aware of the organizational and structural changes needed to embrace Industry 4.0 philosophy. Instead, they partially employ some Industry 4.0 technologies that are compatible with their business, need low investment, and acquire a naive technological infrastructure.

Going through the readiness six dimensions, the results, summarized in Table 2, show that the Industry 4.0 strategy is not clearly defined nor spread out among employees (SME respondents' mean: 2.56, experts' mean: 2.00), in the absence of indicators to orient the development process. The same is true for Industry 4.0 implementation plans over a fiveyear period (SME respondents' mean: 2.67, experts' mean: 2.80), with modest investments in this aspect. This proves the fact that SMEs are more operation-focused on the expense of strategic activities [26,27]. The smart equipment dimension is not found to be widely applied on the ground (SME respondents' mean: 2.56, experts' mean: 2.33). This could be simply due to financial and technological drawbacks incorporated in the smart equipment's utilization, which is not expected to witness a revolution under the current economic status of Jordan. As a prerequisite to smart factories, digital data collection systems should exist, including smart collection, storage, and processing of data to assure the efficient utilization of resources. The findings have revealed a shortage in real-time, enterprise-wide, and cross-enterprise collaboration between different entities (SME respondents' mean: 2.78, experts' mean: 2.40). This could be attributed to the point that the information technology culture in Jordan is not mature enough to support cyber–physical systems, interoperability, and IIoT, which are essential parts of the infrastructure for efficient digital data systems. The dimensions that show the lowest readiness levels are the autonomous workpiece (SME respondents' mean: 1.78, experts' mean: 1.60) and the smart product (SME respondents' mean: 1.44, experts' mean: 2.40). It is expected to have close findings for these dimensions because they are logically related. The smart product dimension implies equipping items with Information and Communications Technology (ICT) components (e.g., RFID, sensors, communications interface) to collect data related to their status and environment. Without having these features, the workpiece is not be able to interact with its environment and lead itself through the production process autonomously. Since these technologies require

high investments in parallel with advanced technological levels, the Jordanian SMEs are behind in comparison to other Industry 4.0 readiness measures. It is worth mentioning that for the six readiness dimensions, there is no statistical difference (*p*-values are more than 0.05) between SME respondents' and experts' opinions, which reflects the experts' awareness of Industry 4.0's status in Jordan. This concurrence could lead to a consolidated onset of collaboration between theory and practice in order to set a schema for Industry 4.0 implementation at the country level.

**Table 2.** Summary of Industry 4.0 readiness and maturity measures' means.

Regarding Industry 4.0 technologies' implementation in Jordan, it is noticed in Table 2 that none of the surveyed technologies reached acceptable maturity levels (with almost all mean values below three). This is not a surprising outcome based on the modest readiness levels obtained in the previously investigated dimension. However, an interesting observation is that in three out of the nine surveyed technologies, experts overestimated the practice level (experts' mean values were higher than those for SME respondents. They statistically differed with *p*-values less than or equal to 0.05). The two respondents' categories differed in autonomous robots, additive manufacturing, and augmented reality aspects. Most experts who overestimated autonomous robots and augmented reality levels are academics, whose opinion, in these aspects, could be a bit biased toward technology, so we do think that the industries' findings are closer to reality for these items. Additive manufacturing is currently applied in different sectors in Jordan, especially the rubber and plastic industry. For instance, 3D printing, is currently used to produce prototypes and individual components. This feature could provide innovative solutions for supply chain implications, associated with manufacturing processes [49,50]. Experts' opinions reflected this fact. However, due to the SME samples' demography, taken from different sectors, a lower estimate for the status of this technology was obtained. To obtain more insight into each surveyed item within the four studied criteria, the responses of SME participants and experts are illustrated in Figures 3–19.

**Figure 3.** Response to Industry 4.0 Readiness\Strategy question from two categories: (**a**) SME respondents; (**b**) Industry 4.0 experts.

**Figure 4.** Response to Industry 4.0 Readiness\Plans question from two categories: (**a**) SME respondents; (**b**) Industry 4.0 experts.

**Figure 7.** Response to Industry 4.0 Readiness\Autonomous Workpiece question from two categories: (**a**) SME respondents; (**b**) Industry 4.0 experts.

**Figure 8.** Response to Industry 4.0 Readiness\Smart Product question from two categories: (**a**) SME respondents; (**b**) Industry 4.0 experts.

**Figure 10.** Response to Industry 4.0 Maturity\Autonomous Robots question from two categories: (**a**) SME respondents; (**b**) Industry 4.0 experts.

**Figure 11.** Response to Industry 4.0 Maturity\Simulation question from two categories: (**a**) SME respondents; (**b**) Industry 4.0 experts.

**Figure 12.** Response to Industry 4.0 Maturity\Horizontal and Vertical Integration question from two categories: (**a**) SME respondents; (**b**) Industry 4.0 experts.

**Figure 13.** Response to Industry 4.0 Maturity\IIoT question from two categories: (**a**) SME respondents; (**b**) Industry 4.0 experts.

**Figure 14.** Response to Industry 4.0 Maturity\Cybersecurity question from two categories: (**a**) SME respondents; (**b**) Industry 4.0 experts.

**Figure 15.** Response to Industry 4.0 Maturity\The Cloud question from two categories: (**a**) SME respondents; (**b**) Industry 4.0 experts.

**Figure 16.** Response to Industry 4.0 Maturity\Additive Manufacturing question from two categories: (**a**) SME respondents; (**b**) Industry 4.0 experts.

**Figure 17.** Response to Industry 4.0\Augmented Reality question from two categories: (**a**) SME respondents; (**b**) Industry 4.0 experts.

**Figure 18.** Response to Industry 4.0 Drivers multi-response question from two categories: (**a**) SME respondents; (**b**) Industry 4.0 experts.

**Figure 19.** Response to Industry 4.0 Barriers multi-response question from two categories: (**a**) SME respondents; (**b**) Industry 4.0 experts.

To make it easier for the reader to analyze the data in the following figures, Figure 3 is described in detail, while in the other figures, only the major findings are summarized. As depicted in Figure 3, around 44% of the surveyed SMEs do not have a specific strategy for Industry 4.0 implementation, while around 22% of them have a strategy at low or medium levels. Only around 11% of the investigated companies have an Industry 4.0 strategy with a high level. Industry 4.0 experts have a slightly different opinion regarding this item; around 53% of Industry 4.0 experts, in the surveyed sample, see that SMEs in Jordan do not have a specific strategy for Industry 4.0, while 20% see that the SME sector

has Industry 4.0 strategy initiatives in the pilot phase. The same percentage of the experts (around 6.7%) see that the companies have a strategy to apply Industry 4.0 to low or high levels. The rest of the investigated sample (around 13.3%) see that Industry 4.0 strategy is applied to a moderate level. As mentioned earlier in this section, the results obtained for this aspect coincide with the literature, stating that SMEs do not focus on long-term strategies and they are operations-oriented [8,27]. These findings highlight the exigency for country-scale initiatives to strategize Industry 4.0, incorporating government agencies, industry chambers, and experts.

In Figure 4, the major finding is that the highest percentage (around 44%) of the SMEs in the surveyed sample do not have plans to implement Industry 4.0 in the next five years. However, the experts' highest percentage (around 33%) shows that the companies have initiated plans in the pilot phase. Regarding having smart equipment infrastructure (e.g., machines and systems controlled through IT, machine-to-machine communication), the highest percentage of the companies in the sample (around 44%) do not have such infrastructure. The same trend is obvious in the experts' sample; around 33% agree that SMEs in Jordan do not have smart equipment infrastructure (Figure 5). SMEs' preference for the human workforce, at the expense of smart equipment, could be due to financial considerations. In developing countries such as Jordan, labor cost is relatively low and customer quality requirements are not excessive; hence, investing in smart equipment is not feasible. As can be seen in Figure 6, the highest percentage of the surveyed companies (around 44%) lack the availability of digital systems to collect machines and process data during production. Conversely, the experts' highest percentage (40%) supported the idea that SMEs in Jordan have digital data collection systems, which are used at low levels. This could be attributed to the nature of SMEs. The organizational structure of SMEs is simpler than large enterprises, with a lower number of organizational levels. This would decrease the need for traditional or digital data collection systems. Regarding the autonomous workpiece dimension, most of the investigated companies (around 56%) do not have this feature, which is compatible with most of the experts' opinions (60%) (Figure 7). The same is true for smart products, equipped with smart add-on functionalities (e.g., product memory, automatic identification, self-reporting). Most SMEs in the sample stated that they do not have these functionalities. This also complies with the judgment given by around 47% of Industry 4.0 experts (Figure 8). The outcomes obtained in the last two aspects are expected to dominate in Jordan. Autonomous workpieces and smart products are considered advanced Industry 4.0 technologies and having them within SMEs in developing countries is unlikely.

