A CONCEPTUAL DESIGN INITIATION FOR PRODUCTION-INVENTORY SYSTEM BASED ON MACROECONOMICS Marzieh Akhondi a and S. Nurmaya Musa b Department of Mechanical Engineering, Faculty of Engineering, University of Malaya, Malaysia a Marzieh.Akhondi@siswa.um.edu.my, b nurmaya@um.edu.my ABSTRACT In order to analyze processes within factories and make them run in the most optimum way, the first step is to identify the constituents interacting together in meeting final goals and objectives of a company. Among all, the key point triggering all the production plant activities is the number of finished goods needed to be delivered to the end customer. However, all the departments performances must be designed based on the firm s responsibilities encountering customer(s). There is a gap on the study of internal factors on the production-inventory system of a supply chain and the macroeconomics variables. The link between the economic factors and the supply chain of a certain type of product is to be best determined according to the customer consumption. On the other hand, the consumption level is directly determined based on economic situation surrounded by which the customers decide how to react to economic crisis nationally. Therefore, the general approach in this research is to develop a model to connect element of macroeconomics cycle and the production-inventory system of a multi-stage supply chain. In this study, simplifying assumptions are presented to best illustrate these interactions. Eventually, the conceptual design of this integrated model is presented. Keywords: Dynamic Modeling, Production-Inventory System, Gross Domestic Products, Demand- Driven Supply Chain, Conceptual Design 1. INTRODUCTION Over decades of introducing supply chain modeling as an interdisciplinary science, due to rising manufacturing costs, the significance of simulating such systems has become more obvious. Essentially, supply chain as a complex system of interactions among several sections, is mainly triggered by the customer demand. Noticeably, a typical supply chain is a combination of the central supplier(s) providing goods and services for the downstream customer(s) by the aid of upstream supplier(s). It is believed that without having a complete understanding of all constituents interacting within a production system, its dynamic behavior cannot be configured and controlled (Towill 1982). Moreover, in order to perform optimizations for achieving the highest degree of competitiveness by a particular firm in the market, external and internal influential factors should be taken into account. Accordingly, when it comes to the benefits as the critical basic goal for profit-making companies, the customer demand role comes to the first level of interest by which all the supply chain echelons actions are to be designed and monitored. Still, there is one question motivating this study: If the customer demand is taken the most vital factor to push all types of supply chains, what is the extent to which the customer economics affects the order placement procedure? To answer this question, we study propose a model development based on the market and the Production- Inventory system relationships across a supply chain. In this model, the Macroeconomic indicators are considered to have direct impact on the customer behavior while ordering goods and services. Customer requirement in turn directly is taken into consideration by production planning experts within companies. As one of the most potential area in supply chain management, the Inventory management is figured based on production rate and customer demand levels. By developing a level of understanding for the order rate, along with smooth production flow, the most beneficial inventory level can be investigated and modeled based on Lee and Wu (2006). 2. LITERATURE REVIEW 2.1 Demand Driven Supply Chain Modeling Literally, to conduct a supply chain model economic aspects of all entities within the system must be taken into account (Forrester 1961). As well, the time-dependent nature of supply chain modeling and optimization due to delays extant within all the interactions, the principles of system dynamics suits the objective of any supply chain modeling (Towill 1991). Hereby, dynamic approach helps to monitor and control the amplifications happened for 1
