Chapter JIT purchasing threshold value models for the RMC industry 6.1 Introduction The objective of this chapter is to develop JIT Purchasing Threshold Value (JPTV) models. These models consider the inventory physical storage cost, together with the additional costs and benefits resulting from JIT purchasing, which have not been considered by the models of Fazel (1997), Fazel et al. (1998) and Schniederjans and Cao (2000, 2001) or the models in the previous chapters. These JPTV models are developed particularly for the Ready Mixed Concrete (RMC) industry and are applicable for boundary condition (see Section 1.6 for its definition). Section 6.2 expands the annual cost of carrying one unit of inventory in the classical EOQ model ( h ) to include all the components of inventory physical storage cost. The features of the expanded EOQ models are also discussed. Section 6.3 and Section 6.4 develop the EOQ-JIT cost indifference points under the revised EOQ models. These EOQ-JIT cost indifference point models are the JPTV models. 6.2 Revised EOQ model Chapter argued that there were reasons to include the physical storage cost into the annual cost of carrying one unit of inventory in the classical EOQ model ( h ). However, to empirically examine the capability of an inventory facility to carry the EOQ-JIT cost indifference point’s amount of inventories on the platform created by Fazel (1997), Fazel (1998) and Schniederjans and Cao (2000, 2001), the physical plant space was treated as a penalty cost and thus was still excluded from h when deriving the ultimate EOQ-JIT cost 158 indifference point in the previous chapters. To accurately capture the impact of inventory purchasing policies on the selection of the inventory purchasing method and to develop the JPTV models, the physical plant space, which is a component of the physical storage costs, needs be included into h . This is to expand “ h ” to “ H ” which is the “expanded annual cost of carrying one unit of inventory”. The classical EOQ model is thus to be revised. The features of the revised EOQ model are discussed below. 6.2.1 Total annual cost under the revised EOQ model When “ h ” is expanded to become “ H ”, the total costs under the revised EOQ model are given by: HQ TC Er = kD + + PE D Q (6.1) where: TC Er is total costs under the revised EOQ model. “ H ”, which includes all the components of the inventory carrying costs, is greater than “ h ” in Eq. (3.1). Accordingly, TC Er is also greater than TC E in Eq. (3.1) in the classical EOQ model. 6.2.2 Optimal economic order quantity under the revised EOQ model The optimum order quantity of the revised EOQ model derived from Eq. (6.1) is: Qr∗ = 2kD H (6.2) where: Qr∗ is the optimum order quantity of the revised EOQ model. 159 The optimum order quantity of the revised EOQ model is significantly less than that of the classical EOQ model, as H is substantially greater than h , assuming the values of the other parameters, namely, D and k remain unchanged. 6.2.3 Total annual optimal cost under the revised EOQ model The total annual optimal cost under the revised EOQ model derived based on Eq. (6.1) and Eq. (6.2) is: TC Er = 2kDH + 2kDH + PE D = 2kDH + PE D 2 (6.3) Eq. (6.3) is valid only when the inventory is ordered at its economic order quantity. This means that the annual inventory ordering cost item ( kD ) equals the annual inventory Q carrying cost item ( QH ) as shown in Eq. (6.3). To sum up, the revised EOQ model is different from the classical EOQ model on three counts. First, the so called “fixed costs”, such as rental, utilities, personnel salaries, etc, are considered in the inventory carrying cost item in the revised EOQ model. Hence, the annual cost of holding one unit of inventory in the revised EOQ model is greater than that in the classical EOQ model. The total annual cost under the revised EOQ model is also greater than that under the classical EOQ model. Second, the revised EOQ model prefers small lot sizes and frequent deliveries. Last, but not the least, the revised EOQ model aims to reduce the actual total inventory ordering and carrying cost, while the classical EOQ model aims to reduce the sum of the inventory ordering cost and a part of the inventory carrying cost. The last point makes it very clear that the revised EOQ model is 160 more suitable than the classical EOQ model in representing the total annual cost under the EOQ system when comparing the EOQ system with the JIT purchasing system. The JPTV models are developed for two scenarios. The first scenario does not consider a price discount. The second scenario considers a price discount. 