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Research Issues in Supply Chain Management

Research Issues in Supply Chain Management. 서울대학교 산업공학과 제조 통합 자동화 연구실 2002/12/6. 발표자 : 정 성 원. Special issue of IJPE. Special Issue Quality in Supply Chain Management and Logistics Potential topics Organizational Issues

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Research Issues in Supply Chain Management

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  1. Research Issues in Supply Chain Management 서울대학교 산업공학과 제조 통합 자동화 연구실 2002/12/6 발표자 : 정 성 원

  2. Special issue of IJPE • Special Issue • Quality in Supply Chain Management and Logistics • Potential topics • Organizational Issues • Quality management implementation in supply chain and logistics functions • Organizational interface between quality and supply chain management logistics • Organizational and Supply Chain Performance Issues • Quality management and supply chain and logistics performance • Quality performance measures in supply chain management and logistics • Quality Issues in Supply Chain and Logistics • Quality assurance in supply chain and logistics • Inter-organizational relations for supply chain management and logistics management • Quality management issues for supply chain management and logistical practices • Due Day • 30 June, 2003 : Manuscript submission • 31 March, 2004 : Final manuscript submissions to publisher 2/37

  3. Contents • Paper I • Performance simulation of supply chain design • Paper II • The impact of forecasting model selection on the value of information sharing in a supply chain • Discussion 3/37

  4. Performance simulation of supply chain designs Fredrik Persson, Jan Olhager Department of Production Economics, IMIE, Linkopting Institute of Technology, Sweden International journal of production economics 77(2002)

  5. Introduction • The basis for this research • A real case in the mobile communications industry • The desire of the managers at two different manufacturing firms within the same manufacturing group • To capture the relationships among quality, lead-times and cost in their supply chains • The purpose of this research • To evaluate alternative supply chain designs with respect to quality, lead-times and costs • To increase the understanding of the interrelationships among these and other parameters 5/37

  6. The time to carry out the project is estimated and the first set of experiments is defined. The system logic is captured for simulation modeling activity. The conceptual model is examined and corrected if necessary. The conceptual model is transformed to a computer-based simulation model Verification aims at testing the computer-based model against the conceptual model Validation aims at testing the computer-based model against the system itself The effect of varying inputs on the output is examined The experiments defined earlier and run and output data is collected and analyzed The analyzed output data is used to recommend some decision or help in an implementation Simulation study methodology 1. Project planning 2. Conceptual modeling 3. Conceptual model validation 4. Modeling 5. Verification 6. Validation 7. Sensitivity 8. Experimentation and analyzing output data 9. Implementation 6/37

  7. Simulation modeling and validation • Model assumption • For the simulation, two products out of eight were chosen to represent the diverse capacity load throughout the manufacturing process • Customer demands are pushed through the SC • Capacity is limited throughout the SC • Yields for all tests are are available from the firm • All operation time data are collected from the manufacturing planning and control • Supply of raw materials and components are endless and do not cause any disturbances Factory 2 Factory 1 Current SC design 7/37

  8. Current SC structure Next generation of SC structure Simulation model I • Three different SC structures • Three different quality level Old SC structure Two item 8/37

  9. Simulation model II • List of experiments • Model output • Performance measures : 1) Costs, 2) Inventory, 3) the level of Quality, 4) Lead-time 9/37

  10. Result : Costs • Costs • The total costs include all relevant costs, including inventory holding costs and quality control related costs • Total cost reduce both with simpler SC structures and with improved quality levels • From Fig. 3, total costs were approximately reduced to half when going from the old SC with low-quality levels to the current SC with medium-quality levels, and a further reduction by 50% can be expected when adopting the next generation SC with improved quality Current(Medium) Next generation(Low) Current(Medium) Next generation(High) 10/37

  11. Result : Inventory • Inventory • There is a reduction in inventories from the old to the current and further to the next generation SC. • The inventory levels will be non-significant for the next generation SC design. • The poor quality levels have a larger impact on inventories with longer supply chains Old (low) old (high) Next (low) Next (High) 11/37

  12. Result : Quality • Quality • In this simulation study, the level of quality was chosen as input. • However with respect to the complexity of the supply chain process, the actual outgoing quality levels can also considered a result of the simulation experiments • Quality : the number of good products / the total number of products entering the SC • For lead times are shorter, providing faster feedback to operators and individual manufacturing Old Next Generation Low  High 12/37

  13. Result : Lead-time • Lead-time • The total lead-time is calculated from the initiation of the first operation through the whole supply chain • The major single contributor to total lead-time is so-called time test • The next generation SC it will be possible to remove this time-consuming step • Only the SC design has a major impact on the total lead-time Old Next Generation Low  High 13/37

