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Product Return Management at Large Scale: Effective Joint Decision-Making Supported by Comprehensive Performance Metrics
Product Return Management at Large Scale: Effective Joint Decision-Making Supported by Comprehensive Performance Metrics
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211153017
- ISBN
- 9798384046042
- DDC
- 510
- 저자명
- Liang, Alys.
- 서명/저자
- Product Return Management at Large Scale: Effective Joint Decision-Making Supported by Comprehensive Performance Metrics
- 발행사항
- [Sl] : University of Michigan, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 238 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-03, Section: B.
- 주기사항
- Advisor: Jasin, Stefanus;Uichanco, Joline.
- 학위논문주기
- Thesis (Ph.D.)--University of Michigan, 2024.
- 초록/해제
- 요약Product returns have imposed significant challenges on supply chain management. Regarded as a "ticking time bomb", they have cost retailers hundreds of billions of dollars in the US. The goal of this thesis is to understand the impact of returns on the retail industry and to enhance operational strategies to increase the efficiency of supply chain management. Generally speaking, there are three features of returns: a high return rate, labor-intensive return processing procedures, and a high restocking rate, which are typical in industries like fashion. These features introduce three primary challenges. First, since a significant portion of inventory consists of restocked returns from past demands, classical frameworks, such as the Newsvendor Logic, might not work. Secondly, the additional randomness introduced by returns (i.e., purchases made on one day can be returned after a random number of days) makes the inventory path more volatile and unpredictable, which imposes difficulties in capacity planning. To guarantee the service level in the presence of such volatility, retailers usually choose to keep a high safety stock, resulting in tremendous leftover inventory after the selling season, a significant portion of which goes directly to landfill. There are two complementary approaches to tackling high-volume returns: 1) reducing the return rate without compromising demand, and 2) enhancing operational efficiency in the presence of returns. Ideally, the proposed policy can also help reduce the total landfill contribution. Abundant literature has revolved around the first approach, and my PhD dissertation complements the existing literature by focusing on the second approach. The goal is to develop implementable strategies to enhance profitability, stabilize inventory trajectory, and reduce waste. Specifically, we aim to answer: in the presence of large-volume returns, a) how to jointly coordinate various operational decisions, and b) how to assess the performance of the proposed policy. The thesis is structured around three essays (Chapters 2-4). Chapter 2 focuses on joint inventory and pricing policy. Chapter 3 and Chapter 4 focus on the joint inventory and assortment policy under dynamic substitution.
- 일반주제명
- Mathematics
- 일반주제명
- Applied mathematics
- 키워드
- Probability
- 키워드
- Randomness
- 기타저자
- University of Michigan Business Administration
- 기본자료저록
- Dissertations Abstracts International. 86-03B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
008250123s2024 us c eng d■001000017164565
■00520250211153017
■006m o d
■007cr#unu||||||||
■020 ▼a9798384046042
■035 ▼a(MiAaPQ)AAI31631546
■035 ▼a(MiAaPQ)umichrackham005700
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a510
■1001 ▼aLiang, Alys.
■24510▼aProduct Return Management at Large Scale: Effective Joint Decision-Making Supported by Comprehensive Performance Metrics
■260 ▼a[Sl]▼bUniversity of Michigan▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a238 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-03, Section: B.
■500 ▼aAdvisor: Jasin, Stefanus;Uichanco, Joline.
■5021 ▼aThesis (Ph.D.)--University of Michigan, 2024.
■520 ▼aProduct returns have imposed significant challenges on supply chain management. Regarded as a "ticking time bomb", they have cost retailers hundreds of billions of dollars in the US. The goal of this thesis is to understand the impact of returns on the retail industry and to enhance operational strategies to increase the efficiency of supply chain management. Generally speaking, there are three features of returns: a high return rate, labor-intensive return processing procedures, and a high restocking rate, which are typical in industries like fashion. These features introduce three primary challenges. First, since a significant portion of inventory consists of restocked returns from past demands, classical frameworks, such as the Newsvendor Logic, might not work. Secondly, the additional randomness introduced by returns (i.e., purchases made on one day can be returned after a random number of days) makes the inventory path more volatile and unpredictable, which imposes difficulties in capacity planning. To guarantee the service level in the presence of such volatility, retailers usually choose to keep a high safety stock, resulting in tremendous leftover inventory after the selling season, a significant portion of which goes directly to landfill. There are two complementary approaches to tackling high-volume returns: 1) reducing the return rate without compromising demand, and 2) enhancing operational efficiency in the presence of returns. Ideally, the proposed policy can also help reduce the total landfill contribution. Abundant literature has revolved around the first approach, and my PhD dissertation complements the existing literature by focusing on the second approach. The goal is to develop implementable strategies to enhance profitability, stabilize inventory trajectory, and reduce waste. Specifically, we aim to answer: in the presence of large-volume returns, a) how to jointly coordinate various operational decisions, and b) how to assess the performance of the proposed policy. The thesis is structured around three essays (Chapters 2-4). Chapter 2 focuses on joint inventory and pricing policy. Chapter 3 and Chapter 4 focus on the joint inventory and assortment policy under dynamic substitution.
■590 ▼aSchool code: 0127.
■650 4▼aMathematics
■650 4▼aApplied mathematics
■653 ▼aReverse logistics
■653 ▼aStochastic optimization
■653 ▼aProbability
■653 ▼aSupply chain management
■653 ▼aRandomness
■690 ▼a0310
■690 ▼a0796
■690 ▼a0405
■690 ▼a0454
■690 ▼a0364
■71020▼aUniversity of Michigan▼bBusiness Administration.
■7730 ▼tDissertations Abstracts International▼g86-03B.
■790 ▼a0127
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17164565▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