6.3 EOQ-JIT cost indifference point under the revised EOQ model 6.3.1 EOQ-JIT cost difference function under the revised EOQ model Based on assumption No. in Table 1.1, Fazel (1997) and Fazel et al. (1998) suggested that the total annual cost under the JIT purchasing system was the product of the unit price under the JIT purchasing system and the annual demand. As suggested earlier, this proposition did not consider the additional costs and benefits resulting from JIT purchasing. Hence, the total annual cost under the JIT purchasing system for the JPTV models is proposed to be the product of the unit price under the JIT purchasing system ( PJ ) and the annual demand ( D ) plus the additional costs and benefits resulting from JIT purchasing, given by: TC Jr = PJ D + ξ (6.4) where: TC Jr is the total annual cost under the JIT purchasing system for the JPTV models, and ξ is the sum of the additional costs and benefits resulting from JIT purchasing. The additional costs are mainly the increased out-of-stock costs when inventory is purchased in a JIT fashion. The additional costs of JIT purchasing contribute positively to ξ . The additional benefits are mainly the improved quality, the 161 flexibility of production, reduced waste and increased organizational competitiveness under the JIT purchasing system. The additional benefits of JIT purchasing contribute negatively to ξ . It is essential to highlight that the cost of the inventory physical plant space reduction in the JIT purchasing system has been assumed to take its maximum value and is considered in the total annual optimal cost under the revised EOQ system. This is because (a) the maximum value of the inventory physical plant space reduction under the JIT purchasing system is the inventory physical plant space under the EOQ system; and (b) the inventory physical plant space under the EOQ system has been considered as a component of inventory carrying costs and included in “ H ” in Eq. (6.3). The inventory physical plant space reduction was proposed by Schniederjans and Olsen (1999) and Schniederjans and Cao (2000, 2001). The total annual optimal cost under the revised EOQ model where the price discount is not considered ( TC Er ) has been presented in Eq. (6.3). The cost difference between the revised EOQ system and the JIT purchasing system is thus given by: Z r = 2kDH + PE D − PJ D − ξ (6.5) where: Z r is the cost difference between the revised EOQ system and the JIT purchasing system. 6.3.2 EOQ-JIT cost indifference points under the revised EOQ model Z r is continuous and differentiable as D is above zero. Taking the first order derivative of Z r with respect to D in Eq. (6.5), would result in: 162 dZ r = dD 2kH D − 12 + P − P E J (6.6) Taking the second order derivative of Z r with respect to D in Eq. (6.5), would result in: d Z r − 2kH −3 / = D dD2 Note that Hence (6.7) 2kH is always positive. D −3 / is also always positive, as D is above zero. d 2Z r , the second order derivative of Z r with respect to D , is always negative. dD2 According to the theorem of the second derivative test for maxima and minima of functions, two counts for the cost difference function can be concluded. First, the curve of the cost difference function Z r is concave downwards. Second, the cost difference between the EOQ and the JIT system is maximized at the demand level, at which dZr = . This demand level is the maximum cost advantage point and its value is given dD by: Dmax = kH 2(PJ − PE ) (6.8) where: Dmax is the maximum cost advantage point. The maximum cost advantage of using a JIT purchasing system over an EOQ system can be derived by substituting Dmax for D in Eq. (6.5). and its value is given by: Z max r = kH −ξ 2(PJ − PE ) (6.9) where: Z max r is maximum cost advantage of using a JIT purchasing system over an EOQ system. 163 It should be noted that Eq. (6.9) is applicable for computing the maximum cost advantage of using the JIT purchasing system over the EOQ system only if the order size in the EOQ system equals the optimal economic order quantity. Should the order quantity in the EOQ system not follow the optimal economic order quantity, the cost advantage of using the JIT purchasing system over the EOQ system would be given by: HQ Z r = kD + + PE D − PJ D − ξ Q (6.10) Setting Z r in Eq. (6.5) to zero, the roots of Z r (D ) = are the revised EOQ-JIT cost indifference points. Their values are given by: Dindr1 = 2kH − 2kH − 4(PJ − PE )ξ 2(PJ − PE ) (6.11) where: Dindr1 is the lower EOQ-JIT cost indifference point under the revised EOQ system. The value of Dindr1 is given by: Dindr1 = kH −(PJ − PE )ξ − k H − 2kHξ (PJ − PE ) (PJ − PE )2 The value of Dindr = (6.12) Dindr is given by: 2kH + 2kH − 4(PJ − PE )ξ 2(PJ − PE ) (6.13) where: Dindr is the upper EOQ-JIT cost indifference point under the revised EOQ system. 164 The value of Dindr is given by: Dindr kH −(PJ − PE )ξ + k H − 2kHξ (PJ − PE ) = (PJ − PE )2 (6.14) 6.3.3 Discussion Eq. (6.12) and Eq. (6.14) indicate that the sum of the additional costs and benefits resulting from JIT purchasing ( ξ ) is an important factor that affects the lower and upper EOQ-JIT cost indifference points. It should be noted that ξ varies from industry to industry and it may not be a constant even when the annual demand remains unchanged for the same manufacturers. However, there can be three scenarios for ξ . 1) ξ is equal to zero; 2) ξ is greater than zero; and 3) ξ is less than zero. Hence, the discussion on the lower and upper EOQ-JIT cost indifference points is presented below for the three scenarios. 6.3.3.1 ξ is equal to zero When the sum of the additional costs and benefits of JIT purchasing equals zero, Eq. (6.12) suggests that the lower EOQ-JIT cost indifference point ( Dindr1 ) equals zero, and Eq. (6.13) suggests the upper EOQ-JIT cost indifference point ( Dindr ) is greater than zero. Meanwhile, the curve of the EOQ-JIT cost difference function is concave downwards. Hence, it can be concluded that an EOQ system is preferred to the JIT purchasing system, provided that the annual demand is greater than the upper EOQ-JIT cost indifference point. 