A Metaheuristic-Based Simulation Optimization Framework For Supply Chain Inventory Management Under Uncertainty

The need for inventory control models for practical real-world applications is growing with the global expansion of supply chains. The widely used traditional optimization procedures usually require an explicit mathematical model formulated based on some assumptions. The validity of such models and approaches for real world applications depend greatly upon whether the assumptions made match closely with the reality. The use of meta-heuristics, as opposed to a traditional method, does not require such assumptions and has allowed more realistic modeling of the inventory control system and its solution. In this dissertation, a metaheuristic-based simulation optimization framework is developed for supply chain inventory management under uncertainty. In the proposed framework, any effective metaheuristic can be employed to serve as the optimizer to intelligently search the solution space, using an appropriate simulation inventory model as the evaluation module. To be realistic and practical, the proposed framework supports inventory decision-making under supply-side and demand-side uncertainty in a supply chain. The supply-side uncertainty specifically considered includes quality imperfection. As far as demand-side uncertainty is concerned, the new framework does not make any assumption on demand distribution and can process any demand time series. This salient feature enables users to have the flexibility to evaluate data of practical relevance. In addition, other realistic factors, such as capacity constraints, limited shelf life of products and type-compatible substitutions are also considered and studied by the new framework. The proposed framework has been applied to single-vendor multi-buyer supply chains with the single vendor facing the direct impact of quality deviation and capacity constraint from its supplier and the buyers facing demand uncertainty. In addition, it has been extended to the supply chain inventory management of highly perishable products. Blood products with limited shelf life and ABO compatibility have been examined in detail. It is expected that the proposed framework can be easily adapted to different supply chain systems, including healthcare organizations. Computational results have shown that the proposed framework can effectively assess the impacts of different realistic factors on the performance of a supply chain from different angles, and to determine the optimal inventory policies accordingly

Finite-horizon operations planning for a lean supply chain system

This dissertation studies an operational policy for a lean supply chain system consisting of a manufacturer, multiple suppliers and multiple buyers. The manufacturer procures raw materials from the suppliers and converts them into finished products, which are then shipped in batches to the buyers at certain intervals of times. Three distinct but inseparable problems are addressed: single supplier and single buyer with fixed delivery size (FD), multiple suppliers and multiple buyers with individual delivery schedule (MD), and time dependent delivery quantity with trend demand (TD). The mathematical formulations of these supply systems are categorized as mixed-integer, nonlinear programming problems (MINLAP) with discrete, non-convex objective functions and constraints. The operations policy determines the number of orders of raw material, beginning and ending times of cycles, production batch size, production start time, and beginning and ending inventories. The goal is to minimize the cost of the two-stage, just-in-time inventory system that integrates raw materials ordering and finished goods production system. The policy is designed for a finite planning horizon with various phases of life cycle demands such as inception (increasing), maturity (level) and phasing out (declining). Analytical results that characterize the exact, optimal policy for the problems described above are devised to develop efficient and optimal computational procedures. A closed-form heuristic that provides a near-optimal solution and tight lower bound is proposed for the problem FD. A network model to represent the problems is proposed and network-based algorithms are implemented to solve the problems FD, MD and TD optimally. The computational complexities of the algorithms are Θ(N2) or O(N3) where N is the total number of shipments in the planning horizon. Numerical tests to assess the robustness and quality of the methods show that the present research provides superior results. Production and supply chain management play an important role in ensuring that the necessary amounts of materials and parts arrive at the appropriate time and place. A manager, using the models obtained in this research, can quickly respond to consumers\u27 demand by effectively determining the right policies to order raw materials, to deliver finished goods, and to efficiently manage their production schedule

An inventory control project in a major Danish company using compound renewal demand models

We describe the development of a framework to compute the optimal inventory policy for a large spare-parts’ distribution centre operation in the RA division of the Danfoss Group in Denmark. The RA division distributes spare parts worldwide for cooling and A/C systems. The warehouse logistics operation is highly automated. However, the procedures for estimating demands and the policies for the inventory control system that were in use at the beginning of the project did not fully match the sophisticated technological standard of the physical system. During the initial phase of the project development we focused on the fitting of suitable demand distributions for spare parts and on the estimation of demand parameters. Demand distributions were chosen from a class of compound renewal distributions. In the next phase, we designed models and algorithmic procedures for determining suitable inventory control variables based on the fitted demand distributions and a service level requirement stated in terms of an order fill rate. Finally, we validated the results of our models against the procedures that had been in use in the company. It was concluded that the new procedures were considerably more consistent with the actual demand processes and with the stated objectives for the distribution centre. We also initiated the implementation and integration of the new procedures into the company’s inventory management systemBase-stock policy; compound distribution; fill rate; inventory control; logistics; stochastic processes

Improving healthcare supply chains and decision making in the management of pharmaceuticals

