This study investigates why there is a social sustainability gap in SMEs. It formulates some propositions and provides strategies to reduce the gap. By doing so, the study makes some notable contributions to the literature. The main contribution of this study is to identify the gaps in SMEs' SSPs and subsequent strategies, which will help the SME sector to be more socially sustainable. While previous reports broadly highlight the importance of social sustainability in SMEs and the difficulties and challenges in implementing SSPs in SMEs, none of them spotted the gap and provided a comprehensive understanding on this issue. Similarly, most of the academic articles that focus on SSPs in SMEs mainly discuss the importance, challenges, and barriers to implementing SSPs. For example, according to MacGregor and Fontrodona [43], the majority of the studies on SSPs in SMEs mostly focus on the development of awareness of SSPs. While enough justifications are already available in the literature as to why SMEs should implement SSPs, at this stage, it is more important to know the specifics of how SMEs can implement SSPs. This study discusses how social sustainability can be achieved in SMEs, which is a unique and noble contribution to the existing body of knowledge. Addressing such adequacy is important given that it is vital to know how SMEs can implement SSPs despite having resource and skill constraints and other challenges.

Next, the study supplements the limited research focusing on social sustainability, or sustainability in general, in SMEs [4,11,65]. Through a systematic literature review on social sustainability, Nakamba, Chan, and Sharmina [3] reported that research to date mostly focuses on large firms, and studies investigating the practices of and approaches to social sustainability in SMEs are inadequate. Finally, the study contributes to the literature on social sustainability. While environmental sustainability has been rigorously investigated in the previous literature, research on social sustainability is scarce [7].

## **7. Conclusions and Future Research Directions**

Aiming to provide guidelines to make SMEs socially sustainable, this study has explored why there is a gap between stakeholders' expectations and the SSPs adopted by SMEs, and how the gap can be minimized. Based on a rigorous literature review, the study has revealed that both internal and external factors are responsible for this social sustainability gap in SMEs. Various internal issues, such as poor organizational orientation and culture and lack of resources, skills, and knowledge, create the gap. Moreover, external factors, such as a lack of a tailored standard for SMEs and a lack of support from stakeholders, are also responsible for creating this gap or making the gap bigger. In order to minimize the gap, we propose that stakeholder support, a supply chain finance mechanism, the improvement of SMEs' efficiency and productivity, and the development of the awareness of employees will lead SMEs to be more socially sustainable. In line with these propositions, we have also provided several strategies, outlining the implications for both SMEs and their stakeholders. The strategies include building cooperative relationships with various stakeholders, developing programs to create awareness of SSPs, creating a sustainable finance package, ensuring a supportive governance structure, developing human capital, innovation in implementing SSPs, and developing a unified social sustainability standard for SMEs.

While the study findings make notable contributions to the literature, it also has a number of limitations that can be seen as the directions for future research. For example, in designing the proposition, this study only uses the current body of literature and does not consider any particular context. Therefore, future studies could empirically investigate and validate the propositions. For validating the findings of this study, a future study could formulate and test several hypotheses grounded in appropriate theories and based on the propositions suggested in this research. Moreover, a comparative study could be undertaken to explore how the proposed strategies contribute to increasing SSPs in SMEs in developed and developing countries. Such a study might provide useful information, as SSPs and their characteristics vary between developed and developing countries [4]. Moreover, there is a difference in implementing SSPs in developing and developed countries. For example, a study by Malesios et al. [18] revealed that French SMEs are different from Indian SMEs in practicing social sustainability. Therefore, a comparative study could be useful to further refine the strategies in the context of developed and developing economies. Finally, we suggest a future study for unveiling whether, how, and to what extent the strategies proposed in this research can contribute to the achievement of the sustainable development goals of the United Nations.

**Author Contributions:** Conceptualization, P.C. and R.S.; Literature review and information search, P.C. and R.S.; Draft writing, P.C. and R.S.; Reviewing and editing: P.C. and R.S., Administration, P.C. All authors have read and agreed to the published version of the manuscript.

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

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

## **References**

© 2020 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 (http://creativecommons.org/licenses/by/4.0/).

## *Article* **A Multi-Item Replenishment Problem with Carbon Cap-and-Trade under Uncertainty**

## **Jiseong Noh 1, Jong Soo Kim 2 and Seung-June Hwang 1,\***

Received: 26 May 2020; Accepted: 12 June 2020; Published: 15 June 2020

**Abstract:** Recently, as global warming has become a major issue, many companies have increased their efforts to control carbon emissions in green supply chain management (GSCM) activities. This paper deals with the multi-item replenishment problem in GSCM, from both economic and environmental perspectives. A single buyer orders multiple items from a single supplier, and simultaneously considers carbon cap-and-trade under limited storage capacity and limited budget. In this case we can apply a can-order policy, which is a well-known multi-item replenishment policy. Depending on the market characteristics, we develop two mixed-integer programming (MIP) models based on the can-order policy. The deterministic model considers a monopoly market in which a company fully knows the market information, such that both storage capacity and budget are already determined. In contrast, the fuzzy model considers a competitive or a new market, in which case both of those resources are considered as fuzzy numbers. We performed numerical experiments to validate and assess the efficiency of the developed models. The results of the experiments showed that the proposed can-order policy performed far better than the traditional can-order policy in GSCM. In addition, we verified that the fuzzy model can cope with uncertainties better than the deterministic model in terms of total expected costs.

**Keywords:** green supply chain management; carbon tax and cap; can-order policy; mixed-integer programming; fuzzy constraints

## **1. Introduction**

Since the 1997 Kyoto protocol, many countries and organizations have presented legislation or policies about managing carbon emissions as global warming destroys the Earth's ecosystem. Accordingly, any company can concentrate on reducing and managing its carbon emissions in a variety of areas such as supply chain management (SCM), production, contracts, inventory, and replenishment, with the result that green supply chain management (GSCM) is quickly spreading [1]. The main purpose of GSCM is to minimize supply chain costs and simultaneously reduce carbon emissions. In line with this goal, many governments have implemented carbon cap-and-trade regulation, which is well known as an effective economic-based mechanism [2]. Under such regulation, each company is allocated limited carbon emissions credits from its government, and it can buy or sell rights to emit carbon emissions with other companies in the carbon trading market [3,4]. In the European Union, the European Union Emissions Trading Scheme, which is the largest carbon trading market, has covered almost 50% of total carbon emissions [5]. Therefore, it becomes important to consider GSCM in the context of carbon cap-and-trade regulation.

Beyond governments' efforts to reduce carbon emissions, some companies have tried to manage their own carbon emissions through their supply chains. For example, the retailers Asda, Tesco, Wal-Mart, and H&M require their suppliers to reduce carbon emissions during multi-item replenishment

activities [6,7]. In this way, a company considers carbon emissions simultaneously with the multi-item replenishment problem under limited resources, such as storage capacity and budget [8]. However, a company could face two realistic situations based on either known or unknown market information [9]. When a company is in a monopoly market, it knows all of the relevant market information so it can easily decide on storage capacity and budgets. In contrast, when a company is in a competitive or a new market, the needed market information is difficult to grasp. In this case, because of weak market information, neither proper storage capacity nor budgets can be estimated in a stochastic sense. It is difficult to predefine them [10]. Therefore, this paper focuses on the multi-item replenishment problem with limited resources under two market information cases, the certain and the uncertain.

Given current real-world practices, this paper considers the multi-item replenishment problem with carbon cap-and-trade under limited storage capacity and budget. This work (1) develops two mixed-integer problem (MIP) models based on a periodic can-order policy, which is a well-known multi-item replenishment policy; (2) includes carbon cap-and-trade for GSCM; and (3) covers two market information cases: certain and uncertain information. This paper makes the following contributions. First, we develop a deterministic model with carbon cap-and-trade for GSCM with certain (known) market information. In this model, both storage capacity and budget are already predefined. The deterministic model can be applied in a monopoly market case. Based on this model, we develop a fuzzy model applying fuzzy constraints. Because of uncertain market information, both storage capacity and budget are considered as fuzzy numbers. The fuzzy model can be applied in a competitive market case. Second, we suggest both MIP models based on the periodic can-order policy. Thus, each model can obtain optimal results under replenishment planning and inventory control.

The structure of this paper is as follows. A literature review is presented in Section 2. Notation, assumptions, and problem definitions are introduced in Section 3. The deterministic model and the fuzzy model are developed in Sections 4 and 5, respectively. We present numerical experiments in Section 6. Academic, managerial, and environmental insights are presented in Section 7. Finally, conclusions are presented in Section 8.

### **2. Literature Review**

This paper is related to three elements of the relevant literature: a multi-item replenishment problem for GSCM with carbon emissions, the can-order policy, and GSCM with fuzzy constraints. The research on multi-item replenishment for GSCM with carbon emissions has been approached in various ways. Konur [11] suggested an integrated inventory-transportation model which considers a carbon cap and the emissions characteristics of trucks during transportation. Nia, Far, and Niaki [8] considered an economic order quantity model under the green vendor-managed inventory (VMI) policy, which includes limited warehouse capacity, pallets, deliveries, and greenhouse-gas emissions. Mokhtari and Rezvan [12] applied VMI policy to solve a multi-item replenishment problem in GSCM. In their model, each retailer decides on a replenishment plan based on a limited amount of total GHG emissions. Noh and Kim [1] considered a single-setup/multiple-delivery policy for a green supply chain contract under an uncertain demand situation. They proved that the cooperative contract is useful to improve the performance of GSCM. Cui et al. [13] focused on a business-to-consumer (B2C) e-business company with distribution centers, and they utilized the strategy of multi-item joint replenishment-distribution.

The theory of the can-order policy was first established by Balintfy [14]. Based on that study, Silver [15] focused on an inventory replenishment problem with Poisson demand and non-zero lead time. He established that a can-order policy performed better than an independent order policy. Liu and Yuan [16] developed a Markov model for a two-item inventory system with coordinated replenishment and a heuristic method for solving the problem. Kayi¸s et al. [17] presented a continuous can-order policy model with two items with Poisson demand under a semi-Markov decision process, and developed a simple enumeration algorithm to solve the problem. Tsai et al. [18] developed an association clustering method that gathers items with similar demand in a hierarchal way to evaluate

the correlated demand for handling a large number of items. Kouki et al. [19] considered a continuous review can-order policy, developed as a Markov process with perishable items and zero lead time.

According to the basic theory of the can-order policy, the inventory system should be continuously reviewed. However, because the supplier has limited replenishment opportunities, Johansen and Melchiors [20] suggested a can-order policy model with a periodic review system. To simulate their idea, they suggested a new method based on Markov decision theory to obtain a near-optimal solution. Nagasawa et al. [21] presented a periodic can-order policy model that uses multi-objective programming to obtain the optimal can-order level. Most previous studies dealt with the continuous review system and focused on deriving the reorder level, can-order level, and order-up-to level based on various algorithms. In the real world, a supplier might ship items once or twice a day, so a company might not receive items as frequently as desired [20]. Although those researchers assumed that those levels cannot be fixed to certain values, a decision maker usually sets those levels according to inventory strategy and service level, respectively. Besides, they did not consider carbon cap-and-trade and limited resources, which are storage capacity and budget in the uncertain market information case.

In a competitive or a new market, it is impossible to describe the market information as a specific stochastic distribution because of this uncertainty. To handle this problem, many researchers apply the fuzzy method, a common way to handle uncertainty and non-stochastic situations [22]. Some researchers have tried to apply the fuzzy method to replenishment models. Sadeghi et al. [23] considered the economic production quantity policy under the consignment stock policy in a fuzzy demand situation, and they applied particle swarm optimization to solve their model. Nia et al. [24] presented a fuzzy resource nonlinear integer programming model that regards customer demand, storage capacity, and the budget as fuzzy numbers. Sadeghi et al. [25] focused on a nonlinear integer programming model with a trapezoidal fuzzy number for demand. However, for handling uncertain resource constraints, no previous studies have applied the fuzzy method to GSCM which focuses on a multi-item replenishment problem.

## **3. Notation, Assumptions, and Problem Definitions**

#### *3.1. Notation*

Decision variables:

## Parameters:

## *3.2. Assumptions*

## *3.3. Problem Definition*

In this GSCM, a single buyer orders multiple items from a single supplier, considering storage capacity, budget, and carbon emissions. Because of limited market information, both the storage capacity and the budget could have some level of uncertainty. For developing multi-item replenishment, we apply a can-order policy to GSCM. Figure 1 compares the inventory patterns with the traditional can-order policy and the proposed can-order policy for two items. By the proposed can-order policy, the order-up-to level *Si t* , where *xi t* = *Si t* − *l i t* , is decided individually through the planning horizon. The inventory level of both items in period *t*0 explains the initial inventory level. In period *t*1, only the inventory level of item 1 is lower than reorder level *s*1, so item 1 is replenished up to *S*1 1. In period *t*2, the inventory levels of items 1 and 2 are lower than the reorder level *s*1 and can-order level *c*2, respectively, so both items are replenished up to *S*1 2 and *S*2 2. In period *t*3, both items are replenished because both of their inventory levels are lower than the reorder level. In contrast, in the traditional can-order policy, when the inventory level of an item drops to or below the reorder level *si* , an order is placed and the order amount *xi t* can make the inventory level up to *Si* . Thus, the order-up-to level *Si* , where *xi t* = *Si* − *l i t* , is always the same through the planning horizon.

