Supply Chain

Traditionally supply chain management has meant factories, assembly lines, warehouses, transportation vehicles, and time sheets. Modern supply chain management is a highly complex, multidimensional problem set with virtually endless number of variables for optimization. An Internet enabled supply chain may have just-in-time delivery, precise inventory visibility, and up-to-the-minute distribution-tracking capabilities. Technology advances have enabled supply chains to become strategic weapons that can help avoid disasters, lower costs, and make money. From internal enterprise processes to external business transactions with suppliers, transporters, channels and end-users marks the wide range of challenges researchers have to handle. The aim of this book is at revealing and illustrating this diversity in terms of scientific and theoretical fundamentals, prevailing concepts as well as current practical applications

Innovation, Research and Development and Capital Evaluation

The role of innovation is becoming increasingly crucial in modern societies. In particular, investments in Research and Development represent an important tool for promoting innovation and for enhancing the capital invested in the market. By now, innovation is linked with different concepts, from entrepreneurship to sustainability, from economics to management. The analysis of the innovation process involves different skills to overcome the high degree of competitiveness and allow higher levels of profits. In the text, the interactions among innovation, entrepreneurship, and ecosystems are investigated. It is an attempt to prepare the theoretical bases to face in an appropriate way the transformations of modern economic and organizational systems and to suggest adequate support policies

Scheduled service network design with synchronization and transshipment constraints for intermodal container transportation networks

In this paper we address the problem of scheduled service network design for container freight distribution along rivers, canals, and coastlines. We propose a new concise continuous- time mixed-integer linear programming model that accurately evaluates the time of occurrence of transportation events and the number of containers transshipped between vehicles. Given the transportation network, the eet of available vehicles, the demand and the supply of containers, the sailing time of vehicles, and the structure of costs, the objective of the model is to build a minimum cost service network design and container distribution plan that denes services, their departure and arrival times, as well as vehicle and container routing. The model is solved with a commercial solver and is tested on data instances inspired from real-world problems encountered by EU carrier companies. The results of the computational study show that in scheduled service networks direct routes happen more often when either the eet capacity is tight or the handling costs and the lead time interval increase. The increase of the same parameters leads to the decrease of the number of containers transshipped between vehicles

Multi-objective inventory optimization problem for a sustainable food supply network under lateral inventory share policy

This study dives deep into a lateral supply chain network for perishable food products and aims to determine optimal re-order and order up to levels for multiple e-groceries within a common network using a simulation-based optimization technique. The algorithm aims to minimize the average inventory carried within the network while accounting for parameters like reduced wastage, improved customer satisfaction level, and a limited number of replenishments

Best matching processes in distributed systems

The growing complexity and dynamic behavior of modern manufacturing and service industries along with competitive and globalized markets have gradually transformed traditional centralized systems into distributed networks of e- (electronic) Systems. Emerging examples include e-Factories, virtual enterprises, smart farms, automated warehouses, and intelligent transportation systems. These (and similar) distributed systems, regardless of context and application, have a property in common: They all involve certain types of interactions (collaborative, competitive, or both) among their distributed individuals—from clusters of passive sensors and machines to complex networks of computers, intelligent robots, humans, and enterprises. Having this common property, such systems may encounter common challenges in terms of suboptimal interactions and thus poor performance, caused by potential mismatch between individuals. For example, mismatched subassembly parts, vehicles—routes, suppliers—retailers, employees—departments, and products—automated guided vehicles—storage locations may lead to low-quality products, congested roads, unstable supply networks, conflicts, and low service level, respectively. This research refers to this problem as best matching, and investigates it as a major design principle of CCT, the Collaborative Control Theory. The original contribution of this research is to elaborate on the fundamentals of best matching in distributed and collaborative systems, by providing general frameworks for (1) Systematic analysis, inclusive taxonomy, analogical and structural comparison between different matching processes; (2) Specification and formulation of problems, and development of algorithms and protocols for best matching; (3) Validation of the models, algorithms, and protocols through extensive numerical experiments and case studies. The first goal is addressed by investigating matching problems in distributed production, manufacturing, supply, and service systems based on a recently developed reference model, the PRISM Taxonomy of Best Matching. Following the second goal, the identified problems are then formulated as mixed-integer programs. Due to the computational complexity of matching problems, various optimization algorithms are developed for solving different problem instances, including modified genetic algorithms, tabu search, and neighbourhood search heuristics. The dynamic and collaborative/competitive behaviors of matching processes in distributed settings are also formulated and examined through various collaboration, best matching, and task administration protocols. In line with the third goal, four case studies are conducted on various manufacturing, supply, and service systems to highlight the impact of best matching on their operational performance, including service level, utilization, stability, and cost-effectiveness, and validate the computational merits of the developed solution methodologies

