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  • 學位論文

供應鏈廠商考量市場與品質風險下利潤最大化與利潤分享問題之研究

Business Operating Strategy for Newsvendor Problems Considering Market Demand and Product Quality Risks

指導教授 : 徐旭昇

摘要


以往供應鏈廠商產品利潤研究中很少同時考量市場風險與品質風險,市場風險包括因市場需求不確定而導致之存貨或缺貨成本,品質風險則將不良產品送至顧客手中而產生之損失成本。本研究探討同時考量市場風險與品質風險之兩種模型:模式1是由下游廠商來承擔市場風險,決定產品售價,以下游自己利潤最大化為目標來決定產品訂購量;上游利潤則來自下游之訂購量收入扣除生產成本與產品品質風險,上游以實施貝氏方法之檢驗計劃來降低產品品質風險,包括考量檢驗誤差與無檢驗誤差兩種情況。在模式2中,下游只扮演銷售角色,利潤則來自市場賣價回扣與總銷售量;上游則決定產品市場售價,承擔市場風險與品質風險,產品訂購量則以雙方總利潤最大化為目標來做決定。本研究之目標在於比較兩模式之優劣,同時探討在模式2中,上下游該如何擬定一套售價回扣機制,以使兩方皆能公平共享多餘利潤。 實驗分析主要針對三個重要參數來做探討,分別是產品失效成本、市場需求標準差及產品剩餘價值。三個參數分析中得到以下幾個共同結論:模式2供應鏈之總利潤大於模式1供應鏈之總利潤;模式2之產品最佳樣本數大於模式1;有檢驗誤差之最佳樣本數皆會大於無檢驗誤差之最佳樣本數,並且有檢驗誤差的產品檢驗成本會大於無檢驗誤差的產品檢驗成本。當產品失效成本越高,產品檢驗 成本也就越高,相對的供應鏈之總利潤也會降低;當市場需求標準差越小則供應鏈之總利潤則會上升,模式2供應鏈之總利潤會非常接近模式1供應鏈之總利潤;當產品剩餘價值越高,產品最佳訂購量會提升,上游或下游廠商的損失也大為下降,導致供應鏈之總利潤也會隨著升高。

並列摘要


This thesis presents a new supply chain model which considers both market demand and product quality risks, and recommends win-win business collaboration approach based on vendor managed inventory (VMI) with consignment arrangement. The newsvendor problem is one of the classic problems in the literature on inventory management. This problem is a three-node supply chain consisting of a supplier, a buyer, and customers. Previous studies were focused on customer demand, supplier costs, and the buyer risk profile. This research presents two models. Model 1 is a traditional model, where the buyer determines market price and order quantity, but he will in turn bear market risks such as inventory cost and shortage cost. The supplier earns profit from the ordered quantity, but will also bear the cost of providing defective products to customers. In Model 2, the buyer will solely play the role of selling the products and earn a profit from the quantity of units sold and a pre-specified percentage of the selling price; in contrast, the supplier will decide the market price and production quantity, and bear market demand and product quality risks. It is assumed that the supplier will employ a Bayesian rectifying inspection plan for both models to minimize the product failure cost. In the study, it is shown that Model 2 is always superior to Model 1 in terms of the total profit earned by the supply chain when only market risks are considered. In addition, the win-win conditions for both supplier and buyer are derived from the comparisons between profits earned in Models 1 and 2, provided that both risks are considered. An example problem is presented to illustrate the two models, along with sensitivity analyses of three model parameters: product failure cost, standard deviation of market demand, and salvage value per unit of unsold product. Based on the numerical results, we conclude the following: (1) production quantity of Model 1 is never less than that of Model 2, and thereby the optimal sample size of Model 1 is never less than that of Model 2; (2) models with inspection errors will result in larger sample sizes and higher product quality cost; (3) total profit difference between Models 1 and 2 tends to be small when uncertainty (standard deviation) of market demand decreases; (4) order quantity of Model 1 or production quantity of Model 2 will increase as salvage value increases; (5) buyer loss in Model 1 or supplier loss in Model 2 will decrease when salvage value increases.

參考文獻


1. Abdul-Jalbar, B., Gutierrez, J., Puerto, J., Sicilia, J. (2003). “Policies for inventory/distribution systems:The effect of centralization vs. decentralization”, International. Journal. Production Economics 81-82:281-293.
2. Achabal, D.D., Mcintyre, S.H., Smith, S.A., Kalyanam, K. (2000). “A Decision Support System for Vendor Managed Inventory”, Journal of Retailing, 76(4):430-454.
3. Anderson, M. T., Greenberg, B. S. and Stokes, S. L. (2001). “Acceptance Sampling With Rectification When Inspection Errors Are Present”, Journal of Quality Technology, 33(4):493-505.
4. Barlow, R. E. and Zhang, X. (1987). “Bayesian Analysis of Inspection Sampling Procedures Discussed by Deming”, Journal of Statistical Planning and Inference, 16(3):285-296.
5. Bollapragada, S., Ganti, P., Osborn, M., Quaile, J. and Ramanathan, K. (2003). “GE’s Energy Rentals Business Automates Its Credit Assessment Process”, Interfaces, 33(5):45-56.

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