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Optimal inventory replenishment strategy for deteriorating items in a demand-declining market with the retailer’s price manipulation

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Abstract

Due to rapid technological innovation and global competitiveness, the demand of many fashion-typed products usually decline significantly over time. A retailer facing such a market can employ replenishment strategies to increase its profit. This study, from the perspective of the retailer in a two-echelon supply chain, develops the optimal replenishment strategy for products experiencing deterioration, continuous decrease in market demand and price changes. This model help determine the optimal product life for products. Numerical examples are systematically conducted to verify the performances of the proposed model.

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References

  • Akella, R., & Kumar, P. R. (1986). Optimal control of production rate in a failure prone manufacturing system. IEEE Transactions on Automatic Control, AC-31, 116–126.

    Article  Google Scholar 

  • Bacsi, Z., & Vizvári, B. (1999). Modelling chaotic behaviour in agricultural prices using a discrete deterministic nonlinear price model. Annals of Operations Research, 89, 125–148.

    Article  Google Scholar 

  • Bakker, M., Riezebos, J., & Teunter, H. R. (2012). Review of inventory systems with deterioration since 2001. European Journal of Operational Research, 221(2), 275–284.

    Article  Google Scholar 

  • Chen, M. S., & Chu, M. C. (2001). The analysis of optimal price control model in matching problem between production and sales. Asia-Pacific Journal of Operational Research, 18, 131–148.

    Google Scholar 

  • Chen, W., Feng, Q., & Seshadri, S. (2012). Sourcing from suppliers with random yield for price-dependent demand. Annals of Operations Research. doi:10.1007/s10479-011-1046-5.

  • Dave, D. S., Fitzpatrick, K. E., & Baker, J. R. (1996). An advertising-inclusive production lot size model under continuous discount pricing. Computers & Industrial Engineering, 30, 147–159.

    Article  Google Scholar 

  • Doorman, G., & Nygreen, B. (2003). Market price calculations in restructured electricity markets. Annals of Operations Research, 124, 49–67.

    Article  Google Scholar 

  • Feng, Y., & Gallego, G. (1995). Optimal stopping times for end of season sales and optimal stopping times for promotional fares. Management Science, 41, 1371–1391.

    Article  Google Scholar 

  • Ghare, P. M., & Schrader, S. F. (1963). A model for an exponentially decaying inventory. Journal of Industrial Engineering, 14, 238–243.

    Google Scholar 

  • Goyal, S. K., & Giri, B. C. (2001). Recent trends in modeling of deteriorating inventory. European Journal of Operational Research, 134, 1–16.

    Article  Google Scholar 

  • Hsu, P. H., Wee, H. M., & Teng, H. M. (2010). Preservation technology investment for deteriorating inventory. International Journal of Production Economics, 124(2), 388–394.

    Article  Google Scholar 

  • Jørgensen, S., Kort, P. M., & Zaccour, G. (1999). Production, inventory, and pricing under cost and demand learning effects. European Journal of Operational Research, 117, 382–395.

    Article  Google Scholar 

  • Lin, P.-C. (2008). Optimal pricing, production rate, and quality under learning effects. Journal of Business Research, 61(11), 1152–1159.

    Article  Google Scholar 

  • Lin, Y. S., Yu, J. C. P., & Wang, K.-J. (2009). An efficient replenishment model of deteriorating items for a supplier-buyer partnership in hi-tech industry. Production Planning & Control, 20, 431–444.

    Article  Google Scholar 

  • Montgomery, D. C. (2005). Design and analysis of experiments (6th ed.). Hoboken: Wiley.

    Google Scholar 

  • Ouyang, L.-Y., Teng, J.-T., Goyal, S. K., & Yang, C.-T. (2009). An economic order quantity model for deteriorating items with partially permissible delay in payments linked to order quantity. European Journal of Operational Research, 194, 418–431.

    Article  Google Scholar 

  • Petruzzi, N. C., & Dada, M. (1999). Pricing and the newsvendor problem: a review with extensions. Operations Research, 47, 183–194.

    Article  Google Scholar 

  • Rosenblatt, M. J., & Lee, H. L. (1986). Economic production cycles with imperfect production processes. IIE Transactions, 18, 48–55.

    Article  Google Scholar 

  • Shemesh, E., Rabinowitz, G., & Mehrez, A. (1999). Optimal capacity and operation of deteriorating chemical production service facilities. Annals of Operations Research, 91, 205–225.

    Article  Google Scholar 

  • Shinn, S. W., & Hwang, H. (2003). Optimal pricing and ordering policies for retailers under order-size-dependent delay in payments. Computers & Operations Research, 30, 35–50.

    Article  Google Scholar 

  • Singer, M., Donoso, P., & Noguer, J. L. (2005). Optimal planning of a multi-station system with sojourn time constraints. Annals of Operations Research, 138, 203–222.

    Article  Google Scholar 

  • Upasani, A., & Uzsoy, R. (2008). Incorporating manufacturing lead times in joint production-marketing models: a review and some future directions. Annals of Operations Research, 161, 171–188.

    Article  Google Scholar 

  • Wang, K.-J., Lin, Y. S., & Yu, J. C. P. (2011). Optimizing inventory policy for products with time-sensitive deteriorating rates in a multi-echelon supply chain. International Journal of Production Economics, 130, 66–76.

    Article  Google Scholar 

  • Wee, H. M. (1999). Deteriorating inventory model with quality discount, pricing and partial backordering. International Journal of Production Economics, 59, 511–518.

