6. Material dynamics

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

To make the aforementioned collaboration more effectively to elaborate the material planning proposals, this chapter presents a generic form for the group decision participants to discuss with. Table 2 illustrates a sample form for the forecasting. The form consists of two portions, the target product and its critical

In this sample form, the product PD portion, which belongs to CA category, currently has PI units in stock, its last period's turnover rate (Δout=Δin) is PT, the maximal can-build quantity is BQ units under current on hand material status,

AM, AS, AC, AP, and AF, and the forecasted quantities are FM, FS, FC, FP, and FF.

Category/product Inventory Turnover Build/suppler Accuracy/forecast Source CA PD PI PT BQ AM FM Marketing MR1 MI1 MT1 MS1 AS FS Sales MR2 MI2 MT2 MS2 AC FC Channels MR3 MI3 MT3 MS3 AP FP Suppliers MR4 MI4 MT4 MS4 AF FF Finance

� �

=Fdactual , were

previous forecast accuracy rates, calculated by Fprevious Fdactual

participants throughout the process.

Advanced Analytics and Artificial Intelligence Applications

5. Effective elaboration

Collaborative decision process.

components.

Table 2.

32

Material forecast sample form.

Figure 2.

The material readiness is essential to the production, especially for those scarce 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 quantities in stock are common measures in practice.

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 products' used quantities qtyi in BOMi,respectively:

$$q\text{t}\mathbf{y}\_{need} = q\text{t}\mathbf{y}\_{stock} - \left(q\text{t}\mathbf{y}\_{use} + q\text{t}\mathbf{y}\_{safe}\right) \tag{6}$$

$$\text{qty}\_{\text{ue}} = (\mathbf{1} + a\mathbf{\forall}) \sum\_{i=1}^{p} \left[ qt\mathbf{y}\_i \ast B\mathbf{OM}\_i \right] \tag{7}$$

$$q\text{ty}\_{\text{safety}} = \text{MA}\left(q\text{ty}\_{\text{us}}, \kappa\right) \* \left(\frac{e^{-\mu} \* \mu^{q\text{ty}\_{\text{order}}}}{q\text{ty}\_{\text{order}}!}\right) \tag{8}$$

The estimation of qtysafety plays a significant role in the forecast accuracy. If the safety stock is overestimated, it will incur the additional financial pressure or discontinues the production due to the shortage of supply if the safety stock is underestimated. This chapter proposes a generic function: qtysafety <sup>¼</sup> MA qtyuse; <sup>κ</sup> <sup>∗</sup> <sup>φ</sup>%, it is based on the moving-average of the material in the past κ terms and multiplied with a given weight for that material. Furthermore, Formula (8) presents more aggressive idea on the estimation of φ% by applying the Poisson probability distribution subject to the product orders that use this material [16, 17].

τð Þ¼ p ∑

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

value of no adjustment to the forecast.

8. Empirical case and discussion

interact or intervene with the control process.

8.1 Economic parameter

trend.

model:

35

n i¼1 pi <sup>2</sup> <sup>þ</sup> <sup>∑</sup> n�1 i¼1 ∑ n�i j¼iþ1

Smart Material Planning Optimization Problem Analysis

pi ∗ pj

where τð Þ p is the forecast adjustment coefficient, 0:0 , τð Þ p , 2:0. p ¼ f g 1:01::<sup>n</sup> means the set containing n perspectives, and each pi ¼ 1:0 which is the expected

The empirical case is about a global production automation equipment manufacturer. Their flag-fleet products are the Computer Numerical Control (CNC) category which is widely used in the production to provide more precise, complicated and repeatable control than just manning the equipment. Basically, each CNC consists of five major components: (1) input, receiving the signals/status from the controlled equipment via various handshaking interfaces; (2) output, sending a set of instructions to the equipment to proceed the next action; (3) control, a number of electrical mechanical units to convert or transform the input signals to the processor and translate the electrical magnetic signals into the output instruction set; (4) processor, performing the signal predefined computations accordingly; and (5) human, providing the interface, usually is through keypad panel, to let worker

The empirical case adopted the stock market performance information as their foundation of setting the peconomic parameter, the most significant weconomic among all perspectives in the PEST evaluation model. They posit that two stock indices, the NASDAQ and their major rival/benchmark in China, can reflect their business

Figure 3 illustrates a sample economic factor parameter analysis against the stock performance of Nasdaq and the rival's in 2018. The X-axis is the dates

StockClose � StockOpen, and the maximal fluctuated is Mag ¼ Stockhigh � Stocklow. The standardization is to transform the indices in to the values between 0 and 1 by

