4 research outputs found

    Optimal pricing strategy:How to sell to strategic consumers?

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    Technological advances are preparing consumers to plan their purchases strategically. Selling to strategic consumers at a fixed price forgoes the profit from salvaging inventory, whereas high-low pricing, as a ubiquitous pricing strategy, is costly due to the offered markdown discount. This research explores the overall impact of consumer's strategic buying behaviour on a pricing strategy, and identifies conditions where fixed pricing, strategic high pricing, or high-low pricing is the best approach by analytically comparing the profits of the three pricing strategies. Our results show that high-low pricing is appropriate only if the offered markdown discount is relatively small. If strategic consumers have a small population and the needed markdown discount is relatively large, retailers can ignore strategic buying behaviour and sell products at a fixed price. Our results emphasize that the markdown discount for clearance sales and the market structure of heterogeneous consumers play vital roles in determining the optimal pricing strategy

    Quality and Operations Management in Food Supply Chains: A Literature Review

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    We present a literature review on quality and operations management problems in food supply chains. In food industry, the quality of the food products declines over time and should be addressed in the supply chain operations management. Managing food supply chains with operations management methods not only generates economic benefit, but also contributes to environmental and social benefits. The literature on this topic has been burgeoning in the past few years. Since 2005, more than 100 articles have been published on this topic in major operations research and management science journals. In this literature review, we concentrate on the quantitative models in this research field and classify the related articles into four categories, that is, storage problems, distribution problems, marketing problems, and food traceability and safety problems. We hope that this review serves as a reference for interested researchers and a starting point for those who wish to explore it further

    ํŒ๋งค์ด‰์ง„์„ ๋„์ž…ํ•œ ์ˆ˜์š” ๋ถˆํ™•์‹ค์„ฑ ์žฌ๊ณ ๊ด€๋ฆฌ ๋ชจํ˜•

