Supply Chain Management — Technical Papers
Alumni of the Supply Chain Leadership Program
SIGN IN HERE
TOPICS
Multi-Echelon Inventory Optimization
Supply Chain Collaboration
Production-Inventory Optimization
Component Inventory Optimization
Service Parts Optimization
Health Care Materials Management
Multi-Echelon Inventory Optimization
- Designing Multi-Echelon Service Parts Networks with Finite Repair Capacity
We propose an approach to model and solve the joint problem of facility location, inventory allocation and capacity investment when demand is stochastic. The objective of the decision problem is to minimize the total expected costs associated with (1) opening repair facilities, (2) assigning each field service location to an opened facility, (3) determining capacity levels of the opened repair facilities and (4) optimizing inventory allocation among the locations. - Determining and Allocating Capacity-Driven Safety Stock in Multi-Item Capacitated Supply Chains
In this paper, we study a multi-item, multi-echelon capacitated production and inventory system under periodic review. The approach is designed to plan and allocate production of items for which it is possible to determine probability distributions describing the stochastic demand process at each location. We begin by presenting a stochastic dynamic program for decision making in this environment. Due to its computational complexity, this formulation is of little practical value. We develop an alternative approximation approach for making production and allocation decisions which is computationally efficient. It uses a combination of the inventory shortfall random variable to describe the state of the system and a new capacity allocation mechanism that mitigates the impact of inventory imbalances among items and locations. We conduct a simulation study which demonstrates the effectiveness of the approach under a wide variety of scenarios. The primary conclusion is that in capacitated systems with short lead times, the existence of imbalance and its impact on costs are negligible. - A Computationally Efficient Approach for Determining Inventory Levels in a Capacitated Multi-Echelon Production-Distribution System
The system under study is a single item, two-echelon production-inventory system consisting of a capacitated production facility, a central warehouse and M regional distribution centers that satisfy stochastic demand. Our objective is to determine a system basestock level which minimizes the long run average system cost per period. Central to the approach are (1) an inventory allocation model and associated convex cost function designed to allocate a given amount of system inventory across locations, and (2) a characterization of the amount of available system inventory using the inventory shortfall random variable. An exact model must consider the possibility that inventories may be imbalanced in a given period. By assuming inventory imbalances cannot occur, we develop an approximation model from which we obtain a lower bound on the per period expected cost. Through an extensive simulation study, we present the quality of our approximation, which on average performed within 0.50% of the lower bound.
Supply Chain Collaboration
- Guidelines for Collaborative Supply Chain System Design and Operation
Over the past decade, firms have adopted supply chain management as a critical element of their corporate strategies. Despite these efforts, it is our observation that many firms do not realize the anticipated benefits of constructing collaborative operating relationships with supply chain partners. Our purpose in this paper is to establish a set of guiding principles for the effective design and execution of supply chain systems. These principles suggest why, what and how collaborative relationships should be constructed.
Production-Inventory Optimization
- Coordination of Information, Inventories and Capacity when Demand is Unpredictable
We examine a periodic-review production environment in which multiple items are produced. Production capacity is finite, and perhaps random in each period. Demand for a large subset of these items is highly erratic and extremely difficult, if not impossible, to predict accurately. We have four objectives in this paper. First, we examine the value of constructing collaborative information relationships between manufacturers and their customers in two alternate operating scenarios. Second, we propose an approach for managing production capacity and inventory in these environments in which demand is highly erratic, called the No B/C Strategy. Third, we describe a model and computationally efficient solution approach for determining base stock levels and capacity allocation decisions to implement this policy. Fourth, we demonstrate the value of the approach in both a simulated and in an industrial environment. Our experiments show that our model and solution methodologies perform well in both environments and result in substantial improvements in system performance over methods found commonly in practice.
- A Model for Level-Loading Production in the Process Industries when Demand is Stochastic
Recent observations of process manufacturing firms have called into question the efficacy of pursuing lean manufacturing approaches in environments where there is inherent inflexibility in the production environment. We examine a multi-item integrated production-inventory system in which changeover times are significant on the process equipment, and customer demand is highly uncertain. We develop and explore a production stabilizing policy in which the length of the production cycle is kept between a lower and upper limit. We discuss how capacity utilization and demand uncertainty interact with lower and upper limits to affect system performance. A numerical study is conducted to evaluate the accuracy of the approach.
- Optimizing Inventory Levels to Stabilize Production Cycles in the Process Industries
Management intuition alone is insufficient to understand the inventory consequences of strategic supply chain decisions in process industries. Senior management may make structural changes to the supply chain through capacity investments or cost-saving initiatives that result in unanticipated inventory levels, operating costs and customer service performance. Manufacturing environments whose production planning is governed by pure rotation schedules, or “product wheels,” are often constrained by upper and lower cycle limits. These types of schedules and constraints are commonly found in process and large batch manufacturing industries. In these types of environments, it is typically more economical to stabilize the production capacity and allow inventory to absorb the shocks of random demand. A critical decision in such environments is that of determining the appropriate inventory level to absorb the demand uncertainty so that planned production cycles may be stabilized while achieving a stated customer service goal. In this paper, we provide a framework to quantify the inventory consequences of strategic management decisions regarding production capacity utilization, product mix, choice of production technology and market demand uncertainty.
