Modeling repair demand in existence of a nonstationary installed base
| Autor: | Mustafa Hekimoğlu, Deniz Karlı |
|---|---|
| Přispěvatelé: | Işık Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Matematik Bölümü, Işık University, Faculty of Engineering and Natural Sciences, Department of Mathematics, Karlı, Deniz |
| Jazyk: | angličtina |
| Rok vydání: | 2023 |
| Předmět: |
System
History Economics and Econometrics Polymers and Plastics Nonstationary demand Life cycle Installed base Original equipment manufacturers Stock control Management Science and Operations Research Industrial and Manufacturing Engineering Stochastic methods Information Customerbase Profitability Business and International Management Investments Upgradability Inventory control Model repair Stochastic systems Inventory theory and control Inventory theory Non-stationary demand General Business Management and Accounting Stochastic control systems Spare parts Spare parts logistics Lifetime extension Decision making Repair |
| Popis: | This research is funded by TUBITAK 3501 National Young Researchers Career Development Program with the grant number 118M477 . Authors are thankful to Ali Kök for his help in organizing our code base that we use in our numerical experiments. Life cycles of products consist of 3 phases, namely growth, maturity, and decline phases. Modeling repair demand is particularly difficult in the growth and decline stages due to nonstationarity. In this study, we suggest respective stochastic models that capture the dynamics of repair demand in these two phases. We apply our theory to two different operations management problems. First, using the moments of spare parts demand, we suggest an algorithm that selects a parametric distribution from the hypergeometric family (Ord, 1967) for each period in time. We utilize the algorithm in a single echelon inventory control problem. Second, we focus on investment decisions of Original Equipment Manufacturers (OEMs) to extend economic lifetimes of products with technology upgrades. Our results indicate that the second moment is sufficient for growing customer bases, whereas using the third moment doubles the approximation quality of theoretical distributions for a declining customer base. From a cost minimization perspective, using higher moments of demand leads to savings up to 13.6% compared to the single-moment approach. Also, we characterize the optimal investment policy for lifetime extension decisions from risk-neutral and risk-averse perspectives. We find that there exists a critical level of investment cost and installed base size for profitability of lifetime extension for OEMs. From a managerial point of view, we find that a risk-neutral decision maker finds the lifetime extension problem profitable. In contrast, even a slight risk aversion can make the lifetime extension decision economically undesirable. TUBITAK 3501 National Young Researchers Career Development Program Publisher's Version Q1 WOS:001015115000001 |
| Databáze: | OpenAIRE |
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