Advanced Search
Article Contents
Article Contents

Inventory replenishment policies for two successive generations price-sensitive technology products

  • * Corresponding author: Gaurav Nagpal

    * Corresponding author: Gaurav Nagpal 
Abstract Full Text(HTML) Figure(5) / Table(6) Related Papers Cited by
  • The high technology products come in generations, where the demand for newer technology generations is strongly influenced by the installed base of earlier generations (such as computers, cameras, notebooks, etc). However, the effect of technology substitution on inventory replenishment policies has received little attention in the supply chain literature. In the hi-technology market, consumers' purchasing capability, the utility of a product along with the entry of the advanced generation product influence the market expansion/contraction of the products. In this study, the impact of parallel diffusion of two successive generations' products on inventory policies of the monopolist has been analysed. The demand models have been characterised by considering the life-cycle dynamics for a P-type inventory system. The purpose of this paper is to develop a model for joint pricing and replenishment of technology generation products. The model has been solved by using a genetic algorithm technique. The impact of yearly price drop and the price sensitivity of demand on the profit margins vis-à-vis on replenishment policies has also been studied. The paper also brings forward the dynamics of the launch of newer generations and the pricing strategies on optimal inventory replenishment policies. Numerical illustrations have also been covered in the paper.

    Mathematics Subject Classification: Primary: 90B05; Secondary: 90B06.


    \begin{equation} \\ \end{equation}
  • 加载中
  • Figure 1.  The behavior of the price of the technology products with the time elapsed after the launch for different values of annual % price drop "$ \Upsilon $"

    Figure 2.  The P Model of inventory management before the launch of the second generation

    Figure 3.  The P Model of inventory management in the $ m^{th} $ planning horizon with $ n $ replenishments before the launch of first-generation

    Figure 4.  Influence of diffusion rate of second generation product on phase-out timing of 1st generation

    Figure 5.  Influence of price elasticity of demand on the diffusion pattern and revenues

    Table 1.  Tabular review of existing literature on multi-item inventory modeling under price-dependent demand

     | Show Table
    DownLoad: CSV

    Table 2.  Optimal number of replenishments ($n_1$ and $n_2$, both of them, say equal n, ) in pooled logistics determined by minimizing the sum of holding cost and carrying costs for each planning horizon (All the costs are in Mn INR)

     | Show Table
    DownLoad: CSV

    Table 3.  Total Revenue, Profits and Opportunity Loss for two generations scenario (The Revenue and Profit figure are in Mn INR)

     | Show Table
    DownLoad: CSV

    Table 4.  Total Profit values $T{P_{{m^{',{n_1}{n_2}*}}}}$ for different values of yearly price drop % with the change in $\beta$ (The Profit figures are in Mn INR)

     | Show Table
    DownLoad: CSV

    Table 5.  Total Profit values $T{P_{{m^{',{n_1}{n_{{2_*}}}}}}}$ for different planning horizons with different values of price sensitivity $\beta$ (The Profit figures are in Mn INR

     | Show Table
    DownLoad: CSV

    Table 6.  Comparison of the replenishment dynamics: Joint vs Dis-joint for both the generations of products (The Profit figures are in Mn INR)