**Figure 1.** Inventory patterns with can-order policies for two items.

## **4. Deterministic Model**

In this section we use MIP to develop a deterministic model for GSCM. The total cost consists of the major ordering cost, minor ordering cost, backorder cost, inventory holding cost, and carbon tax cost. The major ordering cost, incurred when an order is placed during period *t*, is given by:

$$\sum\_{t=1}^{T} u\_t y\_t \tag{1}$$

where

$$y\_t = 0 \text{ or } 1, t = 1, 2, \dots, T.$$

The minor ordering cost of each item occurs when item *i* is ordered in period *t*, and it cannot be incurred unless the major ordering cost is also incurred. It is given by:

$$\sum\_{t=1}^{T} \sum\_{i=1}^{I} v\_t^i z\_t^i \tag{2}$$

The holding cost can be derived as the sum of the order-up-to level and the on-hand inventory at the end of each period. At the beginning of each period, the order-up-to level is delivered, and that amount is decreed by the amount of on-hand inventory at the end of the preceding period. Thus, the expected inventory of each item in each period is approximated as the average of those amounts.

$$\sum\_{t=1}^{T} \sum\_{i=1}^{I} h\_t^i \left( \frac{S\_t^i + I\_t^{i+}}{2} \right) \tag{3}$$

The backorder cost of item *i* at the end of period *t* is:

$$\sum\_{t=1}^{T} \sum\_{i=1}^{I} b\_t^i l\_t^{i-}$$

A buyer has its own carbon cap. If the buyer emits less carbon than the given carbon cap, it would get rewarded by receiving money. Otherwise, the buyer pays penalties in the form of a carbon tax [26]. The buyer's carbon cap cost at period *t* is:

$$\sum\_{t=1}^{T} p(e\_t^+ - e\_t^-) \tag{5}$$

Based on Equations (1)–(5), the following mathematical model can be developed:

$$\text{Min } \sum\_{t=1}^{T} \sum\_{i=1}^{I} \left( h\_t^i \left( \frac{S\_t^i + I\_t^{l+}}{2} \right) + b\_t^i I\_t^{l-} + \sigma\_t^i z\_t^i \right) + \sum\_{t=1}^{T} \left( \mu\_t y\_t + p \left( e\_t^+ - e\_t^- \right) \right), \tag{6}$$

*subject to*

$$S\_t^l - l\_t^l = d\_t^l, \quad \mathbf{i} = \mathbf{1}, \mathbf{2}, \dots, \mathbf{I}, \quad \mathbf{t} = \mathbf{1}, \mathbf{2}, \dots, \mathbf{T}, \tag{7}$$

$$I\_t^i = I\_t^{i+} - I\_t^{i-}, \quad \text{i } = \mathbf{1}, \mathbf{2}, \dots, I, \quad \mathbf{t} = \mathbf{1}, \mathbf{2}, \dots, T,\tag{8}$$

$$\mathbf{x}\_t^i \le \mathbf{M} \mathbf{y}\_{t'} \quad \text{i = 1, 2, \dots, I, \quad t = 1, 2, \dots, T,\tag{9}$$

$$\mathbf{x}\_{t'}^i \le \mathbf{x}\_{t'}^i \quad i = 1, 2, \dots, T,\tag{10}$$

$$I\_{t-1}^{i} + M\alpha\_{t}^{i} \ge \mathbf{s}^{i}, \quad \mathbf{i} = \mathbf{1}, \mathbf{2}, \dots, \mathbf{I}, \quad \mathbf{t} = \mathbf{1}, \mathbf{2}, \dots, \mathbf{T}, \tag{10}$$

$$d\_{t-1}^{i} - M(\mathbf{1} - \alpha\_t^i) \le s^i, \quad \mathbf{i} = \mathbf{1}, \mathbf{2}, \dots, \mathbf{I}, \quad \mathbf{t} = \mathbf{1}, \mathbf{2}, \dots, \mathbf{T}, \tag{11}$$

$$S\_t^i \le I\_{t-1}^i + \mathfrak{x}\_t^i + M(\mathbf{1} - \mathfrak{a}\_t^i), \quad i = \mathbf{1}, \mathbf{2}, \dots, I, \quad t = \mathbf{1}, \mathbf{2}, \dots, T,\tag{12}$$

$$S\_t^i \ge l\_{t-1}^i + \mathbf{x}\_t^i - M(\mathbf{1} - \alpha\_t^i), \quad i = \mathbf{1}, \mathbf{2}, \dots, I, \quad t = \mathbf{1}, \mathbf{2}, \dots, T,\tag{13}$$

$$I\_{t-1}^{i} + M\mathfrak{P}\_t^i \ge c^i, \quad i = 1, 2, \dots, I, \quad t = 1, 2, \dots, T,\tag{14}$$

$$I\_{t-1}^{i} - M\left(\mathbf{1} - \boldsymbol{\beta}\_{t}^{i}\right) \le \mathbf{c}^{i}, \quad i = \mathbf{1}, \mathbf{2}, \dots, I, \quad \mathbf{t} = \mathbf{1}, \mathbf{2}, \dots, T,\tag{15}$$

$$\mathbf{y}\_t^i \le \boldsymbol{\beta}\_{t'}^i \quad \mathbf{i} = \mathbf{1}, \mathbf{2}, \dots, \mathbf{I}, \quad \mathbf{t} = \mathbf{1}, \mathbf{2}, \dots, \mathbf{T}, \tag{16}$$

$$\sum\_{i=1}^{I} \alpha\_t^i \le M\delta\_t, \quad i = 1, 2, \dots, I, \quad t = 1, 2, \dots, T,\tag{17}$$

$$\delta\_t \le \sum\_{i=1}^{I} \alpha\_{t'}^i \quad i = 1, 2, \dots, I, \quad t = 1, 2, \dots, T,\tag{18}$$

$$\mathbf{y}\_{it} \le \delta\_{\mathbf{t}}, \quad \mathbf{i} = \mathbf{1}, \mathbf{2}, \dots, \mathbf{I}, \quad \mathbf{t} = \mathbf{1}, \mathbf{2}, \dots, \mathbf{T}, \tag{19}$$

$$\gamma\_{it} \ge \delta\_t + \beta\_t^i - \mathbf{1}, \quad i = \mathbf{1}, \mathbf{2}, \dots, I, \quad \mathbf{t} = \mathbf{1}, \mathbf{2}, \dots, T,\tag{20}$$

$$S\_t^i \le I\_{t-1}^i + \mathfrak{x}\_t^i + \mathcal{M}(\mathbf{1} - \mathfrak{y}\_t^i), \quad i = \mathbf{1}, \mathbf{2}, \dots, I, \quad \mathbf{t} = \mathbf{1}, \mathbf{2}, \dots, T,\tag{21}$$

$$S\_t^i \ge I\_{t-1}^i + \mathbf{x}\_t^i + M(\mathbf{1} - \mathbf{y}\_t^i), \quad i = \mathbf{1}, \mathbf{2}, \dots, I, \quad \mathbf{t} = \mathbf{1}, \mathbf{2}, \dots, T,\tag{22}$$

$$\mathbf{x}\_t^i \le \mathbf{M} \big( \alpha\_t^i + \mathbf{y}\_t^i \big), \quad i = \mathbf{1}, \mathbf{2}, \dots, \mathbf{I}, \quad t = \mathbf{1}, \mathbf{2}, \dots, \mathbf{T},\tag{23}$$

$$S\_t^i \le I\_{t-1}^i + M(\alpha\_t^i + \mathbf{y}\_t^i), \quad i = 1, 2, \dots, I, \quad t = 1, 2, \dots, T,\tag{24}$$

$$S\_t^i \ge I\_{t-1}^i - M(\alpha\_t^i + \chi\_t^i), \quad i = 1, 2, \dots, I, \quad t = 1, 2, \dots, T,\tag{25}$$

$$\alpha\_t^i + \gamma\_t^i \le \mathcal{M}z\_{t\prime}^i \quad \text{i } = \mathbf{1}, \mathbf{2}, \dots, \mathbf{I}, \quad \mathbf{t} = \mathbf{1}, \mathbf{2}, \dots, \mathbf{T}, \tag{26}$$

$$\sum\_{t=1}^{T} \sum\_{i=1}^{I} \left( \hat{h}\_t^j \left( \frac{S\_t^j + I\_t^{i+}}{2} \right) + \hat{v}\_t^j z\_t^i \right) + \sum\_{t=1}^{T} e\_t^- \le CE + \sum\_{t=1}^{T} e\_t^+,\tag{27}$$

$$\sum\_{i=1}^{I} o^i S\_t^i \le \mathcal{W}\_{t'} \quad \text{t} = \mathbf{1}, \mathbf{2}, \dots, T,\tag{28}$$

$$\sum\_{i=1}^{I} \mathbf{g}^{i} \mathbf{x}\_{t}^{i} \le \mathbf{P}\_{t}, \quad \mathbf{t} = \mathbf{1}, \mathbf{2}, \dots, T,\tag{29}$$

$$y\_{t'} \cdot \alpha^i\_{t'} \cdot \beta^i\_{t'} \cdot \gamma^i\_{t'} z^i\_t \in \langle \mathbf{0}, \mathbf{1} \rangle, \quad i = \mathbf{1}, 2, \dots, I, \quad \mathbf{t} = \mathbf{1}, 2, \dots, T,$$

$$S^i\_{t'} \cdot x^i\_{t'} \cdot l^{i+}\_{t'} \cdot l^i\_{t'} \in \mathbf{Z}\_+ + \langle \mathbf{0} \rangle, \quad l^i\_t \in \mathbf{Z}, \quad i = \mathbf{1}, 2, \dots, I, \quad \mathbf{t} = \mathbf{1}, 2, \dots, T,$$

$$e^+\_{t'} \cdot e^-\_{t} \in \mathbf{Z}\_+ + \langle \mathbf{0} \rangle, \quad \mathbf{t} = \mathbf{1}, 2, \dots, T,$$

Equation (6) presents the objective function that minimizes the total inventory holding cost, backorder cost, major ordering cost, and minor ordering cost. Equation (7) ensures that the inventory position at the end of period *t* is equal to the difference between the order-up-to level and demand. Equation (8) ensures that the inventory at the end of period *t* is equal to the difference between the on-hand inventory and the backorder level. Equation (9) presents the constraint of incurring the major ordering cost whenever at least one item is ordered. Equations (10) and (11) regulate ordering when the inventory position falls below the reorder level. If *l i t*−1, the initial inventory level at *t*, drops below *si* , then α*i t* = 1, otherwise α*i t* = 0. According to Equations (12) and (13), if the inventory position at the end of the previous period is below the reorder level, the item is ordered in the amount equal to the difference between the order-up-to level and the inventory position at the end of the previous period. Equations (14) and (15) regulate whether the inventory position is below the can-order level. If *l i t*−1, the initial inventory level at *t*, drops below *ci* , then β*i t* = 1, otherwise β*i t* = 0. According to Equations (16)–(20), if the inventory position of at least one item is below its reorder point, the following order includes other items whose inventory positions are below their can-order level. According to Equations (21) and (22), if the inventory position at the end of the previous period is below the can-order level, that item is ordered in the amount equal to the difference between its order-up-to level and its inventory position at the end of the previous period. Equation (23) regulates the ordering of items whose inventory position is below the reorder or can-order level. Equations (24) and (25) indicate that the order-up-to level is set based on the inventory position, which is below the reorder level or the can-order level. Equation (26) indicates that any item below its reorder level or can-order level incurs the minor ordering cost. Equation (27) ensures the carbon cap-and-trade constraint. The carbon emissions are incurred from the ordering and holding activities. Based on the amount of emissions, the buyer could buy or sell the carbon credit from the participating companies [26]. Equation (28) ensures that the sum of the inventory position for each item at the beginning of each period and the order-up-to level will not exceed the storage capacity. Equation (29) ensures that the buyer cannot order items over its budget.

## **5. Fuzzy Model**

We here develop a fuzzy model based on the deterministic model. A company that is in a competitive market or moves into a new market usually has less information about that market. In this case, to fit the information as a specific stochastic distribution is usually impossible. It is difficult to predetermine the amount of resources, such as limited storage capacity and/or budget. This uncertain market information can be handled by using the fuzzy method.