The first aspect of Industry 4.0 practice and maturity is big data and analytics. As can be seen in Figure 9, the highest percentage of the SMEs (around 44%) do not use big data and analytics, while 40% of Industry 4.0 experts think that this technology is applied in Jordan to a low degree. The reason for this difference could be the holistic view of Industry 4.0 experts, which is missing within narrow industrial horizons. Experts' opinion is based on their solid academic background in addition to their broad experience in Industry 4.0 implications. An interesting finding about the use of autonomous robots in production processes can be noticed in Figure 10. In total, 80% of the experts are divided into two equal groups, the first group (40% of the sample) think that SMEs in Jordan are not using autonomous robots, while the other group supports the idea that this technology is used in Jordan with low levels. Practically, most of the industry sample (around 78%) stated that they do not utilize autonomous robots. As stated earlier in this section, industry responses, in this aspect, are more realistic and objective than experts' opinions, which could be biased toward technology due to their academic background. The same pattern is noticed for the Industry 4.0 maturity\simulation aspect. Around 27% of the experts believe that this technology is not used in the Jordanian industry. The same percentage thinks that simulation is employed to a moderate level, while most SMEs (around 56%) depicted that they do not employ simulation in their industrial processes (Figure 11). For the horizontal and vertical integration measure, around 56% of the sample admitted that

they are not users of this technology. At the same time, 40% of the experts expected this result (Figure 12). These results agree with the results of a study regarding the use of supply chain management information systems within the Jordanian manufacturing sector [44]. As can be seen in Figures 13–15 and 17, the pattern is the same for IIoT, cybersecurity, the cloud, and augmented reality technologies; the highest percentage of SMEs stated that they do not have this technology (around 56%, 44%, 33%, and 67%, respectively) which coincides with the statements given by the highest percentage of Industry 4.0 experts (around 33%, 27%, 20%, and 54%, respectively). For additive manufacturing technology, although 57% of SMEs stated that they do not use this aspect, the highest percentage of the experts (around 33%) agreed that additive manufacturing is utilized to a low degree in Jordan (Figure 16). This is because additive manufacturing does not require high investments and could be feasibly implemented in Jordanian SMEs, particularly in sectors such as engineering and plastic and rubber.

The third and fourth criteria investigated in this study are Industry 4.0 drivers and barriers (Figures 18 and 19, respectively). As mentioned earlier in this study, these criteria were measured using multi-response questions in which the respondent chose from none to all of the given drivers and barriers. For comparison purposes, any driver or barrier that is chosen by more than 50% of the samples, is considered an influential factor in Industry 4.0 adoption and implementation. As can be seen in Figure 18, there are many commonalities in the Industry 4.0 drivers chosen by industry respondents and Industry 4.0 experts. For the SME sample, the respondents chose four influential drivers which were: customer requirements, cost reduction, productivity improvement, and quality improvement. However, the experts selected customer requirements, cost reduction, and competitors' practice as influential drivers. As satisfying the customer requirements is crucial for all companies and is a key quality principle, it was expected to be the most important driver. Similarly, cost reduction is crucial in driving the adoption of any new technological revolution. For Industry 4.0 barriers, the two samples had different attitudes in selecting influential barriers. For SME respondents, only one barrier, which was lack of knowledge, was chosen by more than 50% of the sample, while for the Industry 4.0 experts, six out of the seven barriers were selected by more than 50% of the sample; these influential barriers were lack of knowledge, lack of data protection, lack of qualified workforce, lack of awareness of the importance of Industry 4.0, lack of financial resources, and lack of governmental support. This can be explained by the fact that the Industry 4.0 experts have a bird eye view toward not only the short-term barriers but also the long-term barriers that companies could face during all stages of Industry 4.0 application. This holistic view, in general, will not be clear for industry practitioners who do not have previous experience in implementing Industry 4.0 technologies in their companies.

Some of the findings in this paper are aligned with other research findings. For instance, it was found that many SMEs in Jordan do not have a specific strategy for Industry 4.0, and they do not assign employees to handle Industry 4.0 implications. The same result is true for the Czech case [40]. It was found that a lack of knowledge and lack of resources are major influencing factors that hamper Industry 4.0 penetration for both the SME respondents and Industry 4.0 experts in Jordan. The same barriers were also found in the Czech companies [40]; however, Raj et al. [11] stated that the most important influencing factor in the developed economy is the lack of maturity in technology. Regarding the other measures investigated in this research, the authors did not find in the literature a previous experience of evaluating Industry 4.0 status in a developing country such as Jordan to compare with.

#### **6. Conclusions, Implications, and Future Work**

This paper surveys the status of the Jordanian SMEs concerning Industry 4.0 readiness, maturity, drivers, and barriers. Two samples are selected from different parties: SME respondents and Industry 4.0 experts. The general outcome of both the respondents' samples

is that SMEs in Jordan are not mature enough or ready to initiate the implementation of Industry 4.0 technologies.

For the six investigated aspects in Industry 4.0 readiness, SME respondents and experts agreed that, on average, the Jordanian SMEs' status is between having initiatives in the pilot phase or implementing concepts to low degrees. It is also noticed that the autonomous workpiece and smart product aspects are the least applicable in Jordan due to their considerable financial and technological requirements.  However, experts coincide with industry respondents in six out of the nine investigated technologies. The overestimation from the experts' side for the remaining three technologies (autonomous robots, additive manufacturing, and augmented reality) could be attributed to experts' possible bias toward technology.

It is concluded, from the empirical findings of this study, that customer requirements, cost reduction, competitors practice, productivity improvement, and quality improvement are essential motives to be considered when designing milestones of Industry 4.0 involvement. It is also found that the major obstacle preventing Industry 4.0 from advancing in SMEs in Jordan is the lack of awareness of the benefits and importance of applying Industry 4.0 technologies, in parallel with a deficiency in technical knowledge crucial to applying Industry 4.0 concepts.

The findings obtained in this study could be significant for current and future SME leaders to draw a roadmap for assessing their companies' status concerning Industry 4.0, measuring the gap between what exists and what is required based on their drivers for Industry 4.0 implementation, and then deploying an Industry 4.0 philosophy within their work environments. The results can also be used pragmatically by managers to identify strengths and weaknesses in the rollout of their Industry 4.0 projects. Helping SMEs to create strategies for Industry 4.0 adoption can lead to positive social changes by improving economies and creating jobs.

The government of Jordan might also use the findings of this study to diagnose how ready the Jordanian market is for Industry 4.0 implementation, providing improvement spaces for policy making on Industry 4.0. For instance, if such studies are performed on a regular time basis, they would be a reference for the government to measure the progress the companies achieve regarding technological advancement. Accordingly, they can target the government's financial and technical support for specific aspects that are promising and influential in the Industry 4.0 transition process. The outcomes of such studies could also help the government to formulate its key performance indicators that are tailored to the Jordanian experiment. The findings can be employed in country-scale initiatives for groundbreaking Industry 4.0 development, incorporating government agencies, industrial parties, and experts, relying on Industry 4.0's readiness and practice status as a starting point, and considering the influential drivers and barriers in steering the development process.

One of the major limitations of this study is the low size of the questionnaire responses. In the absence of Industry 4.0 philosophy in Jordan, it is challenging to find a representative sample from SMEs for which Industry 4.0 concepts are relevant. One recommendation is to increase the sample size and to have a variety of sectors involved. It is expected for a developing country with limited resources such as Jordan to have many struggles while trying to adopt the major changes needed to implement Industry 4.0 technologies. Some of the hampering factors found in this study that should be further investigated are the lack of data protection, lack of a qualified workforce, lack of financial resources, and lack of governmental support. There is a scarcity of country-based studies that investigate the status of Industry 4.0 in general and in developing countries specifically. For future research, these findings from Jordan can be compared with other counties, especially in the Middle East. Another recommendation is to have similar studies, in Jordan, for large enterprises to find out how sensitive the process of Industry 4.0 application is to business size. A good point is to have customized surveys for specific industrial or service sectors and compare the results of the same sector in different countries, investigating how geography and demography can

affect Industry 4.0 implementation. Finally, there will be added value if more studies are conducted on specific industrial sectors in Jordan that are more advanced and more able to absorb Industry 4.0 concepts such as the pharmaceutical and mining industries.

