inventory management with supply chain
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SNU MAI Lab. 2004
1/30
Inventory Management
in Closed-Loop Supply Chain
2004. 8. 21
임 치 훈
SNU MAI Lab. 2004 /302
Business Aspects of Closed-Loop Supply Chains–Rommert Dekker et al., Inventory Control in Reverse Logistics
Karl Inderfurth, Optimal policies in hybrid manufacturing/remanufacturing systems with product substitution
SNU MAI Lab. 2004 /303
The Carnegie Bosch InstituteInternational Conference on Closed-Loop Supply Chains
Business Aspects of Closed-Loop Supply Chains
May 31 – June 2, 2001Pittsburgh, Pennsylvania
Inventory Control in Reverse Logistics
Rommer Dekker and Erwin van der Laan, Erasmus University Rotterdam, The Netherlands
SNU MAI Lab. 2004 /304
A continuous time inventory model for a product recovery system with multiple options
Classification of Inventory Control Problems Classification of Inventory Control Problems
Inventory Control for Direct ReuseInventory Control for Direct Reuse
The Use of Accounting Information
Introduction
Inventory Control for Value-Added RecoveryInventory Control for Value-Added Recovery
Summary and Outlook
SNU MAI Lab. 2004 /305
A schematic overview of reverse logistics situations
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Classification of Inventory Control Problems
Return reason–Rework–Commercial return, outdated product–Product recall–Warranty return–Repair–End-of-use return–End-of-life return
Recovery option–Selling or donation–Store and reuse (direct reuse)–Value-added recovery–Recycle–Disposal
SNU MAI Lab. 2004 /307
Inventory Control for Direct Reuse
Single-period Inventory Decision Problem–Considers only order quantity–Fashion product, final order problem–Vlachos and Dekker (2000)
• Known percentage of returns arrives in time to be resold• Most return recovery options can be reduced to the standard newsboy optimality equation
Multi-period Infinite Horizon Inventory Decision Problem–Considers both reorder point and order quantity–Spare parts control of a refinery–Fleischmann et al. (1997)
• Independent Poisson processes for demands and returns• (s,S) policies remain optimal
Multi-period Finite Horizon Inventory Decision Problem–Considers both reorder point and order quantity–Demand and returns are specified per period–Richter and Sombrutzki (2000)
• Reverse economic lot sizing model with an unlimited return quantity• Zero-inventory regeneration property
SNU MAI Lab. 2004 /308
Inventory Control for Direct Reuse
Netting Approach–Considers returns as negative demands–The net demands are treated with traditional methods for single source inventory control–Van der Laan et al. (1996)
• Satisfactory method when return rates are low• The net demand is much more variable than the total demand
Direct Reuse in Network Inventories–Considers containers and reusable packaging–Determines how many containers are needed at each depot for a given time–Shen and Khoong (1995)
• Decision Support System for this problem
Disposal –When return rates exceed demand rates
SNU MAI Lab. 2004 /309
Inventory Control for Value-Added Recovery
Late 1960’s inventory control for repairable inventory–Physical closed-loop system
• After repair the items stay with or return to the original owner/user
–A demand and a production return always coincide
Product Remanufacturing–Functional closed-loop system–Variability and uncertainty in the timing and quantity of product returns
→ Difficult to balance supply with demand
–Variability and uncertainty in the quality of returned products→ Operations involved with remanufacturing are usually of a very stochastic nature
–Toktay et al. (1999) – Kodak single-use camera–Krikke et al. (1999) – copiers at Océ
SNU MAI Lab. 2004 /3010
Inventory Control for Value-Added Recovery
SNU MAI Lab. 2004 /3011
Inventory Control for Value-Added Recovery
The most common assumptions–Inventory systems are single item, single component systems–Product returns are independent of product demands–The demand and return processes are Poisson processes–Yields are certain–Processes are stationary–Leadtimes are constant and independent of the order size
SNU MAI Lab. 2004 /3012
Inventory Control for Value-Added Recovery
Optimal Policies–Inderfurth (1997)
• (L,M,U) policy • Remanufacturing leadtime = manufacturing leadtime, no fixed setup cost
–Minner and Kleber (1999)• Deterministic setting with dynamic demand and return patterns
–Inderfurth et al. (2001)• n different remanufacturing options, each sold on a separate market