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์‚ฐ์—…๊ณตํ•™๊ณผ, 2020. 8. ๋ฌธ์ผ๊ฒฝ.As the globalization of markets accelerates competition among companies, sales promotion, which refers to short-term incentives promoting sales of products or services, plays a prominent role. Although there are various types of sales promotions, such as price reduction, buy-x-get-y-free, and trade-in program, the common purpose is to induce the purchase of customers by offering benefits. This successful strategy has caught the attention of researchers, including operations management and supply chain management. Thus, various studies have been conducted to examine strategies for ongoing operations and to demonstrate the effects of the sales promotion, which are based on the strategic level. However, research at the tactical or operational level has been conducted insufficiently. This dissertation examines the inventory models considering (i) markdown sale, (ii) buy one get one free (BOGO), and (iii) trade-in program. First, the newsvendor model is considered. By introducing the decision variable, which represents the start time of markdown sale, the retailer can obtain the optimal combination of the start time of a markdown sale and an order quantity. Under certain conditions in a decentralized system, however, the start time of a markdown sale where the retailer obtains the highest profit is the least profitable for the manufacturer. To avoid irrational ordering behavior by a retailer against a manufacturer, a revenue-sharing contract is proposed. Second, the mobile application, ``My Own Refrigerator'', is considered in the inventory model. It enables customers to store BOGO products in their virtual storage for later use. That is, customers can drop by the store to pick up the extra freebies in the future. The promotion involves a high degree of uncertainty regarding the revisiting date because customers who buy the product do not need to take both products on the day of purchase. To deal with this uncertainty, we propose a robust multiperiod inventory model by addressing the approximation of a multistage stochastic optimization model. Third, the trade-in program is considered. It is one of the sales promotions that companies collect used old-generation products from customers and provide them with new-generation products at a discount price. It also helps to acquire the additional products which are required for the refurbishment service. A multiperiod stochastic inventory model based on the closed-loop supply chain system is proposed by incorporating the trade-in program and refurbishment service simultaneously. The stochastic optimization model is approximated to the robust counterpart, which features a deterministic second-order cone program.