Demand Management in Decentralized Logistics Systems and Supply Chains

We analyze issues arising from demand management in decentralized decision-making environments. We consider logistics systems and supply chains, where companies' operations are handled with independent entities whose decisions affect the performance of the overall system. In the first study, we focus on a logistics system in the sea cargo industry, where demand is booked by independent sales agents, and the agents' capacity limits and sales incentives are determined by a central headquarters. We develop models for the central headquarters to analyze and optimize capacity allocation and sales incentives to improve the performance of the decentralized system. We use network flow problems to incorporate agent behavior in our models, and we link these individual problems through an overall optimization problem that determines the capacity limits. We prove a worst-case bound on the decentralized system performance and show that the choice of sales incentive impacts the performance. In the second study, we focus on supply chains in the automotive industry, where decentralization occurs as a result of the non-direct sales channels of the auto manufacturers. Auto manufacturers can affect their demand through sales promotions. We use a game theoretical model to examine the impact of retailer incentive and customer rebate promotions on the manufacturer's pricing and the retailer's ordering/sales decisions. We consider several models with different demand characteristics and information asymmetry between the manufacturer and a price discriminating retailer. We characterize the subgame-perfect Nash equilibrium decisions and determine which promotion would benefit the manufacturer under which market conditions. We find that the retailer incentives are preferred when demand is known. On the other hand, when demand is highly uncertain the manufacturer is better off with customer rebates. We extend this research by analyzing a competitive setting with two manufacturers and two retailers, where the manufacturers' promotions vary between retailer incentives and customer rebates. We find an equilibrium outcome where customer rebates reduce the competitor's profits to zero. We observe in numerical examples that the manufacturers are able to increase their sales and profits with retailer incentives, although this can be at the expense of the retailers' profits under some situations.Ph.D.Committee Chair: Swann, Julie; Committee Member: Ergun, Ozlem; Committee Member: Ferguson, Mark; Committee Member: Griffin, Paul; Committee Member: Keskinocak, Pina

The design of green supply chains under carbon policies: A literature review of quantitative models

Carbon footprinting of products and services is getting increasing attention due to the growing emphasis on carbon related policies in many countries. As a result, many enterprises are focusing on the design of green supply chains (GSCs) with research on supply chains (SCs) focused not only on cost efficiency, but also on its environmental consequences. The review presented in this paper focuses on the implications of carbon policies on SCs. The concept of content analysis is used to retrieve and analyze the information regarding drivers (carbon policies), actors (for example, manufacturers and retailers), methodologies (mathematical modeling techniques), decision-making contexts (such as, facility location and order quantity), and emission reduction opportunities. The review shows a lack of emissions analysis of SCs that face carbon policies in different countries. The research also focuses on the design of carbon policies for emissions reduction in different operating situations. Some possible research directions are also discussed at the end of this review.A NPRP award NPRP No.5-1284-5-198 from the Qatar National Research Fund (a member of The Qatar Foundation).Scopu

Scenario analysis report with policy recommendations: An assessment of sustainability, resilience, efficiency and fairness and effective chain relationships in VALUMICS case studies : Deliverable 8.4

This is an open access article distributed under the Creative Commons Attribution License, to view a copy of the license, see: https://creativecommons.org/licenses/by/4.0/. The final version of this report is available at https://doi.org/10.5281/zenodo.6534011The functioning of food value chains entails a complex organisation from farm to fork which is characterised by various governance forms and externalities which have shaped the overall food system. VALUMICS food value chain case studies: wheat to bread, dairy cows to milk, beef cattle to steak, farmed salmon to fillets and tomato to processed tomato were selected to enable explorative and empirical analysis to better understand the functioning of the food system and, to identify the main challenges that need to be addressed to improve sustainability, integrity, resilience, and fairness of European food chains. The VALUMICS system analysis was executed through four operational phases starting with Groundwork & analysis including mapping specific attributes and impacts of food value chains and their externalities. This was followed by Case study baseline analysis, which provided input to the third phase on Modelling and exploration of future scenarios and finally Policy and synthesis of the overall work. This report is an overall synthesis of the VALUMICS results as follows: • Key findings from the VALUMICS project on the functioning of European food value chains and their impacts on more sustainable, resilient, fairer, and transparent food system are summarised through a compilation of 25 Research Findings and Policy Briefs. • By highlighting the major contributions from the research activities throughout the four phases of the VALUMICS project, this report delivers an assessment of various factors influencing sustainability, resilience, efficiency and fairness and effective chain relationships of different food value chains, and their determinants. • The synthesis of the outcome allows the identification of opportunities and challenges characterising the functioning of food supply chains, and thus, the prospects and potentials for strengthening the EU food sector