165 When the sum of the additional costs and benefits of JIT purchasing equals zero, the upper EOQ-JIT cost indifference point ( Dindr ) equals 2kH . Let D represents indr (PJ − PE )2 2kH . Fazel’s (1997) and Schniederjans and Cao’s (2001) studies were also focused (PJ − PE )2 on the scenarios where ξ was equal to zero. It is thus essential to compare the present study with that of Fazel’s (1997) and Schniederjans and Cao’s (2001) models. A comparison of the present study with that of Fazel’s (1997) model 2kH is similar to the EOQ-JIT cost indifference point DindF (see Eq. (3.7)), which J − PE ) (P is given by 2kh . Since H is significantly greater than h , the revised EOQ-JIT (PJ − PE )2 cost indifference point ( Dindr ) is greater than the DindF , provided that the values of the other parameters, namely, k , PJ and PE remain unchanged. This is because some of the inventory physical storage costs were not accounted for in DindF . It should be noted that the EOQ-JIT cost indifference point proposed by Fazel (1997) is even less than DindF . This has been explained in the explanation notes for Eq. (3.7) in Section 3.3.1. Hence, the revised EOQ-JIT cost indifference point ( Dindr ) is substantially greater than the EOQJIT cost indifference point proposed by Fazel (1997). Again, this finding suggests that the JIT system can still remain cost effective even at a high level of annual demand, thus invalidating Fazel’s (1997) conclusion that JIT was cost effective only at low level of annual demand (Schniederjans and Cao, 2001). This conclusion is in line with what was reached in Chapter 3. 166 A comparison of the present study with that of Schniederjans and Cao’s (2001) model The concept of the carrying capacity of an inventory facility, which has been developed in Chapter 3, can assist to compare the present study with that of Schniederjans and Cao’s (2001) model. Eq. (3.12) suggests that the carrying capacity of an inventory facility is N governed by Qh = αE . When selecting an inventory purchasing approach, it is possible to design the size of the inventory facility proportionate to the optimal economic order quantity amount of inventory, or Qh = bQr∗ , where b is the stock flexibility parameter N and has been explained in Chapter and Chapter 4. Substituting Qh = bQr∗ into αE = Qh , would result in N E = αbQr∗ . This is the formula of the floor area of an inventory facility determined by the inventory optimal economic order quantity. Dindr = Substituting 2kH into Eq. (6.2), the optimal economic order quantity at the revised J − PE ) (P ∗ ∗ EOQ-JIT cost indifference point ( Qrind ) can be derived as Qrind = 2k . Substituting PJ − PE ∗ Qrind for Qr∗ in N E = αbQr∗ , would result in N Eind the minimum area of the inventory facility that can accommodate the EOQ-JIT cost indifference point’s amount of inventory as N Eind = 2αbk . Hence, when a) the inventory space reaches 2αbk , and b) the PJ − PE PJ − PE annual demand reaches 2kH , the total cost under the revised EOQ system will be (PJ − PE )2 equal to the total annual cost under the JIT purchasing system. It should be noted that the cost of the physical inventory plant space has been considered in the total cost under the revised EOQ system. Meanwhile, the physical inventory plant space under the revised 167 found that an EOQ purchasing approach is preferred to the JIT purchasing approach when both the annual demand and the floor area of the inventory facility are greater than their own break-even point. This study also formulated the JPTV models for RMC suppliers. The development of the JPTV models is the fourth objective of this study. The JPTV models suggest that the preferred purchasing method (EOQ or JIT) depends on the market conditions available in the locality. From the JPTV models, three findings were established. First, when the additional costs of JIT purchasing cannot be ignored and the annual demand is low, JIT purchasing is an expensive alternative. Second, when the additional costs of JIT purchasing are substantially high; the EOQ purchasing approach is more cost effective than the JIT purchasing approach. Third, when the additional costs and benefits of JIT purchasing are insignificant, the JIT purchasing approach can be adopted widely. Still, it is possible for the EOQ approach to be more cost effective than the JIT purchasing approach, if the annual demand is substantially high. The additional costs resulting from JIT purchasing include mainly the increased out-of-stock costs. The main additional benefits resulting from JIT purchasing include reduced waste and increased quality and production flexibility. 8.5 Contribution to knowledge Arising from this thesis, seven papers were either published or accepted for publication as the author’s contribution to existing knowledge. Please refer to Appendix for a list of these papers. 