     | Show Table
    DownLoad: CSV
  • [1] F. M. BassT. V. Krishnan and D. C. Jain, Why the Bass Model Fits without Decision Variables, Marketing Science, 13 (1994), 203-223.  doi: 10.1287/mksc.13.3.203.
    [2] N. ChakrabortyS. Mondal and M. Maiti, A deteriorating multi-item inventory model with price discount and variable demands via fuzzy logic under resource constraints, Computers and Industrial Engineering, 66 (2013), 976-987.  doi: 10.1016/j.cie.2013.08.018.
    [3] U. Chanda and R. Aggarwal, Optimal inventory policies for successive generations of a high technology product, The Journal of High Technology Management and Research, 25 (2014), 148-162. 
    [4] O. Duran and P. P. Luis, Solution of the spare parts joint replenishment problem with quantity discounts using a discrete particle swarm optimization technique, Studies in Informatics and Control, 22 (2013), 319-328. 
    [5] F. Fang, Joint pricing and inventory decisions for substitutable perishable products under demand uncertainty, Thesis, University of Southampton, (2016).
    [6] L. FengJ. Zhang and W. Tang, A joint dynamic pricing and advertising model of perishable products, Journal of the Operational Research Society, 66 (2015), 1341-1351.  doi: 10.1057/jors.2014.89.
    [7] M. GhoreishiA. MirzazadehG. W. Weber and I. Nakhai-Kamalabadi, Joint pricing and replenishment decisions for non-instantaneous deteriorating items with partial backlogging, inflation- and selling price-dependent demand and customer returns, Journal of Industrial and Management Optimization, 11 (2015), 933-949.  doi: 10.3934/jimo.2015.11.933.
    [8] R. N. GiriS. K. Mondal and M. Maiti, Analysis of pricing decision for substitutable and complimentary products with a common retailer, Pacific Science Review A: Natural Science and Engineering, 18 (2016), 190-202.  doi: 10.1016/j.psra.2016.09.012.
    [9] R. N. GiriS. K. Mondal and M. Maiti, Bundle pricing strategies for two complimentary products with different channel powers, Annals of Operations Research, 287 (2020), 701-725.  doi: 10.1007/s10479-017-2632-y.
    [10] M. JainG. C. Sharma and R. R. Singh, Multi-item Inventory model with Two Price-breaks under multiple recovery and procurement set-ups, Mathematics Today, 28 (2012), 27-42. 
    [11] D. K. Jana and B. Das, A two-stage multi-item inventory model with hybrid number and nested price discount via hybrid heuristic algorithm, Annals of Operations Research, 248 (2017), 281-304.  doi: 10.1007/s10479-016-2162-z.
    [12] S. KarT. Roy and M. Maiti, Multi-item inventory model with probabilistic price dependent demand and imprecise goal and constraints, Yugoslav Journal of Operations Research, 11 (2001), 93-103. 
    [13] V. B. Kreng and B. J. Wang, An innovation diffusion of successive generations by system dynamics - An empirical study of Nike Golf Company, Technological Forecasting and Social Change, 80 (2013), 77-87.  doi: 10.1016/j.techfore.2012.08.002.
    [14] C. W. Kuo and K. L. Huang, Dynamic pricing of limited inventories for multi-generation products, European Journal of Operational Research, 217 (2012), 394-403.  doi: 10.1016/j.ejor.2011.09.020.
    [15] C. Y. Lee and D. Lee, An efficient method for solving a correlated multi-item inventory system, Operations Research Perspectives, 5 (2018), 13-21.  doi: 10.1016/j.orp.2017.11.002.
    [16] G. LiuJ. Zhang and W. Tang, Joint dynamic pricing and investment strategy for perishable foods with price-quality dependent demand, Annals of Operations Research, 226 (2015), 397-416.  doi: 10.1007/s10479-014-1671-x.
    [17] A. Mahmoodi, Joint pricing and inventory control of duopoly retailers with deteriorating items and linear demand, Computers and Industrial Engineering, 132 (2016), 36-46.  doi: 10.1016/j.cie.2019.04.017.
    [18] S. M. Mousavi, S. T. A. Niaki, A. Bahreininejad and S. N. Musa, Multi-item multi-periodic inventory control problem with variable demand and discounts: A particle swarm optimization algorithm, The Scientific World Journal, 2014 (2014), Article ID: 136047. doi: 10.1155/2014/136047.
    [19] M. Nagarajan and S. Rajagopalan, Inventory models for substitutable products: Optimal policies and heuristics, Management Science, 54 (2008), 1453-1466. 