Agricultural Value Chains in India

This open access book provides a clear holistic conceptual framework of CISS-F (competitiveness, inclusiveness, sustainability, scalability and access to finance) to analyse the efficiency of value chains of high value agricultural commodities in India. It is based on the understanding that agriculture is an integrated system that connects farming with logistics, processing and marketing. Farmer’s welfare being central to any agricultural policy makes it very pertinent to study how a value chain works and can be strengthened further to realize this policy goal. This book adds value to the existing research by studying the value chains end-to-end across a wide spectrum of agricultural commodities with the holistic lens of CISS-F. It is not enough that a value chain is competitive but not inclusive or it is competitive and inclusive but not sustainable. The issue of scalability is very critical to achieve macro gains in terms of greater farmer outreach and sectoral growth. The research undertaken here brings out some very useful insights for policymaking in terms of what needs to be done better to steer the agricultural value chains towards being more competitive, inclusive, sustainable and scalable. The value chain specific research findings help draw very nuanced policy recommendations as well as present a big picture of the future direction of policy making in agriculture

Agricultural Value Chains in India

This open access book provides a clear holistic conceptual framework of CISS-F (competitiveness, inclusiveness, sustainability, scalability and access to finance) to analyse the efficiency of value chains of high value agricultural commodities in India. It is based on the understanding that agriculture is an integrated system that connects farming with logistics, processing and marketing. Farmer’s welfare being central to any agricultural policy makes it very pertinent to study how a value chain works and can be strengthened further to realize this policy goal. This book adds value to the existing research by studying the value chains end-to-end across a wide spectrum of agricultural commodities with the holistic lens of CISS-F. It is not enough that a value chain is competitive but not inclusive or it is competitive and inclusive but not sustainable. The issue of scalability is very critical to achieve macro gains in terms of greater farmer outreach and sectoral growth. The research undertaken here brings out some very useful insights for policymaking in terms of what needs to be done better to steer the agricultural value chains towards being more competitive, inclusive, sustainable and scalable. The value chain specific research findings help draw very nuanced policy recommendations as well as present a big picture of the future direction of policy making in agriculture

Strategic Inventory and Supply Chain Behavior

Based on a serial supply chain model with 2-periods and price-sensitive demand, we present the first experimental test of the effect of strategic inventories on supply chain performance. In theory, if holding costs are low enough, the buyer builds up a strategic inventory (even if no operational reasons for stock-holding exist) to limit the supplier\u27s market power, to increase the own profit share, and to enhance the overall supply chain performance. The supplier anticipates the effect of the strategic inventory and differentiates prices to capture a part of the increased supply chain profits. Our results show that the positive effects of strategic inventories are even more pronounced than theoretically predicted, because strategic inventories empower buyers to reduce payoff inequalities and suppliers exhibit a willingness to reduce inequalities as long as their payoff remains above a certain threshold. Overall, strategic inventories have a double positive effect, a strategic and a behavioral, both reducing the average wholesale prices and damping the double marginalization effect and the latter leading to more equitable payoffs

Blockchain for supply chain traceability and anticounterfeiting: the oracles’ enabling role

Blockchain and physical oracles in the Collectible Industry. Supply chain fairness and bargaining power in agriculture supply chain: the blockchain effect. Unlocking the Blockchain Potentials through Oracles: Empirical Evidences on Supply Chain Challenges and Performance

Demand Learning in Two Operations Models

The rapid advance of information technologies largely facilitated firms' data-driven decision making. Particularly, in operations management practices, firms could continuously collect information to refine their demand knowledge, and integrate this process into their relevant operational decisions, e.g. pricing, inventory, and market entry, known as demand learning. Demand learning in complex business systems is often tangled with complex strategic interactions, thus requiring a deep understanding of how it affects the strategic relationship among players in various business setups. This thesis aims to contribute to the demand-learning literature by studying the strategic interactions in two different business relationships, one vertical and the other horizontal. First, I consider the interactions between a retailer and a supplier in a supply chain subject to demand censorship (i.e. unobservable lost sales) when the retailer is engaging in demand learning through dynamic inventory experimentation. I study the supplier's optimal wholesale prices when the retailer is in three different situations, and find that the retailer and the supply chain may actually benefit from either myopia or censorship in contrast to the existing results, due to the supplier's different collaborative or exploitative responses to the retailer's "willingness to learn". I also identify that, with demand censorship, the collaborative behavior between the players for information acquisition may improve the system's performance. Second, I study an online retail platform's learning process and entry policies as well as the independent seller's pricing distortion behavior to slow down this process, motivated by Amazon.com's unique dual role as both a marketplace and a merchant that allows it to use the transaction data generated by its third-party sellers to decide if to sell the same product itself. I developed a Bayesian statistical model for the platform's demand learning, proposed two types of heuristic entry policies for the platform owner. The model predicts a pattern of price distortion, and describes the product offering choices made by the independent seller. These could potentially serve as testable results for empirical studies