To apply the fuzzy method to our deterministic model, which is also called a crisp model, the model must first be transformed into a fuzzy model. In order to obtain a crisp value from the fuzzy model, the fuzzy model should be converted to a new crisp model through a defuzzification process. Although many researchers have developed various defuzzification methods, we used the symmetry method introduced by Zimmermann [22]. The crisp objective function with fuzzy constraints, where *Aix* ≤ *b i* and *Dix* ≤ *Ci* are a set of fuzzy and crisp constraints, respectively, is formulated as:

$$\mathbf{M}\dot{\mathbf{u}}\,f(\mathbf{x}) = \mathbf{c}^T \mathbf{x},\tag{30}$$

subject to

$$A\_i \mathbf{x} \le b\_{i\nu} \qquad \mathbf{i} = \mathbf{1}, \mathbf{2}, \dots, m\_{\nu}$$

$$D\_i \mathbf{x} \le C\_{i\nu} \qquad i = \mathbf{1}, \mathbf{2}, \dots, m\_{\nu}$$

$$\mathbf{x} \ge \mathbf{0}.$$

Now, using a membership function for a fuzzy set, an element *x* of *X* is mapped to a value between 0 and 1. The membership function for the fuzzy sets that represent the fuzzy constraints can be defined as:

$$\mu\_i(\mathbf{x}) = \begin{pmatrix} \mathbf{1} & \text{if } A\_i \mathbf{x} \le b\_i \\\\ \frac{b\_i + p\_i - A\_i \mathbf{x}}{p\_i} & \text{if } b\_i  b\_i + p\_i \end{pmatrix}, \quad i = \mathbf{1}, \mathbf{2}, \dots, m+\mathbf{1}. \tag{31}$$

In Equation (31), μ*i*(*x*) could be 1 when the constraints are well satisfied, otherwise 1. The *pi* is the tolerance interval which is assumed a constraint to be linearly increasing. Defining the membership function of the objective function requires solving the following two problems. The first problem is the original crisp model, and the optimal result is set as *supR*1 *f* = *cTx opt* = *f*1.

$$\mathbf{M} \| \mathbf{u} \, f(\mathbf{x}) = \mathbf{c}^T \mathbf{x},\tag{32}$$

subject to

*Aix* ≤ *bi*, *i* = **1**, **2**, ... , *m*, *Dix* ≤ *Ci*, *i* = **1**, **2**, ... , *m*, *x* ≥ **0**

In the second problem, the fuzzy constraints in Equation (32) are changed to the crisp constraints with tolerances, and the optimal result is set as *supS*( *R*) *f* = *cTx opt* = *f*0.

$$\mathbf{M} \| \mathbf{y}(\mathbf{x}) = \mathbf{c}^T \mathbf{x},\tag{33}$$

subject to

$$A\_i \mathbf{x} \le b\_i + p\_\prime \quad i = \mathbf{1}, \mathbf{2}, \dots, m\_\prime$$

$$D\_i \mathbf{x} \le \mathbf{C}\_{i\prime} \quad i = \mathbf{1}, \mathbf{2}, \dots, m\_\prime$$

$$\mathbf{x} \ge \mathbf{0}.$$

In short, *f*0 and *f*1 are the minimum total cost with and without tolerances for resources, respectively. Based on that, the membership function of the objective function is obtained as:

$$\mu\_{\widehat{G}}(\mathbf{x}) = \begin{pmatrix} \mathbf{1} & \mathrm{if} f\_{\mathbf{1}} < \mathbf{c}^{T} \mathbf{x} \\\\ \frac{\mathbf{c}^{T} \mathbf{x} - f\_{\mathbf{0}}}{f\_{\mathbf{1}} - f\_{\mathbf{0}}} & \mathrm{if} f\_{\mathbf{0}} \le \mathbf{c}^{T} \mathbf{x} \le f\_{\mathbf{1}} \\\\ \mathbf{0} & \mathrm{if} \mathbf{c}^{T} \mathbf{x} < f\_{\mathbf{0}} \end{pmatrix}. \tag{34}$$

Based on Equations (30)–(34), both the objective function and the constraints have 'symmetry' such that the crisp model by the defuzzification process is transformed.

$$
\lambda \text{Max} \qquad \lambda,\tag{35}
$$

subject to

$$\mathbf{c}^T \mathbf{x} \le \lambda \mathbf{\hat{z}}\_0 - f\_1 \mathbf{\hat{z}} + f\_{1'}$$

$$A\_i \mathbf{x} \le \mathbf{b}\_i + (\mathbf{1} - \lambda) p\_{i'} \qquad i = \mathbf{1}, \mathbf{2}, \dots, m\_i$$

$$D\_i \mathbf{x} \le \mathbf{C}\_{i'} \qquad i = \mathbf{1}, \mathbf{2}, \dots, m\_{i'}$$

$$\mathbf{x} \ge \mathbf{0},$$

$$\mathbf{0} \le \lambda \le \mathbf{1}.$$

As mentioned earlier, we considered the storage capacity and the budget as fuzzy numbers. The following table methodically shows the fuzzy constraints of Equations (28) and (29).

$$\begin{aligned} \sum\_{i=1}^{I} \sigma^i S\_t^i \le \overline{W}\_{t\prime} \quad t = 1, 2, \dots, T, \\\\ \sum\_{i=1}^{I} \mathbf{g}^i \mathbf{x}\_t^i \le \overline{P}\_{t\prime} \quad t = \mathbf{1}, 2, \dots, T. \end{aligned}$$

Next, those constraints are converted to the defuzzification of the storage capacity and budget, where *p*1,*t* and *p*2,*t* are their respective tolerances:

$$\sum\_{i=1}^{I} \sigma^{i} S\_{t}^{i} \le \mathcal{W}\_{t} + (\mathbf{1} - \boldsymbol{\lambda}) p\_{\mathbf{1},t'} \qquad \mathbf{t} = \mathbf{1}, \mathbf{2}, \dots, T,\tag{36}$$

$$\sum\_{i=1}^{I} \mathbf{g}^{i} \mathbf{x}\_{t}^{i} \le P\_{t} + (\mathbf{1} - \lambda) p\_{2,t}, \qquad t = 1, 2, \dots, T. \tag{37}$$

Finally, the equivalent crisp model, transformed from the fuzzy model using the method of Zimmermann [22], is obtained:

$$
\begin{array}{cccc}
\text{Max} & & \lambda\_{\prime} \\
\end{array}
\tag{38}
$$

subject to

$$\sum\_{t=1}^{T} \sum\_{i=1}^{I} \left( h\_t^j \left( \frac{S\_t^i + l\_t^{i+}}{2} \right) + b\_t^j I\_t^{i-} + v\_t^j z\_t^i + G^i e^i x\_t^j \right) + \sum\_{t=1}^{T} u\_t y\_t \le \lambda \left( f\_0 - f\_1 \right) + f\_1,$$

$$\sum\_{i=1}^{I} o^i S\_t^i \le W\_t + (1 - \lambda) p\_{1,t'} \qquad t = 1, 2, \dots, T,$$

$$\sum\_{t=1}^{I} g^i x\_t^i \le P\_t + (1 - \lambda) p\_{2,t'} \qquad t = 1, 2, \dots, T,$$

with Equations (7)–(27)

$$y\_{t'}, \boldsymbol{\alpha}\_{t'}^{i}, \boldsymbol{\beta}\_{t'}^{i}, \boldsymbol{z}\_{t}^{i} \in \langle \mathbf{0}, \mathbf{1} \rangle, \qquad i = \mathbf{1}, \mathbf{2}, \dots, I, \qquad t = \mathbf{1}, \mathbf{2}, \dots, T,$$

$$S\_{t'}^{i} \; \mathbf{x}\_{t'}^{i} \; I\_{t}^{i+}, \boldsymbol{I}\_{t}^{i-} \in \mathbf{Z}\_{+} + \langle \mathbf{0} \rangle, \ I\_{t}^{i} \in \mathbf{Z}, \qquad i = \mathbf{1}, \mathbf{2}, \dots, I, \qquad t = \mathbf{1}, \mathbf{2}, \dots, T,$$

$$\boldsymbol{e}\_{t}^{+}, \boldsymbol{e}\_{t}^{-} \in \mathbf{Z}\_{+} + \langle \mathbf{0} \rangle, \qquad t = \mathbf{1}, \mathbf{2}, \dots, T,$$

## **0** ≤ λ ≤ **1**.

We denote *p*1,*t* and *p*1,*t* as the 'tolerance intervals' of *Wt*, and *Pt*, respectively.

## **6. Numerical Experiments**

To validate our proposed two MIP models, we tested three kinds of numerical experiments. First, we conducted an efficiency test for the deterministic model by comparing it to the traditional can-order policy with predetermined values, (*si* , *ci* , *Si* ). Second, we compared the traditional can-order policy with the proposed can-order policy. In this experiment, we determined which the policy best fit with GSCM. Third, we tested the fuzzy model with various tolerances. In all the experiments we set the very large number *M* at 1,000,000. To solve the MIP models, we used LINGO 17.0 software.

#### *6.1. E*ffi*ciency Test*

To show the efficiency of the proposed can-order policy, we compared it to the traditional can-order policy with predetermined values. In the traditional can-order policy, a decision maker sets the values for (*si* , *ci* , *Si* ) based on previous data or their experience. Thus, this comparison is necessary to show whether the proposed deterministic model can reduce the total cost compared to the traditional can-order policy with predetermined values.

Table 1 presents the basic input parameters for three items in 12 periods. The major ordering cost (*ut*) is \$1000/order. It is difficult to apply constraints, such as storage capacity, budget, or carbon cap-and-trade, to it in the traditional can-order policy with predetermined values, so we did not consider those constraints in this test.

**Table 1.** Input parameters for the basic test.

Notes: *U*(∗,∗): uniform distribution; *N*(∗,∗): normal distribution.

Table 2 shows the initial inventory position, the reorder level, and the can-order level that we used during the test of the traditional can-order policy with predetermined values. The order-up-to level for the traditional can-order policy with predetermined values was assumed to be *S*1 = 697, *S*2 = 590, and *S*3 = 184, the highest demand for each item during the 12 periods. We used Excel 2017 for calculating the traditional can-order policy with predetermined values.

**Table 2.** Initial inventory position, can-order level, and reorder level.

Table 3 shows that the proposed model led to a total cost of \$757,120.50. To see the precise efficiency of the proposed model, we tested it against the traditional can-order policy with predetermined values. Otherwise, it is difficult to discover whether the order-up-to level is biased in predetermined values. We set the order-up-to level from 95% to 75% of the highest demand in 5% decrements. Table 4 shows the results of this test.

### **Table 4.** The effect of the order-up-to level.

As shown in Table 4, the set of order-up-to level *S*1 = 662, *S*2 = 561, and *S*3 = 175 obtains the best result, \$928,417.00. The total cost is increased with a lower order-up-to-level because the company cannot cope with the demand. The proposed model strongly outperforms the traditional can-order policy with predetermined values. This result shows that predetermined values, which are biased by a decision maker, rarely set the order-up-to level properly, which increases the total cost.

## *6.2. Comparison Test between the Proposed Can-Order Policy and the Traditional can-order Policy*

To determine the better policy, we compared the proposed can-order policy to the traditional can-order policy. Based on the deterministic model, we developed a new model about the traditional can-order policy, which can obtain the same order-up-to level through the planning horizon. We conducted this test using an experimental design in which we varied the number of items and periods from 6 to 10 and 10 to 20, respectively. The major ordering cost was \$1000/order and the initial inventory position was zero. The amount of carbon emissions for holding and ordering were assumed as 10% of each cost. Both carbon tax and carbon cap were assumed as \$2 and \$80,000, respectively. The storage capacity was set as 4,000 m2 and the amount of budget was set as \$80,000. Table 5 presents each item's input data. The demands for each item were generated using a normal distribution. The reorder level was set as 99% of the safety stock, and the can-order level was assumed as 20% above the reorder level.

**Table 5.** Input parameters for the comparison test.

Table 6 shows the results of the comparison test. In all cases, the proposed can-order policy outperformed the traditional can-order policy because the proposed policy produced less total cost than the traditional policy. In some of the five-period tests the total costs are negative values, which is an interesting point. Those cases illustrate that a company sells carbon caps to other companies so that

it receives profits. The result proves that the proposed policy benefits the company that replenishes multi-items under carbon cap-and-trade. The proposed policy could be efficacious in the large-scale multi-item replenishment problem in GSCM. Thus, based on these discussions, the proposed model is promising for practical applications.

**Table 6.** The results of the comparison test.

## *6.3. Fuzzy Model Test*

We also tested the fuzzy model to demonstrate the effects of changing the tolerances. To initialize and solve the model in Equation (38), we first solved the two models in Equations (33) and (35) such that we could obtain the values of *f*0 and *f*1. We considered three items during 12 periods and used the parameters in Section 6.2. Table 7 shows the tolerances for the parameters of the storage capacity and the budget. The base values of the storage capacity and the budget were 1000 m2 and \$8000, respectively. Table 8 also shows the value of *f*1 which is the total cost of the deterministic model, and the result of the fuzzy model.