**Author Contributions:** Conceptualization, S.A.A.; methodology, M.I.S. and S.A.A.; software, M.I.S.; formal analysis, M.I.S. and S.A.A.; data curation, M.I.S.; writing—original draft preparation, M.I.S.; writing—review and editing, S.A.A.; supervision, S.A.A. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** The study was conducted in accordance with the Declaration of Helsinki and approved by the Deanship of Scientific Research of German Jordanian University on September 27th, 2022 (In reference to the Regulations for Scientific Research at the German Jordanian University of 2008).

**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.

**Data Availability Statement:** Not applicable.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

## *Article* **Quantifying the Benefits of Digital Supply Chain Twins—A Simulation Study in Organic Food Supply Chains**

**Tom Binsfeld and Benno Gerlach \***

Chair of Logistics, Berlin University of Technology, 10623 Berlin, Germany<EMAIL_ADDRESS>**\*** Correspondence<EMAIL_ADDRESS>
**Abstract:** *Background*: Digital supply chain twins (DSCT) are gaining increased attention in academia and practice and their positive impact on logistics and supply chain management (LSCM) performance is often highlighted. Still, LSCM executives are hesitant regarding DSCT implementation. One reason is the difficulty of making a reasonable cost–benefit comparison, because the benefits of using a DSCT are rarely quantified. Moreover, there seems to be no method of quantifying these benefits as of today. *Methods*: This article builds upon an extensive simulation study of a constructed organic food supply chain (FSC), containing as many as 40 simulation experiments. In this simulation study, three volatility scenarios in the FSC were simulated and their effects on LSCM performance were measured. Subsequently, dynamic simulation experiments were run to emulate DSCT use. The benefits of using a DSCT were then quantified using a newly developed approach. *Results*: A conclusive method for quantifying the benefits of using a DSCT is presented and validated. Moreover, the performance evaluation of using a DSCT for the multi-echelon inventory management of an organic FSC is given. *Conclusions*: The study leads towards a method for quantifying the use of DSCTs that is of importance for research and practice alike. For managers, it additionally provides an exemplary application of said method in the context of organic FSCs.

**Keywords:** digital supply chain twins; logistics and supply chain management; digital twin; logistics; supply chain management; organic food supply chains; agent-based simulation; discrete event simulation

#### **1. Introduction**

Supply chains (SC) today are facing a variety of challenges. Globally distributed production sites, a growing world population, natural disasters and even crises such as the COVID-19 pandemic or global financial crises are placing a strain on companies worldwide. SC volatility often leads to consequences such as supply bottlenecks and lost sales. In addition, the topic of sustainability is becoming increasingly important in LSCM. The demand for sustainable SCs requires companies to come up with new ways of managing LSCM activities. To overcome these challenges, digitization offers new, innovative approaches [1]. While it presents companies with difficulties, digitization also offers a lot of problem-solving potential. Processes and business models are changing, and the appropriate implementation of innovative digital concepts provide LSCM companies with the opportunity to increase efficiency [2].

Furthermore, the COVID-19 pandemic has increased the issue of SC resilience in the face of SC volatilities. SCs have been put under pressure and customers often could not be supplied on time. In addition, there was a sharp increase in demand for individual product categories. For example, there was a shortage of various products in supermarkets and shelves remained empty [3]. Another example of vulnerable SCs was the Suez Canal obstruction in 2021, which disrupted international SCs, resulting in various products being delivered several weeks late. This revealed the problem of SCs featuring dependencies on individual, globally distributed suppliers [4]. In order to prevent fulfillment delays,

**Citation:** Binsfeld, T.; Gerlach, B. Quantifying the Benefits of Digital Supply Chain Twins—A Simulation Study in Organic Food Supply Chains. *Logistics* **2022**, *6*, 46. https:// doi.org/10.3390/logistics6030046

Academic Editors: Xue-Ming Yuan and Anrong Xue

Received: 27 May 2022 Accepted: 1 July 2022 Published: 8 July 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

companies must rely on resilient SCs in order to ensure customer satisfaction. In this regard, supplier selection plays a critical role in order to be able to respond to volatile customer demand [5].

Meanwhile, Digital Twins (DT) are receiving more and more attention in research and in practice. It is considered an innovation that is seen as an opportunity across industries and sectors to improve the planning and control of all kinds of systems. Since only a few concepts have been implemented in science and in companies and most of these concepts are in the development phase, there is a need for research in this area [1]. The technology has great potential for growth. Estimates predict an annual market growth for DTs of 38 percent by 2025 [6]. Even less researched and implemented in practice is the concept of the digital supply chain twin or digital logistics twin (DSCT), which represents the application of the DT concept in the domain of LSCM. It promises, for example, greater SC transparency, increased network resilience, and lower inventory levels [2]. LSCM is a suitable application area for DTs because of the ever-increasing amounts of data and the interdependencies in decision making [6]. DSCTs can find applications in a variety of industries, such as pharmaceuticals, organic food, and precision agriculture [7]. All in all, the DSCT is discussed as a promising innovative solution to overcome the aforementioned challenges in LSCM.

One industry where volatility and resilience are especially significant is the organic food industry. Issues such as visibility and traceability play a major role here to ensure product quality and customer satisfaction. Optimizing logistics processes for companies in the food sector is particularly important, as logistics costs in the food sector account for 6–10% of total sales [8]. Annual sales in the organic food industry have been growing steadily since 1999, rising from €96.7 billion to €106.4 billion between 2018 and 2019. This trend is expected to continue to strengthen due to the change in customers' environmental awareness [9]. Still, the industry is characterized by SC inefficiencies. More than one third of food is lost from farm to fork [10]. In order to reduce food waste, better planning and control of the relevant logistics system is required. Through greater transparency and the use of technological advancements food loss can be reduced and additional costs can be saved [8]. Therefore, the organic food sector presents itself as a promising application domain for DSCT development [7].

Several studies discuss the benefits of using the simulation capabilities of a DSCT in order to better be able to react to SC volatility. However, as of today, a small number of implemented DSCTs exist, both in practice and in research. Although some publications describe the benefits of using a DSCT qualitatively, few are shown to quantify the benefits [2]. In particular, the benefits of a DSCT in the organic food industry have not yet been investigated, although there are great potentials, such as an increase in product quality. As the costs and benefits are not easily estimated, it is difficult to determine the overall value of a DSCT [7]. This makes it all the more difficult for practitioners to generate a reasonable cost–benefit comparison, which presents a major barrier for the implementation of DSCTs.

Considering these developments and recent shortcomings of the current literature in this field, this study seeks to contribute to a much-needed method for quantifying the benefits of using a DSCT in terms of LSCM performance improvement, using the example of an organic FSC. More specifically, this study aims at the following research objectives (RO):

*RO1: Develop a method for quantifying the effects of using a DSCT in terms of LSCM performance*. *RO2: Evaluate the effect of using a DSCT in multi-echelon inventory management of an organic FSC in terms of LSCM performance*.

For this purpose, an extensive simulation study was conducted. Subject of investigation is a case study featuring a constructed organic FSC, which was depicted in a simulation model. Different volatility scenarios regarding both customers and suppliers were then simulated and their effects on LSCM performance were measured. Subsequently, the simulation model was used to emulate the use of a DSCT, altering the system's inventory, procurement, and order strategies. The benefits of a DSCT were then measured using a newly developed quantification method. Thereby, this study provides a first attempt of a quantitative analysis of a DSCT application, thus filling a considerable gap in DSCT research.

In order to contribute to RO1, Section 2 first provides a detailed description of a DSCT in organic FSC. The specific use case of multi-echelon inventory management is then described, as well as the processual and analytical improvements the DSCT provides. Moreover, the case study is defined, introducing the structure of the organic FSC in question, as well as the existing challenges with regard to volatility. Finally, a new method for quantifying the benefits of using a DSCT is derived and described in Section 2.3. In accordance with RO2, a simulation study containing 40 simulation experiments is presented in Section 3, with the ultimate goal of quantifying the effect of using a DSCT in multiechelon inventory management of an organic FSC in terms of LSCM performance. In Section 4, an analysis of the experiment results is shown, demonstrating the DSCT's effect on different dimensions of LSCM performance. This does not only demonstrate and verify the usability of the quantification method presented in Section 2, but also gives a good overview of what benefits to expect when using a DSCT in this specific use case. Afterwards, the results are discussed in Section 5. Finally, Section 6 provides a conclusion.

