*4.6. Cytochrome c*

Cytochrome *c*, a low-molecular weight heme protein loosely tethered to the inner mitochondrial membrane, is an important component of the respiratory chain that accepts electrons from Complex III and transfers them to Complex IV. Cytochrome *c* also functions as an initiator of apoptosis. In humans, cytochrome *c* is encoded by *CYCS* and in yeast by two isoforms *CYC1* and *CYC7*, the first one being predominant. To date, four mutations in *CYCS* have been associated with non-syndromic and mild thrombocytopenia, a rare autosomal dominant disorder characterized by low platelet levels in the blood but no other hematological findings. These mutations include a G41S substitution found in a New Zealander family [283], a Y48H mutation in an Italian family [284], an A52V mutation in a British family [285], and a K101del deletion in a 64-year-old woman from a Japanese family with multiple cases of Hemophilia A [286]. Unlike other non-syndromic thrombocytopenias, the morphology of circulating platelets in these patients was normal [287]. Some of these mutations, recreated using yeast model systems, led to reduced cytochrome *c* expression, reduced respiratory rate, and increased apoptotic rate [284,286]. However, the link between loss-of-function mutations in *CYSC* and abnormal platelet formation has yet to be elucidated.

The addition of the heme moiety to cytochrome *c* is catalyzed by a cytochrome *c* type heme lyase encoded by the *HCCS* gene, the human homolog of yeast *CYC3*. HCCS is able to complement the yeast Cyc3 deficiency [288]. Mutations in *HCCS* have been associated with MLS, an X-linked, male-lethal disorder described in the Complex III section [289]. It has been hypothesized that the characteristic phenotype of MLS patients is a consequence of the activation of non-canonical, caspase-9 mediated cell death in the brain and eyes as a result of HCCS impairment [290].


*2.1. Data Collection*

As researchers involved in this mixed-methods research project, we acknowledge the potential for power differentials and biases that may arise from our active roles as instructors or trainers when administering the Achieving Styles Inventory (ASI) to participants. We rigorously adhered to ethical guidelines [23] (p. 2) throughout the study to address these concerns. We employed an "assessment as learning" [24] approach during the feedback interviews, prioritizing the educational value for participants over solely gathering data for research purposes, thus minimizing potential power dynamics. Furthermore, to mitigate any biases, different researchers led discussions with different participants, and we cross-checked each other's consultation transcripts during the coding process to ensure consistency and reduce individual perspectives or biases. We did not seek full IRB approval given the nature of the study design; however, we committed to ethical conduct, including safeguarding participant well-being, ensuring confidentiality, and obtaining informed consent throughout the research process. These measures contributed to the validity and reliability of our study, and we remained fully transparent about our roles as researchers and instructors to address any potential researcher influence on the study's outcomes. (See Appendix D for a more thorough discussion for not seeking Ethical Review; Appendix E to view a copy of the Connective Leadership Institute's Participant Privacy documentation; Appendix F to review the research team's Consent to Participate in Research script.).

In early 2022, identified participants (who had completed the ASI prior to 2020) were asked to participate in an hour-long interview. Prior to the interview, the researchers requested that the participants complete the ASI, based on their current position and situation. The researcher/interviewer reviewed and compared the ASI taken pre-COVID-19 and the recently completed post ASI. The interviewer led the participant through several reflective questions about how COVID-19 impacted their work and personal life situations and how they responded, pivoted, or adjusted their leadership behaviors in response. Then, the interviewer presented the two ASI profile graphs and facilitated a dialogue on the observations of the shapes and sizes of the two graphs and the relationship with the reported situations and pivots.

#### *2.2. Analysis Approach*


3.1. Thermal conductivity of nanofluid

Researchers have widely studied nanofluid thermal conductivity. Investigation the increase of analytical thermal conductivity a solid-liquid mixture by adding solid particles of micro-size balls into a liquid known as the Maxwell model.

$$k\_{\rm nf} = \frac{k\_p + 2k\_b + 2\rho \left(k\_p - k\_b\right)}{k\_p + 2k\_b - \rho \left(k\_p - k\_b\right)} k\_b \tag{1}$$

where φ is the volume fraction of the nanofluid, kb is the thermal conductivity of the base fluid, and kp is the thermal conductivity of the nanoparticles.

Hamilton and Crosser [13] proposed a model for nonspherical particles by introducing a shape factor n given by n = 3/φ. The thermal conductivity is expressed as follows:

$$k\_{\eta^{\circ}} = \frac{k\_{\mathcal{p}} + (n - 1)k\_{\mathcal{b}} - (n - 1)\varphi(k\_{\mathcal{b}} - k\_{\mathcal{p}})}{k\_{\mathcal{p}} + (n - 1)k\_{\mathcal{b}} - \varphi(k\_{\mathcal{b}} - k\_{\mathcal{p}})} k\_{\mathcal{b}} \tag{2}$$

A modified Maxwell's model was proposed by Xuan and Li [14] by considering the Brownian motion of the particles in the base fluid for the thermal conductivity enhancement given as

$$k\_{nf} = \frac{k\_p + 2k\_b + 2\left(k\_b - k\_p\right)\varrho}{k\_p + 2k\_b - \left(k\_b - k\_p\right)\varrho}k\_b + \frac{\rho\varrho\mathbb{C}\_p}{2}\sqrt{\frac{k\_b T}{3\pi r\_c \mu}}\tag{3}$$

where kB is the Boltzmann constant, rc is the apparent radius of the cluster, and μ is a dynamic viscosity.