์‹œ์žฅ์˜ ์„ธ๊ณ„ํ™”์— ๋”ฐ๋ฅธ ๊ธฐ์—… ๊ฐ„์˜ ๊ฒฝ์Ÿ์ด ๊ฐ€์†ํ™”๋จ์— ๋”ฐ๋ผ, ๋‹จ๊ธฐ ์ธ์„ผํ‹ฐ๋ธŒ๋ฅผ ํ†ตํ•ด ๊ณ ๊ฐ์˜ ์ œํ’ˆ ๋˜๋Š” ์„œ๋น„์Šค ๊ตฌ๋งค๋ฅผ ์œ ๋„ํ•˜๋Š” ํŒ๋งค์ด‰์ง„์˜ ์—ญํ• ์ด ์ค‘์š”ํ•ด์กŒ๋‹ค. ๊ฐ€๊ฒฉ ์ธํ•˜, ํ–‰์‚ฌ์ƒํ’ˆ ์ฆ์ •, ํŠธ๋ ˆ์ด๋“œ์ธํ”„๋กœ๊ทธ๋žจ๊ณผ ๊ฐ™์€ ๋‹ค์–‘ํ•œ ์œ ํ˜•์˜ ํŒ๋งค์ด‰์ง„ ์ „๋žต์ด ์กด์žฌํ•˜์ง€๋งŒ, ๊ณตํ†ต๋œ ์ฃผ์š” ๋ชฉ์ ์€ ๊ธฐ์—…์ด ๊ณ ๊ฐ์—๊ฒŒ ํ˜œํƒ์„ ์ œ๊ณตํ•˜์—ฌ ๊ณ ๊ฐ์˜ ์ˆ˜์š”๋ฅผ ์ฆ๋Œ€์‹œํ‚ค๋Š” ๊ฒƒ์ด๋‹ค. ํŒ๋งค์ด‰์ง„์˜ ์„ฑ๊ณต์ ์ธ ์ „๋žต์€ ๊ฒฝ์˜๊ณผํ•™ ๋˜๋Š” ๊ณต๊ธ‰๋ง๊ด€๋ฆฌ ๋ถ„์•ผ๋ฅผ ํฌํ•จํ•œ ๊ด€๋ จ ํ•™๊ณ„์˜ ๊ด€์‹ฌ์„ ์ด๋Œ์—ˆ๋‹ค. ์ง€์†์ ์ธ ์šด์˜์„ ์œ„ํ•œ ์ „๋žต์„ ๊ฒ€ํ† ํ•˜๊ณ  ์ „๋žต์  ์ˆ˜์ค€ ๊ณ„ํš์„ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•˜๋Š” ํŒ๋งค ์ด‰์ง„์˜ ํšจ๊ณผ๋ฅผ ์ž…์ฆํ•˜๊ธฐ ์œ„ํ•œ ๋‹ค์–‘ํ•œ ์—ฐ๊ตฌ๊ฐ€ ์ˆ˜ํ–‰๋˜์—ˆ์Šต๋‹ˆ๋‹ค. ํ•˜์ง€๋งŒ ์šด์˜ ์ˆ˜์ค€์˜ ์†Œ๋งค์—…์ฒด ์ž…์žฅ์—์„œ์˜ ์—ฐ๊ตฌ๋Š” ๋ฏธํกํ•œ ์‹ค์ •์ด๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” (i) ๋งˆํฌ ๋‹ค์šด (ii) buy one get one free (BOGO), ๋ฐ (iii) ํŠธ๋ ˆ์ด๋“œ์ธํ”„๋กœ๊ทธ๋žจ์„ ๊ณ ๋ คํ•œ ์žฌ๊ณ ๊ด€๋ฆฌ๋ชจํ˜•์„ ๋‹ค๋ฃฌ๋‹ค. ๋จผ์ €, ์‹ ๋ฌธ๊ฐ€ํŒ์› ๋ชจํ˜•์— ๋งˆํฌ ๋‹ค์šด ์‹œ์ž‘ ์‹œ์ ์„ ๋‚˜ํƒ€๋‚ด๋Š” ๊ฒฐ์ • ๋ณ€์ˆ˜๋ฅผ ๋„์ž…ํ•˜์—ฌ ์ตœ์ ์˜ ๋งˆํฌ ๋‹ค์šด ์‹œ์ž‘ ์‹œ์ ๊ณผ ์ฃผ๋ฌธ๋Ÿ‰์˜ ์กฐํ•ฉ์„ ์ œ๊ณตํ•˜๋Š” ๋ชจํ˜•์„ ์ œ์•ˆํ•œ๋‹ค. ๋ถ„์‚ฐ ์‹œ์Šคํ…œ์˜ ํŠน์ • ์กฐ๊ฑด์—์„œ๋Š” ์†Œ๋งค์—…์ž๊ฐ€ ๊ฐ€์žฅ ๋†’์€ ์ด์ต์„ ์–ป๋Š” ์‹œ์ ์ด ์ œ์กฐ์—…์ž์—๊ฒŒ ๋‚ฎ์€ ์ˆ˜์ต์„ฑ์„ ์•ผ๊ธฐํ•  ์ˆ˜ ์žˆ๋‹ค. ๋”ฐ๋ผ์„œ ๋ณธ ์—ฐ๊ตฌ๋Š” ์ œ์กฐ์—…์ž์— ๋Œ€ํ•œ ์†Œ๋งค์—…์ž์˜ ๋น„ํ•ฉ๋ฆฌ์  ์ฃผ๋ฌธ์„ ๋ง‰๊ธฐ ์œ„ํ•œ ์ด์ต๋ถ„๋ฐฐ๊ณ„์•ฝ์„ ์ œ์•ˆํ•œ๋‹ค. ์ด์ต๋ถ„๋ฐฐ๊ณ„์•ฝ์„ ํ†ตํ•œ ์ค‘์•™์ง‘๊ถŒํ™” ์‹œ์Šคํ…œ์€ ๋ถ„์‚ฐ ์‹œ์Šคํ…œ์—์„œ ์–ป์€ ์ด์ต์— ๋น„ํ•ด ์†Œ๋งค์—…์ž์™€ ์ œ์กฐ์—…์ž์˜ ์ด์ต์„ ํ–ฅ์ƒ์‹œํ‚ด์„ ์ˆ˜์น˜์‹คํ—˜์„ ํ†ตํ•ด ํ™•์ธํ•˜์˜€๋‹ค. ๋‘˜์งธ, ๋ชจ๋ฐ”์ผ ์–ดํ”Œ๋ฆฌ์ผ€์ด์…˜ ``๋‚˜๋งŒ์˜ ๋ƒ‰์žฅ๊ณ ''๋ฅผ ๊ณ ๋ คํ•œ ์žฌ๊ณ ๋ชจํ˜•์„ ๊ณ ๋ คํ•œ๋‹ค. ์ด ์•ฑ์„ ํ†ตํ•ด BOGO ํ–‰์‚ฌ์ œํ’ˆ์„ ๊ตฌ๋งคํ•œ ๊ณ ๊ฐ์€ ์ฆ์ •ํ’ˆ์„ ๊ตฌ๋งค ๋‹น์ผ ๋‚  ๊ฐ€์ ธ๊ฐ€์ง€ ์•Š๊ณ  ๋ฏธ๋ž˜์— ์žฌ๋ฐฉ๋ฌธํ•˜์—ฌ ์ˆ˜๋ นํ•  ์ˆ˜ ์žˆ๋Š” ํ˜œํƒ์„ ๋ฐ›๋Š”๋‹ค. ํ•˜์ง€๋งŒ ์†Œ๋งค์—…์ž ์ž…์žฅ์—์„œ๋Š” ๊ณ ๊ฐ์ด ์ฆ์ •ํ’ˆ์„ ์–ธ์ œ ์ˆ˜๋ นํ•ด ๊ฐˆ ์ง€์— ๋Œ€ํ•œ ๋ถˆํ™•์‹ค์„ฑ์ด ์กด์žฌํ•˜๋ฉฐ ์ด๋Š” ๊ธฐ์กด์˜ ์žฌ๊ณ ๊ด€๋ฆฌ ์šด์˜๋ฐฉ์‹์—๋Š” ํ•œ๊ณ„์ ์ด ์žˆ์Œ์„ ์‹œ์‚ฌํ•œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๊ณ ๊ฐ์˜ ์žฌ๋ฐฉ๋ฌธ์— ๋Œ€ํ•œ ๋ถˆํ™•์‹ค์„ฑ์„ ๊ณ ๋ คํ•œ ๋ณต์ˆ˜๊ธฐ๊ฐ„ ์ถ”๊ณ„๊ณ„ํš ์žฌ๊ณ ๋ชจํ˜•์„ ์ˆ˜๋ฆฝํ•˜๋ฉฐ ์ด๋ฅผ ํšจ์œจ์ ์œผ๋กœ ๊ณ„์‚ฐํ•˜๊ธฐ ์œ„ํ•œ ๊ฐ•๊ฑด์ตœ์ ํ™” ๋ชจํ˜•์œผ๋กœ ๊ทผ์‚ฌํ™”ํ•˜์˜€๋‹ค. ์…‹์งธ, ๋ฆฌํผ์„œ๋น„์Šค์™€ ํŠธ๋ ˆ์ด๋“œ์ธํ”„๋กœ๊ทธ๋žจ์„ ๊ณ ๋ คํ•œ ํํšŒ๋กœ ๊ณต๊ธ‰๋ง ์‹œ์Šคํ…œ ๊ธฐ๋ฐ˜์˜ ๋ณต์ˆ˜๊ธฐ๊ฐ„ ์žฌ๊ณ ๊ด€๋ฆฌ๋ชจํ˜•์„ ์ œ์•ˆํ•œ๋‹ค. ์‹ ์„ธ๋Œ€ ์ œํ’ˆ, ๋ฆฌํผ์„œ๋น„์Šค ๋ฐ ํŠธ๋ ˆ์ด๋“œ์ธํ”„๋กœ๊ทธ๋žจ์— ๋Œ€ํ•œ ์„ธ ๊ฐ€์ง€ ์œ ํ˜•์˜ ๋ถˆํ™•์‹คํ•œ ์ˆ˜์š”์— ๋Œ€ํ•œ ์ƒ๊ด€๊ด€๊ณ„๋ฅผ ๋ฐ˜์˜ํ•จ์— ๋”ฐ๋ผ ๋ณต์ˆ˜๊ธฐ๊ฐ„ ์ถ”๊ณ„๊ณ„ํš ์žฌ๊ณ ๋ชจํ˜•์ด ์ˆ˜๋ฆฝ๋œ๋‹ค. ๋ณต์ˆ˜๊ธฐ๊ฐ„ ์ถ”๊ณ„๊ณ„ํš ์žฌ๊ณ ๋ชจํ˜•์˜ ๊ณ„์‚ฐ์ด ์–ด๋ ต๋‹ค๋Š” ํ•œ๊ณ„๋ฅผ ๊ทน๋ณตํ•˜๊ณ ์ž ๊ฐ•๊ฑด์ตœ์ ํ™” ๋ชจํ˜•์œผ๋กœ ๊ทผ์‚ฌํ™”ํ•˜์˜€๋‹ค.Chapter 1 Introduction 1 1.1 Sales promotion 1 1.2 Inventory management 3 1.3 Research motivations 6 1.4 Research contents and contributions 8 1.5 Outline of the dissertation 10 Chapter 2 Optimal Start Time of a Markdown Sale Under a Two-Echelon Inventory System 11 2.1 Introduction and literature review 11 2.2 Problem description 17 2.3 