Decision support for build-to-order supply chain management through multiobjective optimization

This paper aims to identify the gaps in decision-making support based on multiobjective optimization for build-to-order supply chain management (BTOSCM). To this end, it reviews the literature available on modelling build-to-order supply chains (BTO-SC) with the focus on adopting multiobjective optimization (MOO) techniques as a decision support tool. The literature has been classified based on the nature of the decisions in different part of the supply chain, and the key decision areas across a typical BTO-SC are discussed in detail. Available software packages suitable for supporting decision making in BTO supply chains are also identified and their related solutions are outlined. The gap between the modelling and optimization techniques developed in the literature and the decision support needed in practice are highlighted and future research directions to better exploit the decision support capabilities of MOO are proposed

Decision support for build-to-order supply chain management through multiobjective optimization

This is the post-print version of the final paper published in International Journal of Production Economics. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2010 Elsevier B.V.This paper aims to identify the gaps in decision-making support based on multiobjective optimization (MOO) for build-to-order supply chain management (BTO-SCM). To this end, it reviews the literature available on modelling build-to-order supply chains (BTO-SC) with the focus on adopting MOO techniques as a decision support tool. The literature has been classified based on the nature of the decisions in different part of the supply chain, and the key decision areas across a typical BTO-SC are discussed in detail. Available software packages suitable for supporting decision making in BTO supply chains are also identified and their related solutions are outlined. The gap between the modelling and optimization techniques developed in the literature and the decision support needed in practice are highlighted. Future research directions to better exploit the decision support capabilities of MOO are proposed. These include: reformulation of the extant optimization models with a MOO perspective, development of decision supports for interfaces not involving manufacturers, development of scenarios around service-based objectives, development of efficient solution tools, considering the interests of each supply chain party as a separate objective to account for fair treatment of their requirements, and applying the existing methodologies on real-life data sets.Brunel Research Initiative and Enterprise Fund (BRIEF

Efficient Inventory Management of Hospital Supply Chains Using a Sim-Heuristic Approach

Inventory management is a vital section of a supply chain system. In a hospital setting, where delivering high quality patient care is a prime concern, inventory management is often overlooked. With the ever increasing demand for products, it becomes challenging to manage inventory in a dynamic facility such as a hospital. Although there is abundant research in supply chain, seldom have the proposed methods found their way into execution in actual hospital settings. Additionally, much of the literature focuses on particular aspects of the supply chain. Current methods used in practice lead to system performance that is suboptimal, resulting in too much or too short inventory in stock, overtime work to manage supplies, expedited shipments and potentially substandard quality of care delivered to patients. Having the right products available at the point-of-use is important to the efficient and effective treatment of patients. With cost and budget constraints, merely managing demand is not sufficient. There is a need to develop a system design which enables hospitals and healthcare institutions to implement and benefit from methods that have been developed or are being developed for optimal inventory management systems. In this research, we study the hospital supply chain from manufacturers/distribution centers to the point-of-use within a hospital unit, taking into account the integration and implementation of the various echelon of the supply chain system. In particular, we design and develop a sim-heuristic methodology using operations research to evaluate inventory and operational decision variables based on service level and operational costs, subject to variability in demand and lead-time. In addition, we demonstrate the capabilities and limitations of the methodology and compare alternate system configurations including a (Q, r) inventory system and Kanban system

Operations research models and methods for safety stock determination: A review

In supply chain inventory management it is generally accepted that safety stocks are a suitable strategy to deal with demand and supply uncertainty aiming to prevent inventory stock-outs. Safety stocks have been the subject of intensive research, typically covering the problems of dimensioning, positioning, managing and placement. Here, we narrow the scope of the discussion to the safety stock dimensioning problem, consisting in determining the proper safety stock level for each product. This paper reports the results of a recent in-depth systematic literature review (SLR) of operations research (OR) models and methods for dimensioning safety stocks. To the best of our knowledge, this is the first systematic review of the application of OR-based approaches to investigate this problem. A set of 95 papers published from 1977 to 2019 has been reviewed to identify the type of model being employed, as well as the modeling techniques and main performance criteria used. At the end, we highlight current literature gaps and discuss potential research directions and trends that may help to guide researchers and practitioners interested in the development of new OR-based approaches for safety stock determination.This work has been supported by FCT – Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020, and by the European Structural and Investment Funds in the FEDER component, through the Operational Competitiveness and Internationalization Program (COMPETE 2020) [Project no. 39479, Funding reference: POCI-01-0247-FEDER-39479]