#### **2. A Digital Supply Chain Twin in Organic Food Supply Chains**

#### *2.1. A DSCT for Multi-Echelon Inventory Management*

Today, many definitions and understandings of the DSCT concept exist in the scientific literature. According to Ivanov et al. [5], a DSCT serves as a decision-making aid for the physical value network through data support. Accordingly, the DSCT reflects the SC in real-time with existing stocks, demands, transport routes and other logistical parameters. Niaki and Shafaghat [11] criticize that this understanding reflects common practices of SC planning and modelling, but does not describe the properties of a DSCT. They define the DSCT as a detailed simulation model of the SC, which can be analyzed in order to better understand, learn, and reason in regard to the real-world system. To provide a conceptual clarification, Gerlach et al. [2] conducted an extensive literature review, where they came up with the following definition, which serves as a basis for this study. Figure 1 acts as a visual representation of the concept.

**Figure 1.** The digital supply chain twin (DSCT) concept Reprinted with permission from Ref. [2]. Copyright 2021, copyright Benno Gerlach.

*A digital logistics twin or digital supply chain twin (DSCT) is a digital dynamic simulation model of a real-world logistics system, which features a long-term, bidirectional and timely data-link to that system. The logistics system in question may take the form of a whole value network or a subsystem thereof.*

*Through observing the digital model, it is possible to acquire information about the real logistics system to draw conclusions, make decisions, and carry out actions in the real world. The DSCT enables the use of diagnostic, predictive and prescriptive methods with the ultimate goal of holistically improving the logistics performance along the whole customer order process* [2].

Among other aspects, the authors underline the importance of dynamic simulation capabilities in a DSCT, which in turn enable the user to run what-if scenarios. It is also made clear, that not all attributes of a DSCT are predefined when talking about the general concept. While some DSCTs are updated in real-time, for example, for others a lower updating frequency is sufficient. These attributes are determined by the specific use case, in which the DSCT creates benefits.

In the study at hand, a DSCT on the network-level is analyzed. It is applied in the area of network management, more exactly multi-echelon inventory management. In multiechelon inventory management, the DSCT represents a SC from suppliers to customers. This model is then used to test inventory and procurement strategies and evaluate their SC performance [2]. In this sense, the DSCT might also be used to improve a company's ability to react to SC disruptions and other risks in order to improve SC resilience [12]. In order to do so, the DSCT is updated in a timely manner. Real-time updating seems to be unnecessary, because relevant changes in the system do not occur within seconds or minutes. SC disruptions may be addressed in day-to-day operations, where the user is still able to respond efficiently. Therefore, a daily frequency seems to be sufficient. A DSCT might be used both reactively and proactively. Some SC disruptions, such as demand spikes, can be anticipated before they occur. In this case, scenarios can be proactively simulated in the DSCT in order to test the best possible response [13]. In other cases, risks are not predictable. The DSCT then helps to make reactive decisions immediately after the incident occurs. In this study, only reactive use is examined.

Several researchers have addressed the use of a DSCT in the food industry. Defraeye et al. [14] describe the optimization of a fruit SC by modeling the temperature of a transcontinental value network. DSCTs are considered a useful addition in the perishable food sector to minimize food losses due to improper storage. Burgos and Ivanov [15] develop a model of a DSCT in risk management using the COVID-19 pandemic as a motivation, as it can help value networks recover after a breakdown. AnyLogistix (ALX) is used as a simulation tool. However, their approach notably lacks the feedback of the DSCT into company processes and therefore does not differ significantly from a basic SC simulation model. Nikitina et al. [12] develop a DSCT of a food network with the help of a mathematical simulation model. The complex properties of the food products play a decisive role in their calculations.

In recent years, FSCs have been subject to numerous simulation studies, observing their ability to react to SC disruptions. Lohmer et al. [16] analyzed different resilience strategies with an emphasis on blockchain technology. Singh et al. [17] as well as Zhu and Krikke [18] examined the effects of COVID-19 related disruptions on FSCs. All these studies underline the importance of resilience enhancing technologies to ensure SC performance even in the face of SC disruptions. Accordingly, Yuan et al. [19] stress the importance of horizontal logistics collaboration. This study builds on this area of research by examining the use of DSCTs to improve decision making in FSCs in the face of SC disruptions.

The results of this study will tie in with various studies in DSCT research. First, the organic food industry is identified as a relevant industry for the use of a DSCT by Srai et al. [7], as it promises higher transparency. Moreover, a continuation of some aspects of the publication by Burgos and Ivanov [15] is given, in which the authors perform scenario analyses using a simulation model of an FSC. This study further developed their approach by extending the quantification method to integrate a feedback loop into the real logistics system. Finally, Gerlach et al. [2] identified a research gap in quantifying DSCT use. Accordingly, through the investigation of a DSCT in the organic food industry conducted in this study, insights can be derived that have not yet been addressed in academia.

This study seeks to further investigate the use of DSCTs in the organic food industry. Therefore, a case study featuring an organic FSC was constructed, which will be described in detail in the next section.

#### *2.2. A Case Study in Organic Food Supply Chains*

In this study, subject of investigation is a SC in the organic food industry. Organic food products are usually available in conventional retail supermarkets as well as in organic supermarkets, in which up to 10,000 different organic food articles can be found. Often, the procurement is carried out regionally in small-scale and decentralized transports [8]. The FSC is one of the most complex and fragmented value networks, with production mostly worldwide. The wide range of products and the large number of individual product categories lead to a variety of logistical challenges. Products such as dairy products, fresh meat, fish, fruit and vegetables or frozen foods must be temperature-controlled [20]. This complexity is a further argument for the use of a DSCT. Figure 2 shows the individual actors and functional areas of an FSC.

**Figure 2.** Functional areas and actors in international food value chains. Reprinted with permission from Ref. [21]. Copyright 2020, copyright Julia Kleineidam.

In 2021, Patidar et al. [22] conducted a structured review of the literature published on FSC management in the past 15 years to give an overview of the research field and identify research gaps. Among other implications, the authors found that even though a large number of articles discussed and highlighted the problems, challenges, and issues in the FSC, few studies presented strategies to overcome them. Especially the prevention of food loss through LSCM efficiency as well as the proper utilization of emerging technologies are identified as possible research gaps. This work aims at filling these gaps by examining the DSCT as a potential technological solution in the context of a domain-specific case study.

The case study examined in this work features a European FSC, with an emphasis on the German sales market. It includes a value network from suppliers to supermarkets (retail stores). Even though production takes place worldwide, the immediate suppliers of finished products are located in Europe, predominantly in Germany. There are twelve European suppliers, who receive their products from global producers. These suppliers supply a central distribution center (DC) in Germany, which in turn supplies five wholesalers, that are spread across Germany. These sites act as hubs, storing goods and distributing them to the retail stores all across the country. The retail stores are characterized as organic food supermarkets and act as the final customer for the FSC in question. Hence, the system in question is a four-stage value network. Only four product categories are considered. Figure 3 shows a simplified representation of the FSC.

**Figure 3.** Food supply chain of the case study.

The case study company sells organic food produce at German retail stores. It is based on one of the three largest retailers in Germany, which sources about 6000 organic products worldwide [23]. In order to achieve greater visibility and transparency along the FSC, the company decides to use a DSCT. It hopes to be able to better manage its network-wide inventory as well as react to SC risks operationally and strategically.

The user of the DSCT is the SC manager in Germany, who coordinates and optimizes the company's network-wide product supply. She is responsible for the procurement of products, up to the delivery of the products to the supermarkets. His tasks include developing procurement, inventory and order strategies, drawing up contingency plans and evaluating suppliers. The SC manager sits at the DC but is in close contact with dispatchers in the respective hubs, in order to optimize inventory levels across different SC echelons. One of the user's goals is to ensure efficient hub utilization, enabling timely product deliveries to the retail stores, even under changing SC conditions. The scope of the SC manager in this case study thus includes both operational and strategic tasks. Accordingly, she is responsible for procurement logistics, determining the ordering policies as well as defining inventory policies. Thus, a holistic approach to LSCM tasks is given [24].

The user in question has access to a network-wide DSCT, representing the SC from the suppliers to the retail stores. In the event of changing SC conditions, he can run "what-if" scenarios in order to develop a suitable strategy. For this purpose, it is assumed that the user feeds the knowledge gained back into the real logistics system by adopting different strategies. The DSCT evaluates these strategies across different dimensions of SC performance. For this purpose, several performance measurement systems exist in theory and practice, which include financial as well as non-financial measures [25,26].