demand throughout supply chain entities through simulation basics (Gjerdrum, Shah et al. 2001). Noticeably, the significance of accurate information system within the supply chain is taken into consideration since practitioners started to model supply chain echelons behavior. In these models, to investigate beneficial customer demand information systems, the need for systematic approach for designing a supply chain as a whole is inevitable (Towill 1992). On the other hand, to stay competitive and to win customer(s) satisfaction by all profit-making companies, the internal factors such as production rate and inventory level must be investigated (Caridi and Cavalieri 2004). In this manner, researches conducted to model customer demand patterns, helped to introduce forecasting methods for minimizing the so called Bullwhip effect by the aid of computer software (Berry, Naim et al. 1995). All these methods have been initiated to enhance the supply chain flexibility in terms of customer services (Milner and Kouvelis 2005). In this paper, a model is proposed to address the flexibility of production-inventory systems based on the data that is drawn directly from the market surrounded by which customer place orders. 3. PRODUCTION-INVENTORY (P-I) SYSTEM 3.1 P-I System Main Parameters As stated, a particular production-inventory system of manufacturing firm is modeled using Table 1 containing time parameters: Time Average Consumption Time Inventory Adjustment Time Lead time Table 1: Production-Inventory system time parameters Parameters Indicator Work in Process Adjustment Time Fulfillment time Target Fulfillment Time T T a T in T p T w T f T tf For this system to be modeled, the amount of customer order is the basic consumption (CONS) taken from the Macroeconomic loop. This amount equals the quantity for the aggregated demand initiated from the need of society for production excluding services. 3.2 P-I System Mathematic Formulations When the required order quantity is recorded, the Desired Inventory level (DINV) is changing due to changes of Average Consumption (AVGCONS) yearly. This amount is calculated as below: DINV t = AVGCONS t T a As depicted, the Average Consumption here is derived from the forecasted demand utilizing the exponential smoothing methods as a feed-forward control factor used in control systems: AVGCONS= (1/T a ) (CONS) t-1 + (T a -1/ T a ) (AVGGCONS) t-1, t 2 Where in t=1, AVGCONS= 0; In real cases, the amount of DINV is not the same as the actual one (AINV). For this, within the system we call it output of completed goods based on the Completion Rate (COMRATE): AINVt= COMRATE t SR t + AINV t-1 ; where SR t is the Shipment Rate. In case of shipment of goods to the downstream customers, the MAX amount of shipment rate (MSR) is calculated as below: MSR t =AINV t /T f 2
However, the amount of Desired Shipment Rate (DSR) always in reality differ the amount that the factory can fulfill its customer demand. So, the SR can be figured out regarding both: SR t =min (DSR t, MSR t ). Due to the time that it takes to produce goods and the deadline for shipping finished goods to the downstream customers, along with the need for closeness to reality, an amount of unfulfilled orders is considered as BACKLOG: BACKLOG t = CONS t SR t + BACKLOG t-1 Noticeably, one undeniable fact of all manufacturing firms is the delay extant within the production and assembly processes in high service disciplines as nth order delay (Forrester 1961). In this research, we claim that the production time known as Lead Time (T p) is considered as the average delay time and is constant considering the similarity amongst product family types with no disruptions in production process. The first-order delay is taken in model designing (n=1): COMRATE t = Delay n (SR t, T p ); (Entering Material (t)-finished Goods t, Material in Transit t ); Outflow of Finished Goods=Inflow (t-average Delay Time)(Sterman 2000); In addition to that, the amount of Work in Process (WIP) throughout an inventory system depends on the amount of order that has been placed by the customers. Accordingly, WIP subsumes the number of incoming units under completion. Again, a perceived amount of lead time for production must be taken into account instead of the actual lead time (Geary, Disney et al. 2006): WIP t = SR t - COMRATE t + WIP t-1 ; It must be mentioned that the Desired Work in Process is just dependent to the Average consumption derived from the exponential smoothing method: DWIP t = AVGCONS t T p ; Overall, there is a need to calculate the amount of Order Rate (ORATE). To assure that order amount is a nonnegative amount the formula below has been taken into consideration: ORATE= MAX (AVGCONS t + ((DINV t -AINV t )/T in ) + ((DWIP t -WIP t )/T w ), 0); 3.3 Production-Inventory System Causal Loop Diagram We consider a production-inventory system operating under customer demand rate that is drawn from the market for the sake of closeness to reality (Schwarz and Schrage 1975). This system is presented in Figure 1 Figure 1.Production-Inventory System 3