209 The surveys conducted in Chongqing and Singapore found that both the EOQ and JIT purchasing approaches were adopted for managing the procurement of raw materials in the RMC industry. This finding invalidated the general understanding developed through the previous study in the US context that current practices for managing the concrete supply chain upstream in terms of raw materials acquisition or prerequisite work on site were not geared toward JIT production (Tommelein and Li, 1999). It revealed that outside the US context, at least in cities like Chongqing and Singapore, both the two methods are concurrently adopted in practice. The reasons for this case were stated in Section 2.6.1 and Section 2.6.2. The ultimate EOQ-JIT cost indifference point models and the JPTV models have made three contributions to the presently available knowledge base. First, these models invalidated the conclusions derived from the models of Fazel (1997) and Fazel et al. (1998) which suggested that JIT purchasing was cost effective only at a low annual demand level. It is only at this specific point that the author’s opinion is the same as that of Schniederjans and Cao (2000, 2001). Second, the ultimate EOQ-JIT cost indifference point models and the JPTV models also invalidated the conclusions derived from the models of Schniederjans and Olsen (1999), and Schniederjans and Cao (2000, 2001), which suggested that the JIT purchasing system would virtually always be preferable to an EOQ system provided that the JIT operations experienced or could take advantage of physical plant space square footage reduction (Schniederjans and Cao, 2001, p.116). Third, the JPTV models theoretically suggested that the smaller company might have 210 difficulty in implementing a JIT purchasing policy, provided that the additional costs of JIT purchasing could not be ignored and the annual demand of the small company fell below the lower EOQ-JIT cost indifference point. It should be noted that the models in this thesis were developed for boundary condition (see Section 1.6 for its definition), which is based on assumption No. in Table 1.1. For this reason, most of the equations, except Eqs. (3.4), (6.16), (7), (11), (12) (14) and (16), were developed for the first time by the author. This is explicit in the text. 8.6 Contribution to industry The JPTV models developed in this study can be helpful to RMC suppliers to select the purchasing approaches for their raw materials. The findings in this study hold universal lessons for RMC industries in other cities / countries and are not limited only to the cities referred to in this study (namely, San Francisco, Chongqing and Singapore). The study shows that the procurement of raw materials in RMC batching plants may be made through the traditional EOQ (or non-JIT) approach, the JIT purchasing approach or a combination of both approaches. Depending on the EOQ-JIT cost indifference point that can be derived based on their operational constraints, some batching plants may continue to regard their traditional EOQ procurement system to be more viable than the JIT purchasing system for procuring raw materials, and vice-versa. Nevertheless, the study confirmed that regardless of physical locations, it would appear that RMC suppliers universally used the demand pull system in the batching and delivery of RMC to sites. 211 The case studied in Chapter showed that the out-of-stock costs resulting from JIT purchasing was the major reason preventing the batching plants in Chongqing from adopting the JIT approach to purchase their sand and aggregates. The JPTV models suggested that JIT purchasing can be adopted widely, provided that the additional costs resulting from JIT purchasing are insignificant. Hence, the RMC suppliers in Chongqing should work together with their sand and aggregates suppliers and endeavor to reduce the additional costs, particularly the out-of-stock costs, resulting from JIT purchasing, if they intend to adopt the JIT purchasing approach to manage the procurement of their sand and aggregates. 8.7 Limitations of the study The research strategy of this study was to extend only one of the assumptions of the platform created by Fazel (1997), Fazel et al. (1998) and Schniederjans and Cao (2000, 2001) to develop JPTV models for the RMC industry. This was to demonstrate that the reasonable extension of only one assumption can lead to substantially different conclusions than those reached by the previous researchers. Based on the research strategy, the proposed ultimate EOQ-JIT cost indifference point models and the JPTV models have been developed based mainly on the platform built by the previous researchers. As such, to be consistent with the work of the earlier researchers, deterioration of inventory items, inflations, demand rate changes and price competitiveness were not taken into consideration in the ultimate EOQ-JIT cost indifference point models and the JPTV models. The inventory ordering policy for deteriorating items is usually determined by the shelf-life of the inventory items (Silver, 212 et al., 1998). Hence, JPTV models are not suitable for explaining the procurement of deteriorating items. Demand rate changes usually need to be considered. Hence, a system dynamics model for reducing the out-of-stock costs in RMC batching plants is proposed in Section 8.9.2 for future study. Besides, each of the assumptions in Table 1.1 has limitations of its own and therefore, imposes limitations on the ultimate EOQ-JIT cost indifference point models and the JPTV models. 