    Article  Google Scholar 

  • Yang, P. C., & Wee, H. M. (2006). A collaborative inventory system with permissible delay in payment for deteriorating items. Mathematical and Computer Modelling, 43, 209–221.

    Article  Google Scholar 

  • Yang, P. C., Wee, H. M., & Yu, J. C. P. (2007). Collaborative pricing and replenishment policy for hi-tech industry. Journal of the Operational Research Society, 58, 894–900.

    Article  Google Scholar 

  • You, P.-S. (2007). Optimal times of price reductions for an inventory model with partial backorder and vertical shift demand. RAIRO. Operations Research, 41, 35–47.

    Article  Google Scholar 

  • Yu, J. C. P., Lin, Y. S., & Wang, K.-J. (2012). Coordination-based inventory management for deteriorating items in a two-echelon supply chain with profit sharing. International Journal of Systems Science. doi:10.1080/00207721.2012.659701.

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Acknowledgements

The authors gratefully acknowledge the valuable comments and suggestions of the anonymous referees. This work is partially supported by the National Science Council of the Republic of China to the first author.

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Correspondence to Kung-Jeng Wang.

Appendices

Appendix A: Proof of Proposition 1

The analysis of production (Fig. 1) is valid only if the condition \(I_{0}^{S}(t_{0}) \ge q_{1}^{*}\) is satisfied. From (20),

$$ I_{0}^{S}(t_{0}) = \frac{p_{0}}{\theta} \bigl( 1 - e^{ - \theta \cdot t_{0}} \bigr) $$
(A.1)

Then

$$ \frac{p_{0}}{\theta} \bigl( 1 - e^{ - \theta \cdot t_{0}} \bigr) \ge q_{1}^{*} $$
(A.2)

Therefore,

$$ p_{0} \ge\frac{q_{1}^{*} \cdot \theta}{1 - e^{ - \theta \cdot t_{0}}} $$
(A.3)

Similarly, from (21), for the initial period of production is \(t_{0} = \frac{1}{\theta} \ln[ \frac{p_{0}}{p_{0} - q_{1}^{*}\theta} ]\), the following condition is satisfied: \(t_{0} \le\frac{1}{\theta} \ln[ \frac{P_{0}}{P_{0} - q_{1}^{*}\theta} ]\).

Appendix B: Proof of Proposition 2

Assume that a production rate, \(p'_{i}\), is larger than p i , the value of Eq. (21).

$$ p'_{i} > p_{i} = \frac{q_{i + 1} \cdot \theta}{ ( 1 - e^{ - \theta \cdot t_{i}} )}, \quad i = 0,1,2,\dots,n - 1 $$
(B.1)

The supplier’s revenue is the same because the redundant products will not be sold

$$ \mathit{RE}_{i}^{S'} = \mathit{RE}_{i}^{S}, \quad i = 0,1,2,\dots,n - 1 $$
(B.2)

Then the production and storage cost will increase by

$$ \mathit{PC}_{i}^{S'} = U \cdot p_{i}^{\prime} \cdot t_{i} > \mathit{PC}_{i}^{S} = U \cdot p_{i} \cdot t_{i}, \quad i = 0,1,2,\dots,n - 1 $$
(B.3)
(B.4)
(B.5)
(B.6)

Therefore, the supplier’s total profit is decreased to

(B.7)

This ends the proof.

Appendix C: Design of experiments

Run

Basic design

TP S

TP R

h

A

k

B

O r

C

U

D

ν

E

ρ

F=ABCD

η

G=ABCE

ω

H=ABDE

A

J=ACDE

θ

K=BCDE

1

+

+

+

+

+

62,865

1,750,296

2

+

+

161,944

163,802

3

+

+

29,376

102,998

4

+

+

+

+

+

102,573

1,060,363

5

+

+

41,103

559,582

6

+

+

+

+

+

105,557

78,236

7

+

+

+

+

+

100,131

383,125

8

+

+

+

+

+

+

61,594

805,549

9

+

+

113,713

163,627

10

+

+

+

+

+

84,735

474,552

11

+

+

+

+

+

14,451

1,169,658

12

+

+

+

+

+

+

178,344

245,895

13

+

+

+

+

+

128,364

238,772

14

+

+

+

+

+

+

141,427

1,737,788

15

+

+

+

+

+

+

25,695

835,875

16

+

+

+

+

+

53,620

51,935

17

+

+

71,341

105,999

18

+

+

+

+

+

130,025

874,818

19

+

+

+

+

+

28,660

2,033,061

20

+

+

+

+

+

+

112,553

159,524

21

+

+

+

+

+

205,224

371,875

22

+

+

+

+

+

+

93,536

979,951

23

+

+

+

+

+

+

13,192

453,066

24

+

+

+

+

+

77,834

80,191

25

+

+

+

+

+

36,729

993,226

26

+

+

+

+

+

+

236,794

250,660

27

+

+

+

+

+

+

49,100

159,321

28

+

+

+

+

+

66,838

587,698

29

+

+

+

+

+

+

64,928

1,024,148

30

+

+

+

+

+

72,690

50,371

31

+

+

+

+

+

60,423

247,587

32

+

+

+

+

+

+

+

+

+

+

97,107

1,464,150

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Wang, KJ., Lin, YS. Optimal inventory replenishment strategy for deteriorating items in a demand-declining market with the retailer’s price manipulation. Ann Oper Res 201, 475–494 (2012). https://doi.org/10.1007/s10479-012-1213-3

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  • DOI: https://doi.org/10.1007/s10479-012-1213-3

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