4. MACROECONOMIC SYSTEM Within a country s boundary during a specific period of time, the gross amount of production is the main indicator of living standards. Table 2 represents the main factors contributing in macroeconomic cycle as discussed by Harvey (2010): Table 2: GDP loop Parameters in General Condition Parameters Indicator Expectation Formation Time EFT Production Adjustment Time PAT Gross Domestic Product GDP Gross Domestic Product growth rate GDPg Expected Income EXPINC Expected Income growth rate EXPINCg Macroeconomic Loop Consumption CONS m Marginal Propensity to Consume MPC Total Investment Total Government Spending Aggregate Demand INVEST GOVSPEND AD The GDP amount is calculated regarding the short delay extant in the procedure of adjustment to the aggregated demand: GDP= (change in GDP, AD); Likewise, Change in GDP= GDP g = (AD-GDP)/PAT; On the other hand: AD=INVEST+ GOVSPEND+ CONS m ; The fraction of the income that people spend to the amount they earn is the Marginal Propensity to Consume (MPC). Hence, the Consumption is calculated using this factor: CONS m = MPC EXPINC; On the other hand, the expected income is accumulated in the GDP (Sterman, 2000): Expected Income== (change in Expected Income, GDP); Change in Expected Income= EXPINCg= (GDP Expected Income)/PAT; As an assumption in this simple model, both GOVSPEND and INVEST are exogenous and constant over time(sterman 2000). The casual diagram developed for the GDP loop without considering product type X is as shown in the figure. This model first was designed to illustrate the consumption trend explaining fundamental fact for the Keynesian economic theory (Samuelson 1939). Simply, the theory states that the amount of customers Aggregated Demand (AD) is dependent to their expectations of incomes (Sterman 2000). In fact, the total production of a country (GDP) affects the households expectations of their standards of living leading to their level of consumption. The increasing customer consumptions further leads to the increase of GDP. That is why the loop is considered a multiplier loop as presented in Figure 2. 4
Figure 2.Macroeconomic System in General Condition 4.1 Macroeconomic Loop Regarding Product Family of X In order to design a GDP loop for a specific product type X, some verification is needed. First of all, the total amount for GDP when is considered for manufactured products, must exclude the services produced. Second of all, the expected income in this model is considered Disposable Income simply meaning that all the taxes is included in the Expected Income for the sake of closeness to reality. Moreover, for INVEST, CONS m (Consumption Rate within the Macroeconomic loop), and GOVSPEND we need to consider those percentage of these measures contributing in manufacturing the product type X regardless of other types of products produced nationally. These assumptions are indicated in Table 3: Table 3: Macroeconomic Loop for Manufacturing Product type X Parameters Indicator Consumption for Product X Investment for Product X Government Spending for Product X Aggregate Demand for Product X Average Price of the Product X Aggregate Demand Quantity CONSforX INVESTforX GOVSPENDforX ADforX AVGPforX ADQ The casual diagram developed for the GDP loop considering product type X is as in Figure 3. 5
Figure 3: Macroeconomic System for Product type X 5. INVESTIGATING THE INTEGRATION OF MACROECONOMIC MULTIPLIER LOOP AND PRODUCTION-INVENTORY SYSTEM Essentially, the significance of the surrounding atmosphere for the customer as the most important element of supply chain must be taken into consideration. Almost all product families can benefit the studies conducted to introduce the role of inventory management. This is proved as a key point to obtain internal flexibility allowing production plans to adapt changes in customer demand instantly (Bonney 1994). Existing studies consider the most optimal system for inventory control using JIT to avoid inventory wastes, making the role of customer demand inevitable. So far, the fact that the customer behavior can be best identified and patterned according to the economic level surrounded by which the amount of demand must be set has been taken for granted. Again, the need for the problem solving procedure to be as much realistic as it can be, led us to search for one external factor affecting customer consumption level in this study. The innovative work here is to acknowledge the impact of these economic factors on the customer behavior mainly customer demand. For this, the macroeconomics by considering total amount of production nationally can illustrate economic behavior of potential and actual customer(s) more clearly (Sterman 2000). In order to illustrate the interactions, the two models above are connected to make an integrated model by the aid of which the downstream consumption of the customers is directly drawn from the macroeconomic state of the market. As presented in Figure 4. 6