**Table 7.** The various tolerances for the parameters.

**Table 8.** The results of the fuzzy model test.

Table 8 shows that all the results of the fuzzy model test produced better results than the deterministic model, \$1,400,277. Two factors explain this. First, the uncertain market information on storage capacity and budget might lead to large on-hand inventories to avoid large backorders. Second, the strict constraints of storage capacity and budget in the deterministic model are strongly

fixed. Based on the value of λ, the decision maker can obtain the values of the storage capacity and budget, being (1 − λ)*p*1,*t* and (1 − λ)*p*2,*t*, respectively. This test illustrates that the fuzzy model not only handles the uncertainty but also improves the system performance. Thus, the fuzzy model is a good option for a decision maker when the market information has uncertainty.

## **7. Academic, Managerial, and Environmental Insights**

## *7.1. Academic Insights*

We developed two MIP models dealing with the periodic can-order policy for GSCM with limited storage capacity, limited budget, and carbon cap-and-trade. We also developed a deterministic model under certain (known) market information, based on which we suggested a fuzzy model that considers the fuzzy numbers of storage capacity and budget. This is the first study to develop both deterministic and fuzzy models of the can-order policy under carbon cap-and-trade for GSCM. Thus, our study can be considered initial research which considers multi-item replenishment with carbon cap-and-trade for GSCM.

## *7.2. Managerial Insights*

A company interested in GSCM could benefit from our study. The deterministic and fuzzy models developed here can help a company systematically replenish multi-items with carbon cap-and-trade regulation. The deterministic model suggests practical insights on multi-item replenishment for minimizing the total cost under limited resources and carbon cap-and-trade regulation. It can also help a company in a monopoly market make sound investment decisions. For a company that has uncertain market information, the fuzzy model can support preparation of appropriate storage capacity and budget, and also planning for cost minimization. Thus, the correct implementation of these models will give a company better decisions in managing GSCM and reducing its total cost.

## *7.3. Environmental Insights*

This paper incorporates carbon cap-and-trade regulation into GSCM. Considering carbon cap-and-trade regulation, the companies can increase resource-use efficiently and grow together. This sustainable situation is regarded on the 2030 Agenda and the Sustainable Development Goal 9 (SDG 9), which is industry, innovation, and infrastructure. Thus, using this paper, the company can help protect environment while earning profits.

## **8. Conclusions**

This paper presents two MIP models, a deterministic model and a fuzzy model, which address the multi-item replenishment problem with carbon cap-and-trade for GSCM under limited resources. We developed the two models based on the can-order policy, which is one of the well-known multi-item replenishment policies. Reflecting real-world situations, we considered limited storage capacity, budget, and carbon cap-and-trade regulation. The deterministic model can be used when a decision maker has solid market information, while the fuzzy model can be applied when a decision maker faces uncertain market information in a competitive or a new market. In this model, both limited storage capacity and budget are denoted as fuzzy numbers.

We carried out three experiments to test the efficiency of the two models. First we compared our deterministic model with the traditional can-order policy which already has predetermined values (*si* , *ci* , *Si* ), and proved that our deterministic model was significantly better because it resulted in lower total costs. In the second experiment, by using our deterministic model, we compared the proposed can-order policy with the traditional can-order policy. The result showed that the proposed can-order policy outperformed the traditional can-order policy by, again, resulting in lower total costs. Finally, we quantified the effects of the fuzzy model with various tolerances. The results showed that applying fuzzy constraints is useful to make decisions under uncertain situations. We demonstrated the validity and practicality of our models in those experiments, confirming that our models can be useful for multi-item replenishment in GSCM.

There are some research limitations in this study and also some indications for possible future works. First, this paper only considered a single supplier and a single buyer. In the real world there are many suppliers, so it is an important decision to select one among several. Our current models could be applied to extend GSCM with this supplier-selection problem. Also, this paper assumed the deterministic demand. For replenishment planning, forecasting demand theory could be considered.

**Author Contributions:** Conceptualization, J.S.K.; Funding acquisition, S.-J.H.; Methodology, J.N.; Project. administration, S.-J.H.; Writing–original draft, J.N.; Writing–review & editing, J.S.K. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2019S1A5C2A04083153).

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

## **References**

© 2020 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 (http://creativecommons.org/licenses/by/4.0/).

## *Article* **Narrowing the Gaps: Assessment of Logistics Firms' Information Technology Flexibility for Sustainable Growth**

## **Jeong Hugh Han 1,\*, Yingli Wang 2 and Mohamed Naim 2**

Received: 28 April 2020; Accepted: 21 May 2020; Published: 26 May 2020

**Abstract:** In a supply chain management context, the effective management of Information Technology (IT) flexibility has been an issue to be resolved. However, no analytical method that calculates the required and actual level of IT flexibility dimensions has been proposed. This paper aims to provide an analytical tool that measures the required and actual levels of IT flexibility dimensions to provide the best value from a logistics firm's IT flexibility. To do so, we propose a combined Importance-Performance Analysis (IPA) and Partial Least Squared Structured Equation Modelling (PLS-SEM) method based on a multidimensional IT flexibility model. By comparing industry-level data with client firm data, our method allows for effective identification of a client logistics company's multiple IT flexibility gaps and indicates where particular management interventions are required. By proposing importance and performance as measurement scales, our research suggests an analytical tool that managers can utilize to assess IT flexibility and identify any gaps that exist between actual and required flexibility levels. This allows managers to effectively address areas that demand further attention. This approach also leads to an improved understanding of how organisations can extract the best value from their investment in IT flexibility to contribute to sustainable growth.

**Keywords:** flexibility; IT flexibility; importance-performance analysis; partial least squared structured equation modelling; performance gap; sustainable growth

## **1. Introduction**

Information Technology (IT) flexibility is one of the most widely used concepts for identifying a firm's ability to cope with the variation generated by its business environment [1–4]. With the recognition of IT flexibility as a multidimensional concept, previous research has focussed on the identification of, and validated the dependence of firm performance on, IT flexibility dimensions [5–9]. Further, as echoed by several researchers [4,10–13] an investigation of the mismatches between the actual and required level of each flexibility dimension is required to execute efficient resource allocation to each dimension so that finite firm resources can be used effectively. However, little attention has been given to a method to improve firm performance through a flexibility requirements analysis. Specifically, there has been a lack of analytical tools that calculate the required and actual level of IT flexibility dimensions, hence impeding strategic decision-making in resource investment.

To fill this research gap, we suggest the use of Importance-Performance Analysis (IPA), combined with Partial Least Squared Structured Equation Modelling (PLS-SEM), to identify the gaps between the required and actual levels of each flexibility dimension. We particularly highlight the usefulness of this application, which indicates particular dimensions that might be under- or over-resourced. The combined use of IPA and PLS-SEM is a largely neglected method, particularly in the Technology

Management field. To the best of our knowledge, our paper is the first to apply combined IPA and PLS-SEM in the context of operational flexibility more generally, and IT flexibility specifically.

The rest of the paper is organised as follows. Section 2 offers a theoretical background for managing multiple flexibility dimensions. Section 3 discusses our methodological approach in applying IPA to examine the IT flexibility gap. This is followed by a discussion of our research findings in Section 4. We draw conclusions in Section 5 by highlighting our theoretical and practical contributions. We also acknowledge our research limitations and discuss future research directions.

### **2. Literature Review—Managing Multiple Dimensions of IT Flexibility**

There is general agreement that IT should be flexible to help companies deal with outward uncertainties through advancing, adapting or coordinating the functionalities of the IT. IT flexibility is thought to increase the capacity for adjust to variations in internal and external business circumstances. For instance, adaptability to novel or dissimilar circumstances and scalability [8], IT investment and IT infrastructure to adapt to a changing business environment [10], support to alter business strategies [14], information system functionality, database, interface and processing capacity [3], compatibility, information sharing, modularity and capacity to handle multiple applications [15], adaptability for changing business partners and environment, reconfiguration of communication linkages and capability to redesign business process are highlighted as the principal components of IT flexibility [1].

IT flexibility is a multidimensional concept. The seminal work of Duncan [2] categorised IT flexibility dimensions into compatibility, connectivity and modularity, focusing on IT infrastructure. On the other hand, Saraf et al. [6] claimed that the value of IT flexibility depends on the ability to adapt to the different types of business requirements that emerge from different organisational levels (e.g., operational tactical and strategical levels). So, to generate value, a continuous redesign of IT infrastructure that supports incremental and revolutionary environmental changes is required. By defining IT flexibility as the manner in which a firm's IT is organised and integrated to adapt to rapid changes, Saraf et al. [6] proposed scalability, system design for new business relationships, and system design for rapid business requirement change as the primary components of IT flexibility. Gosain et al. [16] give examples of different resource uses. According to Gosain et al. [16], if there is a need to change business partners quickly then IT should be exploited to lower the switching costs. If the capability requirement is to increase the volume of interfirm business transactions, IT should enable enhanced information sharing via the standardisation of processes.

Lee and Xia's [17] findings also strongly support the multidimensional characteristics of IT flexibility. They identified that two types of IT flexibility co-exist, namely response extensiveness and response efficiency. The multidimensionality of IT flexibility is also recognised in a comprehensive review by Kumar and Stylianou [4]. They pointed out that prior research focused on information technology infrastructure flexibility and highlighted the importance of IT flexibility as both a strategic and an organizational capability. Specifically, flexibility in IT operations, IT service development and IT management that responds to changing business process and consumer requirements are viewed as IT flexibility categories. Another contribution to defining the different roles of IT flexibility dimensions was supplied by Han et al. [11]. By integrating the traditional infrastructure-focused view and value creation in their IT flexibility dimensions, they revealed that IT flexibility encapsulates transactional, operational and strategic flexibilities, and further tested the causal effect between three dimensions and firm performance. From the perspective of a technology acceptance model, Kwak et al. [18] showed that information reliability, networking capability and security of a logistics platform are the logistics platform dimensions that increase the logistics platforms' dynamic capabilities. In particular, Kwak et al. [18] highlighted the role of scalability as a part of networking capability. They argue that the scalability of the logistics platform enables network effects so increases the flexibility of the logistics platform in responding to changing business demand.

Despite previous research efforts in articulating the multiple dimensions of IT flexibility and their influence on firm performance, there have been limited discussions of how to interrogate the effectiveness of each flexibility dimension. Lee and Xia [17] and Kumar and Stylianou [4] are the notable exceptions that discuss the possible trade-offs among different dimensions of flexibility for firm performance. Kumar and Stylianou [4] argued that different IT flexibility dimensions play different roles and make synergies or conflicts in different situations requiring IT flexibility. Even though Kumar and Stylianou [4] did not provide the exact meaning of the trade-off, synergies and conflict, they implicitly argued that a set of concurrent actions that address specific needs for IT flexibility could be developed when the required and actual levels of flexibility are identified. Lee and Xia [17] argued that the gap between existing and desired IT flexibility should be identified by helping "managers attack the flexibility gap by developing theories" (p. 88). However, none of them was able to propose how such trade-offs or gap closing could be conducted.

Given the limited IT flexibility literature on assessing how well a firm performs against industry norms, we refer to the well-established OM flexibility literature that indicates the following practices are required to measure the flexibility gap. First, the desired configuration of heterogeneous dimensions of flexibility should be identified; that is, identifying the required level of flexibility needed within each dimension to yield performance benefits [19–22]. Second, an analysis of actual performance within each flexibility dimension is undertaken to identify performance mismatches between the required and observed levels of IT flexibility [20,23–26]. This process of performance measurement identifies where the flexibility levels may need to be raised or reduced [21]. Third, once a given performance gap has been identified, a management decision is required as to where to close the gaps at an acceptable cost [21,23,26,27].

The aforementioned process may provide a good guideline to identify the gaps in IT flexibility. However, the flexibility measuring process still has a number of limitations. One of the core reasons is the absence of universal criteria that encompass different flexibility dimensions. Different flexibility dimensions are not homogenous and hence require different measures [28–30]. For instance, Cousens et al. [27] propose a series of steps for increasing manufacturing flexibility and suggested six key performance indicators (KPIs) to measure volume and mix flexibility in manufacturing. However, because the KPIs are very factory-specific, such as the number of variants per key product family, one cannot use them as a total set for the measurement for other dimensions such as labour or material handling flexibility. Seebacher and Winkler [31] also measured flexibility by developing a two-dimensional framework to identify the performance and usefulness of batch production systems. Their method of evaluating manufacturing flexibility is to compute a coefficient of variation from the deviations of the manufacturing order lead times and then calculate an efficiency in manufacturing performance. Although their approach is effective for evaluating manufacturing flexibility, they acknowledge that the application of the model is restricted to discrete manufacturing due to the specific parameters adopted (i.e., coefficient of variation).

Flexibility is a relative, situation-specific concept, so a certain dimension is viewed as a more imperative dimension when a specific environmental necessity emerges [32]. This relative importance in different situations also makes the flexibility measurement difficult. For instance, when a large variety of service accessibility and speedy transition proficiency exists, they both denote the flexibility concept. When the marketplace needs an advanced level of service diversity for a specific situation or time, the flexible capability for a large variety of services would take greater value [32,33]. The biased measurements used for flexibility performance are a similar problem. For example, existing empirical studies, such as Chang [34], have only prioritized the required flexibility dimensions in environmental uncertainty, and therefore are unable to identify actual levels of flexibility dimensions. As a result, they offer limited insights into identifying the flexibility gap to be closed.