#### *2.3. A Quantification Method for DSCT Benefits*

Ironically enough, most DSCT studies do not feature a real-world DSCT. They mostly use a basic simulation model, which is not connected to any real-world system and therefore does not qualify as a DSCT. Still, it is carried out so in a variety of studies: A SC disruption is first being simulated, after which the level of SC performance drops. Subsequently, the features of the simulation model are being used to improve SC performance due to

strategies, that better fit the new situation. The level of improvement is then presented as the expected benefit of the DSCT.

However, this approach neglects the fact, that in a real use case a user would have to use the DSCT during ongoing operations. There has to be a feedback loop returning the simulation results into company processes. This process needs to be emulated in order to obtain a better understanding of the effect a DSCT use has on SC performance. Even if this feedback loop is not executed using real-world company data, it can be emulated using a fitting simulation model. For this purpose, an adequate method should be developed in order to be able to adequately quantify the benefits of using a DSCT, as a basic simulation model is not sufficient. Therefore, in accordance with RO1, the following approach is presented as a possible solution:

	- a. Logistics System Description
	- b. Input Parameters
	- c. Output Parameters
	- a. Baseline Scenario
	- b. Modified Scenarios
	- a. Objectives
	- b. Resources
	- a. Initial Baseline Scenario
	- b. Modified Baseline Scenarios
	- c. Optimized Modified Scenarios

**Use case definition**: First, the DSCT use case is to be defined in detail. This begins with a description of the logistics system in question. Furthermore, the input and output parameters have to be set. The input parameters consist of the decisions and actions that the user in question inputs into the real system. The output parameters are certain LSCM performance measures. The user's performance is evaluated on the basis of these. The measures used should always form a holistic performance evaluation of the logistics system in question, as is appropriate in LSCM.

**Scenario selection**: Next, scenarios to be investigated are to be selected. The scenarios chosen should reflect possible applications of the DSCT in the use case described prior. The first scenario should always be an initial baseline scenario. On the one hand, this initial baseline is used as a validation. The output parameters should be compared with real-world measures to ensure model accuracy. On the other hand, the initial baseline serves as a means of comparison. Afterwards, the modified scenarios have to be selected. These scenarios should represent alternative situations the DSCT user can find himself in during ongoing operation. For this purpose, domain experts should be consulted. The scenarios should be both probable and relevant in terms of the use case in question. For an application in risk management, for example, these modified scenarios might be the occurrence of various SC risks.

**Process emulation**: The first level of simulation is the emulation of the user process. This should be a replication of all the steps a user would perform in the real-world optimization process. During this phase, the simulation model acts as a tool for reacting to the modified scenarios defined in the prior step when trying to optimize the output parameters. For this purpose, objectives have to be defined. They may take the form of measures and respective target values the user tries to achieve with her actions. As the user would not have unlimited time in a real-world application, money and other resources, these resources also have to be defined beforehand. This may be carried out by setting a fixed maximum number of iterations for the optimization process.

**System simulation**: Ultimately, the different final scenarios have to be fed into the model to simulate the system's behavior over time, thus forming the second level of simulation. During this phase, the simulation model acts as a tool to reflect the system's behavior over time. For this purpose, the results of the prior process emulation step are taken. These results represent the user's decisions that were made using the DSCT as a decision support tool. These decisions are then input into the simulation model in the form of altered input parameters. In order to be able to put the results into perspective, the initial baseline scenario should first be simulated. Subsequently, further baseline scenarios should follow—one for each modified scenario to be investigated. The results of these experiments reflect the system's behavior when the modified scenarios occur, but no DSCT is used. Lastly, the optimized modified scenarios should be simulated. The results of these experiments reflect the system's behavior when the modified scenarios occur and a DSCT is used to optimize the system. The input parameters for these experiments are taken from the process emulation step. The reaction time of a user should also be considered in this step.

Following these steps should give a realistic overview of the expected benefits of using a DSCT in a given use case. A comparison between the initial baseline scenario and the modified baseline scenarios gives an estimate of the expected outcomes of the SC modifications on SC performance. In the example of risk management, this may form a means of risk assessment. A comparison between the modified baseline scenarios and the optimized modified scenarios gives an estimate of the effect DSCT use has on LSCM performance regarding the use case in question. In the following, the method was applied to the use case of this study:

**Use case definition**: The use case in question is multi-echelon inventory management of an FSC. A detailed use case description is presented in Section 2.2. The input parameters are the inventory, procurement, and order strategies of the DC and the hubs as described in the case study. In order to evaluate the DSCT's effect on SC performance, a fitting performance measurement system was derived from theory. The performance measurement system is divided into the five categories of efficiency, flexibility, responsiveness, food quality and sustainability [25–27]. Each category is measured by key performance indicators (KPI) that are presented in Table 1:

**Table 1.** Supply Chain Performance Measurement System of the Case Study.

**Scenario selection**: As the use case in question deals with multi-echelon inventory management of volatile SC, the scenarios to be examined are different volatility scenarios. Three scenarios including two different kinds of SC disruptions were examined. First, an increase in demand. Second, the breakdown of a supplier. Third, both a demand increase and a supplier breakdown.

**Process emulation**: The user utilized a simulation model in order to test different strategies in response to SC disruptions. Therefore, he ran experiments for different what-ifscenarios with the DSCT and made quantitatively justified decisions on suitable solutions

based on the experiment results. His ultimate goal was the optimization of the logistics system in the face of the given SC disruption. For reasons of simplicity, only one objective was defined for this study: Achieving a service level of at least 98% per product for all locations within the scope. Still, all SC performance measures were evaluated in the end. For resource constraints, a minimum of 5 iterations and a maximum of 15 iterations were selected. The what-if scenarios were carried out using a consistent procedure, which will be further described in Section 3.3. This process led to a final set of strategies for each of the three volatility scenarios, determined by the user in order to best handle the given SC disruptions.

**System simulation**: In this step, the system's behavior over time was simulated for each volatility scenario. For this purpose, the initial baseline, the modified baseline, and the optimized modified scenario were compared. Thus, the use of a DSCT in the given use case could be evaluated. A visual representation of the applied method is shown in Figure 4.

**Figure 4.** Quantification method for DSCT benefits of the case study.

#### **3. Research Design**

In order to apply and test the developed method and its application to the given use case, a simulation study was conducted. ALX was used as a simulation tool. Furthermore, a well-proven process model for conducting simulation studies by Rabe et al. [28] was used as a methodological guideline. The sub-processes of said model were slightly adapted and are described in the following part. Similar approaches have been utilized in the recent scientific literature, where simulation studies were chosen as a methodology in order to examine the effects on organizational or technological measures on FSC performance [16–19,29].

#### *3.1. Objectives and Tasks*

Objective description: The objective of the simulation study is quantifying the effects of using a DSCT for managing inventory and procurement in an organic FSC. Simulation is used as it constitutes a major component of the DSCT. Thus, this simulation study directly targets RO2.

Task description: The objective is met through conducting what-if scenarios, emulating the use of a DSCT in the face of different volatility scenarios. The selection of the volatility scenarios is further described in Section 3.3. The effects of different strategies on certain LSCM measures are then evaluated in order for them to be comparable. Therefore, the user performs an optimization for each volatility scenario in order to deal with the DC disruption she is facing. By means of dynamic factor design, a step-by-step attempt is made to improve LSCM performance.

#### *3.2. System Analysis and Model Formalization*

The system in question is the FSC described in Section 2.2, with focus on four product categories: fruits, vegetables, meat, and fish. For reasons of simplicity and feasibility, a consideration of sub-categories or single products was refrained from. To gain insights into the system, several data sources were used. Some of the data used were freely available on the Internet, while other data were obtained from publications. These are either statistical calculations, real data, or estimates. All data sources are provided in the following paragraph.

The location data regarding the retail stores in Germany were taken from a publicly available database [30]. The demand values of the individual product categories are based on a calculation using the per capita consumption per year with the respective market share of the given retail chain. This was then multiplied by the share of organic food in the total food market in Germany. Therefore, the demand per day per inhabitant in Germany was determined. Finally, the demand of all retail stores was automatically generated in ALX, using the number of inhabitants of the different locations [23]. The sales prices were determined by the average prices of the given food categories in the year 2022 [9].

For reasons of simplicity, shipping costs were included in the purchase price. Initial stock levels as well as costs for stock refilling were also reflected in the purchase price. [15] To measure CO2 emissions, truck emission factors based on load weight and distance travelled were derived using the publicly available EcoTransit calculation tool, which is recognized in research. In order to take energy consumption for truck loading activities into consideration, an additional coefficient was added per delivery [31]. Furthermore, a distance- and weight-based calculation of travel costs was selected.

