Integrated optimisation of pricing manufacturing and procurement decisions of a make-to-stock system operating in a fluctuating environment
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Date
2023
Authors
Oktay Karabağ
Burak Gökgür
Journal Title
Journal ISSN
Volume Title
Publisher
Taylor and Francis Ltd.
Open Access Color
HYBRID
Green Open Access
No
OpenAIRE Downloads
OpenAIRE Views
Publicly Funded
No
Abstract
Manufacturers experience random environmental fluctuations that influence their supply and demand processes directly. To cope with these environmental fluctuations they typically utilise operational hedging strategies in terms of pricing manufacturing and procurement decisions. We focus on this challenging problem by proposing an analytical model. Specifically we study an integrated problem of procurement manufacturing and pricing strategies for a continuous-review make-to-stock system operating in a randomly fluctuating environment with exponentially distributed processing times. The environmental changes are driven by a continuous-time discrete state-space Markov chain and they directly affect the system's procurement price raw material flow rate and price-sensitive demand rate. We formulate the system as an infinite-horizon Markov decision process with a long-run average profit criterion and show that the optimal procurement and manufacturing strategies are of state-dependent threshold policies. Besides that we provide several analytical results on the optimal pricing strategies. We introduce a linear programming formulation to numerically obtain the system's optimal decisions. We particularly investigate how production rate holding cost procurement price and demand variabilities customers' price sensitivity and interaction between supply and demand processes affect the system's performance measures through an extensive numerical study. Furthermore our numerical results demonstrate the potential benefits of using dynamic pricing compared to that of static pricing. In particular the profit enhancement being achieved with dynamic pricing can reach up to 15% depending on the problem parameters. © 2023 Elsevier B.V. All rights reserved.
Description
Keywords
Dynamic Programming, Linear Programming Dynamic Pricing, Manufacturing Systems, Markov Modelling, Stochastic Models, Continuous Time Systems, Costs, Investments, Linear Programming, Markov Processes, Profitability, Stochastic Models, Stochastic Systems, Dynamic Pricing, Environmental Fluctuations, Linear Programming Dynamic Pricing, Linear-programming, Make-to-stock Systems, Markov Modeling, Pricing Decision, Procurement Decisions, Procurement Strategy, Stochastic-modeling, Dynamic Programming, Continuous time systems, Costs, Investments, Linear programming, Markov processes, Profitability, Stochastic models, Stochastic systems, Dynamic pricing, Environmental fluctuations, Linear programming dynamic pricing, Linear-programming, Make-to-stock systems, Markov modeling, Pricing decision, Procurement decisions, Procurement strategy, Stochastic-modeling, Dynamic programming, 670, 330
Fields of Science
0209 industrial biotechnology, 0211 other engineering and technologies, 02 engineering and technology
Citation
WoS Q
Scopus Q

OpenCitations Citation Count
9
Source
International Journal of Production Research
Volume
61
Issue
Start Page
8423
End Page
8450
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Citations
CrossRef : 12
Scopus : 12
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Mendeley Readers : 42
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