Figure 3 shows the variation of knf =kb as a function of alumina volume fraction with and without CTAB along with its best fits with and without interfacial resistance. The knf =kb value

semi-concentrated nanofluids with 5–10 vol.% with aggregation of nanoparticles, and concen-

There are some existing theoretical formulas to estimate the viscosity of nanofluid. Among them, equation suggested by Einstein [22] is a pioneer in determining the viscosity equation. The assumptions based on the linear viscous fluid containing spherical particles and low

where μnf is the viscosity of nanofluid, μb is the viscosity of the base fluid, and φ is the volume fraction. It is a linear increase of the viscosity with increasing volume concentration. This formula has a limitation that is very small particle concentration. Later on, many researchers

In 1952, Brinkman [23] extended Einstein's formula to be used with moderate particle concentrations, and this correlation has more acceptance among the researchers. For particle concen-

Considering the effect due to the Brownian motion of particles on the bulk stress of an approximately isotropic suspension of rigid and spherical particles, Batchelor [24] proposed

It is clear from the above two relations that, if the second or higher order of φ is ignored, then these formulas will be the same as Einstein's equation has been validated for a particle volume

Nguyen et al. [17] showed that both the Brinkman [23] and Batchelor [24] equations severely underestimate nanofluid viscosities, except at very low particle volume fractions (lower than 1%). They have proposed two correlations for nanofluids consisting 47 and 36 nm of Al2O3

μnf ¼ μb � 0:904e

Both of these models determine the viscosity by only considering base fluid viscosity and the particle volume fraction. Furthermore, they proposed a correlation for computing CuO water

μnf ¼ μbð Þ 1 þ 2:5φ (4)

Performance Evaluation Criterion of Nanofluid http://dx.doi.org/10.5772/intechopen.74610 283

μnf ¼ μbð Þ 1 � φ 2:5 (5)

μnf ¼ μb 1 þ 2:5φ þ 6:5φ2 (6)

μnf ¼ μb 1 þ 0:025φ þ 0:015φ2 (8)

0, 1842φ (7)

trated nanofluid with 10 vol.% concentration, is out of the usual nanofluids [19–21].

particle volume fractions (φ < 0.02). The suggested formula is as follows:

Theoretical investigations.

contributed to correct this formula [16].

the following formula in 1977:

fraction up to φ < 0:1 [16].

viscosity as shown in Eq. (9) [16]:

trations less than 4%, the expression is as follows:

nanoparticles with water, respectively, as follows:

Figure 3. Variation of knf =kb as a function of alumina nanoparticle volume fraction with and without CTAB and its best fit with an interfacial resistance.

of 70 CMC surfactant is also shown. The effective medium theory (EMT) fits on the experimental data indicated by the solid line, which shows a perfect agreement with empirical data. It can be seen that the value of knf/kb with pure surfactant was negative, while it was positive at all other concentrations of nanoparticles [15]. In general the higher the particle volume fraction, the higher the nanofluid thermal conductivity of nanofluids.


**3.5 Design of the floor plan of the smart house**

**Figure 5** below which shows the simulated floor plan of the smart house. The sample house has the following floor plan. A1 and A2 are bedrooms for sleeping. B1 and B2 are corridors. C is the toilet; D is the Bathroom and E is the kitchen for cooking.


*<IP_ADDRESS>.5 Cool pressing method*

Also kwon as scarification method, this is one of the best methods to extract EOs. The term cool pressed theoretically means that the oil is expeller-pressed at low temperature and pressure. This process insures that the resulting oil is 100% pure and retains all the properties of the plant. Here the heat is reduced and minimized throughout the batching of the raw material. EOs are then separated from the material by centrifugation [36].

Since economy, competitiveness, eco-friendly, sustainability, operation costs, high efficiency, and good quality become keywords of the modern industrial production, the development of EO extraction techniques has never been interrupted. The most relevant disadvantage of conventional techniques are time and solvent consumption and also related to the thermolability of EOs components which undergo chemical alteration (hydrolyze, isomerization, oxidation) due to the high

**Figure 4.** *Soxhlet equipment [35].*

applied temperatures [37]. The quality of the obtained oil is damaged, particularly if the extraction time is long. It is important that the extraction method maintain the chemical composition and the natural proportion at its original state. Strictly speaking, conventional methods are not the only way for the removal of EOs. Novel techniques known as innovative have been developed for this purpose but may not necessarily be widely used for commercial production due to the high cost of production of oils without any alteration of their thermosensitive components (**Figure 5**).