Formula 11 defines a composite scoring function for the economic factors. The ΔScorei is the first-order difference of the composite scores; by applying the product of these difference vectors, Formula 12 derives each si in the evaluation vector S. The trend, showing both indices are moving toward the same direction, is the proportion of all positive si in S illustrated in Formula 13. Choosing the appropriate stock indices by the data analyst to reflect the current sector's business state will determine the usefulness of this trend function. Formula 14 introduces the matrix cosine similarity method to facilitate this choosing process, especially in targeting the appropriate rivals in the volatile stock market. The committee can reference these figures to determine the comfortable peconomic to fit in the group decision

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

<sup>2</sup> <sup>þ</sup> Volumei

2

(11)

<sup>2</sup> <sup>þ</sup> Magi

and Y-axis is the standardized ratios. The stock index changed is Scale ¼

Scalei

q

applying ½index� minð Þ� index =½ � maxð Þ� index minð Þ index .

Scorei ¼

ð Þ p ¼ f g 1:01::<sup>n</sup> (10)

� � " # <sup>∗</sup> <sup>τ</sup>�<sup>1</sup>

#### 7. Uncertain demand

The "bullwhip effect" is a classic problem in the supply chain management; the obvious symptom is the overstocking in the whole supply chain. When the market demand declines not as the forecast expected, it will potentially impose the financial risk significantly. More overproduced products will push to the distribution channels, and the channels might sacrifice their margin in order to attract the consumers to buy more until the demand has saturated. Both the product and the material inventory levels will hike and thus incur the warehouse management cost and the value depreciation. This symptom will impact more when the optimistic supply chain tiers are deep. It is simply because the suppliers in each tier might magnify their forecasts under the asymmetric market demand information [18]. The root cause of this effect is that the market demand does not always follow the trend derived from the past. It is very challenging to forecast the demand of the individual product because the order quantity is slim. But the products in the same category may share a common component structure in the majority. In the configure-toorder model, let the consumer to optionally select the components from the configuration of the product; the differences among these products can be as simple as just a few components vary than one another [19]. This implies that the forecast model can be applied to reduce the inventory overstock and understock risk, as long as the quantity volatile product demands shares common materials.

The increasing economic disturbance such as the trade barriers has annoyingly amplified the market demand uncertainty. For instances, recently, the US-China trade tensions [20] and the Brexit [21] are the perfect examples of this. In order to assess business potential risk, we must consider the big picture and be aware of the impact of various economic parameter through the use of PEST analysis: (1) political harmony, such as shared visions, diplomatic situation, polarization; (2) economic factors, such as disposable income, interest rate, wealth inequality; (3) social trend, such as product adoption preferences, living style expectation, stability of community; and (4) technology novelty, such as the maturity of supply chain, innovation capability. Certainly, the firm can consider more perspectives than PEST or apply other perspectives that are more comprehensive to the firm's business environment.

This chapter proposes the material planning committee to set the confidence levels (a sort of weights) on these firm external perspectives to adjust the demand forecast. The pi is the confidence level of a perspective; as Formula (9) suggests, pi ⇓ is set when the committee think the forecast is too optimistic; or giving pi ⇑ to amplify the scale of business otherwise. A new scoring scheme is presented in this chapter, as illustrated in Formula (10):

$$p\_i \in \Re, 0.0 \le p\_i \Downarrow \le 1.0 \le p\_i \Downarrow \le 2.0 \tag{9}$$

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

$$\pi(p) = \left[\sum\_{i=1}^{n} p\_i^2 + \sum\_{i=1}^{n-1} \sum\_{j=i+1}^{n-i} \left(p\_i \ast p\_j\right)\right] \ast \tau^{-1}(p = \{1.0\_{1\ldots n}\})\tag{10}$$

where τð Þ p is the forecast adjustment coefficient, 0:0 , τð Þ p , 2:0. p ¼ f g 1:01::<sup>n</sup> means the set containing n perspectives, and each pi ¼ 1:0 which is the expected value of no adjustment to the forecast.