Further, previous research has also suggested the need to show the link between an increased level of flexibility and the improvement of firm performance. To show that firm performance is conditional and dependent on flexibility levels, concurrent validation of the positive impact of flexibility configurations should also be undertaken [35–40]. However, much of the performance research in flexibility-related works has focused on the justification of IT flexibility dimensions and/or their impact on firm performance and has not extended to requirements analysis [38–40]. The most relevant work regarding the decision-making process and how to fill the performance gap is probably the work of He et al. [23]. By developing the concept of "flexibility fit," they showed that the levels of required and available flexibility can be determined through a set of simulations. However, as their guidelines were limited to a single dimension of process flexibility, i.e., range, it is not clear if their approach can be applied to the multiple flexibility dimensions found within a firm. Focusing on a single dimension overlooks the fact that multidimensionality is an essential attribute of a flexibility construct, and resources within a firm need to be shared in a cost-effective manner.

Therefore, given the limitations of existing flexibility research as Table 1 presents, namely the lack of objective measurements for different dimensions of flexibility and the lack of methods to determine the correlation between the level of IT flexibility and firm performance, the opportunity to close the flexibility gaps is lacking in previous literature. Furthermore, it would be difficult to pinpoint specific areas where a proper action plan can be devised for resource allocation or adjustment. The same problem exists for IT flexibility gap measurements. There is therefore a need for an effective tool that measures both the required and observed flexibility levels exploiting comprehensive, objective criteria. Those criteria also need to be aligned with different flexibility dimensions while showing uniformity towards performance improvement.

**Table 1.** *Cont.*

### **3. Methodology**

## *3.1. A Combined IPA and PLS-SEM Method: Background*

This study proposes the combined use of an IPA matrix and PLS-SEM to identify the flexibility gap. While the IPA matrix concept has its origins in marketing [45], it has since been applied in a number of different industry management settings [46–50]. IPA allows a company to detect which attributes of its product or service ought to be improved. Its main structure is a four-dimensional grid based on the importance and performance level of the identified attributes. For instance, for a particular product attribute, importance ratings could be obtained, from "extremely important" to "not important," and, similarly, performance ratings could be obtained, from "excellent" to "poor." Attributes can then be classified according to their relative importance and performance ratings by mapping the scores in a four-dimensional plot. Its introduction to operations management was through Slack [49], who modified the classic 2 x 2 importance-performance grid into alternative zones allowing a more constant evolution in inferred priorities. The matrix was later extended by Tontini and Silveira [50]. By incorporating the Kano Model for the arrangement of service features, namely basic, performance and excitement attributes, they developed a way to identify gaps between expected satisfaction, current and average market satisfaction.

A notable aspect of Slack's research [49] is that, based on focus group discussions with company personnel, he configured a zoning representation, as shown in Figure 1. In this representation, companies must determine how well the performance aligns with the line AB, which represents the "best fit" with respect to the performance level. Anything below the line requires improvement—or, in extreme cases, as defined by curve CD, urgent action. Anything above the line may be deemed appropriate or, if above the curve EF, questionably excessive. As indicated by Slack [49], the AB line does not provide a clear cutoff point where being over (or under) the line definitely indicates being over- (or under-) resourced. Nonetheless, it helps to reveal the potential gap and signals that organisations may need to investigate that are the potential causes of the performance gap.

**Figure 1.** Zoning representation of the IPA matrix (Source: Adapted from Slack 1994).

Recent developments of the IPA matrix have combined the analysis with PLS-SEM applications by Hair et at al. [46] and Ringle and Sarstedt [51]. The process of identifying the flexibility performance gap is elaborated as follows. On the one hand, the importance level on the x-axis of a given matrix denotes the valuation of the direct, indirect and overall relationships between latent constructs. This is computed with the inner and outer coefficients. The coefficients range from 0 to 1.0. On the other hand, the performance level is rescaled to 0 to 100 on the y-axis of a given matrix, according to the average scores of the latent construct values. In addition, the scores for the importance level and performance level of each variables are united in a matrix with a bootstrapping method that is employed to measure the significance level of the indicators' importance scores.

## *3.2. Application of the Method to IT Flexibility Dimensions*

In this analysis, one can interpret the importance level as the required flexibility level and the performance as the actual flexibility level. Ideally, the most important dimension will show the highest performance score. If the actual performance does not meet the required level, the IPA matrix identifies the performance gap (i.e., the mismatch). Moreover, with this tool we can expand our analysis to the indicator level, thus identifying specific areas that may contribute to the under- or overperforming flexibility dimensions, which may then require rectifying actions [46,51,52].

This method resolves the aforementioned issues in flexibility measurement. First, by providing a universal, all-encompassing measurement, namely performance, different dimensions can be measured in a consistent manner. Second, by providing the two types of measurement, i.e., the importance and performance, this method measures the required and actual flexibility levels simultaneously. Further, as PLS-SEM structural model analysis can demonstrate the impact of independent variables (flexibility dimensions in this case) on dependent variables (firm performance in this case) [53,54], it validates the correlations between flexibility dimensions and firm performance concurrently while testing the uniformity of the dimensions towards firm performance.

Applying IPA with PLS-SEM in our research, we undertook the following steps. First, the results of the IT flexibility model analysis with PLS-SEM with industry-level data are incorporated into an IPA matrix. This step is meant to validate our method in a generalized industry setting, and also to determine whether the general performance levels are consistent with the line AB in Figure 1. If the performance level of each dimension is appropriate when compared to its importance level, that is, consistent with AB, then this tends to indicate that the resources are fairly distributed, and no resource reallocation is required. Second, a case firm that needed IT flexibility improvement was selected, and its data were analysed with the same method to determine if the performance levels are consistent with the line AB in Figure 1. We also compared the importance and performance levels of the case firm to the general industry results. If the performance levels show different distribution patterns compared to the industry norm, with performance gaps, this strongly indicates that there may be under- or over-resourcing from the case firm. Third, where the case firm's data are not consistent with the line AB, a further analysis was conducted to identify which indicators may require more or fewer resources.

To apply IPA combined with the PLS-SEM method in closing IT flexibility gaps, a model that meets IT flexibility-specific requirements needs to be employed. We applied our method to an existing IT flexibility model proposed by Han et al. [11]. By incorporating exploitive purposes of IT use and an explorative view [55,56], Han et al. [11] classified multiple dimensions of IT flexibility such as transactional IT flexibility, operational IT flexibility and strategic IT flexibility. Transactional (TR) IT flexibility refers to a capability to utilise advances in IT infrastructure. Operational (OP) flexibility is the ability to use IT for information distribution and process enhancement. Strategic (STR) flexibility is the ability to use IT expertise to generate novel, future-oriented operations together with supply chain partners [11]. Such a classification enables us to measure the gap in IT flexibility given that each dimension has different roles. We required a prevalidated model that shows the uniformity of each firm's IT flexibility performance. Based on the theory that IT interacts with intermediate business practices [57], Han et al. [11] showed that IT flexibility is created when the integration of supply chain operations inside the firm and with external business partners is ensured. Thus, Han et al.'s model is well placed to assess the firm performance. One can refer to Han et al. [11] for detailed model development discussions and the hypothesis of the IT flexibility research model. Measurement

indicators of the three different dimensions of IT flexibility and the hypotheses are presented in Appendix A.

### *3.3. Data Collection*

Due to the different IT flexibility dimensions at different organizational levels, the current research requires every respondent to acquire adequate interfirm and functional understanding and experiences at all areas and levels. Our rationale is that senior executives would have a more integrative and strategical perspective, but they might not automatically possess in-depth understanding or experience regarding present working systems. On the other hand, more junior member of staff may be well acquainted with certain IT systems, due to their close engagement with the systems, but may lack a holistic view. Further, the respondents should be capable of assessing the promoting roles of IT flexibility for intra-/interfirm process improvement and the enhancement of firm performance. Respondent validation ensures that the acquired data are reliable and credible for our empirical analysis. Such complex qualifications inevitably reduce the availability of suitable respondents. Because of the aforementioned constraint, this study opted for nonprobability sampling.

Specifically, a mixture of purposive data collection and convenience data collection method was chosen for the current research. The purposive data collection method was selected because it employs the experiences and skills of the researcher to gain well-informed respondents [58,59]. With this data collection method, researchers stipulate the features of a population of interest and attempt to find people who have those features. In the convenience data collection method, the informants are asked to contact new informants, as a focal contact point, who satisfy specific requirements and are willing to take part in specific research [58]. Our survey was organized into five parts (1–5). In order to confirm that the informants satisfy the inclusion principles and measure their ability to assess the dimensions of IT flexibility, a supplementary check was conducted within part 1. It also inquired about the technologies that the informants' firms use to check if the informants are familiar with different IT use patterns, based on the recommendation of Kumar et al. [60]. Further, only the responses from respondents successfully answering the full questionnaire were used for empirical analysis. This data collection method is in line with prior IT flexibility research. By employing a nonprobability data collection method, Gosain et al. [16] collected dependable information from both senior-level respondents and junior-level employees. This is to integrate the insights from the workers associated in daily operations. Rai et al. [61] used similar data collection method as high-level managers are not responsible for repetitive problem settlement and the principal responsibility of the executives is more tactical in nature than the operations conducted by junior-level workers. Parts 2-4 supply questions measuring the level of different dimensions of IT flexibility and other variables (Appendix A). The questions in Part 5 in our survey questionnaire supply general information on the respondents.

For the industry data, the professional network at the authors' university was accessed, which contains professionals who worked at the university for several years on shared research work and information assimilation projects, as well as graduates likely to be familiar with the current research subject. Furthermore, professionals were encouraged to distribute the survey to their co-workers to invite them to take part in the current study. A firm-level dataset was collected within a case company, hereafter known as MultiLogistics. MultiLogistics is a multinational logistics service provider that provides a diverse range of logistics services, such as warehousing, transportation, custom clearance and freight forwarding. Acting as an intermediary, it provides order fulfilment services for a large number of customers, such as telecommunication manufacturers and fashion retailers. It also works with shipping lines and freight transport companies for supply chain execution. The nature of the business indicates that it transmits a large quantity of information and has exhaustive information interchange activities with its customers and business partners. Therefore, IT is critical to the successful execution of MultiLogistics' operations. Under intensifying marketing pressures, whereby logistics services are increasingly seen as a commodity, senior executives feel IT flexibility is an important enabler to allow the company to respond and adapt to a changing environment

quickly and to remain competitive. Therefore, they feel the need to assess their current IT flexibility and compare it with industry practices to see whether the company is underperforming in certain areas, and to identify opportunities for improvement. We distributed the survey to 62 key informants from over 20 operating units of MultiLogistics globally. In doing so, an international director of the MultiLogistics was actively involved in the data collection. He distributed the survey questionnaire to potentially suitable respondents. Follow-up emails were sent two weeks and four weeks after the initial questionnaire distribution to encourage the respondents to participate in the survey. We followed up our quantitative analysis with qualitative interviews with key respondents from the case company in order to understand the significance of the results from the quantitative stage.

Regarding the industry data (*n* = 128), the analysis of the questionnaire showed that the questionnaires were answered by professionals from production and manufacturing (22%, *n* = 29), warehousing and inventory management service (28%, *n* = 36), integrated transport service providers (30%, *n* = 38), logistics and transport service brokers like 3PL firms (16%, *n* = 20) and others (4%, *n* = 5). The sample included vice presidents or higher position professionals (4%, *n* = 5), directors or vice directors (16%, *n* = 21), managers or assistant managers (42%, *n* = 54), supervisors (12%, *n* = 15), operators and clerks (24%, *n* = 31) and others (2%, *n* = 2), so the survey obtained information covering different areas and levels of an interorganizational business, namely transactional, operational and strategic operations. A majority of informants were from supply chain-related areas (13%, *n* = 17) and the transport and logistics field (71%, *n* = 90). The sample also included marketing position informants (8%, *n* = 10), IT personnel (2%, *n* = 3), CEOs (2%, *n* = 3) and others (4%, *n* = 5). The company's age and number of staff were also captured. To preserve the case firm's anonymity, we will not disclose detailed background information on MultiLogistics. We were able to attain 35 returns. Our sample size seems to be relatively small. However, this study falls into the category of exploratory research. So, a 10% significance level was thought to be theoretically adequate [46,62]. With a minimum *R2* of 0.25–0.50, the required sample size was 34–53 [46] (p. 38). Bearing in mind that the *R2* from the client firm's model analysis was 0.261–0.735, the acquired sample size of 35 satisfies the recommended criteria.

## **4. Data Analysis**

We investigated the industry-level data by employing PLS-SEM, and then extended the investigation to an IPA matrix method by using SmartPLS 3.0. The scores we calculated serve as the foundation for our investigation.