**1. Introduction**

Food that is useful when taken as part of a diverse diet on a routine basis, whole foods, and fortified, enhanced, or improved foods can improve health, and they are an exciting contemporary development in the food and nutrition sector [1]. The significant research acceptance level of proof should be used to support health claims on food items, particularly functional foods. The fast-developing trend of functional foods raises a slew of new concerns and opportunities for public health, particularly in terms of giving truthful data [2]. Nutritional study advanced slowly until the 1940s when several deficiency disorders and the nutrients that may "treat" them became the focus. Top Scientists and others found nutrients that were important for many existing disease illnesses during this golden period of biochemistry [3].

Companies that make functional foods and dietary supplements began to clash with scientists who demanded scientific proof to increase their profitability. Organic food shops expanded in quantity and power to the point that they produced major chain stores that competed with regular supermarkets [4]. The National Center for Complementary and Alternative Medicine, which sponsored university-affiliated research institutions, was established inside the National Institutes of Health in 1998, further cementing government acknowledgment of the unconventional proactive health approach advocated by functional foods [5]. This time of life Hippocrates phrase, "Let food be thy medicine, and medicine be thy food," is the credo of today's modern health-conscious populace. Following that, Metchnikoff's "Law of Longevity" was linked to extended youth and healthful old age, as seen mostly in the Russian population. Commoners in the Balkans at the time consumed cultured kinds of milk [6].

Since that day, researchers have been working to better understand the impact of a variety of food components and nutrients in improving health and avoiding chronic illnesses. Functional foods are the product of this field's study, which has culminated in a slew of new brands for foods that have been linked to certain health advantages [7]. The notion of functional ingredients stresses that food is important not only for sustenance but also for preventing and reducing potential risks for a spectrum of ailments, as well as increasing some critical physiological activities. Functional foods also supply the body with essential nutrients, lipids, proteins, carbs, and other nutrients. Now we'll delve deeper into the topic of Functional Foods [8].

Registered dietitians obey Hippocrates' lead by advocating food as medical nutrition treatment to cure a variety of illnesses and/or to protect, recover, and enhance wellness and health. As a consequence, understanding and incorporating functional foods within their treatment is crucial. They serve as a link between scientific proof studies and overall wellness for consumers. The number of functional food items on the market will keep growing. RDs must know how these foods get to market and how they are marketed [9].


1. Introduction

In the pursuit of smart manufacturing, to satisfy the customer needs with quality, responsiveness and cost-effectiveness become the major challenge of nowadays factories [1]. The market demands are uncertain [2] and prone to be influenced by the composite effects of the driven forces, including the following: Market saturation: most known potential customers have already had the similar kind of product, and the new market segments are still not thriving to prove be the promising revenue fountain [3]. Product innovation: the customers hardly pay more for these products with slim marginal utility [4]. Product differentiation: most product features are common with the rivals, and the price-cutting thus becomes the inevitable survival strategy [5]. Rival initiative: in facing the same situation of demand slump, all rivals are endeavoring to stimulate the demand on their products to gain

the shrinking profit [6]. Customized features: in the business-to-business (B2B) model, the purpose-intended design is the key to make the customers' final product differentiable to their rivals. While in the business-to-consumer (B2C) market, the customers are willing to pay more for those personalized products [7].

disadvantage that the problem frames are not friendly to the business process improvement. Therefore, this chapter seeks to describe the essential framework of the material planning problem (Figure 1) in a more intuitive fashion, by using

by a solid customer base. (2) Adaptive products: these are the extended or enhanced version of mature products or the long tail ones. (3) Long-tailed products: used by the existing customers for a period of time, but the demand is getting slim. (4) New products: used as the market penetration tool to explore the niche

The sales orders usually are not placed at the same time, but in a certain "random" way instead. If the materials take longer time in preparation than the order requested delivery time, consequently, the requested orders cannot be fulfilled, and the business responsiveness (one of the essentials of the smart manufacturing) will be compromised. Therefore, the factory must procure these materials in advance based on the market forecast. This forecast must be able to reflect the confidence level on the estimated quantities of the following: (1) Mature products: the firm's major revenue source, with a long, steady predictable sales history, usually adopted

Each product type may share common parts (materials) with one another. For example, if a new product is an enhanced version of the existing mature product, it will share many common parts with its predecessor. As product versions upgraded, a long-tailed product line is formed, the common parts usually will gradually decrease through generations. To keep as many common parts as possible in the new product design so that the material requisition planning can be further optimized is the key to lower the overstock risk. Nevertheless, in many occasions, the suppliers may discontinue to supply their legacy materials that will force the firm to

After the material preparation process completes, the inventory should be adequate to support the following procedures, including the production, shipping products as the sales orders requested, and deploying the products to the customers. Formula (1) depicts the qty ið Þsales which is the total requested quantity of a product aggregated from a group of sales orders; Formula (2) depicts the qty ið Þforecast which consists of two parts, namely qty ið Þmature and qty ið Þnew; and Formula (3) depicts the qty ið Þtotal which is the overall quantity at that batch. It is worth noting that the qty ið Þmature can be either subjectively determined by the executives or conformed by a series of probabilistic-driven formulae over time.

an expanded "Business Process Model and Notation" (BPMN).