  14. Result : Cost, Quality and lead-time relationships • Cost, Quality and lead-time relationships • The main purpose for the company initiating this study was to capture the relationship into consideration when discussing outsourcing option • Below figure shows how total costs are affected by quality levels and supply chain structures • Both factors contribute to cost reductions • Quality levels have almost no impact on lead-time N C O N : Next generation SC structure C : Current SC structure O : Old SC structure 14/37

  15. Conclusion • Which would be the most profitable move in a given situation • Technology advances would allow for a simper supply chain, facilitating both higher quality levels and shorter lead-times N C O 1 : Current(Medium)Next generation(Medium) 2 : Current(Medium) Current(high) 3 : Current(Medium)Next generation(high) 15/37

  16. The impact of forecasting model selection on the value of information sharing in a supply chain Xiande Zhao*, Jinxing Xie**, Janny Leung*** *Department of Decision Sciences and Managerial Economics, The Chinese University of Hong Kong, China ** International journal of production economics 77(2002)

  17. Introduction • The performance of a supply chain • The accuracy of the forecasts • The information sharing • Some problems in the information sharing • The costs for setting up and operating an information sharing system between ‘link’ of a supply-chain are still substantial • Many companies are reluctant to share information with their trading partners, afraid that the information will be used unfairly to their disadvantage • The purpose of this research • In order to motivate companies to share information, they need to be aware of the benefits that information-sharing systems can bring • The purpose of this research is to examine how the selection of forecasting models influence the performance of the supply chain and the value of information sharing 17/37

  18. The simulation model and procedures I • Basic assumptions • This study focuses on a piece of the supply chain consisting of one supplier and four retailers • The supplier is a manufacturer who produces a single product for four retailers • No explicit manufacturing lead-time will be considered, as this would depend on the supplier’s capacity and will be implicitly determined in the supplier’s production decision • Shipments are made from the supplier to retailers by truck and the transportation lead-time is assumed to be one period • The truck capacity is large enough so that all units ordered by a retailer in each period can be shipped by a single truck • The retailers face uncertain customer demands, with the average demand per period for each retailer being 1000units. • The retailer replenish their inventories by placing orders to the supplier, thus average demand per period for the supplier is 4000 • The initial inventory for the ith retailer is set at (4+i)*1000 (i = 1,2,3,4) 18/37

  19. The simulation model and procedures II • Generation of demand and capacity • All four retailers are assumed to face identical demand patterns generated by the following formula • Demandt is the demand in period t, snormal() is a standard normal random number generator • Four demand patterns(CON,SEA,SIT,SDT) representing different combinations of trends and seasonality are used in this study(DP) • CON : CON produces demand with neither trends nor seasonality • SEA : SEA produces demand with seasonality but without trends • SIT : SIT produces demand with seasonality and an increasing trend • SDT : SDT produces demand with seasonality and decreasing trend • Capacity tightness(CT) • Total capacity available for available for all the periods is equal to the total demand multiplied by CT factor 19/37

  20. The simulation model and procedures III • Retailer’s ordering decision • The retailers are assumed to use the EOQ rule to determine their ordering quantity. In each period, the retailers use a forecasting method to forecast demand for the future periods • Five typical forecasting models (FM) • A naïve method(NAV) • A simple moving average(SMA) • A two-parameter double exponential smoothing(DES) • A no-trend Winters’ method(NTW) • A three parameter winter’s model(WIN) SMA NTW/WIN b1 : The basic signal b2 : The linear trend component ct : The multiplicative seasonal factor DES 20/37

  21. The simulation model and procedures IV • Supplier’s production and delivery decision • The supplier/manufacturer applies a single item-capacitated lot-sizing rule in planning his/her production activities • The supplier receiver orders from different retailers and makes production-planning decisions based on information available. We consider three case(IS) • When there is no information sharing between the supplier and the retailers(NIS) • When the retailers share their forecasted net requirements with the supplier (DIS) • When their retailer share their planned orders with the supplier (OIS) 21/37

  22. Experimental design and research hypotheses • Independent variable • Environmental factors • The demand pattern (DP) faced by retailer – CON, SEA, SIT, DIT • The capacity tightness (CT) faced by the supplier – Low (1.33), Medium(1.18), High(1.05) • Parameters for SCM • The forecasting model (FM) – NAV, SMA, DES, NTW, WIN • The way that information is shared between the supplier and the retailer (IS) – NIS, DIS, OIS • Dependent variables • Total cost for retailers (TCR) • Total cost for the supplier (TCS) • Total costs for the entire supply chain (TC) • The service level of the supplier (SLS) • The customer service level of the retailers (SLR) 22/37