## *4.1. Industry-Level Analysis*

In Table 2, each score of importance and performance level of the different IT flexibility dimensions using the industry data is provided. This is produced using the method described in Section 3. TR IT flexibility showed the highest importance score (0.369). OP IT flexibility had the second-highest score (0.201). STR IT flexibility had the lowest score (0.186) in importance among the three flexibility dimensions. Such an output strongly indicates that TR flexibility's score in performance (i.e., desired performance) should be the highest among the three dimensions. In fact, TR flexibility's actual score in performance was 26.276. This was the highest score, while OP IT flexibility's performance score is the second highest (23.835). STR flexibility's performance came in third (20.459). The output of the structural model analysis is summarised in Appendix C. In this analysis, the direct influence of STR flexibility on firm performance and indirect influence of TR and OP flexibility on firm performance via PIC is captured. Therefore, correlations between IT flexibility dimensions and firm performance are demonstrated. A validity test for the measurement models is as reported in Han et al. [11].

**Table 2.** Importance–performance level analysis of industry: construct scores.

Source: authors.

From Table 2, we observe that the actual performance levels of different IT flexibility dimension are suitable to their relative levels of importance. Specifically, at the industry level, the resource distribution for the three different IT flexibility dimensions follows a line of "best fit," as given in Figure 1. This is also demonstrated by presenting a trend line among the three different flexibility levels that establish the AB line in Figure 2.

**Figure 2.** Importance–performance analysis of MultiLogistics: construct matrix. Source: authors. (Note: flexibility\_I stands for industry and flexibility\_ML is for MultiLogistics.)

## *4.2. Firm-Level Analysis*

Table 3 shows that MultiLogistics' importance scores are in line with the findings from the industry-level data investigation. However, its performance scores in the three dimensions are inconsistent with the industry results. Notably, TR flexibility's performance score (i.e., desired performance) should be the highest among the three dimensions, as this dimension has been deemed the most important among the three. However, in the case of MultiLogistics, TR IT flexibility showed an actual performance score of 39.013 (the lowest score). In terms of OP flexibility's importance, it had the second-highest score (40.184). With regard to the STR IT flexibility's performance, it came in first (45.363). This indicates that MultiLogistics performed best at the STR dimension, followed by OP, then TR. The detailed PLS-SEM analysis results for MultiLogistics are provided in Appendix D.

**Table 3.** Importance–performance analysis of MultiLogistics: Construct scores.

Source: authors.

Both the industry and MultiLogistics scores were united in a plot, given in Figure 2. As can be identified from Figure 2, MultiLogistics' perception of the order of importance of the three IT flexibility dimensions conforms with the industry-level analysis. However, its performance for the three dimensions is greater than the general industry measure. In particular, MultiLogistics' STR flexibility performance far exceeds the industry norm. This indicates that MultiLogistics may have invested excessively and hence overperformed in this dimension. If the company invested unnecessarily in resources in an effort to build STR flexibility, it might not get the rewards it expected. Alternatively, resources could have been better allocated to other areas, which would have a bigger influence on firm performance. What exactly could have contributed to this potential overperformance? The next section offers further insights via an indicator-level analysis.

## *4.3. Indicator-Level Analysis—Development of a Resource Allocation Action Plan*

To identify which indicators of STR flexibility may be consuming resources that could be reallocated to other indicators, the performance of indicators is analysed. Table 4 provides the performance scores of each indicator for both MultiLogistics and the industry as a whole, as well as the score difference between MultiLogistics and the industry. We examine the three STR indicators in particular.

**Table 4.** The relative performance analysis for MultiLogistics versus the industry: indicator scores.

Source: authors.

All three indicators of STR show high performance scores, as expected. However, if one examines the score difference between MultiLogistics and the industry as a whole, there is evidence that some indicators have consumed extensive resources. First, the actual performance of PTN 1 (the ability of the company to establish and adjust information connections with existing supply chain partners) is considerably higher than the industry norm with a gap of 31.111—the largest gap among all 14 flexibility indicators. Moreover, the observed performance of OFF (the ability of the company to use ICT in offering novel products and services to their customers) (47.059) also shows a relatively high score difference (23.830) when compared to the industry score (23.228). The performance of PTN2 (the

ability of the company to establish and adjust information linkages with new supply chain partners) is of less concern as it is more in line with the other indicators from the TR and OP dimensions.

Such potential overcommitment on PTN1 and OFF could be appropriate due to MultiLogistics' mission to be a truly customer-centric company. Given the increasing market pressure, and the fact that logistics and freight forwarding services are increasingly being perceived as commodities, the company has seen its profit margin being squeezed to a single digit in some regions. Therefore, the company has invested heavily in IT, including implementing a popular commercial transportation management system (TMS) globally, and an enterprise resource planning (ERP) customer relationship management suite to manage its air and freight transactions. According to its annual report, it expected that the flexibility brought about by such investments would provide better visibility to existing and new customers, simplify and speed up information flows, and streamline financial transactions across all its divisions. A lack of information on how the rest of the industry has performed could have led to a potential overcommitment. However, the seeming overinvestment could also be a deliberate act from the company in order to outperform its competitors and retain its leading position in the marketplace.

In order to find the underlying reasons why there is a large gap in PTN1 and OFF between the case company and the industry norm, we conducted a follow-up study. Interviews were conducted with four senior staff from the company: global innovation manager, IT manager, country fulfilment manager and a senior supply chain executive from one of the company's biggest clients, which, in order to retain anonymity, we call TelCo. With each interviewee, we first talked through the rationale of our study and the data analysis results. We then pointed out the gap we identified in the strategic dimension and asked our interviewees what might have contributed to the gap.

The interviews with participants from the case company largely confirmed our initial speculation that the "customer-centric" strategy drives the company's investment decisions in strategic IT flexibility. Investments have been made to streamline internal information integration (e.g., investment on Transport Management Systems and Enterprise Resource Planning) for efficiency and productivity gains. "This is the area that we can control," commented the IT manager. Areas that the case company has less control over but nonetheless must be committed to are building interorganisational information links and improving communications with various clients, particularly with large clients. The biggest challenge is that those large clients tend to have different in-house information systems, and the case company often has to build a dedicated information link with each of the clients, rather than a standard and cost-effective interface with all. The bespoke information connectivity demands a heavy resource commitment and contributes largely to the gap identified in factor PTN1.

The interviewee from TelCo explained why bespoke connectivity is needed in order for them to work with MultiLogistics: "We are a large global manufacturing company and have multiple factories in Europe. MultiLogistics is in charge of our UK order fulfilment process. This means the company needs to be able to interact with a number of our in-house systems. For inbound logistics, they also need to interact with each factory's ERP system to manage and receive goods coming into the UK. We have a central inventory management system that they need to access in order to gain visibility to stock levels. This means their WIS [warehouse information system] needs to integrate with our WIS. For outbound logistics, every time we issue them our customer's PO [purchasing order] and a packing list. They will then have to pick and configure the parts needed for that PO and send us a picking list via EDI [Electronic Data Interchange] link to our CRM [Customer Relationship Management] system."

As to the gap identified in OFF, the global innovation manager from the case company explained that, different from other logistics companies that tend to outsource many of their IT functions, MultiLogistics manages most of its IT in-house and sees technology as the core of its ability to adapt to changing demands in industry. Unlike asset-heavy logistics companies like DHL, the company's core competitiveness lies in its dynamic capability to continuously innovate and provide value-adding services to their clients. "So rather [than] just managing stocks for our clients, we actually work with our clients to reduce their overall inventories. We'll get paid less [for] warehousing, but we could then implement [a] VMI [vendor-managed inventory] type of exercise and take over our client's

replenishment function," commented the innovation manager. Other interviewees also commented that a number of explorative initiatives took place in MultiLogistics—for example, introducing manufacturing services and 3D printing. Hence it seems that "overcommitment" under the factor of OFF is a deliberate act that the company undertook to differentiate itself from its competitors. They did acknowledge that they had limited knowledge about how the rest of industry performed, and therefore our tool helped them to gain a clear sense of their competitive position in the marketplace.

By translating the PLS-SEM analysis output to the IPA matrix, the current research has assessed the performance gap in different IT flexibility dimensions. The assessment was based on their relative levels of importance to firm performance. In the case of MultiLogistics, our analysis shows that STR flexibility may overperform, given that its importance does not deviate much from the industry standard. A further indicator-level analysis offers additional insight as to which factors contribute to the possible excessive performance of STR flexibility and where downscaling might be possible. Our follow-up study reveals the complex coercive and competitive forces that drove the case company's deliberate action to commit considerable resources.

## **5. Conclusions**

## *5.1. Theoretical Contribution*

Previous research has stressed the importance of effective IT flexibility management through a requirements analysis that mitigates deficiencies or excesses in dissimilar flexibility dimensions. Yet there has been a lack of analytical tools that evaluate the required and actual level of IT flexibility dimensions. To resolve this issue, we propose a combined method of IPA and PLS-SEM. Our method shows that prioritization among multiple dimensions of IT flexibility is made by employing the two universal measurements, namely importance and performance. Furthermore, the distribution of firm resources to the most important dimensions is advised.

Application of this method to a client company's data (Multilogistics in this study) also visualizes how this client firm can distribute its resources through the prioritization of different IT flexibility dimensions. In doing so, we proposed an action plan to distribute finite resources to different IT flexibility dimensions in a well-organized and efficient method. The method of PLS-SEM combined with the IPA matrix revealed the most important variables that contribute to the highest level of performance. To the best of the authors' knowledge, there has been no study that has proposed such an analytical tool that assesses multiple dimensions of IT flexibility. The unique contribution of this study is, therefore, an improved understanding of how to get the greatest value out of an investment in IT resources by revealing areas where particular management attention and subsequent rectification may be necessary. By proposing a combined method of IPA and PLS-SEM, we provide a novel insight about the mechanisms that firms can utilize to control their IT flexibility levels in rapidly changing business environments.

The IPA-PLS-SEM tool, however, does not offer detailed explanations as to why companies overcommit in certain areas. There may well be positive and legitimate reasons why companies acted in this way—for example, to maintain a competitive advantage by managing clients who require a certain IT flexibility, as illustrated in the case of MultiLogistics. Or equally, the areas identified could be "blind spots," or mismatches between required and committed investment, that senior executives were not aware of. Those areas could then be targeted for further improvement, or resources could be redeployed elsewhere to deliver improved value to the businesses. Furthermore, without this tool it is not possible to detect the IT flexibility gaps at an indicator level. Consequently, it will be difficult for organisations to develop a strategy for better resource exploitation. Once organisations are confident that the flexibility gaps identified via the use of the IPA—PLS-SEM tool are due to misinvestment in certain areas, action can be undertaken to rectify the situation. Certain resources could be transferred from one area to another in order to achieve the desired IT flexibility outcomes. The concept of resource mobility is well established in the literature, and reallocating resources from one dimension to another often incurs lower penalties when compared with additional investment.

## *5.2. Practical Contribution*

Our research also suggests a measurement tool that managers can utilize to assess IT flexibility and identify any gaps that exist between current and desired flexibility levels. This output encourages logistics managers to consider the importance of IT flexibility in a more integrative and clear manner. It also helps logistics managers to decide how to coordinate their IT flexibility dimensions efficiently to deal with upcoming changes with finite firm resources. The current literature on quantifiable flexibility measurements has been accused of not being relevant to real-life industry settings. However, in this study, we propose that the IPA tool can be applied directly to industrial practice. Both supply chain and IT managers can use the IPA tool to understand what their competitive priorities should be when examining the performance of their IT flexibility, and identify whether some dimensions may be overor underperforming. This will enable managers to review their IT flexibility capabilities and make more informed choices about where to best concentrate their resources, ultimately increasing managers' abilities to control and manipulate organizational factors to increase firm performance.

## *5.3. Limitations and Future Research*

Even though the we did our best to gain many samples for applying the IPA and PLS-SEM combined method to the client firm, the application was performed with a comparatively small sample size (*n* = 35). This is because we required respondents to be key informants who are skilled and well-informed about inclusive IT use, interorganizational process connections and firm performance attributes. Moreover, acquiring 100 individuals from a single firm was a difficult task as the sample pool in a specific company is narrower than in an overall industry sector. If we acknowledge the exploratory characteristics of this study, the sample size is statistically acceptable [46]. However, even though our sample size satisfies the recommended criteria, future research should seek to collect more data to enhance the validity of both industry- and firm-level data.

Although IPA is particularly useful for indicating areas that may either under- or overperform, it does not offer an explanation as to why this happens. As the development of IT flexibility is specific to the operational context, environmental (e.g., industrial advances and market circumstances), organisational (e.g., firm scale, tactics and monetary condition) and technological (e.g., IT proficiency and human resources, architecture and IT merchants) issues can affect the required IT flexibility level. Further investigation is thus needed to understand whether a company is indeed over- or underperforming in a specific dimension. As the IPA matrix assumes linear relationships between importance and performance, this study does not address lines CD and EF (shown in Figure 1). Considerations of possible nonlinear relationships, such as the one proposed by Tontini and Silveira [50], could complement our approach. Future research could also attempt to apply this tool to other, non-IT types of flexibility.