8.8 Applicability of the JPTV models The ultimate EOQ-JIT cost indifference point models and the JPTV models are suitable for providing guidelines for a firm to select its material purchasing approach for nondeteriorating inventory items, when the assumptions in Table 1.1 can be approximately met and demand rate changes, inflation and price competitiveness are not considered. To appropriately use these models to solve practical problems, four points need to be highlighted. First, the JPTV models are applicable only for selecting inventory purchasing approach. This is resulted from assumption No. in Table 1.1. The JPTV models assumed that the physical storage costs under the EOQ system, for example, rental, utilities and personnel salaries were proportional to the average inventory level and thus expanded the annual cost of carrying one unit of inventory in the classical EOQ model ( h ) to become the expanded annual cost of carrying one unit of inventory ( H ). H includes, among other components of the inventory carrying costs, the inventory physical storage cost. Although it is possible to adjust the area of an inventory facility, the number of logistics 213 staff and utilities based on the average inventory level when selecting an inventory purchasing approach, or if the inventory facilities have not been built and the logistics staff can easily be allocated to or from other positions. These physical storage costs may be difficult to be adjusted with the average inventory level during the inventory operation stage. This assumption, thus, limits the application of the JPTV models only for selecting inventory purchasing approaches. Second, the JPTV models are applicable only for the scenario where the price under the JIT approach ( PJ ) is higher than that under the EOQ system ( PE ). The price under the JIT system has two scenarios. One is the “ideal” scenario; the other is the “real life” scenario (Fazel, 1997). Under the “ideal” scenario, the material supplier is also better off to use JIT production and to frequently deliver small quantities to a JIT company. In such a scenario, the price under the JIT purchasing approach will be even lower than the price under the EOQ approach ( PE ), as the supplier can pass some of the savings to the JIT company (Waters-Fuller, 1996; Golhar and Sarker, 1992). In reality, suppliers usually produce their products in large batches and keep large quantities of items in their inventory and then deliver them in small quantities to the JIT company (Newman, 1988; Chhikara and Weiss, 1995). In the “real life” scenario, the price under the JIT approach ( PJ ) is higher than that under the EOQ system ( PE ) (Chandrashekar, 1994; Fazel, 1997). It was from this “real life” scenario that the JPTV models have been developed. This is explicit in assumption No. in Table 1.1. Hence, the JPTV models are not applicable in the “ideal” scenario. Nevertheless, the site studies indicated that the procurement of raw materials in the RMC industry was not in line with the “ideal” scenario. 214 Third, the JPTV models are not applicable for the scenario where the price under the JIT approach ( PJ ) is equal to that under the EOQ system ( PE ) either. Moily (1986), Pan (1987), Hayya et al. (1987) and Pan and Liao (1989) suggested that order quantity splitting can reduce the inventory costs and thus can affect the selection of the inventory purchasing policy. Pan and Liao (1989) proved that the annual demand would not affect the decision to select the inventory purchasing approach under a special scenario. Among other things, that special scenario assumed that the purchasing price in that JIT purchasing approach was the same as that in the EOQ approach, even when order quantity was split. It should be noted that the scenarios studied in this thesis are different from that studied by Pan and Liao (1989). This is explicit in assumption No. in Table 1.1. Finally, the output of the JPTV models may not make sense until accurate inputs can be made available from professionals in the industry. This is because that the additional costs and benefits resulting from JIT purchasing in the JPTV models are usually difficult to be estimated precisely (Johnson and Stice, 1993). 8.9 Areas for future research The areas for future research are discussed below: 8.9.1 Further study on EOQ-JIT cost indifference point Fazel (1997), Fazel et al. (1998), Schniederjans and Olsen (1999), and Schniederjans and Cao (2000, 2001) had developed a series of innovative mathematical models to derive the 215 EOQ-JIT cost indifference point to help companies who were still using the EOQ approach to consider switching over to the JIT purchasing approach. However, the material suppliers who not act in a JIT pattern were excluded from their studies. In addition, the models developed in this present study were based on the classical EOQ model and the platform created by previous researchers where demand variability or the use of safety stock to protect against it – either in JIT or EOQ, were not discussed. Further investigations to study the economic conditions where a supplier can be justified economically to act in a JIT pattern and further research to investigate how the demand variability or safety stock may impact the EOQ-JIT cost indifference point are therefore warranted. More insightful understanding of the impact of these factors on the EOQ-JIT cost indifference point may then be achieved by those working towards the full implementation of the JIT philosophy in the industry. 