  23. Research hypotheses • Hypothesis 1 • FM selection by the retailer will significantly influence the performance of the supply chain and the value of information sharing • The forecasting model with higher forecast accuracy will reduce costs, improve service level, and make information sharing more beneficial by improving the performance of the supply chain • Hypothesis 2 • DP faced by the retailers significantly influences the impact of forecasting model on the value of the information sharing • The presence of trends and seasonality in the DP will make the impact of forecasting model selection on the value of information sharing more significant • Hypothesis 3 • CT faced by the supplier will also significantly influence the impact that the forecasting model will have on the value of the information sharing • When the supplier faces a higher CT, the more significant will be the impact that the selection of forecasting model by the retailer will have on the value of the information sharing 23/37

  24. Results – Hypothesis I • The impact of FM on supply chain performance and the value of IS • Overall, the results indicate that higher benefits will be achieved through information sharing when the forecasting accuracy is higher, thus supporting Hypothesis 1 • Comparison of forecasting model performance under different levels of information sharing indicates that moreaccurate forecasts may not help to improve the performance of the supply chain dramatically when the retailers do not share information with the supplier 24/37

  25. Results – Hypothesis I • The impact of FM on supply chain performance and the value of IS 25/37

  26. Results – Hypothesis I • The impact of FM on supply chain performance and the value of IS NIS-ACT : 142-144 OIS-ACT : 100-124 26/37

  27. Results – Hypothesis II • The interaction between DP, FM, IS • DP = CON, SEA • All retailers face demands without trends thus the total demand of the supply chain is smooth over the time horizon and is usually below suppliers capacity in every period • Therefore, when retailers share their planned orders with the supplier, the supplier can make a better trade-off between setup costs and inventory costs through the capacitated lot-sizing procedure. • The retailer’s service level, however is already very high even without information sharing, thus sharing information did not significantly reduce the backorder costs for the retailers when they share information with the supplier. On the other hand, the retailers’ inventory carrying costs increase dramatically • DP = SIT, SDT • When all the retailers face demands with trends,sharing information significantly reduce the costs for both the supplier and retailers 27/37

  28. Results – Hypothesis II • DP = CON OIS-ACT  NIS-ACT : 100359(TCS) OIS-ACT  NIS-ACT : 117100(TCR) 28/37

  29. Results – Hypothesis II • DP = SEA OIS-ACT  NIS-ACT : 100356(TCS) OIS-ACT  NIS-ACT : 117100(TCR) 29/37

  30. Results – Hypothesis II • DP =SIT OIS-ACT  NIS-ACT : 100176(TCS) OIS-ACT  NIS-ACT : 100127(TCR) 30/37

  31. Results – Hypothesis II • DP =SDT OIS-ACT  NIS-ACT : 100148(TCS) OIS-ACT  NIS-ACT : 100149(TCR) 31/37

  32. Results – Hypothesis III • The interaction between CT, FM and IS • When CT is low, sharing planned order information with the supplier results in significant low costs for the supplier and for the entire supply chain . However it results in higher total costs for the retailers • When CT is medium and high, sharing information significantly reduce the costs for both the supplier and retailers 32/37

  33. Results – Hypothesis III • CT = Low OIS-ACT  NIS-ACT : 100307(TCS) OIS-ACT  NIS-ACT : 112100(TCR) 33/37

  34. Results – Hypothesis III • CT = Medium OIS-ACT  NIS-ACT : 100243(TCS) OIS-ACT  NIS-ACT : 100146(TCR) 34/37

  35. Results – Hypothesis III • CT = High OIS-ACT  NIS-ACT : 100156(TCS) OIS-ACT  NIS-ACT : 101133(TCR) 35/37

  36. Conclusion • Information sharing can significantly influence the performance of the supply chain. • The benefits to different parties in the supply chain may be quite different under different conditions • The total costs and service level for retailers may even worsen when they share information with the supplier under some demand conditions when capacity tightness is low • Therefore, the supplier must provide some incentives to the retailers under these conditions, or retailers may not be willing to participate in an information-sharing project 36/37

  37. 1차 납품업체 a 고객 공장a 물류창고 a 고객 1차 납품업체 b 물류창고 b 공장b 고객 고객 1차 납품업체 c Discussion • The impact of production and distribution planning in the supply chain 37/37

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