An Optimistic-Robust Approach for Dynamic Positioning of Omnichannel Inventories

We introduce a new class of data-driven and distribution-free optimistic-robust bimodal inventory optimization (BIO) strategy to effectively allocate inventory across a retail chain to meet time-varying, uncertain omnichannel demand. While prior Robust optimization (RO) methods emphasize the downside, i.e., worst-case adversarial demand, BIO also considers the upside to remain resilient like RO while also reaping the rewards of improved average-case performance by overcoming the presence of endogenous outliers. This bimodal strategy is particularly valuable for balancing the tradeoff between lost sales at the store and the costs of cross-channel e-commerce fulfillment, which is at the core of our inventory optimization model. These factors are asymmetric due to the heterogenous behavior of the channels, with a bias towards the former in terms of lost-sales cost and a dependence on network effects for the latter. We provide structural insights about the BIO solution and how it can be tuned to achieve a preferred tradeoff between robustness and the average-case. Our experiments show that significant benefits can be achieved by rethinking traditional approaches to inventory management, which are siloed by channel and location. Using a real-world dataset from a large American omnichannel retail chain, a business value assessment during a peak period indicates over a 15% profitability gain for BIO over RO and other baselines while also preserving the (practical) worst case performance

Agent Based Modeling and Simulation Framework for Supply Chain Risk Management

This research develops a flexible agent-based modeling and simulation (ABMS) framework for supply chain risk management with significant enhancements to standard ABMS methods and supply chain risk modeling. Our framework starts with the use of software agents to gather and process input data for use in our simulation model. For our simulation model, we extend an existing mathematical framework for discrete event simulation (DES) to ABMS and then implement the concepts of variable resolution modeling from the DES domain to ABMS and provide further guidelines for aggregation and disaggregation of supply chain models. Existing supply chain risk management research focuses on consumable item supply chains. Since the Air Force supply chain contains many reparable items, we fill this gap with our risk metrics framework designed for reparable item supply chains, which have greater complexity than consumable item supply chains. We present new metrics, along with existing metrics, in a framework for reparable item supply chain risk management and discuss aggregation and disaggregation of metrics for use with our variable resolution modeling

Lowering outbound shipping costs in an online retail environment by making better fulfillment and replenishment decisions

Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 181-185).As online retailing - or e-tailing - continues to grow as more and more customers buy physical goods on the internet, finding ways to reduce the cost and environmental impact of outbound shipping in this sector will become increasingly important. We investigate the impact of making poor fulfillment and replenishment decisions using data obtained from a large American online retailer. Then, we propose implementable - i.e., computationally tractable and relatively intuitive - solutions for both the fulfillment and replenishment problems, both tested either on actual data from our industrial partner or on small but realistic models. We first focus on the fulfillment problem, namely, deciding from which warehouse(s) to fulfill a customer's order when several options exist. We propose a heuristic that utilizes the dual values of a transportation linear program to estimate the opportunity cost of depleting inventory from a warehouse. This linear program values inventory at a warehouse due to both its geography and the size of its catalogue. After showing that this linear program is asymptotically optimal - using concepts developed in airline network revenue management - we then test the heuristic on industry data, showing a 1% reduction in outbound shipping costs as compared to a myopic fulfillment policy. The last part of the thesis focuses on replenishment. Every period, for each item, the network places an order to restock all the warehouses. Complicating this decision are two factors. First, the orders will not arrive immediately, but rather require a lead time to be delivered. During this time a random number of customers will place orders with the network. Second, any customer's order may be filled from any warehouse, which becomes important when warehouses stock out of an item. Therefore, it is not trivial to calculate the optimal inventory to order to each warehouse. We show that using a standard replenishment policy - popular in practice - can lead to dynamics that result in increased outbound shipping costs. We propose a replenishment policy heuristic that is intuitive and performs well on examples. This heuristic has two variants: a simpler one that assumes deterministic demand, and a more complicated one that accounts for stochasticity.by Jason Andrew Acimovic.Ph.D