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

Modeling and Controlling of an Integrated Distribution Supply Chain: Simulation Based Shipment Consolidation Heuristics

Increasing competition due to market globalization, product diversity and technological breakthroughs stimulates independent firms to collaborate in a supply chain that allows them to gain mutual benefits. This requires collective knowledge of the coordination and integration mode, including the ability to synchronize interdependent processes, to integrate information systems and to cope with distributed learning. The Integrated Supply Chain Problem (ISCP) is concerned with coordinating the supply chain tires from supplier, production, inventory and distribution delivery operations to meet customer demand with an objective to minimize the cost and maximize the supply chain service levels. In order to achieve high performance, supply chain functions must operate in an integrated and coordinated manner. Several challenging problems associated with integrated supply chain design are: (1) how to model and coordinate the supply chain business processes; (2) how to analyze the performance of an integrated supply chain network; and (3) how to evaluate the dynamic of the supply chain to obtain a comprehensive understanding of decision-making issues related to supply network configurations. These problems are most representative in the supply chain theory’s research and applications. A particular real life supply chain considered in this study involves multi echelon and multi level distribution supply chains, each echelon with its own inventory capacities and multi product types and classes. Optimally solving such an integrated problem is in general not easy due to its combinatorial nature, especially in a real life situation where a multitude of aspects and functions should be taken into consideration. In this dissertation, the simulation based heuristics solution method was implemented to effectively solve this integrated problem. A complex real life simulation model for managing the flow of material, transportation, and information considering multi products multi echelon inventory levels and capacities in upstream and downstream supply chain locations supported by an efficient Distribution Requirements Planning model (DRP) was modeled and developed named (LDNST) involving several sequential optimization phases. In calibration phase (0), the allocation of facilities to customers in the supply chain utilizing Add / Drop heuristics were implemented, that results in minimizing total distance traveled and maximizing the covering percentage. Several essential distribution strategies such as order fulfillment policy and order picking principle were defined in this phase. The results obtained in this phase were considered in further optimization solutions. The transportation function was modelled on pair to pair shipments in which no vehicle routing decision was considered, such an assumption generates two types of transportation trips, the first being Full Truck Load trips (FTL) and the second type being Less Truck Load trips (LTL). Three integrated shipment consolidation heuristics were developed and integrated into the developed simulation model to handle the potential inefficiency of low utilization and high transportation cost incurred by the LTL. The first consolidation heuristic considers a pure pull replenishment algorithm, the second is based on product clustering replenishments with a vendor managed inventory concept, and the last heuristic integrates the vendor managed inventory with advanced demand information to generate a new hybrid replenishment strategy. The main advantage of the latter strategy, over other approaches, is its ability to simultaneously optimize a lot of integrated and interrelated decisions for example, on the inventory and transportation operations without considering additional safety stock to improve the supply chain service levels. Eight product inventory allocation and distribution strategies considering different safety stock levels were designed and established to be considered as main benchmark experiments examined against the above developed replenishment strategies; appropriate selected supply chain performance measures were collected from the simulation results to distinguish any trading off between the proposed distribution strategies. Three supply chain network configurations were proposed: the first was a multi-echelon distribution system with an installation stock reorder policy; the second proposed configuration was Transshipment Point (TP) with a modified (s,S) inventory; and the last considered configuration was a Sub-TP, a special case from the second configuration. The results show that, depending on the structure of multi-echelon distribution systems and the service levels targets, both the echelon location with installation stock policy and advanced demand information replenishment strategy may be advantageous, and the impressive results and service level improvements bear this out. Considering the complexity of modeling the real life supply chain, the results obtained in this thesis reveal that there are significant differences in performance measures, such as activity based costs and network service levels. A supply chain network example is employed to substantiate the effectiveness of the proposed methodologies and algorithms

Performance Evaluation of Stochastic Multi-Echelon Inventory Systems: A Survey

Globalization, product proliferation, and fast product innovation have significantly increased the complexities of supply chains in many industries. One of the most important advancements of supply chain management in recent years is the development of models and methodologies for controlling inventory in general supply networks under uncertainty and their widefspread applications to industry. These developments are based on three generic methods: the queueing-inventory method, the lead-time demand method and the flow-unit method. In this paper, we compare and contrast these methods by discussing their strengths and weaknesses, their differences and connections, and showing how to apply them systematically to characterize and evaluate various supply networks with different supply processes, inventory policies, and demand processes. Our objective is to forge links among research strands on different methods and various network topologies so as to develop unified methodologies.Masdar Institute of Science and TechnologyNational Science Foundation (U.S.) (NSF Contract CMMI-0758069)National Science Foundation (U.S.) (Career Award CMMI-0747779)Bayer Business ServicesSAP A