### 8. Empirical case and discussion

The estimation of qtysafety plays a significant role in the forecast accuracy. If the

The "bullwhip effect" is a classic problem in the supply chain management; the obvious symptom is the overstocking in the whole supply chain. When the market demand declines not as the forecast expected, it will potentially impose the financial risk significantly. More overproduced products will push to the distribution channels, and the channels might sacrifice their margin in order to attract the consumers to buy more until the demand has saturated. Both the product and the material inventory levels will hike and thus incur the warehouse management cost and the value depreciation. This symptom will impact more when the optimistic supply chain tiers are deep. It is simply because the suppliers in each tier might magnify their forecasts under the asymmetric market demand information [18]. The root cause of this effect is that the market demand does not always follow the trend derived from the past. It is very challenging to forecast the demand of the individual product because the order quantity is slim. But the products in the same category may share a common component structure in the majority. In the configure-toorder model, let the consumer to optionally select the components from the configuration of the product; the differences among these products can be as simple as just a few components vary than one another [19]. This implies that the forecast model can be applied to reduce the inventory overstock and understock risk, as long as the

The increasing economic disturbance such as the trade barriers has annoyingly amplified the market demand uncertainty. For instances, recently, the US-China trade tensions [20] and the Brexit [21] are the perfect examples of this. In order to assess business potential risk, we must consider the big picture and be aware of the impact of various economic parameter through the use of PEST analysis: (1) political harmony, such as shared visions, diplomatic situation, polarization; (2) economic factors, such as disposable income, interest rate, wealth inequality; (3) social trend, such as product adoption preferences, living style expectation, stability of community; and (4) technology novelty, such as the maturity of supply chain, innovation capability. Certainly, the firm can consider more perspectives than PEST or apply other perspectives that are more comprehensive to the firm's

This chapter proposes the material planning committee to set the confidence levels (a sort of weights) on these firm external perspectives to adjust the demand forecast. The pi is the confidence level of a perspective; as Formula (9) suggests,

⇑ to

⇑ , 2:0 (9)

⇓ is set when the committee think the forecast is too optimistic; or giving pi

pi ∈ R, 0:0 , pi

amplify the scale of business otherwise. A new scoring scheme is presented in this

⇓ , 1:0 , pi

quantity volatile product demands shares common materials.

safety stock is overestimated, it will incur the additional financial pressure or discontinues the production due to the shortage of supply if the safety stock is underestimated. This chapter proposes a generic function: qtysafety <sup>¼</sup> MA qtyuse; <sup>κ</sup> <sup>∗</sup> <sup>φ</sup>%, it is based on the moving-average of the material in the past κ terms and multiplied with a given weight for that material. Furthermore, Formula (8) presents more aggressive idea on the estimation of φ% by applying the Poisson probability distribution subject to the product orders that use this material [16, 17].

Advanced Analytics and Artificial Intelligence Applications

7. Uncertain demand

business environment.

chapter, as illustrated in Formula (10):

pi

34

The empirical case is about a global production automation equipment manufacturer. Their flag-fleet products are the Computer Numerical Control (CNC) category which is widely used in the production to provide more precise, complicated and repeatable control than just manning the equipment. Basically, each CNC consists of five major components: (1) input, receiving the signals/status from the controlled equipment via various handshaking interfaces; (2) output, sending a set of instructions to the equipment to proceed the next action; (3) control, a number of electrical mechanical units to convert or transform the input signals to the processor and translate the electrical magnetic signals into the output instruction set; (4) processor, performing the signal predefined computations accordingly; and (5) human, providing the interface, usually is through keypad panel, to let worker interact or intervene with the control process.

#### 8.1 Economic parameter

The empirical case adopted the stock market performance information as their foundation of setting the peconomic parameter, the most significant weconomic among all perspectives in the PEST evaluation model. They posit that two stock indices, the NASDAQ and their major rival/benchmark in China, can reflect their business trend.

Figure 3 illustrates a sample economic factor parameter analysis against the stock performance of Nasdaq and the rival's in 2018. The X-axis is the dates and Y-axis is the standardized ratios. The stock index changed is Scale ¼ StockClose � StockOpen, and the maximal fluctuated is Mag ¼ Stockhigh � Stocklow. The standardization is to transform the indices in to the values between 0 and 1 by applying ½index� minð Þ� index =½ � maxð Þ� index minð Þ index .

Formula 11 defines a composite scoring function for the economic factors. The ΔScorei is the first-order difference of the composite scores; by applying the product of these difference vectors, Formula 12 derives each si in the evaluation vector S. The trend, showing both indices are moving toward the same direction, is the proportion of all positive si in S illustrated in Formula 13. Choosing the appropriate stock indices by the data analyst to reflect the current sector's business state will determine the usefulness of this trend function. Formula 14 introduces the matrix cosine similarity method to facilitate this choosing process, especially in targeting the appropriate rivals in the volatile stock market. The committee can reference these figures to determine the comfortable peconomic to fit in the group decision model:

$$Score\_i = \sqrt{\text{Scale}\_i^2 + \text{Mag}\_i^2 + Volume\_i^2} \tag{11}$$

Figure 3. A sample economic factor parameter analysis.

