In-Season Transshipments Among Competitive Retailers

Cataloged from PDF version of article.A decentralized system of competing retailers that order and sell the same product in a sales season is studied. When a customer demand occurs at a stocked-out retailer, that retailer requests a unit to be transshipped from another retailer who charges a transshipment price. If this request is rejected, the unsatisfied customer may go to another retailer with a customer overflow probability. Each retailer decides on the initial order quantity from a manufacturer and on the acceptance/rejection of each transshipment request. For two retailers, we show that retailers' optimal transshipment policies are dynamic and characterized by chronologically nonincreasing inventory holdback levels. We analytically study the sensitivity of holdback levels to explain interesting findings, such as smaller retailers and geographically distant retailers benefit more from transshipments. Numerical experiments show that retailers substantially benefit from using optimal transshipment policies compared to no sharing. The expected sales increase in all but a handful of over 3,000 problem instances. Building on the two-retailer optimal policies, we suggest an effective heuristic transshipment policy for a multiretailer system

Policy Reform Impact on Food Manufacturing

The impact of agricultural policies and their reform is of major concern when addressing issues of growth, innovation and consolidation in the food manufacturing sector. Growth is one of the forces fueling the globalization of food manufacturing activities. Market- and policy-driven forces present a myriad of opportunities to influence growth and reorientation of patterns at the nexus where food manufacturing links the food system. The productivity and international competitiveness of the food manufacturing sector must be evaluated in the context of governmental incentives, international standards and the emerging supply- and value-chains.total factor productivity growth, intercountry impacts, dairy products, meat products, sugar, Agribusiness, Agricultural and Food Policy,

Distribution-free Inventory Risk Pooling in a Multi-location Newsvendor

With rapidly increasing e-commerce sales, firms are leveraging the virtual pooling of online demands across customer locations in deciding the amount of inventory to be placed in each node in a fulfillment network. Such solutions require knowledge of the joint distribution of demands; however, in reality, only partial information about the joint distribution may be reliably estimated. We propose a distributionally robust multi-location newsvendor model for network inventory optimization where the worst-case expected cost is minimized over the set of demand distributions satisfying the known mean and covariance information. For the special case of two homogeneous customer locations with correlated demands, we show that a six-point distribution achieves the worst-case expected cost, and derive a closed-form expression for the optimal inventory decision. The general multi-location problem can be shown to be NP-hard. We develop a computationally tractable upper bound through the solution of a semidefinite program (SDP), which also yields heuristic inventory levels, for a special class of fulfillment cost structures, namely nested fulfillment structures. We also develop an algorithm to convert any general distance-based fulfillment cost structure into a nested fulfillment structure which tightly approximates the expected total fulfillment cost.https://deepblue.lib.umich.edu/bitstream/2027.42/146785/1/1389_Govindarajan.pd

"Trade Credit, Bank Loans, and Monitoring: Evidence from Japan"

Firms in modern developed economies can choose to borrow from banks or from trade partners. Using first-difference and difference-in-differences regressions on Japanese manufacturing data, we explore the way they make that choice. Whether small or large, they do borrow from their trade partners heavily, and apparently at implicit rates that track the explicit rates banks would charge them. Nonetheless, they do not treat bank loans and trade credit interchangeably. Disproportionately, they borrow from banks when they anticipate needing money for relatively long periods, and turn to trade partners when they face short-term exigencies they did not expect. This contrast in the term structures of bank loans and trade credit follows from the fundamentally different way bankers and trade partners reduce the default risks they face. Because bankers seldom know their borrowers' industries first-hand, they rely on guarantees and security interests. Because trade partners know those industries well, they instead monitor their borrowers closely. Because the costs to creating security interests are heavily front-loaded, bankers focus on long-term debt. Because the costs of monitoring debtors are on-going, trade creditors do not. Despite the enormous theoretical literature on bank monitoring, banks apparently monitor very little.

Essays on Procurement with Information Asymmetry.