Component Inventory Optimization
- Determining Component Inventory Levels in Capacitated Assembly Systems
We explore the problem of establishing inventory base stock levels for products and components in large scale, capacitated assembly environments. Customer demand for products is stochastic and accurate forecasts may not be available. Procurement lead times for individual components may be lengthy. Component use in finished products is defined through a single level bill of materials. We are interested in a computationally efficient approach for jointly computing inventory levels of components and products that minimize the per period expected holding and backorder costs over an infinite horizon. We define an operating policy and develop a solution approach for making joint stocking decisions. We examine the system performance under different capacity and component allocation rules, including a simplistic firs-come, first-served rule and a real-time optimization rule. In our numerical study, our solution approach estimates expected costs within an average error of 4.0\%. The real time optimization rule reduces the average number of backorders by 33.3\% over the first-come, first-served policy. Our approach was implemented in an industrial application that resulted in a 45\% reduction in inventories with no reduction in customer service levels. - Procurement of Common Components in a Stochastic Environment
We consider a multi-period, two-level, product-component configuration where many of the components are common to several products. The demands for the products in any period are stochastic and independent. Our objective is to determine the component quantities that are to be ordered every period that satisfy a pre-specified service level placed jointly on the products. We consider three approximations and compare the quality of solutions obtained. Our focus is on quickly solving medium- to large-scale problems. Through a large simulation study, we demonstrate the performance and behavior of our algorithms as well as provide insights into the benefits of commonality for complex product structures.
Service Parts Optimization
- Integrated Real-Time Capacity and Inventory Allocation for Reparable Service Parts in a Two-Echelon Supply System
Numerous models exist in the literature for determining reparable item inventory levels in multi-echelon systems. These models are typically based on steady state analyses and are not appropriate for making real-time allocation decisions in highly dynamic operating environments, as are often found in service parts repair and logistics systems. In such systems, procurement lead times for service parts are lengthy, operational requirements change frequently, and repair capacity is limited and variable. As a consequence, it is common to have many items in short supply while others are in long supply. This imbalance requires a real-time decision-support system that responds appropriately to the non-stationary nature of the environment. In this paper, we examine a multi-echelon system for reparable service parts that can be applied in dynamic logistics systems. We formulate a finite-horizon, periodic-review mathematical programming decision model for determining the optimal allocation of service parts across multiple field stocking locations as well as the optimal allocation of repair capacity. Multiple modes of transportation are allowed. We also develop practical heuristic methods for making these decisions. Of particular concern is the value of employing these real-time decision models over simple allocation rules in highly dynamic logistics environments. We also demonstrate the quality and computational efficiency of the heuristic approaches. - Optimal Stocking in Reparable Parts Networks with Repair Capacity and Inventory Pooling
This paper addresses a tactical planning problem for a three-echelon, reparable-parts service network characterized by a central repair facility and local opportunities for inventory pooling. The problem is to find the optimal total system stock. The model proposed is appropriate for high-cost, high-criticality, low-demand-rate parts for which transport times are short and for which optimization-based stock allocation is performed in the distribution system. The model includes parameters that characterize design and management decisions in the resupply system. The model can be solved in time that is nlog(n) in the number of part number- location combinations, making it a practical technique for large-scale inventory problems. One implication of this approach is to emphasize the importance of optimization-based stock allocation and repair priority routines in inventory management execution systems.
- Optimizing Service Parts Inventory in a Multi-Echelon, Multi-Item Supply Chain with Time-Based Customer Service Level Agreements
In the realm of service parts management, customer relationships are often established through service agreements that extend over a period of months or years. These agreements typically apply to a piece of equipment (or system) that the customer has purchased, and they specify the type of service that will be provided, as well as the timing with which the service will take place. In the case of a customer that operates in multiple locations, service agreements may apply to several pieces of equipment across several locations. In this paper we describe a continuous-review inventory model for a multi-item, multi-echelon service parts distribution system in which time-based service level requirements exist for general groups of items. The time limits specified by the requirements, if not instantaneous, are presumed to coincide with transport times from replenishment sites within the distribution network. Our goal is to determine base stock levels for all items at all locations so that the service level requirements are met at minimum investment. - Efficient Computation Of Time-Based Customer Service Levels In A Multi-Item, Multi-Echelon Supply Chain: A Practical Approach For Inventory Optimization
Time-based item fill rates, or “channel” fill rates, are the building blocks needed to evaluate steady-state compliance with time-based customer service agreements. Exact computation of channel fill rates is both difficult and time consuming, yet their accurate assessment is essential for system-wide inventory optimization. We describe and validate a practical method for computing channel fill rates in a multi-item, multi-echelon service parts distribution system. A simulation study is presented which shows that, in a three-echelon setting, our estimation errors are very small over a wide range of base stock level vectors. A more accurate, though less efficient, approximation method is also evaluated for comparison.
Health Care Materials Management
- A Rolling Horizon Materials Planning Model to Support Operating Room Schedules
Healthcare costs continue to rise at unprecedented rates in the U.S. While past research has addressed optimal patient scheduling to improve the utilization of operating theater resources (physicians, specialized equipment and operating rooms), there is little research that attempts to optimize material requirements and safety stocks to support these optimized schedules. While classical inventory models can and should be used, there are additional considerations in hospital environments that remain unaddressed. Based on our empirical observations, there are significant opportunities to reduce waste by improving the planning and coordination of materials and information in hospital operating theaters. To quantify these opportunities, we develop an analytic optimization framework for planning and executing material quantity preparation and placement decisions to support hospital operating theaters. Our model is novel and considers the non-stationary stochastic demand associated with block scheduling of surgical departments and a stochastic bill of materials common in surgical treatments. It is computationally efficient and flexible, allowing fast solutions and unique considerations for individual hospitals. We conduct a numerical study and sensitivity analysis to pinpoint the leverage points in the system for reducing inventory carrying and materials management costs. Reducing the uncertainty in the bill of materials exhibited the greatest potential for savings.
