Controlling inventories in a supply chain: a case study

This article studies specific aspects of the joint replenishment problem in a real supply chain setting. Particularly we analyze the effect on inventory performance of having minimum order quantities for the different products in the joint order, given a complex transportation cost structure. The policies suggested have been tested in a simulation model with real data.Inventory;Supply chain management;Minimum order quantities;Joint replienishment

Assessment of joint inventory replenishment: a cooperative games approach

This research deals with the design of a logistics strategy with a collaborative approach between non-competing companies, who through joint coordination of the replenishment of their inventories reduce their costs thanks to the exploitation of economies of scale. The collaboration scope includes sharing logistic resources with limited capacities; transport units, warehouses, and management processes. These elements conform a novel extension of the Joint Replenishment Problem (JRP) named the Schochastic Collaborative Joint replenishment Problem (S-CJRP). The introduction of this model helps to increase practical elements into the inventory replenishment problem and to assess to what extent collaboration in inventory replenishment and logistics resources sharing might reduce the inventory costs. Overall, results showed that the proposed model could be a viable alternative to reduce logistics costs and demonstrated how the model can be a financially preferred alternative than individual investments to leverage resources capacity expansions. Furthermore, for a practical instance, the work shows the potential of JRP models to help decision-makers to better understand the impacts of fleet renewal and inventory replenishment decisions over the cost and CO2 emissions.DoctoradoDoctor en Ingeniería Industria

Revenue Management and Demand Fulfillment: Matching Applications, Models, and Software

Recent years have seen great successes of revenue management, notably in the airline, hotel, and car rental business. Currently, an increasing number of industries, including manufacturers and retailers, are exploring ways to adopt similar concepts. Software companies are taking an active role in promoting the broadening range of applications. Also technological advances, including smart shelves and radio frequency identification (RFID), are removing many of the barriers to extended revenue management. The rapid developments in Supply Chain Planning and Revenue Management software solutions, scientific models, and industry applications have created a complex picture, which appears not yet to be well understood. It is not evident which scientific models fit which industry applications and which aspects are still missing. The relation between available software solutions and applications as well as scientific models appears equally unclear. The goal of this paper is to help overcome this confusion. To this end, we structure and review three dimensions, namely applications, models, and software. Subsequently, we relate these dimensions to each other and highlight commonalities and discrepancies. This comparison also provides a basis for identifying future research needs.Manufacturing;Revenue Management;Software;Advanced Planning Systems;Demand Fulfillment

Modeling Industrial Lot Sizing Problems: A Review

In this paper we give an overview of recent developments in the field of modeling single-level dynamic lot sizing problems. The focus of this paper is on the modeling various industrial extensions and not on the solution approaches. The timeliness of such a review stems from the growing industry need to solve more realistic and comprehensive production planning problems. First, several different basic lot sizing problems are defined. Many extensions of these problems have been proposed and the research basically expands in two opposite directions. The first line of research focuses on modeling the operational aspects in more detail. The discussion is organized around five aspects: the set ups, the characteristics of the production process, the inventory, demand side and rolling horizon. The second direction is towards more tactical and strategic models in which the lot sizing problem is a core substructure, such as integrated production-distribution planning or supplier selection. Recent advances in both directions are discussed. Finally, we give some concluding remarks and point out interesting areas for future research

Centralization of inventory management for spare parts

Background Arriva DK currently has a decentralized organization where each depot is responsible for the inventory control of their spare parts. This type of organization often presents challenges when it comes to control and management. Arriva DK suffers from high order costs due to lack of coordination between the depots and the suppliers. To address these issues Arriva wants to introduce a central warehouse. Purpose The aim of the thesis is to optimize inventory management by creating simulation models that from a cost perspective explore the effects of introducing a central warehouse for spare parts. To provide Arriva DK with simulation models of the supply chain and inventory management, both with – and without a central warehouse. Method The thesis is built on Hillier and Lieberman’s Operations research method. Data used in the thesis consist of both primary and secondary quantitative and qualitative data. Literature studies have also been done and are the basis for the theory. Two simulation models were built. The first model represents an optimized decentralized situation that uses joint replenishment. The second model represents a system with a central warehouse, which also uses joint replenishment. Thereafter the models were compared to determine if an investment in a centralized system is profitable. Conclusions The results from the simulation study showed that the total cost for the CW-model is higher than the DC-model in all scenarios. Both the holding- and order cost will be higher with a central warehouse. The major advantage of using a central warehouse is that the number of orders to the suppliers will be reduced by more than 50%. This along with the suppliers only have to deliver to one location will result in price reductions on the products. Even with small discounts, will a central warehouse be profitable since the procurement cost represents such a large part of the total cost

Minimizing food waste in grocery store operations: literature review and research agenda