Smart Material Planning Optimization Problem Analysis

DOI: http://dx.doi.org/10.5772/intechopen.84614

market.

Figure 1.

29

Material planning problem frame.

change the design accordingly.

To effectively fulfill the business model of uncertain sales orders ensuring the product responsive delivery, the factory must prepare adequate resources, including the material and the workforce in advance. The more prepared resources are in advance, the more cost will be incurred; thus the revenue shrinks [8]. In the manufacturing practice, the bill of material (BOM) is an information to keep the product structural data of materials, such as part numbers, the quantity of need, and the associated specification [9]. To manage the material requisition, the total material needed shall be aggregated by the queued sales orders; the minimal quantity of a material is the required product quantities multiply the usage of that material in the BOM, respectively. The supplier material replenishment schedule may not be equivalent to one another due to their various conditions of production and delivery [10]. In most cases, the procurement of material in an economic scale will impact the production cost. This implies that the factory needs provision more and in advance for those materials that have greater variability in delivering.

Of those manufacturing automation equipment products, the sales may not aware of the gaps between the customer's expectations and the equipment limitations, including the required working environment, the excess inputs, and the unsynchronized outputs to the next step of productions. The factory product development team must customize the equipment in order to fit in the customer's application. The dilemma is whether the development team just tweaks the design for this specific case or puts more efforts on triggering the whole engineering change process to enhance the product features. If the decision is to enhance the product, that means a new BOM will be created, and some parts must be replaced; inevitably, the development team will commence a series of rigorous test on this design change; some tests take time. Consequently, the objective of material planning is to find the appropriate cost-effective solution under the constraints of order fulfillment and economic scale of the procurement.

The objective of this chapter is to articulate how the firm's material forecasting under the uncertain business environment can be improved from both management and advanced analytics perspectives.

#### 2. Framing the problem

Apparently, it is a challenge to articulate the overall processes in which the aforementioned uncertainties might occur. Without a comprehensive expression, the firm cannot effectively collaborate on and make contribution to solve the problem. Thus, this chapter applied the problem frame analysis framework to disclose the complexity of the material planning in this smart manufacturing theme. Through this framework, all task-related participants can elaborate their actions to improve the forecast within and also look the problems a bigger firm-level picture. Essentially, the material forecast is an overall optimization in the firm. Such an optimization requires the synergy of the participants through the analytical models among tasks.

The problem frame is a method often used in the requirement engineering to describe a complicated problem's boundary and analyze the mutual influences among the problem factors in rigorous mathematic logic expressions [11]. One of the advantages of applying this method is these mathematic logic expressions can be easily transformed into the analytical forecast models. But it also brings its major

#### Smart Material Planning Optimization Problem Analysis DOI: http://dx.doi.org/10.5772/intechopen.84614

the shrinking profit [6]. Customized features: in the business-to-business (B2B) model, the purpose-intended design is the key to make the customers' final product differentiable to their rivals. While in the business-to-consumer (B2C) market, the

To effectively fulfill the business model of uncertain sales orders ensuring the product responsive delivery, the factory must prepare adequate resources, including the material and the workforce in advance. The more prepared resources are in advance, the more cost will be incurred; thus the revenue shrinks [8]. In the manufacturing practice, the bill of material (BOM) is an information to keep the product structural data of materials, such as part numbers, the quantity of need, and the associated specification [9]. To manage the material requisition, the total material needed shall be aggregated by the queued sales orders; the minimal quantity of a material is the required product quantities multiply the usage of that material in the BOM, respectively. The supplier material replenishment schedule may not be equivalent to one another due to their various conditions of production and delivery [10]. In most cases, the procurement of material in an economic scale will impact the production cost. This implies that the factory needs provision more and in advance for those materials that have greater variability in delivering. Of those manufacturing automation equipment products, the sales may not aware of the gaps between the customer's expectations and the equipment limitations, including the required working environment, the excess inputs, and the unsynchronized outputs to the next step of productions. The factory product development team must customize the equipment in order to fit in the customer's application. The dilemma is whether the development team just tweaks the design for this specific case or puts more efforts on triggering the whole engineering change process to enhance the product features. If the decision is to enhance the product, that means a new BOM will be created, and some parts must be replaced; inevitably, the development team will commence a series of rigorous test on this design change; some tests take time. Consequently, the objective of material planning is to find the appropriate cost-effective solution under the constraints of order

The objective of this chapter is to articulate how the firm's material forecasting under the uncertain business environment can be improved from both management