Heuristic Policies–Muckstadt and Isaac (1981)
• Manufacturing – continuous review (s,Q) policy• Product returns are remanufactured upon arrival with stochastic service times and limited capacity
–Van der Laan et al. (1997)• Continuous review push and pull policies• The push policy concentrates stocks in serviceable inventory
–Inderfurth and Van der Laan (1998)• Treats the remanufacturing leadtime as a decision variable
SNU MAI Lab. 2004 /3013
Inventory Control for Value-Added Recovery
Dependency relation between demands and returns–Enables forecasts for timing and quantity of product returns–Kiesmüller and Van der Laan (2001)
• If good forecasts are incorporated in the inventory policy, they considerably improve system performance
–Kelle and Silver (1986)• Tracking and tracing of individual products leads to superior return forecasts
SNU MAI Lab. 2004 /3014
Optimal policies in hybrid manufacturing/remanufacturing systems
with product substitution
International journal of production economics 90 (2004) 325-343
Karl Inderfurth*
*Faculty of Economics and Management, Otto-von-Guericke-University Magdeburg, Magdeburg, Germany
SNU MAI Lab. 2004 /3015
A continuous time inventory model for a product recovery system with multiple options
General model formulation General model formulation
Case A. Short manufacturing leadtimeCase A. Short manufacturing leadtime
Case B. Short remanufacturing leadtimeCase B. Short remanufacturing leadtime
Decision problemDecision problem
Managerial insights
Further research
SNU MAI Lab. 2004 /3016
Decision problem
If remanufactured products are significantly different from new ones, they are sold in different markets to different customers at different prices
If a company is willing to offer its customers of remanufactured items a higher-valued original one in an out-of-stock situation. (downward substitution)
SNU MAI Lab. 2004 /3017
General model formulation
Optimally coordinated manufacturing/remanufacturing policy under product substitution
–Objective : maximize the expected profit –Single-stage, single-period–Independent stochastic demands for both product types–Deterministic leadtimes for manufacturing and remanufacturing–Stochastic returns of used products–Returned items which are not remanufactured will be disposed of
SNU MAI Lab. 2004 /3018
General model formulation
Notation–i = M for manufacturing(MP)–i = R for remanufacturing(RP)
SNU MAI Lab. 2004 /3019
General model formulation
Revenues from selling and salvaging MPs/expected revenues
Substitution quantity/expected amount of substitution
Expected total profit
Bound
SNU MAI Lab. 2004 /3020
Case A. Short manufacturing leadtime
At the time of the manufacturing decision the number of returns R which can be used for remanufacturing is known with certainty
Optimization problem
SNU MAI Lab. 2004 /3021
Case A. Short manufacturing leadtime
Theorem 1–TP(yM, yR) is jointly concave in yM and yR
→ Optimal reaction function
Theorem 2, 3–SM(yR) is monotonously decreasing with
• : Newsboy solution of the separate manufacturing problem (yR →∞)
• : Solution in case of zero RP inventory (yR = 0)
–SR(yM) is monotonously decreasing with • : Newsboy solution of the separate remanufacturing problem (yM = 0)
• : Solution in case of unlimited MP inventory (yM →∞)
optimal ‘order-up-to-levels’
SNU MAI Lab. 2004 /3022
Case A. Short manufacturing leadtime
Optimal policy structure in Case A
SNU MAI Lab. 2004 /3023
Case B. Short remanufacturing leadtime
At the time of the manufacturing decision the return uncertainty may not yet have been completely revealed
Optimization problem for remanufacturing
SNU MAI Lab. 2004 /3024
Case B. Short remanufacturing leadtime
Theorem 4–TPR(yR, yM, xR ,R) is jointly concave in yR
→ Optimal reaction function
from
Theorem 5–UR (yM) is identical to function SR(yM) in Case A
–So it is monotonously decreasing with
Optimal remanufacturing decision
Optimal profit from remanufacturing
SNU MAI Lab. 2004 /3025
Case B. Short remanufacturing leadtime
Optimization problem for manufacturing
Theorem 6–TPM(yM, xM, xR) is concave in yM
→Optimal reaction function
from
‘Manufacture-up-to policy’
SNU MAI Lab. 2004 /3026
Case B. Short remanufacturing leadtime
Optimal policy structure in Case B
SNU MAI Lab. 2004 /3027
정리
Closed-Loop Supply Chain 의 Production Planning and Control–현재까지는 Inventory control 분야에 많이 집중되어 있음–Optimal policy 에 관한 연구들은 실제 사례에 사용하기 어려움–Heuristic method 사용한 inventory control 에 대한 논문 review 계획