Sourcing, once seen as a tactical function of vertically integrated firms, has today become strategic for firms that now rely on extensive, vertically disintegrated supply chains. Vertical disintegration leads to complex relationships within a supply chain; for example, dependence between firms across different tiers and competition among firms within each tier often coexist. In addition, firms generally harbor private information. The complex relationships and information asymmetry make firms’ interactions highly strategic. How should firms in supply chains of various structures make strategic procurement decisions in the presence of information asymmetry? The three essays in this dissertation study three specific problems on this topic. The essay “Does Pooling Component Demands when Sourcing Lead to Higher Profits?” studies whether pooling purchases for a component used in multiple products with uncertain demands always results in increased profits for the buyer, when the component must be purchased from a sole-source strategic supplier. The essay “Simple Auctions for Supply Contracts” designs a simple and easily implementable optimal procurement mechanism for a newsvendor-like problem, where the buyer’s (newsvendor’s) purchase price is not fixed, but determined through interactions with candidate suppliers who possess private information about their own production costs. Finally, the essay “Price-Quoting Strategies of a Tier-Two Supplier” studies how a tier-two supplier of a crucial component should best quote prices to her tier-one customers, who will compete for an OEM’s indivisible contract based on cost. This dissertation has the potential to help procurement managers understand certain business situations more clearly and make better decisions. In particular, it highlights the impact of information asymmetry on procurement, and suggests strategies to tackle resulting challenges. More broadly, this dissertation adds to the nascent and growing Operations Management scholarship on procurement, and contributes to the field’s general understanding of supply chain management.Ph.D.Business AdministrationUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/86363/2/hub_1.pd

An economic order quantity stochastic dynamic optimization model in a logistic 4.0 environment

This paper proposes a stock dynamic sizing optimization under the Logistic 4.0 environment. The safety stock is conceived to fill up the demand variability, providing continuous stock availability. Logistic 4.0 and the smart factory topics are considered. It focuses on vertical integration to implement flexible and reconfigurable smart production systems using the information system integration in order to optimize material flow in a 4.0 full-service approach. The proposed methodology aims to reduce the occurring stock-out events through a link among the wear-out items rate and the downstream logistic demand. The failure rate items trend is obtained through life-cycle state detection by a curve fitting technique. Therefore, the optimal safety stock size is calculated and then validated by an auto-tuning iterative modified algorithm. In this study, the reorder time has been optimized. The case study refers to the material management of a very high-speed train

Edge Computing for AI and ML: Enhancing Performance and Privacy in Data Analysis

Centralised cloud computing paradigms are encountering difficulties with latency, bandwidth, privacy, and security due to the exponential growth of data volumes produced by sensors and Internet of Things (IoT) devices. One potential approach to these constraints is edge computing, which moves computers and storage closer to the data sources. With this paradigm change, data privacy is improved, network congestion is decreased, and real-time processing is made possible. Aiming to improve the efficiency and confidentiality of data analysis applications powered by artificial intelligence (AI) and machine learning (ML), this article investigated the possibility of edge computing. We provide a thorough analysis of the latest developments in edge computing frameworks, algorithms, and architectures that allow for safe and fast training and inference of AI/ML models at the edge. We also go over the main obstacles and where the field may go from here in terms of research. Our research lays the groundwork for future intelligent edge systems by demonstrating the substantial advantages of edge computing in facilitating low-latency, energy-efficient, and privacy-preserving AI/ML applications. &nbsp

IEEE Access Special Section Editorial: Big Data Technology and Applications in Intelligent Transportation

During the last few years, information technology and transportation industries, along with automotive manufacturers and academia, are focusing on leveraging intelligent transportation systems (ITS) to improve services related to driver experience, connected cars, Internet data plans for vehicles, traffic infrastructure, urban transportation systems, traffic collaborative management, road traffic accidents analysis, road traffic flow prediction, public transportation service plan, personal travel route plans, and the development of an effective ecosystem for vehicles, drivers, traffic controllers, city planners, and transportation applications. Moreover, the emerging technologies of the Internet of Things (IoT) and cloud computing have provided unprecedented opportunities for the development and realization of innovative intelligent transportation systems where sensors and mobile devices can gather information and cloud computing, allowing knowledge discovery, information sharing, and supported decision making. However, the development of such data-driven ITS requires the integration, processing, and analysis of plentiful information obtained from millions of vehicles, traffic infrastructures, smartphones, and other collaborative systems like weather stations and road safety and early warning systems. The huge amount of data generated by ITS devices is only of value if utilized in data analytics for decision-making such as accident prevention and detection, controlling road risks, reducing traffic carbon emissions, and other applications which bring big data analytics into the picture