An Adaptive Inventory Management System for Hospital Supply Chain

In healthcare, delivering high quality care to the patients typically requires significant investment in supply chain management systems. Inventory management is an important part of any supply chain system. Researchers have indicated great potential for optimizing existing healthcare inventory systems, especially within hospitals. With ever changing needs, product prices and policies, managing inventory of products in hospitals becomes difficult. As time progresses, the inventory policies of products become sub-optimal. In this research, we study multiple echelons of a hospital supply chain considering the distributors, to address the need for an efficient and effective hospital inventory management system. We propose a method consisting of two components: (1) system design and optimization; and (2) system monitoring, evaluation, and forecasting. The system design and optimization methodology includes a sim-heuristic approach where optimization of inventory levels and hospital operations is considered. As time evolves, to monitor the relevant system performance measures over time, control-chart like methods are used. When significant deviations in system performance occur, a re-evaluation of the inventory decision variables and/or system operations is conducted to maintain an efficient inventory system. A hierarchical procedure is used to determine the extent of evaluation of the system. Experimental results are presented to demonstrate the effectiveness of this methodology

Entropy-based Demand Splits in a Hospital-Warehouse Profit Center

Financial pressures on healthcare industry in the United States and elsewhere have forced the industry to address their supply costs, their fastest growing cost sector currently comprising over 40 % of their total spend. In the USA, the healthcare supplies market is dominated by a few large distributors and significant barriers to entry. Cost reducing measures to date have relied on Group Purchasing Organizations to leverage economies of scale in negotiating price reductions. Recently, the healthcare industry has been deemphasizing this practice. In doing so, healthcare organizations have merged to form large Integrated Delivery Networks, leveraging their collective purchasing capacity to negotiate price reductions. These organizations have essentially created their own internal Group Purchasing Organizations to compete with external suppliers. Although these ventures have been publicized to be “successful”, their overall success cannot be independently validated. Furthermore, the operational details of creating these ventures, financial analyses, and operations are not publically available. Our ultimate objective is to model the creation of ventures in which healthcare organizations enter price competitions with their external vendors using the currently prevalent market parameters and practices. Specifically, the models would identify and quantitate the parameters that determine venture success, here referred to as Venture Success Metrics. Such models would comprise multiple external suppliers of different products that belong to different categories. This thesis is our first step towards that objective. It represents a simplified venture in which the hospital runs its own warehouse as a profit center that competes with one external vendor on a single supply item. The model is based on currently prevalent healthcare industry practices. In particular, it incorporates discount schedules that accurately account for the unique healthcare industry practice of offering year-2 volume-based discounts based on year-1 volumes, restricted only to the contract period. Modeling a simplified venture enabled us to identify and quantitate the parameters that determine venture success. These parameters comprise the vendor and warehouse year-1 profit objectives as well as the bias of the hospital’s purchases from its own warehouse. Pursuing the models of the thesis induced the development of healthcare-relevant sigmoidal discount schedules. These functions accurately represent the tabular step-function discount schedules while averting the infinite and discontinuous derivatives of the latter. Their “continuous derivatives” advantage renders the sigmoidal discounts readily useable in computing price equilibria, a feat that was not easily achievable with the rigid step-function discounts. The thesis also introduces novel demand split functions in which a customer’s total demand can be equitably apportioned across all suppliers subject to diverse business objectives such as price constraints or biasing purchases in favor of one or more suppliers while retaining equitability. The ultimate economic goal for achieving equitability is to conserve supply source. The demand split methodology introduced in this thesis can be characterized as “achieving equitability under business constraints”. A series of examples are provided to illustrate the methods developed in this research. Finally, the thesis concludes with a synopsis of the findings and future extensions.1 yea