8.9.2 A system dynamics model for reducing the out-of-stock costs in RMC batching plants The JPTV models suggest that the reduction of the out-of-stock costs resulting from JIT purchasing is important for the successful implementation of the JIT purchasing policy. The out-of-stock costs may be reduced through the co-ordination of the inventory level and the production rate. This task may be achieved through a holistic and dynamic approach, as JIT is a continuous improvement process (Koskela, 1992; Low and Chan, 1997). 216 System dynamics assume a holistic view of the organization focusing on the behavioral trends of projects and their relationship with managerial strategies (Forrester, 1961; Rodrigues and Bowers, 1996; Sterman, 2000; Hu, 2003). System dynamics modeling is thus a good methodology that may help RMC suppliers to alleviate the out-of-stock risk resulting from JIT purchasing of raw materials in RMC batching plants. Hence, a system dynamics model for the co-ordination of the production rate and the inventory level is proposed in Figure 8.2. Stocks Order rate + - A Adjustment for stock Desired order rate + - Stock adjustment time Material lot size + Desired stock + - + RMC supplier JIT production strategy - RMC supplier quality control strategy Production rate + Vendor lead time - - Vendor quality control Vendor training RMC supplier Communication management with vendor commitment Expected production rate + Figure 8.1 Co-ordinating the inventory level and production rate in RMC batching plants The model in Figure 8.1 aims to set an optimal desired stock level while considering the endogenous JIT element variables in the RMC batching plant and the exogenous JIT element variables in the materials supplier side. The endogenous JIT element variables in the RMC batching plant include the RMC supplier’s JIT production strategy, quality 217 strategy, management commitment and communication with the vendor (i.e. sand and aggregates suppliers). The exogenous JIT element variables in the materials supplier side include vendor training, vendor quality control, vendor lead-time and material lot size. Figure 8.1 shows that a better customer-vendor relationship between a RMC batching plant and its vendors can result in a reduced inventory level, thus a better implemented JIT purchasing system. Simulation and identification of the momentum policy of the model are recommended for future research. For varying demand rate, production capacity, storage capacity and carrying cost, a dynamic modeling approach is recommended. 8.9.3 A JIT implementation priority setting model for the RMC industry The surveys in Chapter found that some important JIT characteristics were seldom practiced by the RMC suppliers surveyed (see Section 2.6). The JIT elements were interrelated (Schonberger, 1982b; Hall, 1983; Voss and Robinson, 1987; Hay, 1988; Lubben, 1988; Wantuck, 1989; Koskela, 1992; Monden, 1998; Fan and Chong, 1999; Low and Choong, 2001a, b, c, d, e). Resources are always limited. It is thus essential to scrutinize all the JIT elements again and to set the priority for implementing these JIT techniques in RMC batching plants. This is to help the RMC suppliers to increase their working efficiency and effectiveness. A JIT element priority setting model is recommended in Figure 8.2. 218 Increase RMC supplier’s working efficiency and effectiveness Waste elimination Productivity improvement Quality improvement JIT vendor strategy JIT production strategy Quality control strategy Management commitment & employee involvement JIT customer strategy 1. Communicate with vendor 2. Vendor training 3. Quality certificates from vendor 4. Reducing vendor lead time 5. Sole source vendor 6. Small lot size 1. Reduce machine set up time 2. Reduce inhouse lot size 3. Group technology 4. Cross training 5. Preventive maintenance 6. Schedule stability 1. Jidoka 2. Statistical process control 3. Quality circles 1. Outside consultants 2. JIT champion 3. JIT education 4. JIT team 5. Co-worker relationship 6. Suggestion system 1. Long term relationship 2. Communicate with contractors Figure 8.2 JIT implementation priority setting model for the RMC industry The priority to implement JIT elements in RMC batching plants can be determined through the model in Figure 8.2. The first level of the model is the goal to implement the JIT philosophy in RMC batching plants, namely, to increase the RMC supplier’s working efficiency and effectiveness. The second level of the model includes the sub goals for implementing the JIT philosophy, namely, to reduce waste, improve quality and increase productivity. JIT vendor strategy, JIT customer strategy, JIT production strategy, quality control strategy, and management commitment and employee involvement are in the third level of the model. The various JIT elements are in the fourth level of the model. It 219 is essential to highlight that the JIT elements and JIT strategies overlap one another with respect to the sub goals for implementing the JIT philosophy. It is therefore not appropriate to infer that a specific JIT element, for example, the JIT champion, only contributes to one sub goal among waste elimination, quality control and productivity improvement. A JIT element usually affects waste elimination, quality control and productivity improvement simultaneously. However, the degree of this influence can be different because the local weightings of a specific JIT element with respect to the different sub goals are often not the same. Hence, in the model shown in Figure 8.2, the three sub goals of JIT implementation were “wrapped” together. A multi-attribute decision technique is needed to obtain the weighting of each of these JIT elements. A higher weighting for a JIT element in this model would indicate the higher priority which this JIT element deserves. However, the priority setting of the JIT elements in the context of the RMC industry is presently beyond the scope of this study. 