Since only some product categories are included, the key figure of truck utilization is not decisive. Accordingly, trucks could drive at less-than-truck-load (LTL) between locations. Two types of trucks were considered. Between the suppliers, the DC and the hubs, trucks with larger capacity (26 t) were used. The last-mile delivery to the supermarkets was executed using trucks with smaller capacity (7.5 t), as they have to drive to city centers.

Inventory policies of the DC and the hubs were set to be identical: make-to-stock (MTS). By selecting a Min-max policy (s, S), excessively high and low inventories can be avoided. For this purpose, a replenishment point (s) and a target stock (S) were defined. A new order is placed whenever the inventory level falls below the replenishment point (s). The ordered quantity is the delta between S and the current inventory level. The target stock (S) at the DC was set to be larger than the daily demand in Germany as a whole. The replenishment point (s) at which reordering takes place is usually half of the value of S. At the hubs, inventory levels are lower. The target stock was set to be equal to the daily local demand per product. Inventory levels are checked twice a day, so that orders are placed a maximum of twice a day.

To calculate the system's inventory cost, the storage costs at the DC and the hubs were calculated. The storage costs for the suppliers were not considered. All suppliers had the same capacity and could order from the producer as often as they wanted. The expected lead time of the retail stores was set to be two. An overview of the case characterization is presented in Table 2, while Figure 5 shows the model SC implemented in ALX.

**Table 2.** Case study characteristics.

**Figure 5.** Food supply chain of the case study implemented in the simulation tool.

#### *3.3. Experiment Plan and Analysis*

**Table 2.** *Cont.*

Four different kinds of scenarios were tested in order to simulate the system's behavior in the face of different forms of SC volatility. There is one initial baseline scenario and three volatility scenarios. In the initial baseline scenario, the status quo of the FSC was simulated. In this scenario, the FSC was set up so that all suppliers are available and deliver to the DC in Lorsch. The SC performance measurement system presented in Section 2.3 was used to evaluate LSCM performance. It should be noted that some of the KPIs have different names when implemented in ALX. The dropped orders, for example, were derived from the product backlog measured in the simulation tool, as both measures represent the lack of order fulfillment to the customer.

Scenario 1 examines the occurrence of a customer risk. One reason for this could be a worldwide pandemic, for example, which causes a strong and spontaneous increase in demand. It was assumed that demand increased by 100% during a defined period. The duration of the demand increase was set between 15 and 120 days. In a sensitivity analysis, the influence of the duration of the demand increase was determined first.

In scenario 2, there are supplier disruptions. The suppliers of meat products in Germany close their sites and can no longer supply the DC. The company needs to react quickly and use the DSCT to develop a strategy to still be able to supply the retail stores. The breakdown duration was set between 15 and 90 days. Again, a sensitivity analysis was performed to evaluate the effect of different durations of supplier downtime on the LSCM performance.

In scenario 3, it is assumed that both effects mentioned above occur simultaneously. Possible reasons for this are global pandemics causing unexpected demand spikes and factory closures. The duration of both the demand increase and the supplier breakdown was assumed to be 60 days. The magnitude of the demand increase was set between 50% and 200%. Again, a sensitivity analysis of different magnitudes of demand increase was performed.

An overview of the different scenarios and the respective experiments is presented in Table 3.

**Table 3.** Volatility Scenarios analyzed in the Case Study.

For each scenario, the SC performance measurement system presented in Section 2.3 was used to evaluate different inventory and procurement strategies. The system was implemented in ALX in the form of a dashboard, which is a good representation of an actual DSCT being deployed in a real-world use case. For reasons of readability, not all parts of the dashboard can be shown in all steps.

For each scenario, the user then performed an optimization in order to deal with the DC disruption she is facing. By means of dynamic factor design, a step-by-step attempt was made to improve LSCM performance. A maximum of 15 iterations were conducted per scenario, with the goal of achieving a service level of at least 98%.

#### **4. Results**

In this section, the experiment results are presented. The section is structured in accordance with the different volatility scenarios presented in the previous section. For each volatility scenario, an overview of the LSCM performance is given. Additionally, a description of the disruption effects on the FSC is given in scenarios 1–3. Subsequently, the effects of the DSCT use are described. This is done not only in terms of LSCM performance measures, but also in terms of the system's behavior over time.

#### *4.1. Scenario 0: Initial Baseline Scenario*

First, the initial baseline scenario was examined. For this purpose, one simulation experiment was conducted. The KPIs measured present the status quo of the FSC without any SC disruptions.

The lead time for each order is less than 0.54 days, the service level for each product is 100% at all times, and the available inventory in the hubs and the central DC is constant. Furthermore, there are no delayed or dropped orders. This overview reflects a functioning and efficient logistics system. Figure 6 shows an overview of some important KPIs in the form of a dashboard. This dashboard represents the simulation tool, which is available to the real-world user for decision-making support.

**Figure 6.** Initial baseline scenario: Dashboard.

The complete performance evaluation of scenario 0 is presented in Table 4. In the efficiency category, the total costs are about €309 million. Of this, 86% is attributable to purchasing costs, while 13% is attributable to inventory carrying costs and less than 1% to transport costs. This leaves an overall profit of €42.16 million, which accounts for approximately 13% of total revenue. The purchasing costs of 86% are rather high. However, this corresponds to the common share in the organic food industry, since the products are not being produced, but rather procured. In addition, other cost components are included in this amount, as described in the model assumptions in Section 3.2.

**Table 4.** Performance evaluation of scenario 0.

It is observed that all orders can be delivered on time and that there are no dropped orders at any time. The fill rate is 100% and the average lead time is 0.32 days. Therefore, the logistics system can meet the retail stores' lead time requirement of less than two days at all times. The service level per location and per product category is 100%. The average stock level per day is 281,159 kg. Accordingly, the DC and hubs are always ready to meet the customer demand. The total CO2 emissions from transport and storage are 4856 t CO2.

This evaluation acts a mean of comparison for the scenarios to come.

#### *4.2. Scenario 1: Demand Increase*

**Disruption effects**: After a demand increase occurs, some orders can no longer be fulfilled. Moreover, some orders cannot be fulfilled on time. This is reflected in the occurrence of dropped orders, as well as in the OTD and service level drop. As demand at the retail stores increases, demand at the hubs and at the DC in Lorsch also increases during this period. As a result, inventory levels in the hubs drop sharply, while the inventory level in the DC increases. The DC is supplied by several suppliers who normally do not use their maximum delivery capacity. The bullwhip effect is evident here, as the inventory fluctuation, that begins at the retail stores, is highest at the supplier level. After inventory levels in the hubs plummet, it takes a while for service levels to recover.

Figure 7 provides an overview of the disruption effects on the KPIs over time for different durations of demand increase. It shows a visualization of four important KPIs for two consecutive iterations. A sensitivity analysis shows that the longer the demand increase, the longer the average lead times. Furthermore, a linear relationship is observed between the duration of the demand increase and the effects described above. The longer an increase lasts, the longer the logistics system takes to recover. This figure compares an increase of 30 and 60 days. More iterations have been made subsequently, up to a duration of 120 days, thus ending the sensitivity analysis. As the analysis shows a linear relationship between the disruption duration and the effect, the duration can be chosen freely within the given range without affecting the interpretability of further results.

**Figure 7.** Scenario 1: Sensitivity analysis (iteration 2 = 30 days, iteration 3 = 60 days).

**Iterative Optimization:** After the sensitivity analysis, a duration of 60 days was selected as a modified baseline. In the event of a 60-day demand increase, the user tries to improve the LSCM performance through various adjustments. Figure 8 contains the dashboard with the relevant KPIs, on the basis of which various measures are taken. By means of dynamic factor design, a step-by-step approach is made to improve LSCM performance. Three steps are selected representatively to show the iterative improvement. By adjusting the order frequency, the number of delayed orders can be significantly reduced (iteration 8). Next, the availability of new suppliers leads to an improvement in service levels and fewer orders are delivered late (iteration 10). Finally, an increase in inventory leads to less dropped orders and thus to a stabilization of service levels (iteration 13).

**Figure 8.** Scenario 1: Iterative optimization (iteration 8 = adjusted order strategy, iteration 10 = adjusted procurement strategy, iteration 13 = adjusted inventory strategy).