The rising cost of quality healthcare is becoming an increasing concern. A significant part of healthcare cost is the pharmaceutical supply component. Improving healthcare supply chains is critical not only because of the financial magnitude but also because it impacts so many people. Efforts such as this project are essential in understanding the current operations of healthcare pharmacy systems and in offering decision support tools to managers struggling to make the best use of organizational resources. The purpose of this study is to address the objectives of a local hospital that exhibits typical problems in pharmacy supply chain management. We analyze the pharmacy supply network structure and the different, often conflicting goals in the decisions of the various stakeholders. We develop quantitative models useful in optimizing supply chain management and inventory management practices. We provide decision support tools that improve operational, tactical, and strategic decision making in the pharmacy supply chain and inventory management of pharmaceuticals. On one hand, advanced computerized technology that manages pharmaceutical dispensation and automates the ordering process offers considerable progress to support pharmacy product distribution. On the other hand, the available information is not utilized to help the managers in making the appropriate decisions and control the supply chain management. Quantitative methods are presented that provide simplified, practical solutions to pharmacy objectives and serve as decision support tools. For operational inventory decisions we provide the min and max par levels (reorder point and order up to level) that control the automated ordering system for pharmaceuticals. These parameters are based on two near-optimal allocation policies of cycle stock and safety stock under storage space constraint. For the tactical decision we demonstrate the influence of varying inventory holding cost rates on setting the optimal reorder point and order quantity for items. We present a strategic decision support tool to analyze the tradeoffs among the refill workload, the emergency workload, and the variety of drugs offered. We reveal the relationship of these tradeoffs to the three key performance indicators at a local care unit: the expected number of daily refills, the service level, and the storage space utilization

Developing an optimal selective procurement matrix for resale business - Case Studiotec Oy

OBJECTIVES OF THE STUDY The foundation of this study is to understand the procurement challenges that the case company Studiotec Oy and its sister company Soundtools Oy are currently confronted with. Moreover, this study aims to develop procurement directions in order to increase inventory turnover and improve customer satisfaction whilst releasing working capital. Academic literature is filled with recommendations on how to profile inventories into classes, on forecast approaches and with inventory control policies. However, a clear link between classes and different forecast systems and replenishment systems does not exist yet. Therefore, the aim of this study is to develop a practical SKU classification framework that guides the inventory decision and aids in the selection of the most appropriate combination of forecast approach and replenishment system. This study furthermore tries to assist the responsible purchasing employees to reconsider customary purchasing manners and enables management to think of the fresh ways of inventory management. ACADEMIC BACKGROUND AND METHODOLOGY In order to classify items and to test the designed framework, the sales and order data from the beginning of 2010 until September 2012 were analyzed. The empirical recommendations are based on heuristics and cost comparisons that were carried out for 90 items. Company's top management, product managers and the logistics manager who are responsible for making the purchase decisions were interviewed in order to get a better overall understanding about the situation, including the entity being evaluated and the circumstances. The inventory management literature was thoroughly reviewed as to select the best classification factors is the first step, followed by determining the most suitable forecasting methods and inventory control policies for the different classes. FINDINGS AND CONCLUSIONS The pivotal reason for increased inventory lies on demand intermittence and erratic. In addition, product managers unjustifiable large order quantities played a big role. By way of the new framework, items were categorized into six classes based on their contribution to net sales and demand frequency. Based on academic research, an optimal forecasting method and an inventory replenishment system were addressed for each class. Empirically optimal procurement tool differed from theoretical suggestion. The study suggests that the case company should abolish stocking items sold less frequently than every three months. This way, the case company could decrease its inventory value by 20 % and dramatically deplete the risk of obsolescence. Moreover, the new procurement tool embedded in product life cycle directs on overall procurement decisions

Managing humanitarian operations: the impact of amount, schedules, and uncertainty in funding

Global health spending has increased manyfold in the last few decades reaching US $6.5 trillion in 2012. Despite these increases, humanitarian organizations from around the world, working on different diseases including Malaria and Tuberculosis, have warned about potential funding shortfalls in the near future. Facing a growing need for health services and commodities, resource-constrained organizations are constantly looking for ways to maximize health outcomes through efficient and effective use of available resources. In this dissertation, we develop approaches to make efficient operational decisions under variable and unpredictable donor funding, a situation that is commonly faced by many humanitarian organizations. In the first chapter, we study the problem of managing inventory of a nutritional product under variable funding constraints. Despite the complexities associated with funding, we show that the optimal replenishment policy is easy to compute and straightforward to implement. We also provide several insights into how the funding amount, funding schedules and uncertainty in funding impact operating costs in this setting. In chapter 2, we look at the problem of dynamically allocating a limited amount of donor funding to patients in different health states in a humanitarian health setting. We show that the optimal allocation policy is state-dependent and prove several structural properties of the optimal policy that would help simplify its computation. Due to the complexity involved in calculating the optimal policy, we develop two heuristics to handle real-size problems with longer planning horizons. Computational results suggest that both heuristics perform well in many cases but one of the heuristics is more robust across a wide variety of settings. In addition to the allocation policy, we also provide some interesting insights into the impact of funding level and funding uncertainty in the multiple health states setting. In the third chapter, we focus on the supply- vs. demand-side investment dilemma frequently faced by public health managers who have a limited budget at their disposal. First, we consider a centralized setting where a single entity, referred as the principal, makes both supply- and demand-side investment decisions. We determine the principal's optimal investment mix in this budget constrained environment and provide insights into how the investment mix varies with the different supply- and demand-side parameters. We then consider a decentralized setting where the principal invests in improving the supply chain while demand mobilization activities are contracted to an agent, who is a profit maximizer. For the decentralized setting, we identify two contracts that ensure that the coverage in the decentralized setting is at least as high as the centralized case.Doctor of Philosoph