8.9.4 Optimization of EOQ/JIT benefit through planning policy and management measures A comparison between the sand and aggregates purchasing by RMC suppliers in Singapore and Chongqing suggested that the planning policy and management measures may affect the adoption of the EOQ/JIT purchasing policy. The JPTV models suggest that when the out-of-stock costs resulting from JIT purchasing are insignificant, the JIT purchasing approach can be widely adopted, provided that the 220 annual demand of the inventory is still less than its upper EOQ-JIT cost indifference point. As suggested earlier, Pan and Liao (1989) proved that JIT purchasing could always be cost effective than EOQ purchasing, provided that the company operates in a special scenario. This special scenario encompassed four assumptions. The first is that a long term purchasing agreement, in which an overall quantity to be delivered during a period is specified, can be set up with the material supplier. The second is that the unit price and the cost of carrying one unit of inventory remain constant during order quantity splitting. The third is that “the ordering cost remains unchanged no matter how many deliveries are scheduled in one cycle” (Pan and Liao, 1989, p.49). The last assumption is that the deliveries are arranged in such a way that “each succeeding delivery arrives at the time that the quantity from the previous delivery has just been depleted” (Pan and Liao, 1989, p.50). Because of the geographical condition in the market and the strategic location of the RMC suppliers resulting from Singapore’s urban planning policy, the assumptions proposed by Pan and Liao (1989) were largely satisfied by the purchase of sand and aggregates in the RMC industry in Singapore. Hence, all the RMC suppliers in Singapore purchased their sand and aggregates in a JIT fashion. Those RMC suppliers, who were also sand and aggregates suppliers for the local construction market, purchased their sand and aggregates using 4,000-tonne barges and 2,500-tonne barges in a JIT fashion from Indonesia. The sand and aggregates yards of these RMC suppliers were located in the industry parks near to the beach, as shown in 221 Figure 8.3. Figure 8.3 also shows a 4,000-tonne aggregates barge. It should be noted that the 4,000-tonne and 2,500-tonne barges were the standard transportation vehicle for sand and aggregates deliveries from Indonesia to Singapore; no matter how large the order size might be. Sand and aggregates yards of these RMC suppliers, together with the yards of other sand and aggregates suppliers in Singapore were largely located in the industry parks near Tuas, Pasir Ris and Sungei Kadut. These RMC suppliers included BS , C S and DS (see the surveys in Chapter 2). Figure 8.3 A 4,000-tonne aggregate barge for RMC supplier BS in Singapore The smaller RMC suppliers also purchased sand and aggregates from the local market in a JIT fashion, but using the 12-tonne truck. These RMC suppliers were largely set up in Kaki Bukit, Sungei Kadut, Tuas and Changi. (This information was provided in Chapter 2). These industry parks were linked by well developed highways to the sand and 222 aggregates supplier. This is shown in Figure 8.4. Figure 8.4 shows an eight-lane highway that linked the sand and aggregates yard of supplier BS and the batching plant of RMC supplier K S in Singapore. Figure 8.4 also shows a 12-tonne sand truck. It should be noted that the 12-tonne truck was the widely adopted transportation tool for the delivery of sand and aggregates within Singapore. This is because Singapore’s surface area is only about 1,000 square kilometers. The RMC suppliers included AS , E S , FS , G S , H S , I S , J S , K S , LS , M S , N S and OS (see surveys in Chapter 2). Figure 8.4 Eight-lane highway from the sand supplier to the RMC supplier K S in Singapore 223 The JPTV models also suggested that depending on the market conditions available in the locality of the company, the EOQ approach may be preferred to the JIT purchasing approach for raw materials procurement. The surveys in Chapter and the case studies in Chapter showed that the RMC batching plants in Chongqing were currently located in the region where JIT delivery of sand and aggregates might result in high out-of-stock costs. Hence, all the RMC suppliers in Chongqing purchased their sand and aggregates by using the EOQ approach. The comparison between the purchase of sand and aggregates by RMC suppliers in Singapore and Chongqing suggested that further study on how EOQ/JIT benefits can be optimized through planning policy and management measures is essential. Based on the above discussions, the possible directions for future research are below: • Improvement upon the JPTV models by considering demand variability and economic conditions from the materials supplier’s side. • Development of a system dynamics model to reduce the out-of-stock costs by coordinating the inventory level and production rate in RMC batching plants. • Development of a model to set the priority for implementing the JIT elements in RMC batching plants. • Optimization of EOQ/JIT benefits through planning policy and management measures 224 [...]