$$\mathcal{S} = \prod\_{i=1}^{2} \left( \Delta Score\_i \right), s\_i \in \mathcal{S} \tag{12}$$

purchase will be made; (5) when the inventory is short to fill the order, a purchase of the lead time multiply the economic scale will be made (3000 units in this model); and (6) the supplier will deliver the sample material after the lead time of

In Figure 4, the sales orders related to this sample material have shown the demand, with the star markers, slumped from the expected 1000 units down to near 750. The triangle markers represent the purchases, and the round markers are the remained inventory. The green circle represents the stock on hand at the end of the forecast period. With the exception of the last circle (leftover stock), they coincide with every purchase made (triangle). By applying this model, the production may stop because of the material shortage; finding the sufficient safety stock

params <sup>¼</sup> qtyorder; qtySafety; qtyeconomic; timelead; qtyloss (15)

This chapter applies the iterative method by changing the qtySafety, illustrated in Formula 16, and evaluating the return values. The risksafety is the occurrences when inventory is below the qtySafety, and the stopsafety is how many times that the product has stopped. The model function Model params ð Þ behavior depends on the settings of the given qtyorder, qtyeconomic, timelead, qtyloss, and the variable qtySafety. In the sample material case, the minimal qtySafety to prevent the disruption of product is 1000 units (coincidently matched with the qtyeconomic). It is worth noting that if the qtyeconomic is underestimated, the production disruptions are inevitable in this fixed

An enhanced variable input of the material requisition model is illustrated in Figure 5. It has the same configuration as the fixed input, but (1) suppose the sample material economic scale of supply is per 1000-unit; (2) when the inventory is below the safety stock; an economic scale purchase will be made; (3) when the inventory is short to fill the order; a purchase of the lead time multiply the economic scale will be made; and (4) each purchased quantity will be based on the moving average of the quantities of the previous lead time of the orders, illustrated in

risksafety, stopsafety ¼ Model params ð Þ (16)

quantity is a challenge to prevent the disruption of production:

Smart Material Planning Optimization Problem Analysis

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

the purchase.

input model.

Figure 4.

37

Sample material fixed input requisition model.

8.2.2 Variable input

$$Trand = \frac{count(\mathbb{S}, \forall s\_i \ge \mathbf{0})}{count(\mathbb{S})} \tag{13}$$

$$\text{Similarity} = 1 - \frac{\text{Score}\_i^T \* \text{Score}\_j}{||\text{Score}\_i|| \* ||\text{Score}\_j||} \tag{14}$$

### 8.2 Material requisition models

#### 8.2.1 Fixed input

The proposed fixed input material requisition model (Figure 5) makes the following assumptions (1) suppose the sample material fulfillment lead time takes three terms (usually in weeks); (2) suppose the sample material economic scale of supply is 1000 units; (3) the predicted loss ratio is set on 5% of each procurement quantity; (4) when the inventory is below the safety stock, an economic scale

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

purchase will be made; (5) when the inventory is short to fill the order, a purchase of the lead time multiply the economic scale will be made (3000 units in this model); and (6) the supplier will deliver the sample material after the lead time of the purchase.

In Figure 4, the sales orders related to this sample material have shown the demand, with the star markers, slumped from the expected 1000 units down to near 750. The triangle markers represent the purchases, and the round markers are the remained inventory. The green circle represents the stock on hand at the end of the forecast period. With the exception of the last circle (leftover stock), they coincide with every purchase made (triangle). By applying this model, the production may stop because of the material shortage; finding the sufficient safety stock quantity is a challenge to prevent the disruption of production:

$$parameters = \left(q\text{ty}\_{order}, q\text{ty}\_{Safety}, q\text{ty}\_{comomic}, time\_{lead}, q\text{ty}\_{las}\right) \tag{15}$$

$$risk\_{\text{safety}} \cdot stop\_{\text{safety}} = Model(params) \tag{16}$$

This chapter applies the iterative method by changing the qtySafety, illustrated in Formula 16, and evaluating the return values. The risksafety is the occurrences when inventory is below the qtySafety, and the stopsafety is how many times that the product has stopped. The model function Model params ð Þ behavior depends on the settings of the given qtyorder, qtyeconomic, timelead, qtyloss, and the variable qtySafety. In the sample material case, the minimal qtySafety to prevent the disruption of product is 1000 units (coincidently matched with the qtyeconomic). It is worth noting that if the qtyeconomic is underestimated, the production disruptions are inevitable in this fixed input model.