Apparently, it is a challenge to articulate the overall processes in which the aforementioned uncertainties might occur. Without a comprehensive expression, the firm cannot effectively collaborate on and make contribution to solve the problem. Thus, this chapter applied the problem frame analysis framework to disclose the complexity of the material planning in this smart manufacturing theme. Through this framework, all task-related participants can elaborate their actions to improve the forecast within and also look the problems a bigger firm-level picture. Essentially, the material forecast is an overall optimization in the firm. Such an optimization requires the synergy of the participants through the analytical models

The problem frame is a method often used in the requirement engineering to describe a complicated problem's boundary and analyze the mutual influences among the problem factors in rigorous mathematic logic expressions [11]. One of the advantages of applying this method is these mathematic logic expressions can be easily transformed into the analytical forecast models. But it also brings its major

customers are willing to pay more for those personalized products [7].

Advanced Analytics and Artificial Intelligence Applications

fulfillment and economic scale of the procurement.

and advanced analytics perspectives.

2. Framing the problem

among tasks.

28

disadvantage that the problem frames are not friendly to the business process improvement. Therefore, this chapter seeks to describe the essential framework of the material planning problem (Figure 1) in a more intuitive fashion, by using an expanded "Business Process Model and Notation" (BPMN).

The sales orders usually are not placed at the same time, but in a certain "random" way instead. If the materials take longer time in preparation than the order requested delivery time, consequently, the requested orders cannot be fulfilled, and the business responsiveness (one of the essentials of the smart manufacturing) will be compromised. Therefore, the factory must procure these materials in advance based on the market forecast. This forecast must be able to reflect the confidence level on the estimated quantities of the following: (1) Mature products: the firm's major revenue source, with a long, steady predictable sales history, usually adopted by a solid customer base. (2) Adaptive products: these are the extended or enhanced version of mature products or the long tail ones. (3) Long-tailed products: used by the existing customers for a period of time, but the demand is getting slim. (4) New products: used as the market penetration tool to explore the niche market.

Each product type may share common parts (materials) with one another. For example, if a new product is an enhanced version of the existing mature product, it will share many common parts with its predecessor. As product versions upgraded, a long-tailed product line is formed, the common parts usually will gradually decrease through generations. To keep as many common parts as possible in the new product design so that the material requisition planning can be further optimized is the key to lower the overstock risk. Nevertheless, in many occasions, the suppliers may discontinue to supply their legacy materials that will force the firm to change the design accordingly.

After the material preparation process completes, the inventory should be adequate to support the following procedures, including the production, shipping products as the sales orders requested, and deploying the products to the customers.

Formula (1) depicts the qty ið Þsales which is the total requested quantity of a product aggregated from a group of sales orders; Formula (2) depicts the qty ið Þforecast which consists of two parts, namely qty ið Þmature and qty ið Þnew; and Formula (3) depicts the qty ið Þtotal which is the overall quantity at that batch. It is worth noting that the qty ið Þmature can be either subjectively determined by the executives or conformed by a series of probabilistic-driven formulae over time.

Figure 1. Material planning problem frame.

The material aggregation is to calculate the required quantity for each material in the BOM; this chapter uses the column vector notation of Xi ¼ x1⋯p ∈Pi to represent the materials that belong to the product Pi. Thus, the total required material quantities to fulfill the batch is also a column vector of qty ið Þtotal ∗Xi. Let Xs i represents the quantities of these materials in the stock; therefore, the batch demand of these materials is qty ið Þtotal ∗Xi � Xs i . But it is common that the material procurement should be in an economic scale denoted as Xp i ; the factor often considers the minimal purchase quantity for an order, the strategy of quantity-price advantage, and the safety quantity in stock. Formula (4) shows the total procured quantities of the materials in that batch which is a column vector of Xr i :

$$(q \text{ty} (i)\_{\text{sales}} = \sum\_{j=1}^{n} (order\_{i,j}) \tag{1}$$

$$q\text{ty}(i)\_{forecast} = q\text{ty}(i)\_{matter} + q\text{ty}(i)\_{new} \tag{2}$$

$$q \text{t} \mathbf{y}(i)\_{\text{total}} = q \text{t} \mathbf{y}(i)\_{\text{sales}} + q \text{t} \mathbf{y}(i)\_{\text{forecast}} \tag{3}$$

$$X\_i^r = \min \left[ qt\mathbf{y}(i)\_{total} \* X\_i - X\_i^t, X\_i^p \right] \tag{4}$$

The participants in the supply chain can reach the consensus about the market demand prospects of coming period, if information visibility is improved. This improved visibility will also relieve the information asymmetry side effect on the participants' planning. Fully documented product specifications and well-trained field engineers will overcome the deployment obstacles at customers' operating environment. The consented market demand prospect and the visible information are the tangible artifacts of the decision-making which is a collaborative process within the factory's departments and even with the external participants of the supply chain. Therefore, the more effective collaboration in improving the quality

Workforce size The suppliers shrank their operation and impacted the replenishment, or the workers went on the