Multi-echelon Inventory Control with Integrated Shipment Decisions

Rising fuel prices and increasing environmental awareness emphasizes the importance of the transportation aspect in logistics. This calls for new improved inventory control methods that consider the effects of shipment strategies in a more realistic manner. This thesis, consisting of an introduction and three scientific papers, studies how shipment decisions can be included in the inventory control of distribution systems. The systems studied in the papers consist of a central warehouse that supplies goods to a number of retailers that face stochastic customer demand. The first two papers consider a system where shipments from the central warehouse are consolidated to groups of retailers periodically. This means that replenishment orders of one or several items from different retailers are consolidated and dispatched at certain time intervals. By doing so, transportation cost savings can be realized and emissions can be reduced. This is achieved by filling the vehicles or load carriers to a higher extent and by using cheaper and more environmentally friendly, transportation modes. The first paper explicitly focuses on how to include more realistic transportation costs and emissions. This is done by obtaining the distribution of the size of an arbitrary shipment leaving the central warehouse (directly affected by the shipment frequency). It is thereby easy to evaluate any system where the transportation costs and emissions are dependent on the size of the shipment. The paper also provides a detailed analysis of a system where there is an opportunity to reserve shipment capacity on an intermodal truck-train-truck solution to at least one of the retailer groups. For this system it is shown how to jointly optimize the shipment intervals, the reserved capacities on the intermodal transportation modes and the reorder points in the system. The presented optimization procedure is applicable in three scenarios; (i) the emissions are not considered, (ii) there is a fixed cost per unit of emission, and (iii) there is a constraint on the maximum emissions per time unit. The second paper extends the analysis of a similar time-based shipment consolidation system to handle compound Poisson demand (instead of pure Poisson demand). This system has a simpler transportation cost structure, but the more general demand structure makes the model applicable for a broader array of products. The paper also extends the model to handle fill rate constraints, which further improves the practical applicability. The cost analysis is performed with a new methodology, based on the nominal inventory position. This variable is a helpful tool for analyzing the dynamics of distribution systems. Another system where this tool can be used is studied in the third paper. In this paper all stock points use installation stock (R,Q) ordering policies (batch ordering). This implies that situations can occur when only part of a requested retailer order is available at the central warehouse. The existing literature predominantly assumes that the available units are shipped immediately and the remaining units are shipped as soon as they arrive to the central warehouse, referred to as partial delivery. An alternative is to wait until the entire order is available before dispatching, referred to as complete delivery. The paper introduces a cost for splitting the order and evaluates three delivery policies; the PD policy (only partial deliveries are used), the CD policy (only complete deliveries are used), and the state-dependent MSD policy (an optimization between a partial and a complete delivery is performed for each delivery). The MSD policy is proven to perform better than both the PD and the CD policy. In a numerical study it is shown that significant savings can be made by using the MSD policy

Exact Methods for Multi-echelon Inventory Control : Incorporating Shipment Decisions and Detailed Demand Information