... model for the RMC industry The revised EOQ with a price discount model for the RMC industry is developed by incorporating the price discount scheme of the RMC industry in Eq (6.17) into the 1 73 revised EOQ model in Eq (6.1) The total annual cost under the revised EOQ with a price discount model is the sum of the inventory ordering cost, the expanded inventory carrying cost and the cost of the actual... is the sum of the additional costs and benefits resulting from JIT purchasing It is mainly the additional costs resulting from the tardiness in concrete delivery by the RMC supplier 188 Tommelein and Li (1999) also observed that those contractors who engaged in residential or office building projects usually adopted the first approach for purchasing their concrete, as the amount of concrete needed in. .. that it was possible for an inventory facility to hold the EOQ-JIT cost indifference point’s amount of inventory when the floor area of an inventory facility reached N Eind : the minimum area of the inventory facility to house the EOQ-JIT cost indifference point’s amount of inventory This conclusion is in line with what was reached in Chapter 3 Another expression of this finding is that an EOQ based system... quantity under the EOQ system cannot be economically split, and the annual demand is high enough The additional costs of JIT purchasing include mainly the increased out-of-stock costs 182 The additional benefits of JIT purchasing include mainly reduced waste and increased quality and production flexibility The JPTV models are tested with data from the RMC industry in Chapter 7 1 83 CHAPTER 7 TESTING THE JPTV... their work experience in the RMC industry 6.4 .3 A price discount scheme for the RMC industry Based on the above analysis, to fit the RMC industry, a new price discount scheme that incorporate the reality in the RMC industry is suggested based on the price discount scheme proposed by Fazel et al (1998) and is given below The delivery price per unit starts from PEQmin , where Qmin is the lowest order quantity... alternative In the RMC industry, Wang et al (2001b) also suggested that 10 per cent of the concrete needed by the construction industry in Singapore was site mixed, rather than delivered in a JIT fashion from off-site RMC batching plants The Chongqing Construction Committee stipulated that concrete was allowed to be site mixed for a construction project in the urban area, provided that the amount of concrete. .. collected in December 20 03 by interviewing the production manager and the general manager of RMC supplier L Due to further development in the western region of China and the construction of the “Three Gorges Dam Project”, the construction industry in Chongqing was booming (Wong and Wu, 2002; Wong et al 2001; Wong et al 2001a, b, c and d) The increased construction activities resulted in increasing demand... 7.1 Introduction The aim of this chapter is to test the JIT Purchasing Threshold Value (JPTV) models developed in the previous chapter with the data on inventory procurement approaches adopted by the Ready Mixed Concrete (RMC) firms in Chongqing (China) and Singapore The JPTV models can have three implications These implications were summarized in Section 6.5 The JPTV models were thus tested through the. .. had his trucks pulled into any batching 185 plant during operating hours The contractor-owned trucks simply joined the line of plant trucks waiting to be loaded The driver then went to the batching plant operators’ walk-up window and ordered the needed mix design and quantity The batching plant filled the contractor’s trucks in the same ways as it filled its own The contractor will then be billed on a... cost indifference points Their values are given by: Dindrd 1 = ( ) ( ) kH −ξ PJ − PEQmin − π E Qmin − k 2 H 2 − 2kHξ PJ − PEQmin − π E Qmin − 4π E kξ 2 (P J −P Qmin E ) 2 − π E Qmin +4π E k 175 for Qmin ≤ Q ≤ Qmax (6.22) where: Dindrd1 is the lower EOQ-JIT cost indifference point under the revised EOQ with a price discount system for the RMC industry Dindrd 2 = ( ) ( ) kH −ξ PJ − PEQmin − π E Qmin + . for the RMC industry is developed by incorporating the price discount scheme of the RMC industry in Eq. (6.17) into the max Q Q E P min Q min Q E P 174 revised EOQ model in Eq. (6.1). The. cost under the JIT purchasing system for the JPTV models, and ξ is the sum of the additional costs and benefits resulting from JIT purchasing. The additional costs are mainly the increased. EOQ-JIT cost indifference point ( ∗ rind Q ) can be derived as EJ rind PP k Q − = ∗ 2 . Substituting ∗ rind Q for ∗ r Q in ∗ = rE bQN α , would result in Eind N the minimum area of the inventory