Production rate Additional or unexpected cost incurred, the suppliers increased their material prices, or the rival

Back orders The suppliers canceled the procurement orders owing to their poor capacity planning, or the

Regulations The authorities imposed new regulations that increased the firm additional costs, such as taxation or

Based on the previous experience on business cycle, the firms had overprovisioned their resources

customers postponed the purchase plans for business reasons; and these numbers were counted in

There is no doubt that the extreme weather, including heavy snow, flood, or tsunami, has impacted

The objective of conducting the collaborative decision-making process is to reach the consensus on the scale of the demand forecast in the next period. The diversity of this collaborative team is essential. The team members should cover the roles from (1) material planner, to report the current inventory status; (2) procurement, to report the collected forecasts from the suppliers; (3) sales, to present the products' front log with their selling confidence levels for each customer, respectively; (4) channel, to present the products' front log with their selling confidence levels for each distributor, respectively; (5) marketing, to disclose the overall demand from the external professional analysis and the competitor's recent launched initiatives; (6) finance, to present the current cash flow status and the capital capacity of procurement and suggest the forecast quantity based on the analysis of management accounting; and (7) data analyst, a critical role in the forecasting under the uncertainty, who designs the analytical process, including constructing the optimization formulae, collecting and compiling the datasets, disclosing the insight about how and where the inaccuracy of previous forecasts came from, and, the most importantly, making the prediction closer to the coming busi-

Figure 2 illustrates this collaborative decision process; after the group decision reaches the consensus on the material planning, the participants draft a couple of proposals and submit it to the material planning committee composed of the firm executives, the decision group participants, and the external industry professionals.

of decision-making, the less uncertainty bias shall be incurred.

4. Collaborative decision-making

ness reality.

31

Source of change

Seasonal overstocked

Extreme weather

The uncertain change sources.

Table 1.

Reasons

DOI: http://dx.doi.org/10.5772/intechopen.84614

strike

than expected

the forecast

lowered down their market prices

Smart Material Planning Optimization Problem Analysis

the equipment replacement

the economic growth globally

Using common parts across the BOMs is a key to manage the risk and costs; this means, in the simplest case of two products Pi and Pj, the common parts Xij will exist in the material vectors Xi and Xj. Either of the qty ið Þforecast does not occur, and more qty j ð Þsales arrive or customers cancel orders causing the qty ið Þsales drops and qty j ð Þforecast is doing well beyond the expectation, the Xij can be used to support the business. The worst case is neither sales orders arrive, nor the forecasted market blooms as expected. The more common parts of Xij have, the more flexible the product will be.

Furthermore, in some cases, the material xi is a substitutable part-set with priorities, xi ¼ xk i ; k ¼ 1⋯m , and usually, the priority implies the material received order, first-in-first-served (FIFS), or the release versions of parts, which serves the lower version part first. It will make the material planning more challenge when those legacy products are still in service at the customers'sites.

### 3. Supply chain optimization

In the smart manufacturing theme, the production planning is a multiperiod, multiproduct problem; the factory makes appropriate schedules based on a scenario tree containing all possible combinations to build the products optimally under the resource constraints. Both demand and supply uncertainties are driven by dynamic stochastic processes. The optimality is to satisfy the minimal resource consumed and the stochastic uncertainty of changes [12]. When multiple manufacturers at different sites collaborate to build products, the uncertainty may root from various external changes, illustrated in Table 1.

This problem can be resolved as multiobjective linear programming functions to minimize the total costs of supply chain and the total order fulfillment gaps across the factory sites [13]. However, both aforementioned approaches did not answer the fundamental question: how to determine the uncertainty of each forecast? This uncertainty causing the poor performance may be attributed from (1) over- or underprovisions on the different market demand prospects; (2) planning with the limited information; (3) misperception of customers' operating environment; and (4) quality of decision-making [14]. Therefore, this chapter incorporates the concepts from the multiobjective method with the consideration of overcoming the information asymmetry to present a novel approach as follows to tackle the problem.

#### Table 1.

The material aggregation is to calculate the required quantity for each material

i

orderi,j

qty ið Þforecast ¼ qty ið Þmature þ qty ið Þnew (2) qty ið Þtotal ¼ qty ið Þsales þ qty ið Þforecast (3)

> i ;Xp i (4)

to

. But it is common that the material

(1)

i ; the factor often con-

i :

i

in the BOM; this chapter uses the column vector notation of Xi ¼ x1⋯p ∈Pi

represent the materials that belong to the product Pi. Thus, the total required material quantities to fulfill the batch is also a column vector of qty ið Þtotal ∗Xi. Let Xs

represents the quantities of these materials in the stock; therefore, the batch

quantities of the materials in that batch which is a column vector of Xr

qty ið Þsales ¼ ∑

siders the minimal purchase quantity for an order, the strategy of quantity-price advantage, and the safety quantity in stock. Formula (4) shows the total procured

i ¼ min qty ið Þtotal ∗Xi � Xs

worst case is neither sales orders arrive, nor the forecasted market blooms as

expected. The more common parts of Xij have, the more flexible the product will be. Furthermore, in some cases, the material xi is a substitutable part-set with

received order, first-in-first-served (FIFS), or the release versions of parts, which serves the lower version part first. It will make the material planning more challenge when those legacy products are still in service at the customers'sites.