**Performance evaluation:** In the final iteration 13, order strategies as well as procurement and inventory strategies have been adjusted in order to better adapt the logistics system to the SC disruptions. Table 5 gives an overview of the LSCM performance of scenario 1 with the use of a DSCT (optimized) and without (baseline). However, the user

can only react to the increase in demand in a reactive way. Therefore, a delay is simulated between the occurrence of the SC disruption and the point at which the changes are implemented in the logistics system. This leads to delayed orders and a slightly higher lead time.

**Table 5.** Performance evaluation of scenario 1: Demand increase.

When comparing the scenario 1 baseline with the optimized final iteration, an overall increase in costs is observed. Still, there is an increase in profit of around €11.5 million. This is because after the demand increase, there are 548 dropped orders, which in turn lead to lost sales and therefore less revenue. With the use of a DSCT this can be prevented. The service level of 0.99 is also notably better than without DSCT use (0.84).

#### *4.3. Scenario 2: Supplier Risk*

**Disruption effects**: After a supplier breakdown occurs for one product category (meat), it is observed that the lead time for meat products grows significantly for the duration of the breakdown. Meat orders from the retail stores arrive constantly and they cannot be serviced. Therefore, dropped orders are observed, which lead to lost sales. Inventory levels in the DC and the hubs are lower and the service level for meat products decreases. Figure 9 visualizes the effects described above. A sensitivity analysis analogue to scenario 1 is carried out. Iteration 2 (30-day supplier downtime) and iteration 3 (60-day supplier downtime) are displayed in the figure.

**Figure 9.** Scenario 2: Sensitivity analysis (iteration 2 = 30 days, iteration 3 = 60 days).

**Iterative Optimization:** After the sensitivity analysis, a duration of 60 days was selected as a modified baseline. The user has different strategies to follow in the event of a supplier breakdown. Since no suppliers are available in Germany, an attempt is made to involve suppliers from abroad. Adding a supplier from Poland has the effect that no more orders are delivered late. The inventory levels in the DC and the hubs can also be increased slightly. As a result, the service level improves (iteration 5). Adjusting the order frequency for meat products in the hubs and distribution center results in the supplier not being able to re-deliver quickly enough because they have limited capacity and their location is further away from the DC (iteration 7). Finally, establishing and selecting a supplier relationship in the Netherlands with the same capacity but with a shorter distance leads to a significant improvement in service level and other relevant KPIs as compared to prior iterations (iteration 9). In addition, the DSCT helps to determine that the supplier from the Netherlands is sufficient. The costs for establishing new supplier relationships are neglected. The effects of adjusting the strategies iteratively are shown in Figure 10.

**Figure 10.** Scenario 2: Iterative optimization (iteration 5 = adjusted procurement and inventory strategies, iteration 7 = adjusted order strategy, iteration 9 = adjusted procurement strategy).

**Performance evaluation:** In scenario 2, the effects are similar to scenario 1. In the final iteration 9, order strategies as well as procurement and inventory strategies have been adjusted in order to better adapt the logistics system to the SC disruptions. Table 6 gives an overview of the LSCM performance of scenario 2 with the use of a DSCT (optimized) and without (baseline). Again, the user can only react to the supplier breakdown in a reactive way. Therefore, a delay is simulated between the occurrence of the SC disruption and the point at which the changes are implemented in the logistics system. This again leads to a slightly higher lead time.

**Table 6.** Performance evaluation of scenario 2: Supplier breakdown.

The profit increases by €8 million when using a DSCT relative to the scenario 2 baseline. This occurs due to higher revenue, although total costs increase by 9%. The increase in revenue is due to the prevention of almost 7800 dropped orders. The main drivers for the higher total costs are the higher inventory costs of 28% and the higher purchasing costs of 9%. The lead time is also significantly lower after optimization (0.32) as compared to the scenario 2 baseline (1.46).

#### *4.4. Scenario 3: Customer and Supplier Risk*

**Disruption effects**: In this scenario, both a demand increase and a supplier breakdown happened simultaneously for a duration of 60 days. Effects from both scenario 1 and scenario 2 can be observed. As the demand in some retail stores cannot be satisfied, dropped orders occur. The performance measures related to the product category affected by the supplier breakdown (meat) suffer particularly in this scenario. The service level drops, as well as inventory levels at the hubs. The effects on the KPIs are significantly higher than in the prior two scenarios, as the logistics system has to deal with two disruptions at the same time. Figure 11 gives an overview of the observed effects.

**Figure 11.** Scenario 3: Sensitivity analysis (iteration 2 = 50% demand increase, iteration 3 = 100% demand increase days).

**Iterative optimization:** After the sensitivity analysis, a demand increase of 100% was selected as a modified baseline. The user then again tries to handle the SC disruptions properly. The supplier breakdown is prioritized, as its impact is more severe when compared to

the demand increase. Figure 12 shows another exemplary excerpt of the user dashboard, where the inventory levels of the DC for different strategies are shown. In the first two visualizations, stock outs can be observed. In the last one the supplier selection seems to be appropriate, as no stock outs occur. This way the demand can be met.

**Figure 12.** Scenario 3: Inventory levels of the DC for different procurement strategies.

Figure 13 shows some significant iterations in the optimization process. The addition of two suppliers stabilizes inventory levels in the DC. It also improves service levels and reduces the number of dropped orders (iteration 7). Increasing order frequency leads to higher inventory levels at the hubs as well as an improved service level (iteration 8). Finally, an adjusted inventory strategy leads to the required service level (iteration 14).

**Figure 13.** Scenario 3: Iterative optimization (iteration 7 = adjusted procurement strategy, iteration 8 = adjusted order strategy, iteration 14 = adjusted inventory strategy).

**Performance evaluation:** In scenario 3, it took the user 14 iterations to teach the final result. Order strategies as well as procurement and inventory strategies have been adjusted in order to better adapt the logistics system to the SC disruptions. Table 7 gives an overview of the LSCM performance of scenario 3 with the use of a DSCT (optimized) and without (baseline). Again, the user can only react to the SC disruptions in a reactive way. Therefore, a delay is simulated between the occurrence of the SC disruptions and the point at which the changes are implemented in the logistics system. This leads to delayed orders and a slightly higher lead time and a reduced service level.

**Table 7.** Performance evaluation of scenario 3: Customer and Supplier Risk.

After optimization, the profit increases by €10 million. Even though the total costs increase by 21% due to the build-up of a higher inventory, the company is more profitable, since more than 8000 dropped orders can be prevented. The service level after the optimization (0.993) is significantly higher than without DSCT use (0.741).

#### **5. Implications**

This study shows how a DSCT can be used in multi-echelon inventory management of an organic FSC. A method for adequately measuring SC performance was developed and tested. When using this method as a guideline, one is able to not only make an estimation of the effects a DSCT in a certain use case has on LSCM performance, but also obtain a better idea of the user process during ongoing operations. Additionally, an analysis of the conducted case study gives a good overview of what benefits are to be expected when implementing a DSCT in a comparable use case in the food industry. This study is therefore of great use for researchers and practitioners alike who are concerned with DSCT.

The quantification method presented in Section 2.3 generates multiple added values for DSCT research. First, it is a structured approach, which helps to generate reproducible and verifiable results when trying to measure DSCT benefits. Second, it displays some major advantages of using merely a simulation model for this purpose, which is still common practice as of today. By emulating and documenting the user process, a realistic application is ensured. Both the user's objectives and the user's resources are determined beforehand. Insights into the utility and usability of the DSCT can be obtained. Furthermore, a feedback loop of the optimization results into the logistics system is given. The user's reactiveness is being simulated as well, thus creating a realistic overview of the system's behavior in case of a real-world application. Lastly, the quantification method presents different levels of comparableness. Depending on what baseline is used, different conclusions can be drawn.

All in all, the quantification method is an improvement of the methods being used commonly in DSCT research. Therefore, when using this method as a guideline, researchers may be able to achieve more realistic and comprehensible results when trying to evaluate DSCT benefits. Practitioners may benefit from the method as well, as they may use it as an assessment tool when deciding on implementing a DSCT in their company. However, one has to keep in mind, that this study completely neglects the cost of DSCT implementation. In order to carry out a cost–benefit comparison, reasonable cost assumptions would have to be made beforehand.

The extensive simulation study conducted, which is described in Sections 3 and 4, showed a realistic application of said method in a case study featuring an organic FSC. Thus, a first verification of the method is given. The organic food industry seems to be a plausible application domain. The volatility scenarios analyzed, namely demand increase and supplier breakdown, appear to be realistic scenarios, as they have been observed during the COVID-19 pandemic. For both scenarios, the DSCT presents itself as a feasible solution with promising effects on LSCM performance. An evaluation of the experiment results gives an estimate of the quantified benefits.