Research on grocery waste in food retailing has recently attracted particular interest. Investigations in this area are relevant to address the problems of wasted resources and ethical concerns, as well as economic aspects from the retailer’s perspective. Reasons for food waste in retail are already well-studied empirically, and based on this, proposals for reduction are discussed. However, comprehensive approaches for preventing food waste in store operations using analytics and modeling methods are scarce. No work has yet systematized related research in this domain. As a result, there is neither any up-to-date literature review nor any agenda for future research. We contribute with the first structured literature review of analytics and modeling methods dealing with food waste prevention in retail store operations. This work identifies cross-cutting store-related planning areas to mitigate food waste, namely (1) assortment and shelf space planning, (2) replenishment policies, and (3) dynamic pricing policies. We introduce a common classification scheme of literature with regard to the depth of food waste integration and the characteristics of these planning problems. This builds our foundation to review analytics and modeling approaches. Current literature considers food waste mainly as a side effect in costing and often ignores product age dependent demand by customers. Furthermore, approaches are not integrated across planning areas. Future lines of research point to the most promising open questions in this field

Demand Prediction and Inventory Management of Surgical Supplies

Effective supply chain management is critical to operations in various industries, including healthcare. Demand prediction and inventory management are essential parts of healthcare supply chain management for ensuring optimal patient outcomes, controlling costs, and minimizing waste. The advances in data analytics and technology have enabled many sophisticated approaches to demand forecasting and inventory control. This study aims to leverage these advancements to accurately predict demand and manage the inventory of surgical supplies to reduce costs and provide better services to patients. In order to achieve this objective, a Long Short-Term Memory (LSTM) model is developed to predict the demand for commonly used surgical supplies. Moreover, the volume of scheduled surgeries influences the demand for certain surgical supplies. Hence, another LSTM model is adopted from the literature to forecast surgical case volumes and predict the procedure-specific surgical supplies. A few new features are incorporated into the adopted model to account for the variations in the surgical case volumes caused by COVID-19 in 2020. This study then develops a multi-item capacitated dynamic lot-sizing replenishment model using Mixed Integer Programming (MIP). However, forecasting is always considered inaccurate, and demand is hardly deterministic in the real world. Therefore, a Two-Stage Stochastic Programming (TSSP) model is developed to address these issues. Experimental results demonstrate that the TSSP model provides an additional benefit of $2,328.304 over the MIP model

Truckload Shipment Planning and Procurement

This dissertation presents three issues encountered by a shipper in the context of truckload transportation. In all of the studies, we utilize optimization techniques to model and solve the problems. Each study is inspired from the real world and much of the data used in the experiments is real data or representative of real data. The first topic is about the freight consolidation in truckload transportation. We integrate it with a purchase incentive program to increase truckload utilization and maximize profit. The second topic is about supporting decision making collaboration among departments of a manufacturer. It is a bi-objective optimization model. The third topic is about procurement in an adverse market. We study a modification of the existing procurement process to consider the market stochastic into marking decisions. In all three studies, our target is to develop effectively methodologies to seek optimal answers within a reasonable amount of time

Demand Prediction and Inventory Management of Surgical Supplies

Effective supply chain management is critical to operations in various industries, including healthcare. Demand prediction and inventory management are essential parts of healthcare supply chain management for ensuring optimal patient outcomes, controlling costs, and minimizing waste. The advances in data analytics and technology have enabled many sophisticated approaches to demand forecasting and inventory control. This study aims to leverage these advancements to accurately predict demand and manage the inventory of surgical supplies to reduce costs and provide better services to patients. In order to achieve this objective, a Long Short-Term Memory (LSTM) model is developed to predict the demand for commonly used surgical supplies. Moreover, the volume of scheduled surgeries influences the demand for certain surgical supplies. Hence, another LSTM model is adopted from the literature to forecast surgical case volumes and predict the procedure-specific surgical supplies. A few new features are incorporated into the adopted model to account for the variations in the surgical case volumes caused by COVID-19 in 2020. This study then develops a multi-item capacitated dynamic lot-sizing replenishment model using Mixed Integer Programming (MIP). However, forecasting is always considered inaccurate, and demand is hardly deterministic in the real world. Therefore, a Two-Stage Stochastic Programming (TSSP) model is developed to address these issues. Experimental results demonstrate that the TSSP model provides an additional benefit of $2,328.304 over the MIP model

A Heuristic Solution Technique to the Joint Replenishment Problem with Quantity Discounts and Full Truck Loads

In this project a stochastic multi-item inventory problem is considered. A wholesaler buys multiple products, with stochastic demand and similar holding and purchase costs, from a single supplier. The supplier offers an all-unit quantity discount whenever a full truckload is replenished. For the delivery of the products trucks with a finite capacity are available. The dispatched trucks arrive at the wholesaler after a constant leadtime and with each truck fixed shipping costs are charged independent on the number of units shipped. Since fixed transportation costs are high coordination of orders and full truckload shipments can benefit from economies of scale and quantity discounts. A new heuristic solution to this problem is proposed. The solution includes a direct grouping strategy and considers the optimal solution from both, the shipping trucks and products perspectives. In implementing the proposed solution an adjusted periodic review system is used. An excellent performance of the proposed solution can be observed when the fixed cost per order is high and the demand of the different products is similar. While the proposed solution presented in this project to the joint replenishment problem under consideration has been shown to be reliable, it nevertheless represents the first step towards the development of more efficient and versatile future solutions to the problem