In the smart manufacturing theme, the production planning is a multiperiod, multiproduct problem; the factory makes appropriate schedules based on a scenario tree containing all possible combinations to build the products optimally under the resource constraints. Both demand and supply uncertainties are driven by dynamic stochastic processes. The optimality is to satisfy the minimal resource consumed and the stochastic uncertainty of changes [12]. When multiple manufacturers at different sites collaborate to build products, the uncertainty may root from various

This problem can be resolved as multiobjective linear programming functions to minimize the total costs of supply chain and the total order fulfillment gaps across the factory sites [13]. However, both aforementioned approaches did not answer the fundamental question: how to determine the uncertainty of each forecast? This uncertainty causing the poor performance may be attributed from (1) over- or underprovisions on the different market demand prospects; (2) planning with the limited information; (3) misperception of customers' operating environment; and (4) quality of decision-making [14]. Therefore, this chapter incorporates the concepts from the multiobjective method with the consideration of overcoming the information asymmetry to present a novel approach as follows to tackle the problem.

i ; k ¼ 1⋯m , and usually, the priority implies the material

Using common parts across the BOMs is a key to manage the risk and costs; this means, in the simplest case of two products Pi and Pj, the common parts Xij will exist in the material vectors Xi and Xj. Either of the qty ið Þforecast does not occur, and more qty j ð Þsales arrive or customers cancel orders causing the qty ið Þsales drops and qty j ð Þforecast is doing well beyond the expectation, the Xij can be used to support the business. The

n j¼1

demand of these materials is qty ið Þtotal ∗Xi � Xs

Advanced Analytics and Artificial Intelligence Applications

procurement should be in an economic scale denoted as Xp

Xr

priorities, xi ¼ xk

30

3. Supply chain optimization

external changes, illustrated in Table 1.

The uncertain change sources.

The participants in the supply chain can reach the consensus about the market demand prospects of coming period, if information visibility is improved. This improved visibility will also relieve the information asymmetry side effect on the participants' planning. Fully documented product specifications and well-trained field engineers will overcome the deployment obstacles at customers' operating environment. The consented market demand prospect and the visible information are the tangible artifacts of the decision-making which is a collaborative process within the factory's departments and even with the external participants of the supply chain. Therefore, the more effective collaboration in improving the quality of decision-making, the less uncertainty bias shall be incurred.

## 4. Collaborative decision-making

The objective of conducting the collaborative decision-making process is to reach the consensus on the scale of the demand forecast in the next period. The diversity of this collaborative team is essential. The team members should cover the roles from (1) material planner, to report the current inventory status; (2) procurement, to report the collected forecasts from the suppliers; (3) sales, to present the products' front log with their selling confidence levels for each customer, respectively; (4) channel, to present the products' front log with their selling confidence levels for each distributor, respectively; (5) marketing, to disclose the overall demand from the external professional analysis and the competitor's recent launched initiatives; (6) finance, to present the current cash flow status and the capital capacity of procurement and suggest the forecast quantity based on the analysis of management accounting; and (7) data analyst, a critical role in the forecasting under the uncertainty, who designs the analytical process, including constructing the optimization formulae, collecting and compiling the datasets, disclosing the insight about how and where the inaccuracy of previous forecasts came from, and, the most importantly, making the prediction closer to the coming business reality.

Figure 2 illustrates this collaborative decision process; after the group decision reaches the consensus on the material planning, the participants draft a couple of proposals and submit it to the material planning committee composed of the firm executives, the decision group participants, and the external industry professionals.

The critical components section contains four major parts—MR1⋯4, inventory levels are denoted by MI1⋯4, turnover rates denoted by MT1⋯4, and the suppliers of critical components are denoted by MS1⋯4 respectively. It is worth noting that all the figures in the form depend on the information capability of firm, especially the

The final agreed decision on the forecast of the product can be systematically measured by Formula (5). The outer summation adds up the forecast of the five groups and multiplies by their wi weights, respectively. The inner summation adds up the group's forecast decision. Each group has the pi participants, and there is also

> wi ∑ pi j¼1

The reason why previous forecast accuracy rates were excluded from Forecastfinal

The material readiness is essential to the production, especially for those scarce

The challenge of making the decision on the quantities of these safe stocks is that the procurement and the planner must be aware of the supply market's movements and take action in a proactive manner at all times. Formula (6) illustrates the general material acquired function; when qtyneed is a negative value, it means the reserved stock is no longer able to support the production, and thus the further procurement is needed. Each material more or less will have waste during the production; it can be attributed to the poor quality or mishandling by the workers. The ω% is the additional ratio—can be an average number from the past—to compensate the production loss. Formula (7) shows the total quantity of use qtyuse which is the multiplication of the loss and the summation of total p forecast prod-

p

i¼1

qtysafety ¼ MA qtyuse; κ � � ∗ e�μ ∗ μqtyorder

qtyneed ¼ qtystock � qtyuse þ qtysafety � � (6)

qtyi ∗ BOMi

qtyorder!