    [20] G. Nagpal and U. Chanda, Economic Order Quantity model for two generation consecutive technology products under permissible delay in payments, International Journal of Procurement Management, 14 (2021). doi: 10.1504/IJPM.2020.10027606.
    [21] G. Nagpal and U. Chanda, Adoption and diffusion of hi-technology product and related inventory policies - an integrative literature review, International Journal of e-Adoption, 12 (2020), 1-14. 
    [22] F. Nascimento and W. Vanhonacker, Optimal strategic pricing of reproducible consumer products', Management Science, 34 (1988), 921-937. 
    [23] T. S. NedaH. Mirmohammadi and I. Mehdi, Joint optimization of dynamic pricing and replenishment cycle considering variable non-instantaneous deterioration and stock-dependent demand, Computers and Industrial Engineering, 123 (2016), 232-241. 
    [24] M. Nunez-LopezJ. X. Velasco-Hernandez and P. A. Marquet, The dynamics of technological change under constraints: Adopters and resources, Discrete and Continuous Dynamical Systems - B, 19 (2014), 3299-3317.  doi: 10.3934/dcdsb.2014.19.3299.
    [25] J. A. Norton and F. M. Bass, A diffusion theory model of adoption and substitution for successive generations of high-technology products, Management Science, 33 (1987), 1068-1086.  doi: 10.1287/mnsc.33.9.1069.
    [26] F. OtrodiY. R. Ghasemy and S. AliTorabi, Joint pricing and lot-sizing for a perishable item under two-level trade credit with multiple demand classes, Computers and Industrial Engineering, 127 (2016), 761-778. 
    [27] D. Panda and M. Maiti, Multi-item inventory models with price dependent demand under flexibility and reliability consideration and imprecise space constraint: A geometric programming approach, Mathematical and Computer Modelling, 49 (2009), 1733-1749.  doi: 10.1016/j.mcm.2008.10.019.
    [28] P. M. Parker, Price elasticity dynamics over the adoption and life cycle, Journal of Marketing Research, 29 (1992), 358-367. 
    [29] S. PaulM. I. M. Wahab and P. Ongkunaruk, Joint replenishment with imperfect items and price discount, Computers and Industrial Engineering, 74 (2014), 179-185.  doi: 10.1016/j.cie.2014.05.015.
    [30] H. Simon, Dynamic of price elasticity and brand life cycles: An empirical study, Journal of Marketing Research, 16 (1979), 439-452. 
    [31] K. Y. Tam and K. L. Hui, Price elasticity and the growth of computer spending, IEEE Transactions on Engineering Management, 46 (1999), 190-200. 
    [32] M. TalebianN. Boland and M. Savelsbergh, Pricing to accelerate demand learning in dynamic assortment planning, European Journal of Operational Research, 237 (2013), 555-565.  doi: 10.1016/j.ejor.2014.01.045.
    [33] A. A. TaleizadehS. Tavassoli and A. Bhattacharya, Inventory ordering policies for mixed sale of products under inspection policy, multiple prepayment, partial trade credit, payments linked to order quantity and full backordering, Annals of Operations Research, 287 (2020), 403-437.  doi: 10.1007/s10479-019-03369-x.
    [34] S. TayalS. R. Singh and R. Sharma, A multi item inventory model for deteriorating items with expiration date and allowable shortages, Indian Journal of Science and Technology, 7 (2014), 463-471. 
    [35] Y. C. Tsao and G. J. Sheen, A multi-item supply chain with credit periods and weight freight cost discounts, International Journal of Production Economics, 135 (2012), 106-115.  doi: 10.1016/j.ijpe.2010.11.013.
    [36] J. Wei and J. Zhao, Pricing decisions for substitutable products with horizontal and vertical competition in fuzzy environments, Annals of Operations Research, 242 (2016), 505-528.  doi: 10.1007/s10479-014-1541-6.
    [37] C. T. YangL. Y. OuyangH. F. Yen and K. L. Lee, Joint pricing and ordering policies for deteriorating item with retail price-dependent demand in response to announced supply price increase, Journal of Industrial and Management Optimization, 9 (2013), 437-454.  doi: 10.3934/jimo.2013.9.437.
  • 加载中




Article Metrics

HTML views(1095) PDF downloads(598) Cited by(0)

Access History

Other Articles By Authors



    DownLoad:  Full-Size Img  PowerPoint