**Author Contributions:** Conceptualization, J.H.H.; methodology, J.H.H.; Y.W. and M.N.; formal analysis, J.H.H. and Y.W.; writing—original draft preparation, J.H.H. and Y.W.; writing—review and editing, Y.W. and M.N.; visualization, J.H.H.; supervision, Y.W. and M.N.; project administration, Y.W. All authors have read and agreed to the published version of the manuscript.

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

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

## **Appendix A**

**Table A1.** Multiple dimensions of IT flexibility.

**Table A1.** *Cont.*

Source: adapted from Han et al. [11].

## **Appendix B**

**Table A2.** Hypotheses for IT flexibility research model.

Source: adapted from Han et al. [11].

## **Appendix C**

**Figure A1.** PLS-SEM test results for industry-level data (\*\* *p* < 0.05, \*\*\* *p* < 0.01, NS: Non-Significant).

## **Appendix D**

**Table A3.** PLS-SEM test results for MultiLogistics. Summary of validity test results of the measurement model.

**Table A5.** PLS-SEM test results for MultiLogistics. Cross-loading analysis.

**Table A6.** Effects and variance explained.

Note: \*\*\* *p* < 0.01; \*\* *p* < 0.05; \* *p* < 0.1 (all two-tailed).

**Figure A2.** Result of path analysis—Company A. (\*\* *p* < 0.05, \*\*\* *p* < 0.01, NS: Non-Significant).

## **References**

© 2020 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 (http://creativecommons.org/licenses/by/4.0/).

## *Article* **Sustainability of Investment Projects with Energy Efficiency and Non-Energy Efficiency Costs: Case Examples of Public Buildings**

**Mališa Ðuki´c 1,\* and Margareta Zidar 2**

**Abstract:** According to the European Commission Energy Union strategy from 2015, some of the main objectives are to improve energy efficiency, reduce dependence on energy imports, cut emissions, and drive jobs and growth. Achieving the objectives of the Energy Union requires significant financing, particularly for investments in energy efficiency. Serbia and Croatia included the objectives of the Energy Union in their national strategies and have implemented various investment projects in this area. This paper focuses on the sustainability of energy efficiency projects for public buildings which include not only energy efficiency investment cost but also non-energy efficiency investments. By applying the European Commission methodology for cost-benefit analysis, we assessed the sustainability of several projects in Serbia and Croatia. The sustainability assessment is done by quantifying energy savings, greenhouse gas emission reductions and the social and economic benefits that are related to non-energy efficiency project components. The values of economic performance indicators imply that society would be better off with projects that would contribute to achieving not only the targets set in national energy strategies but also to creating broader social benefits.

**Keywords:** investment projects; financial analysis; economic analysis; energy efficiency; public buildings; sustainability

## **1. Introduction**

The buildings sector in the EU contributes to approximately 36% of CO2 emissions and 40% of final energy consumption, while 75% of the building stock is energy inefficient and 35% of buildings are over 50 years old [1]. Due to the Energy Efficiency Directive (2012/27/EU and 2018/2002) (EED), the annual rate of refurbishment should amount to 3% of the total surface area of buildings occupied by the central government [2]. The Directive on Energy Performance of Buildings (2010/31/EU and 2018/844) (EPBD) requires decarbonizing the national building stocks by 2050 by establishing national long-term renovation strategies [3]. In addition to these energy efficiency related directives, the Directive 2018/2001 on the Promotion of the use of Energy from Renewable Sources (REDII) calls for more renewable-based heating and cooling in buildings [4]. Current annual renovation rates of the building stock across Member States ranges from 0.4% to 1.2%, and to reach the targeted 3%, many barriers to renovation need to be overcome [5].

In this paper, we focus on analyzing the sustainability of investing in several public buildings in Croatia and Serbia. The objective of the study is to assess the technical, financial, and economic feasibility of investment projects in three public buildings by calculating corresponding performance indicators. This paper addresses the following questions: What is the energy consumption in each case? What are the technical options and what are their implications on the energy performance of buildings? Do the options contribute to the decarbonization of the building stock? What are the investment, operation

**Citation:** Ðuki´c, M.; Zidar, M. Sustainability of Investment Projects with Energy Efficiency and Non-Energy Efficiency Costs: Case Examples of Public Buildings. *Sustainability* **2021**, *13*, 5837. https://doi.org/10.3390/su13115837

Academic Editors: Golam Kabir, Sanjoy Kumar Paul, Syed Mithun Ali and Marc A. Rosen

Received: 30 March 2021 Accepted: 18 May 2021 Published: 22 May 2021

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

**Copyright:** © 2021 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/).

and maintenance costs and energy savings per every option? Which option offers the most cost-effectiveness? Is the proposed project financially viable? What are the energy efficiency and non-energy efficiency benefits in every case? Do the energy efficiency benefits justify the costs? If not, how can the economic viability of the cases presented in this paper be assessed?

In Croatia, from 2014 until 2020, the energy refurbishment of buildings was co-financed by the European Structural and Investment funds with 60% for energy efficiency measures and 85% for design documentation, energy audit and energy certificate costs [6]. To qualify for the co-financing, at least 50% of heating energy savings was required and usually this was achieved by the thermal insulation of the building envelope and the installation of energy efficient windows. The market was highly motivated to use grants for financing the energy refurbishment costs. There were 758 projects of public buildings and the total co-financing amount was EUR 211.81 million. In residential buildings, i.e., multiapartment buildings and family houses, which included a total of 16,000 households, there were 584 projects and the total co-financing amount was EUR 100 million [7]. Even though this is a relatively high number of projects and financial resources invested, an annual refurbishment rate of 0.7% of the total building stock was achieved and 1.35 million m2 of the building surface area were refurbished. According to the recently published National building stock refurbishment strategy, a gradual increase in the annual refurbishment rate is targeted, starting from 1% in 2021 and reaching 4% in 2050, and expecting to refurbish a total of 104 million m2 and to achieve 2260.9 ktCO2 savings [8]. Decarbonization of the building stock will include additional energy efficiency measures, such as modernizing the building systems, using on-site renewable energy, and developing neighborhood renovation projects instead of single building projects. In Serbia, according to the Energy sector development strategy until 2025, the energy refurbishment of buildings has been set out as a priority activity.

To assess the sustainability of the investment projects in several public buildings in Serbia and Croatia, we took into consideration both energy efficiency and non-energy efficiency costs and benefits. Economic performance indicators imply that energy efficiency benefits may not be sufficient to justify the project costs. The inclusion of non-energy efficiency benefits improves the results of the economic analysis, making the projects desirable from a socioeconomic perspective.

The paper is organized as follows. Section 2 provides a literature review. Methods on energy audits, certificates of buildings and the cost-benefit analyses of investment projects are introduced in Section 3. Case examples of a building in Croatia and two buildings in Serbia are elaborated in Section 4. Results and discussion on energy cost savings, option analysis and cost-benefit analyses are presented in Section 5. Finally, conclusions are presented in Section 6.

## **2. Literature Review**

Improving energy efficiency has been a way to increase the productivity and sustainability of society, primarily through the delivery of energy savings. The impact of energy efficiency measures can be an important contributor to economic growth and social development [9]. To investigate the complex and controversial relationship between energy consumption and GDP, a study concluded that there is mixed evidence on the direction of causality between energy consumption and GDP [10]. Job creation is an important socioeconomic effect associated with the development of energy efficiency technologies. The approaches to estimate job creation effects of the renewable energy and energy efficiency sectors, as well as the existing estimations of job creation figures by technology and capacity, were summarized in the literature [11,12]. Kerr et al. considered how different benefits have been used within the overall rationale for energy efficient retrofit policies in different contexts [13]. The identification of multiple benefits may not imply multiplied policy support, and instead it is more likely that different rationales will have relevance at different times, for different audiences [13].

Bleyl et al. analyzed the economic and financial implications for renovating an office building to the "Passive House" standard and concluded that the Life Cycle Cost & Benefit Analysis cash flow model can be used not only for deep energy retrofit business case analysis, project structuring, and financial engineering, but also for policy design [14]. Gelatioto et al. simulated a public historic building and some permitted retrofit actions were applied to analyze the effectiveness of national measures in four different climatic zones [15]. Energy efficiency retrofitting of existing buildings is a key program for improving building energy efficiency in northern regions of China. A methodological framework to conduct an economic cost-benefit analysis (CBA) for energy efficiency retrofit projects was applied and the research found that the retrofitting of existing buildings generally lacks attractiveness to investors from an economic perspective [16]. Energy saving retrofits of residential and public buildings positively contributed to economic growth, employment, and protection of the environment in a post-transition country [17].

Novikova et al. analyzed the costs and benefits of the different thermal efficiency retrofits as well as the impact of user behavior, implying that energy usage in the public building sector could increase and mitigate savings resulting from energy-saving measures, making saved energy costs invisible [18]. By reducing energy consumption, other benefits can be achieved such as reduced government subsidies and improved health due to less air pollution and a better indoor climate [19]. Filippidou and Jimenez Navaro [1] have used CBA to identify the cost effective and cost optimal solutions for current and future EU building stock. Both energy efficiency and sustainable heating and cooling should be considered when planning the decarbonization of the building sector, thus avoiding lock-in effects in terms of investments in less than cost-optimal energy efficiency renovations. Costoptimal solutions require moderate thermal insulation in southern Europe, and efficient heating and cooling technologies should be prioritized. On the other hand, in central Europe, deep energy efficiency improvements are cost-optimal and should be combined with efficient heating and cooling technologies.

The renovation of large scale buildings is still a difficult task to be accomplished. The most prevalent reasons for this are, among others, large investments and lack of awareness of the potential benefits and of the skills required [20,21]. Papers published so far evaluate the co-benefits (or multiple benefits) of a portfolio of energy efficiency projects by quantifying the effects of such projects on selected macroeconomic variables [9–11,17–19,22]. In our paper, we try to contribute to the literature by estimating the economic performance of energy efficiency investments in public buildings on a case-by-case basis.

#### **3. Methods**

The European Commission methodology is applied to assess the technical, financial and economic feasibility of proposed investments in three public buildings [23]. Figure 1 presents the appraisal steps.

**Figure 1.** Project appraisal methodology steps (Source: Adapted from Sartori et al. [23]).

Project objectives are elaborated in Section 4. The methodology on technical feasibility with option analysis as well as the financial and economic analysis is presented in Sections 3.1 and 3.2, respectively.

#### *3.1. Energy Audits and Certificates of Buildings*

Energy savings in buildings are determined in an energy audit. There are several standards for conducting energy audits, and the most used ones are ISO 50002:2014 and EN 16247-1:2012, which promote similar basic steps targeted to identify current energy performance of a building and building systems and the energy performance improvement measures. The energy audit process has two implementation steps, (1) Data collection & Site Visit, and (2) Analysis & Reporting.

The data collection includes discussion with the building owner and user to define the energy audit purpose and scope, to acquire data on the building and the energy bills. Next, a site visit is undertaken to record findings on the current condition of the building's elements and systems, and to identify opportunities for energy savings [24].

Savings are achieved by considering various energy efficiency measures that improve energy performance of building components and systems. Baseline energy consumption, costs and CO2 emissions are determined from the energy bills. Feasibility of energy efficiency measures is determined by technoeconomic analysis of the investment costs and the saving potential presented in energy, CO2 emissions and costs (kWh/y, tCO2/y, EUR/y, respectively). Additional financial indicators as simple payback period (SPB), discounted payback period (DPB), internal rate of return (IRR) and investment net present value (NPV) are also prepared to describe the effectiveness of the energy efficiency measures. All findings on the current energy performance, energy efficiency measures and the improved energy performance of a building are described in a report, which is presented to the building owner and user to make the decision on the implementation of energy efficiency measures.

The EPBD has defined a general framework for the calculation of the energy performance of buildings where the heat energy demands for heating and cooling, along with energy efficiency levels of the building systems, are assessed. The energy performance certificate is a document where the unique energy performance indicator is presented, grading the building's energy performance in energy classes, from A+ as the most efficient level, to G as the lowest efficiency level.

For existing buildings, which are usually graded in low energy efficiency classes from D to G, there is a high energy saving potential. Deep energy refurbishment measures, such as thermal insulation of the building envelope and on-site renewable energy-based technologies, achieve energy savings of up to 80% and the highest energy classes. Major refurbishment is usually focused on thermal insulation measures and saving up to 50% more energy and energy classes B or C are reached. Minor refurbishment includes improvements of the existing building systems while savings can range up to 30% and the energy class usually improves one level up.

In the option analysis, cost effectiveness rather than cost efficiency should be targeted. Implementing lower levels of energy performance may lead to locking-in energy consumption to unsustainable patterns due to long-term lack of proper maintenance, which remain unchanged for several decades until the next renovation cycle [25]. As future renovation rates are expected to remain close to the current level, it is highly important to make sure that the best available energy efficiency measures are included [26]. In our paper, the selection of the preferred options is based on cost effectiveness, which allows for high energy savings.