Analysis of the decentralized system 21 2.3.1 Newsvendor model for a retailer 21 2.3.2 Solution procedure for an optimal combination of the start time of the markdown sale and the order quantity 25 2.3.3 Profi t function of a manufacturer 25 2.3.4 Numerical experiments of the decentralized system 27 2.4 Analysis of a centralized system 35 2.4.1 Revenue-sharing contract 35 2.4.2 Numerical experiments of the centralized system 38 2.5 Summary 40 2.5.1 Managerial insights 41 Chapter 3 Robust Multiperiod Inventory Model with a New Type of Buy One Get One Promotion: "My Own Refrigerator" 43 3.1 Introduction and literature review 43 3.2 Problem description 51 3.2.1 Demand modeling 52 3.2.2 Sequences of the ordering decision 54 3.3 Mathematical formulation of the IMMOR 56 3.3.1 Mathematical formulation of the IMMOR under the deterministic demand 58 3.3.2 Mathematical formulation of the IMMOR under the stochastic demand 58 3.3.3 Distributionally robust optimization approach for the IMMOR 60 3.4 Computational experiments 76 3.4.1 Experiment 1: tractability of the RIMMOR 77 3.4.2 Experiment 2: robustness of the RIMMOR 78 3.4.3 Experiment 3: e ect of duration of the expiry date under the different customers' revisiting propensities 78 3.5 Summary 83 3.5.1 Managerial insights 83 Chapter 4 Robust Multiperiod Inventory Model Considering Refurbishment Service and Trade-in Program 85 4.1 Introduction 85 4.2 Literature review 91 4.2.1 Effects of the trade-in program and strategic-level decisions for the trade-in program 91 4.2.2 Inventory or lot-sizing model in a closed-loop supply chain system 94 4.2.3 Distinctive features of this research 97 4.3 Problem description 100 4.3.1 Demand modeling 103 4.3.2 Decision of the inventory manager 105 4.4 Mathematical formulation 108 4.4.1 Mathematical formulation of the IMRSTIP under the deterministic demand model 108 4.4.2 Mathematical formulation of the IMRSTIP under the stochastic demand model 110 4.4.3 Distributionally robust optimization approach for the IMRSTIP 111 4.5 Computational experiments 125 4.5.1 Demand process 125 4.5.2 Experiment 1: tractability of the RIMRSTIP 128 4.5.3 Experiment 2: approximation error from the expected value given perfect information 129 4.5.4 Experiment 3: protection against realized uncertain factors 130 4.5.5 Experiment 4: di erences between modeling demands from VARMA and ARMA 131 4.5.6 Experiments 5 and 6: comparisons of backlogged refurbishment service with or without trade-in program 133 4.6 Summary 136 Chapter 5 Conclusions 138 5.1 Summary 138 5.2 Future research 140 Bibliography 142 Chapter A 160 A.1 160 A.2 163 A.3 163 A.4 164 A.5 165 A.6 166 Chapter B 168 B.1 168 B.2 171 B.3 172 Chapter C 174 C.1 174 C.2 174 ๊ตญ๋ฌธ์ดˆ๋ก 179Docto
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