Figure 4: The Integrated Model of the P-I system and Macroeconomic System For the sake of simplicity, a number of assumptions have been considered as below: The amount of INVEST and GOVSPEND are constant to see the effect of EXPINC on AD directly. MPC is considered equal to 0.8 and that is the average amount of this indicator calculated from US data bases over a period of ten years (Sterman 2000). The EXPINC in the macroeconomic loop mainly refers to total disposable income. Since the aggregate demand is calculated to gain the customer consumption of the manufactured products not services, the GDP exclude the services. In order to obtain the customer demand quantity that enters the manufacturing firm the average price of a product type X must be taken since all the measures in the GDP loop are in monetary basis, where the quantity for aggregate demand is equal to the amount of aggregate demand over the average price of product type X. 7
6. CONCLUSION From the integrated model of Production-Inventory system and the GDP loop from the market, we are able to identify any changes in the quantity of the customer demand resulting from slight and dramatic changes in the economy of a country. This finally results in having a comprehensive control over the internal factors within a profit-making company. The economic behavior of the customers is directly affected by the macroeconomics shaping their expectations and consumptions. Therefore, the need for an integrated model to illustrate all the necessary connection is taken into account in this study. The integrated model proposed here, demonstrates the procedure of connecting the two fundamental echelons of a multi-stage supply chain. This model further can be utilized to predict future level of inventory by the aid of forecasting methods regarding the incidents of the market in which the chain of supply performs. ACKNOWLEDGMENT We are grateful for the partial financial support received from Fundamental Research Grant Scheme (FR024-2012A) and technical support from the Centre of Advanced Manufacturing and Material Processing (AMMP Centre), Faculty of Engineering, University of Malaya. REFERENCE Berry, D., M. Naim and D. R. Towill (1995). "Business process re-engineering an electronic products supply chain." IEE Proceedings-Science, Measurement and Technology 142(5): 395-403. Bonney, M. (1994). "Trends in inventory management." International Journal of Production Economics 35(1): 107-114. Caridi, M. and S. Cavalieri (2004). "Multi-agent systems in production planning and control: an overview." Production Planning & Control 15(2): 106-118. Forrester, J. W. (1961). Industrial dynamics, MIT press Cambridge, MA. Geary, S., S. M. Disney and D. R. Towill (2006). "On bullwhip in supply chains historical review, present practice and expected future impact." International Journal of Production Economics 101(1): 2-18. Gjerdrum, J., N. Shah and L. G. Papageorgiou (2001). "A combined optimization and agent-based approach to supply chain modelling and performance assessment." Production Planning & Control 12(1): 81-88. Lee, H. and J. Wu (2006). "A study on inventory replenishment policies in a two-echelon supply chain system." Computers & Industrial Engineering 51(2): 257-263. Milner, J. M. and P. Kouvelis (2005). "Order quantity and timing flexibility in supply chains: The role of demand characteristics." Management Science 51(6): 970-985. Samuelson, P. A. (1939). "Interactions between the Multiplier Analysis and the Principle of Acceleration." The Review of Economics and Statistics 21(2): 75-78. Schwarz, L. B. and L. Schrage (1975). "Optimal and system myopic policies for multi-echelon production/inventory assembly systems." Management Science 21(11): 1285-1294. Sterman, J. (2000). Business dynamics, Irwin-McGraw-Hill. Tang, O. and S. Nurmaya Musa (2011). "Identifying risk issues and research advancements in supply chain risk management." International Journal of Production Economics 133(1): 25-34. Towill, D. R. (1982). "Dynamic analysis of an inventory and order based production control system." The international journal of production research 20(6): 671-687. Towill, D. R. (1991). "Supply chain dynamics." International Journal of Computer Integrated Manufacturing 4(4): 197-208. Towill, D. R. (1992). "Supply chain dynamics the change engineering challenge of the mid 1990s." Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 206(4): 233-245. 8