Recent advances in information technologies and an increased environmental awareness have altered the prerequisites for successful logistics. For companies operating on a global market, inventory control of distribution systems is often an essential part of their logistics planning. In this context, the research objective of this thesis is: To develop exact methods for stochastic inventory control of multi-echelon distribution systems incorporating shipment decisions and/or detailed demand information.The thesis consists of five scientific papers (Paper I, II, III, IV and V) preceded by a summarizing introduction. All papers study systems with a central warehouse supplying a number of non-identical local warehouses (retailers) facing stochastic demand. For given replenishment policies, the papers provide exact expressions for evaluating the expected long-run system behavior (e.g., distributions of backorders, inventory levels, shipment sizes and expected costs) and present optimization procedures for the control variables. Paper I and II consider systems where shipments from the central warehouse are consolidated to groups of retailers and dispatched periodically. By doing so, economies of scale for the transports can be reached, reducing both transportation costs and emissions. Paper I assumes Poisson customer demand and considers volume-dependent transportation costs and emissions. The model involves the possibility to reserve intermodal (train) capacity in combination with truck transports available on demand. For this system, the expected inventory costs, the expected transportation costs and the expected transport emissions are determined. Joint optimization procedures for the shipment intervals, the capacity reservation quantities, the reorder points and order-up-to levels in the system are provided, with or without emission considerations. Paper II analyses the expected costs of the same system for compound Poisson demand (where customer demand sizes may vary), but with only one transportation mode and fixed transportation costs per shipment. It also shows how to handle fill rate constraints. Paper III studies a system where all stock points use installation stock (R,Q) ordering policies (batch ordering). This implies that situations can occur when only part of a requested retailer order is available at the central warehouse. In these situations, the models in existing literature predominantly assume that available units are shipped immediately (partial delivery). An alternative is to wait until the entire order is available before dispatching (complete delivery). The paper introduces a cost for splitting the order and evaluates a system where optimal choices between partial and complete deliveries are made for all orders. In a numerical study it is shown that significant savings can be made by using this policy compared to systems which exclusively use either partial or complete deliveries. Paper IV shows how companies can benefit from detailed information about their customer demand. In a continuous review base stock system, the customer demand is modeled with independent compound renewal processes at the retailers. This means that the customer inter-arrival times may follow any continuous distribution and the demand sizes may follow any discrete distribution. A numerical study shows that this model can achieve substantial savings compared to models using the common assumption of exponential customer inter-arrival times. Paper V is a short technical note that extends the scope of analysis for several existing stochastic multi-echelon inventory models. These models analyze the expected costs without first determining the inventory level distribution. By showing how these distributions can be obtained from the expected cost functions, this note facilitates the analysis of several service measures, including the ready rate and the fill rate

An Integrated Multiechelon Logistics Model with Uncertain Delivery Lead Time and Quality Unreliability

Nowadays, in order to achieve advantages in supply chain management, how to keep inventory in adequate level and how to enhance customer service level are two critical practices for decision makers. Generally, uncertain lead time and defective products have much to do with inventory and service level. Therefore, this study mainly aims at developing a multiechelon integrated just-in-time inventory model with uncertain lead time and imperfect quality to enhance the benefits of the logistics model. In addition, the Ant Colony Algorithm (ACA) is established to determine the optimal solutions. Moreover, based on our proposed model and analysis, the ACA is more efficient than Particle Swarm Optimization (PSO) and Lingo in SMEIJI model. An example is provided in this study to illustrate how production run and defective rate have an effect on system costs. Finally, the results of our research could provide some managerial insights which support decision makers in real-world operations

Essays in Measuring, Controlling, and Coordinating Supply Chain Inventory and Transportation Operations

Supply chain collaboration programs, such as continuous replenishment program (CRP), is among the most popular supply chain management practices. CRP is an arrangement between two partners in a supply chain to share information on a regular basis for lowering logistics costs while maintaining or increasing service levels. CRP shifts the replenishment responsibility to the upstream partner to avoid the bullwhip effect across the supply chain. This dissertation aims to quantify, measure, and expand the benefits of CRP for the purpose of reducing logistics cost and improving customer service. The developed models in this dissertation are all applied in different case studies supported by a group of major healthcare partners. The first research contribution, discussed in chapter 2, is a comprehensive data-driven cost approximation model that quantifies the benefits of CRP for both partners under three cost components of inventory holding, transportation and ordering processing without imposing assumptions that normally do not hold in practice. The second contribution, discussed in chapter 3, is development of a verifiable efficiency measurement system to ensure the benefits of CRP for all partners. Multi-functional efficiency metrics are designed to capture the trade-off in gaining efficiency between multiple functions of logistics (i.e. inventory efficiency, transportation efficiency, and order processing efficiency). In addition, a statistical process control (SPC) system is developed to monitor the metrics over time. We discuss suitable SPC systems for various time series behaviors of the metrics. The third contribution of the dissertation, discussed in chapter 4, is development of a multi-objective decision analysis (MODA) model for multi-stop truckload (MSTL) planning. MSTL is becoming increasing popular among shippers while is experiencing significant resistance from carriers. MSTL is capable of reducing the shipping cost of shippers substantially but it can also disrupt carriers’ operations. A MODA model is developed for this problem to incorporate the key decision criteria of both sides for identifying the most desirable multi-stop routes from the perspective both decision makers