#### *3.2. CBA Methodology*

CBA is defined as an activity that enables calculating and comparing costs and benefits of an investment project. The impact of financial, economic, social and other factors should be considered in order to assess the financial and economic viability of projects [27]. When the value of benefits exceeds the costs incurred, the project is feasible. When an activity undertaken by an individual or a company affects other individuals or companies who do not pay for that activity or are not paid, the effect can be either positive or negative; therefore, there are positive or negative externalities [28]. Sáez and Requena emphasize that CBA includes our own intergenerational ethics, expressing what our present generation is willing to pass on to future generations and not the future generations' preferences [29]. They propose the use of the common Social Discount Rate for market goods and a lower discount rate (environmental discount rate) for non-market goods in the same CBA exercise as a way to include a certain level of intergenerational equity [29].

In this paper, CBA is performed in line with the European Commission Guide to cost-benefit analysis of investment projects, hereafter the EC CBA Guide [23]. CBA includes financial analysis and economic analysis. The financial analysis is made on behalf of the owner of the infrastructure. The methodology used is the Discounted Cash Flow (DCF) method. Only cash inflows and outflows are considered in the analysis, i.e., depreciation, reserves, price and technical contingencies and other accounting items which do not correspond to actual flows are disregarded. The reference period is 20 years. The analysis is carried out in constant (real) prices, net of value added tax (VAT), both on costs and revenues.

The incremental approach is applied, comparing the with-project scenario and the without-project scenario. The with-project scenario assumes the implementation of the proposed investment and operations within the reference period. The without-project scenario is a counterfactual scenario and indicates what would happen in the absence of the project.

It is assumed that the energy price will be increased according the electricity price forecasts in the South East Europe Electricity Road Map Country Report [30]. Projected electricity price growth rates are applied to estimated electricity cost. Fuel scenarios developed for the Ten-Year Network Development Plan (TYNDP) 2018 from the European Network of Transmission System Operators (ENTSOs) were considered with regards to gas price [31]. Projected gas price growth rates are applied to estimated heating cost.

The economic analysis is made on behalf of the whole society and appraises the project's contribution to the economic welfare of the region or a country. Investment projects often have impacts that have no direct market values such as the impacts on the environment. These effects can be monetized through different valuation techniques. The key objective of the economic analysis is to prove that the present value of the project's economic benefits exceeds the present value of its economic costs, which means that the project has a positive net contribution to society. This is expressed as a positive Economic Net Present Value of the net cash flow, a Benefit/Cost (B/C) ratio higher than 1,0 or a project's economic rate of return (ERR) exceeding the social discount rate. The economic analysis is based on an incremental approach, comparing economic cost and benefits of the project. Constant prices are applied and a social discount rate of 5% is used. As no particular salary distortions are foreseen, the conversion factor of 1 is used. The following five steps are applied: (1) conversion from market to accounting prices, (2) monetization of non-market impacts, (3) inclusion of additional indirect effects, if relevant, and (4) discounting of the estimated costs and benefits and calculation of the economic performance indicators [23].

In this paper, we assessed energy efficiency and non-energy efficiency benefits. Energy efficiency benefits included O&M cost savings, avoided CO2 emissions, avoided cost of airborne pollutants and enhanced security of supply. The non-energy efficiency benefits are specific for every building and may comprise education-related benefits, avoided healthcare costs and avoided cost of child care, if applicable.

The recommended values of European Commission (EC) for the shadow price of CO2 are used in the economic analysis and are available in the European Commission Directorate-General for Climate Action paper on Climate Change and Major Projects [32]. Changes in the emissions of airborne pollutants are also monetized (PM, NOx, SOx) with unit damage values from the NEEDS study [33]. If the with-project temperature will be different from the current temperature, the project would create incremental comfort benefits. The project is expected to generate security-of-supply benefits at a shadow price used by the European Investment Bank (EIB) and the EC for power generated from a combined cycle gas turbine (CCGT) [23].

When applicable, education related benefits are assigned. Such benefits would result from improved quality of health care services and an increase in the number of survivors. Value of a Statistical Life (VSL) and income elasticities are elaborated by Viscusi and Masterman [34].

## **4. Case Examples of Public Buildings**

#### *4.1. Building 1—Hospital*

Building 1 is a clinical hospital built in 1988 and is located in Zagreb, Croatia. The building is graded in energy class F > 220%. Electricity accounts for 48% of total operational costs, natural gas for 19% and water for 33%. Although the investment maintenance has been implemented over the years, the building systems have reached the end of life and many damages can be observed on the building envelope. Leakage of the flat roofs is occurring continuously due to the large roof surface area, damaged stormwater outlets and damaged skylights. Building systems lack all functionalities that would enable stable and efficient supply of energy, as part of the damaged equipment like heat recovery and humidification systems were not replaced due to high investment costs. The window opening mechanisms, window sealing and prefabricated façade panels with thermal insulation layers are damaged, causing increased heat losses. The water supply and hydrant network lack proper system pressure due to frequent pipe bursting while all water fixtures are damaged with continuous leakage. Investment not only in energy efficiency improvement but also in other major building properties is needed, but the respective refurbishment program finds only energy efficiency measures and water saving measures eligible for co-financing.

Energy efficiency measures proposed for this building include reducing heat demand by improving the thermal insulation of the external envelope, the full replacement of the centralized air-conditioning and mechanical ventilation systems and the replacement of steam boilers with hot water boilers. A new lighting system and automation and control for all building systems with integration in the central management and surveillance system are suggested. In addition, the revitalization of laundry and kitchen facilities, refurbishment of the external hydrant network, introducing new efficient water saving fixtures, refurbishment of the entire indoor supply pipeline, photovoltaic system and solar thermal collectors are included. Option analysis is performed for this building indicating various energy efficiency levels of the energy efficiency measures proposed. Option A presents the minimum improvements of the building envelope and building systems. Option B aims at achieving the overall highest energy efficiency level. In addition to the improvements of option B, option C includes a higher building envelope performance. The building will be graded in energy class C < 100% following the implementation of the proposed investment.

## *4.2. Building 2—Emergency Health Care Center*

Building 2 was built in 1975 as a medical facility in Belgrade, Serbia. The building is undergoing significant surface expansion to improve substandard working conditions and to introduce new medical services. The building is graded in energy class E > 150%. Electricity accounts for 30% of total operational costs, heat energy for 67% and water for 3%. The overall poor conditions due to lack of adequate maintenance on all installations and the building structure can be observed, such as leakage of the flat roofs and façade damages. The existing building systems have substandard energy performance levels, and additional power capacity, proper cabling and illumination level of the lighting system, replacement of damaged sanitary furniture, improvement of indoor air quality and indoor comfort both in summer and winter period are required.

The energy efficiency measures proposed for this building include the reduction of heat demand by improving the thermal insulation of the external envelope. The supply of heat from the existing district heating system will remain, but the heat distribution and transfer system will be completely replaced. Moreover, solar thermal collectors, a new centralized cooling system, a ventilation system with heat recovery for high priority areas and building energy management system will be introduced. The sustainability measures include the reinforcement of the existing loadbearing structure, fire protection systems and installations, new water and sewage installations, the complete replacement of the electrical installations, as well as back-up energy generators and reliable and highcapacity telecommunication installations. Auxiliary services (kitchen, restaurant, laundry, car wash), accessibility infrastructure for disabled persons, a video surveillance system and traffic and landscape infrastructure are provided. Option A includes the minimum energy efficiency improvement of all building elements and systems and is in line with national energy efficiency requirements, while option B introduces solar thermal system additionally. Option C incorporates the mechanical ventilation systems with heat recovery in high priority areas. The building will be graded in energy class B < 50% in the withproject scenario. CBA is performed for the preferred option.

## *4.3. Building 3—Kindergarten Complex*

Building 3 is a kindergarten complex with administration offices and a kitchen facility in Belgrade, Serbia. The building was built in 1975 and is graded in energy class E > 200%. Electricity accounts for 37% of total operational costs, heat energy for 58% and water for 5%. The building was thermally insulated in 2009 but with the minimum performance of the façade and windows. The indoor thermal comfort in heating season is low due to insufficient heating capacity of the central light heating oil boiler plant. Individual electric heating units are used for the additional heating of rooms. The power capacity is not sufficient to cover all electric equipment installed.

Energy efficiency measures proposed for this building include reducing heat demand by improving the thermal insulation of the external envelope. Modernization of building systems introduces heat pumps for heating and cooling, a mechanical ventilation system with heat recovery, solar thermal collectors, a new lighting system and a building energy management system. Option A includes the minimum energy efficiency improvement of all building elements and systems and is in line with national energy efficiency requirements. Option B introduces the solar thermal system and heat pump, while option C incorporates the mechanical ventilation and heat recovery. The building will be graded in energy class B < 50% following the implementation of the proposed investment. The with-project scenario of CBA is based on the preferred option.

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

#### *5.1. Building 1—Hospital*

The investment cost in various options is from 432 to 500 EUR/m2 while the operational cost savings range from 21 up to 39 EUR/m2 (Table 1). The simple payback period is from 10 to 21 years. Option B is the most cost-effective one with the highest operational cost saving, and with an annual electricity saving of 24%, heat saving of 67%, water saving of 42% and CO2 saving of 57%. Renovation costs in hospitals in Albania for building envelopes and heating systems were assessed in the literature [18]. Due to the difference in investment objectives, their findings are not comparable to the results of option analysis.

For Option B, the NPV amounts EUR −406,840 and the IRR is 3.73%. With 40% cofinancing, the NPV is EUR 8.8 million and the IRR is 12.53%. The results suggest that an energy efficient renovation investment, if not supported by government grants, may not be financially viable. This is in line with previous findings that government support could significantly improve the financial viability for investors [17,19].

**Table 1.** Investment cost and savings for various options for Building 1.

Source: Authors' calculations.

## *5.2. Building 2—Emergency Health Care Center*

The investment cost ranges from 67 up to 94 EUR/m2 while the operational cost saving varies from 5 up to 14 EUR/m2 (Table 2). Total energy savings vary from 41% up to 49%. Option B has the shortest simple payback period, however, option C is the most cost-effective as it provides higher indoor comfort.

**Table 2.** Investment cost and savings for various options for Building 2.

Source: Authors' calculations.

The investment cost is estimated to be EUR 7.3 million and includes energy efficiency (EE) components and non-EE components with 54% and 46% share, respectively (Table 3).

**Table 3.** Investment cost of Building 2.

Source: Authors' calculations.

Operation and maintenance (O&M) costs include electricity, heating and maintenance costs of both EE and non-EE components (Table 4). Due to the energy cost savings that would result from the project implementation, the incremental operation and maintenance costs are negative from the first year of operations in the amount of EUR −5.804. The energy costs are adjusted to the forecasted changes of energy prices as elaborated in the methodology section of the paper.

**Table 4.** Incremental O&M cost in the first year of project operations.

Source: Authors' calculations.

The economic costs include the investment cost, replacement cost and residual value (Table 5). As the weighted average life time of investment cost items is equal to the reference period of 20 years, the replacement costs and residual value are set to EUR 0. Since the project generates energy savings, incremental O&M costs are presented as economic benefits. The net present value of economic costs is EUR 6.9 million.

**Table 5.** CBA results for Emergency Health Center.

Source: Authors' calculations.

The O&M cost savings are a major energy efficiency related benefit with the net present value of EUR 157 k and a 2.0% share of total benefits. The avoided cost of airborne pollutants follows in importance with a 1.1% share (EUR 85 k). The avoided CO2 emissions and the enhanced security of supply account for 0.9% of the total. The project implementation would not generate incremental revenues. Benefits from education with a net present value of EUR 7.98 million are considered as a non-EE benefit and are a result of improved quality of health care services. The economic net present value is EUR 1.0 million while the benefit/cost ratio is 1.15. Such economic performance indicators imply that the society would be better off with the project. The energy efficiency benefits are not sufficient to justify the EE investment cost. The inclusion of non-EE costs and benefits in the analysis provides the basis for the comprehensive assessment of the project's feasibility. The assessment of co-benefits or multiple project benefits of various projects, which include the impact on GDP, employment, labor income, rental income and building sales price, is presented in the literature [11,14,16–18]. Since these benefits are not included or applicable to the cases in this paper, the results of the CBA are not comparable to the indicators presented in the literature.

A sensitivity analysis was then performed to identify the effect of the choice of a discount rate on the weighing of costs and benefits. If the social discount rate would increase, the net present value would decrease as the costs come up-front and benefits come later. A relevant element of the sensitivity analysis is a value that a variable (social discount rate) would take in order for the net present value of the project to become zero or negative.

If a rate of 7% is used, the benefit/cost ratio is 0.99% and the net present value is negative (Table 6). Such results would suggest that the project is not economically viable only due to the increase in the social discount rate from 5% to 7%. In the case of the Emergency Health Center, an additional increase in the discount rate (7% or higher) would further deteriorate the results of the cost-benefit analysis.

**Table 6.** Results of economic analysis under different social discount rates for the Emergency Health'Center.

## *5.3. Building 3—Kindergarten Complex*