In case of a demand increase, a company's profit goes down. This is because the logistics system is not able to fulfill the customers' needs with the current order, inventory, and procurement strategies. Fill rate, service level, and OTD drop, while the lead time goes up. When a DSCT is used, these effects can be prevented for the most part. Notably, the logistics costs of the FSC go up, but since the revenue rises even more, an overall profit increase can be achieved, while otherwise quasi maintaining the LSCM performance level of the status quo. Interestingly enough, a comparison between the optimized scenario and the initial baseline shows, that a SC disruption in the form of a demand spike acts as both a risk and an opportunity, due to the potential profit gain for the company.

In case of a supplier breakdown, similar effects can be observed. It goes to show that if not properly dealt with, the missing supplier has devastating effects on the LSCM performance. Customer needs cannot be fulfilled and dropped orders go up, which in turn leads to a decrease in revenue. Other LSCM KPIs also deteriorate. When using a DSCT, a proper alternative for the missing supplier can be found, as is shown in the simulation experiments. Again, dropped orders can be prevented, resulting in higher revenue and therefore greater profit, while again maintaining the LSCM performance level of the status quo. Unlike in the event of a demand increase, the supplier breakdown leads to an overall profit loss when compared to the initial baseline. Still, the DSCT can help mitigating negative impacts, therefore potentially preventing major disasters.

These findings should present a first attempt of a realistic quantification method for DSCT benefits and make it easier for researchers and practitioners in LSCM to evaluate DSCT applications in practice. Combined with an adequate simulation study design, an assessment tool for DSCT implementation is formed.

#### **6. Conclusions and Final Remarks**

In conclusion, the aim of this paper was to develop a quantification method for evaluating DSCT benefits (RO1) and evaluate the effect of using a DSCT in multi-echelon inventory management of an organic FSC in terms of LSCM performance (RO2). All ROs have been met sufficiently by the authors. Section 2.3 provides a conclusive method for benefit quantification of DSCT use. Meanwhile Sections 3 and 4 describe the application of said method to a case study in the organic food sector, providing an evaluation of this very use case. Finally, Section 5 derives implications for researchers and practitioners.

The quantification method presented in Section 2.3 provides a conclusive process model for measuring DSCT benefits. This is of great value for practitioners with an interest in implementing a DSCT and DSCT researchers alike, since it paves the way for a reasonable cost–benefit comparison of DSCTs. As pointed out in Section 5, the method features significant advantages over other methods commonly used in the field, most notably simple simulation models. This allows for more realistic DSCT studies, making the research field overall more tangible.

Furthermore, the results of the simulation study offer a first point of reference in terms of what benefits to expect when using a DSCT to improve decision making in an FSC. The use case in question featured decisions in regards to order, procurement, and inventory strategies in a multi-echelon logistics network when facing SC disruptions, namely demand spikes and supplier breakdowns. When utilizing the DSCT, the case study user was able to prevent the devastating effects the disruption would have had on LSCM performance. Through the application of better strategies, lost sales were prevented, resulting in higher company revenue. Even though these strategies sometimes led to increased costs, overall profit went up. Non-financial KPIs were also improved, leading to a better OTD rate, increased service levels, and overall shorter lead times. Nevertheless, these results have to be interpreted with caution, since validity and generalizability of the quantified magnitude of said effects heavily depend on the case study data quality. Since the case study at hand was constructed, the authors would like to refrain from deriving far-reaching implications for the food industry. However, the study results reveal insights into the underlying mechanisms at work in FSCs and how the application of a DSCT in this domain might look like.

Still, this study is not without limitations, which shall be addressed in the following paragraph. First of all, the simulation model used in this study does not classify as a DSCT, since it does not feature a data link to any real-world logistics system. So even though a real-world application process is being emulated, the level of accuracy of a DSCT cannot be matched. Therefore, some assumptions have been made while creating the case study and the respective simulation model. For one, truck utilization has mostly been neglected during evaluation, since only LTL transports have been used. This also led to higher CO2 emissions in the optimized scenarios. Since awareness for sustainability has been rising over the past years in LSCM, this factor should be subject of more detailed investigation in the future. Some costs and efforts have also been neglected during the modelling phase, for example the ones related to building supplier relationships and applying certain order strategies. Additionally, storage capacities were neglected for the DC and the hubs. Still, it was implicitly given through inventory costs. Lastly, it was assumed that the user does not take any actions in the modified baseline scenarios, that is, after the SC disruption occurs, but no DSCT is used. Given proper SC transparency, this seems rather unlikely. However, on the one hand, SC transparency might actually not be given, which would render the assumption somewhat realistic. On the other hand, the magnitude of the positive effects brought by the DSCT use are not crucial for the core findings of this paper. Therefore, even though the study results have to be put into perspective in this regard, the authors are nonetheless confident in the generalizability of this study's results.

This work also lays the foundation for a variety of future research. Firstly, researchers may use the presented quantification method to more accurately evaluate DSCT benefits in their works. Secondly, as pointed out earlier, some dimensions of a LSCM performance have not been focus of this study. Especially the sustainability aspect of DSCT use should be looked into further. Thirdly, the application domain of the presented use case was the organic food industry. Similar studies should be conducted in other domains as well. Lastly, this study focusses solely on the benefits of a DSCT while ignoring the implementation costs for the most part. However, to properly conduct a cost–benefit evaluation, these implementation costs have to be estimated as well. Therefore, it seems crucial for researchers in the DSCT area to look into methods of evaluating DSCT implementation costs.

All in all, while benefits of using DSCT are widely being discussed in academia and practice, they are rarely quantified. As DSCT are a promising solution for handling complex tasks in LSCM, such as inventory and risk management, a detailed quantification of their effects on different LSCM performance measures is an essential instrument for making the concept's discourse more comprehensive. The authors of this paper hope to have underlined the importance of this topic and also to provide guidance for researchers and managers who have an interest in the subject of DSCT.

**Author Contributions:** Conceptualization, T.B. and B.G.; methodology, T.B. and B.G.; software, T.B.; validation, T.B. and B.G; formal analysis, T.B. and B.G; investigation, T.B. and B.G; resources, T.B. and B.G; data curation, T.B. and B.G; writing—original draft preparation, T.B. and B.G; writing—review and editing, T.B. and B.G; visualization, T.B. and B.G; supervision, B.G.; project administration, T.B. and B.G; funding acquisition, T.B. and B.G. All authors have read and agreed to the published version of the manuscript.

**Funding:** We acknowledge support by the German Research Foundation and the Open Access Publication Fund of TU Berlin.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

## *Article* **A Soft Computing View for the Scientific Categorization of Vegetable Supply Chain Issues**

**Rizwan Abbas 1,\*, Gehad Abdullah Amran 2, Irshad Hussain <sup>1</sup> and Shengjun Ma <sup>3</sup>**

**Abstract:** Over the most recent couple of years, the Internet of Things and other empowering innovations have been logically utilized for digitizing the vegetable supply chain (VSC). *Background*: The unpredictable examples and complexity inserted in enormous data dimensions present a test for an orderly human master examination. Hence in an information-driven setting, soft computing (SC) has accomplished critical energy to investigate, mine, and concentrate confidential information data, or tackle complex improvement issues, finding some harmony between good productivity and maintainability of vegetable supply frameworks. *Methods*: This paper presents a new and diverse scientific classification of VSC issues from the SC methodology. It characterizes VSC issues and sorts them in light of how they be demonstrated according to the SC perspective. Moreover, we examine the SC methodologies commonly utilized in each phase of the VSC and their related classes of issues. Accordingly, there is an issue in distinguishing and characterizing VSC issues according to a more extensive point of view, enveloping the different SC strategies that can apply in various phases (from creation to retailing), and recognizing the issues that emerge in these phases according to the SC viewpoint. *Results*: We likewise acquaint some rules with the assistance of VSC analysts and specialists to settle on appropriate strategies while resolving specific issues they could experience. Even though a few latest examinations have arranged the SC writing in this field, they are situated towards a solitary group of SC strategies (a gathering of techniques that share standard qualities) and survey their application in VSC phases. *Conclusions*: We have suggested a novel approach and complete scientific classification of vegetable supply chain concerns about soft computing. We present a view of three delegate supply chains: cruciferous vegetables, dark green leafy vegetables, and tomatoes. We assembled the scientific type in light of different parts to arrange vegetable supply chain issues as per how they can be demonstrated utilizing soft computing methodologies.

**Keywords:** vegetable supply chain; soft computing; neural networks; machine learning; deep learning

#### **1. Introduction**