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دسته بندی:
مدیریت بازاریابی - Marketing Management
سال انتشار:
2020
عنوان انگلیسی مقاله:
Enhancing supply chain production-marketing planning with geometric multivariate demand function (a case study of textile industry)
ترجمه فارسی عنوان مقاله:
تقویت برنامه ریزی تولید و بازاریابی زنجیره تأمین با عملکرد هندسی تقاضای چند متغیره (مطالعه موردی صنعت نساجی)
منبع:
Sciencedirect - Elsevier - Computers & Industrial Engineering, 140 (2020) 106220. doi:10.1016/j.cie.2019.106220
نویسنده:
R. Ghasemy Yaghin
چکیده انگلیسی:
In this paper, a multi-period, multi-product, multi-site, multi-sales channel aggregate production planning
problem including ordering preferences is presented in an integrated two-echelon supply chain to avoid the suboptimality
caused by separate, sequential decisions of production and the marketing/retailing chain. Each
customer demand class is affected by price, marketing expenditures and product quality involving customer
willingness-to-pay. In addition, the immigration of customers between submarkets (i.e. cannibalization) is
considered in the market-segmented environment due to imperfect segmentation. This research develops a
geometric programming model to formulate the issue of joint price differentiation and multi-site aggregate
production planning decisions by maximizing the total profit of the supply chain. To tackle the model and obtain
solutions, we tailor an efficient analytical solution procedure to convert the original highly non-linear programming
model into a convex programming equivalent. Finally, a numerical study of garment supply chain is
presented to demonstrate the performance of the model and solution approaches. The research findings indicate
a positive relationship between the scaling constant of price-dependent demand and the total profit rate.
Moreover, as price gaps grow, the utility of price differentiation is decreased.
Keywords: Multi-site production planning | Integrated production-marketing | Price differentiation | Geometric programming | Non-linear programming
قیمت: رایگان
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