### 8.2.2 Variable input

An enhanced variable input of the material requisition model is illustrated in Figure 5. It has the same configuration as the fixed input, but (1) suppose the sample material economic scale of supply is per 1000-unit; (2) when the inventory is below the safety stock; an economic scale purchase will be made; (3) when the inventory is short to fill the order; a purchase of the lead time multiply the economic scale will be made; and (4) each purchased quantity will be based on the moving average of the quantities of the previous lead time of the orders, illustrated in

Figure 4. Sample material fixed input requisition model.

<sup>S</sup> <sup>¼</sup> <sup>Y</sup> 2

i¼1

Similarity <sup>¼</sup> <sup>1</sup> � Score<sup>T</sup>

8.2 Material requisition models

A sample economic factor parameter analysis.

Advanced Analytics and Artificial Intelligence Applications

8.2.1 Fixed input

36

Figure 3.

Trend <sup>¼</sup> count Sð Þ ; <sup>∀</sup>si <sup>≥</sup><sup>0</sup>

The proposed fixed input material requisition model (Figure 5) makes the following assumptions (1) suppose the sample material fulfillment lead time takes three terms (usually in weeks); (2) suppose the sample material economic scale of supply is 1000 units; (3) the predicted loss ratio is set on 5% of each procurement quantity; (4) when the inventory is below the safety stock, an economic scale

ð Þ ΔScorei , si ∈S (12)

<sup>i</sup> ∗ Scorej

k k Scorei ∗ Scorej � � � �

count Sð Þ (13)

(14)

9. Conclusion

address.

Author details

39

Rich C. Lee\* and Man-ser Jan

The customers buying preferences stimulate and inspire a new way of manufacturing. It has been a trend that the manufacturers are heading toward their ultimate goals of smart manufacturing. Many firms put the equipment automation as the first step of their smart manufacturing initiatives. But soon they found out that the current business challenge is on the uncertain market demand rather than just focusing on the operation automation. In addition, the smart manufacturing initiative is a sort of business reengineering process; it requires all participants to be aware in the problems in a holistic view. This is where this chapter would like to

Smart Material Planning Optimization Problem Analysis

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

In the smart manufacturing theme, the material planning is a challenging task under the uncertain demand environment. The task is not just the responsibility of the planner nor the data analyst but the synergy of all related participants. This chapter presents three material requisition models, for those materials having short lead times or being able to apply the pull model (vendor managed inventory, VMI), the fixed input model is adequate enough; for those materials having the same trend for a period of time, the variable input model can compensate the trend difference and prevent the excessive purchase; and for those volatile demand materials, the trend variable input model has the lowest inventory level than the others. Finally, all proposed modes treat the loss ratio ω% as constant for easy to explain, and this ratio should be measured from the production. To manufacture smarter products nowadays, to create a healthy collaborative culture within the firm is above all to enhance the competence of data analysis, and to improve the information systems is the cornerstone of survival and the business success as well.

Institute of Applied Economics, National Taiwan Ocean University, Taiwan, China

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

\*Address all correspondence to: richchihlee@gmail.com

provided the original work is properly cited.

Figure 5. Sample material variable input requisition model.

Figure 6. Sample material trend variable input requisition model.

Formula 17. When qtySafety is adequate, the purchase quantities are high, but the frequency is less; however, the production disruption will never occur in this variable input model.

$$\text{qty}\_{purchase} = \text{MA}\left(\text{qty}\_{\text{use}}, \kappa\right) \* time\_{lead} \tag{17}$$

#### 8.2.3 Trend variable input

The final proposed model, illustrated in Figure 6, is based on the aforementioned variable input, but each purchased quantity will consider the trend about the previous lead time of qtyorder. The simplest form of the trend function is shown in Formula 18, taking the MAð Þ<sup>1</sup> first-order derivative, and if the trend is positive (demand increasing), the purchase will plus one additional average quantity; if the trend is otherwise, the purchase will lessen one additional average quantity instead. Comparing this model with the aforementioned variable input, the inventory levels are constantly lower, and it implies the risk is also less in the case the demand drops drastically. For the long-term observed material, the trend can be estimated by a proper probability distribution or the decision of PEST:

$$\text{qty}\_{\text{purchase}} = \text{MA}\left(\text{qty}\_{\text{use}}, \kappa\right) \* \left[ \text{time}\_{\text{lead}} + \text{Trend}\left(\text{MA}^{(1)}\left(\text{qty}\_{\text{use}}, \kappa\right)\right) \right] \tag{18}$$