� � (7)

� � (8)

and/or valuable ones. There are several reasons causing the material scarcity: (1) usually these are subcomponents which required the outsourcing, customized design; (2) those materials are provided by the single source or the oligopoly market; and (3) the materials are common but essential in many products, and when these products are hot in the market, these materials become very difficult to acquire the adequate quantities to support the firm's production. To prevent the shortage of materials, reserving and maintaining the materials at some level of

is because the participants will adjust their forecast rates accordingly, based on assigned weights by their group leaders. The purpose of this form is to give a template for the group discussion; it can help the participants make their forecasts

θj ∗ forecasti,j

� � " # (5)

BQ quantity which must be iteratively calculated during the process.

5 i¼1

not relying on the hunches but based on the fact of tangible numbers.

a θj weight for every participant's forecasti,j quantity:

Smart Material Planning Optimization Problem Analysis

DOI: http://dx.doi.org/10.5772/intechopen.84614

quantities in stock are common measures in practice.

ucts' used quantities qtyi in BOMi,respectively:

33

qtyuse ¼ ð Þ 1 þ ω% ∑

6. Material dynamics

Forecastfinal ¼ ∑

Figure 2. Collaborative decision process.

The committee will make the final decision on the material planning. It is worth noting that the data analyst plays the backbone role facilitating the tasks of other participants throughout the process.


**3. Application of licorice products for foods**

Pontefract cakes are made of licorice extract, molasses, sugar, and flour in the Yorkshire town of Pontefract, England, during the seventeenth century. In the nineteenth century, it was used extensively for confectionery [7]. Licorice extracts and glycyrrhizin have the following properties [8]:

Therefore, licorice extracts and glycyrrhizin are used as food additives in a variety of foods such as alcohol beverages, nonalcohol beverages, chewing gum, candy, chocolate, sweet snacks, ice cream, soy sauce, Japanese pickled vegetables, seafood delicacies, steamed fish paste, and sausages in Japan.

Research work by our company in 1994 resulted in a yeast that selectively hydrolyzed the terminal β‐glucuronyl linkage of glycyrrhizin to yield glycyrrhetic acid 3‐*O*‐mono‐glucuro‐ nide (MGGR), a potent sweetener (relative sweetness to sucrose: ×950, **Table 2**) [9, 10]. Moreover, MGGR has a flavor‐enhancing property (**Table 3**) [11].

**Table 2.** Relative sweetness intensity in water or 5% salt solution.

**Table 3.** Flavor‐enhancing effect of MGGR against cocoa, chocolate, and whipped cream.

**3. Application of licorice products for foods**

16 Biological Activities and Action Mechanisms of Licorice Ingredients

**2.** Improving foam stabilization and head‐forming characteristics

Moreover, MGGR has a flavor‐enhancing property (**Table 3**) [11].

**Table 2.** Relative sweetness intensity in water or 5% salt solution.

properties [8]:

of sucrose

**3.** Masking effect of bitter aftertaste

**4.** Flavor‐enhancing effect

**5.** Nonfermented sweetener

**9.** Depression of freezing point

paste, and sausages in Japan.

**10.** Full‐bodied umami and sweetening

**6.** Noncaramelization

**8.** Soften the saltiness

**7.** Heat stable

Pontefract cakes are made of licorice extract, molasses, sugar, and flour in the Yorkshire town of Pontefract, England, during the seventeenth century. In the nineteenth century, it was used extensively for confectionery [7]. Licorice extracts and glycyrrhizin have the following

**1.** High‐intensity sweetener, glycyrrhizin possesses about 200 times the sweetness potency

Therefore, licorice extracts and glycyrrhizin are used as food additives in a variety of foods such as alcohol beverages, nonalcohol beverages, chewing gum, candy, chocolate, sweet snacks, ice cream, soy sauce, Japanese pickled vegetables, seafood delicacies, steamed fish

Research work by our company in 1994 resulted in a yeast that selectively hydrolyzed the terminal β‐glucuronyl linkage of glycyrrhizin to yield glycyrrhetic acid 3‐*O*‐mono‐glucuro‐ nide (MGGR), a potent sweetener (relative sweetness to sucrose: ×950, **Table 2**) [9, 10].

**Glycyrrhizin MGGR Glycyrrhizin MGGR**

**Comparable sucrose concentration (%) In water In 5% salt solution**

 250 1400 - - 170<PHONE_NUMBER> 170<PHONE_NUMBER> 150<PHONE_NUMBER> 100<PHONE_NUMBER>