REVENUE AND ORDER MANAGEMENT UNDER DEMAND UNCERTAINTY

We consider a firm that delivers its products across several customers or markets, each with unique revenue and uncertain demand size for a single selling season. Given that the firm experiences a long procurement lead time, the firm must decide, far in advance of the selling season not only the markets to be pursued but also the procurement quantity. In this dissertation, we present several operational scenarios in which the firm must decide which customer demands to satisfy, at what level to satisfy each customer demand, and how much to produce (or order) in total. Traditionally, a newsvendor approach to the single period problem assumes the use of an expected profit objective. However, maximizing expected profit would not be appropriate for firms that cannot afford successive losses or negligible profits over several consecutive selling seasons. Such a setting would most likely require minimizing the downside risk of accepting uncertain demands into the production plan. We consider the implications of such competing objectives. We also investigate the impact that various forms of demand can have on the flexibility of a firm in their customer/market selection process. a firm may face a small set of unconfirmed orders, and each order will often either come in at a predefined level, or it will not come in at all. We explore optimization solution methods for this all-or-nothing demand case with risk-averse objective utilizing conditional value at risk (CVaR) concept from portfolio management. Finally, in this research, we explore extensions of the market selection problem. First, we consider the impact of incorporating market-specific expediting costs into the demand selection and procurement decisions. Using a lost sales assumption instead of an expediting assumption, we perform a similar analysis using market-specific lost sales costs. For each extension we investigate two different approaches: i) Greedy approach: here we allocate order quantity to market with lowest expediting cost (lowest expected revenue) first. ii) Rationing approach: here we find the shortage (lost sale) then ration it across all the markets. We present ideas and approaches for each of these extensions to the selective newsvendor problem

Now or Later: A Simple Policy for Effective Dual Sourcing in Capacitated Systems

We examine a possibly capacitated, periodically reviewed, single-stage inventory system where replenishment can be obtained either through a regular fixed lead time channel, or, for a premium, via a channel with a smaller fixed lead time. We consider the case when the unsatisfied demands are backordered over an infinite horizon, introducing the easily implementable, yet informationally rich dual-index policy. We show very general separability results for the optimal parameter values, providing a simulation-based optimization procedure that exploits these separability properties to calculate the optimal inventory parameters within seconds. We explore the performance of the dual-index policy under stationary demands as well as capacitated production environments, demonstrating when the dual-sourcing option is most valuable. We find that the optimal dual-index policy mimics the behavior of the complex, globally optimal state-dependent policy found via dynamic programming: the dual-index policy is nearly optimal (within 1% or 2%) for the majority of cases, and significantly outperforms single sourcing (up to 50% better). Our results on optimal dual-index parameters are generic, extending to a variety of complex and realistic scenarios such as nonstationary demand, random yields, demand spikes, and supply disruptions

Supplier Choice: Market Selection under Uncertainty.

Suppliers and Manufacturers generally have some say in which subset of all possible demand they will meet. In some cases that choice is implicit through pricing decisions and feature selection. Other times it is made explicitly by choosing only specific regions to stock a product in. This thesis includes models using both approaches and incorporates random demands. We present several methods for choosing a subset of all candidate customers given uncertain demands. In this thesis we consider four models of demand selection. The first two research problems consider market selection, which has been studied in the literature. The Selective Newsvendor Problem (SNP) looks at a decision maker choosing a subset of candidate markets to serve, and then receiving revenues and paying newsvendor-type costs based on the selected collection. In this thesis we consider a generalization with normally distributed demands which includes a multi-period problem as a special case and develop both exact and heuristic algorithms to solve it. When demands are not normally distributed, the problem is considerably more complex and is in general NP-hard. We develop an approximation algorithm using sample average approximation and a rounding approach to efficiently solve the problem. In addition to the work on market selection, we propose two other models for demand selection. We study auctions as a tool for a supplier with a fixed capacity to allocate the limited supply to retailers with newsvendor-type costs. Finally, we present a model for a supplier who must ensure demand is met in all markets, but has the option to work with subsidiary suppliers to meet that demand.PhDIndustrial and Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/120864/1/zstrinka_1.pd

Analysis of a two-echelon inventory system with two supply modes

In this paper, we consider a serial two-echelon periodic review inventory system with two supply modes at the most upstream stock point. As control policy for this system, we propose a natural extension of the dual-index policy, which has three base-stock levels. We consider the minimization of long run average inventory holding, backlogging, and both per unit and fixed emergency ordering costs. We provide nested newsboy characterizations for two of the three base-stock levels involved and show a separability result for the difference with the remaining base-stock level. We use results for the single-echelon system to efficiently approximate the distributions of random variables involved in the newsboy equations and find an asymptotically correct approximation for both the per unit and fixed emergency ordering costs. Based on these results, we provide an algorithm for setting base-stock levels in a computationally efficient manner. In a numerical study, we investigate the value of dual-sourcing in supply chains and show that it is useful to decrease upstream stock levels. In cases with high demand uncertainty, high backlogging cost or long lead times, we conclude that dual-sourcing can lead to significant savings

Newsvendor bounds and heuristics for serial supply chains with regular and expedited shipping

We study an infinite-horizon, N -stage, serial production/inventory system with two transportation modes between stages: regular shipping and expedited shipping. The optimal inventory policy for this system is a top–down echelon base-stock policy, which can be computed through minimizing 2 N nested convex functions recursively (Lawson and Porteus, Oper Res 48 (2000), 878–893). In this article, we first present some structural properties and comparative statics for the parameters of the optimal inventory policies, we then derive simple, newsvendor-type lower and upper bounds for the optimal control parameters. These results are used to develop near optimal heuristic solutions for the echelon base-stock policies. Numerical studies show that the heuristic performs well. © 2009 Wiley Periodicals, Inc. Naval Research Logistics, 2010Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/64894/1/20388_ftp.pd

Expediting in Two-Echelon Spare Parts Inventory Systems

We consider a two-echelon spare parts inventory system consisting of one central warehouse and multiple local warehouses. Each warehouse keeps multiple types of repairable parts to maintain several types of capital goods. The local warehouses face Poisson demand and are replenished by the central warehouse. We assume that unsatisfied demand is backordered at all warehouses. Furthermore, we assume deterministic lead times for the replenishments of the local warehouses. The repair shop at the central warehouse has two repair options for each repairable part: a regular repair option and an expedited repair option. Both repair options have stochastic lead times. Irrespective of the repair option, each repairable part uses a certain resource for its repair. Assuming a dual-index policy at the central warehouse and base stock control at the local warehouses, an exact and efficient evaluation procedure for a given control policy is formulated. To find an optimal control policy, we look at the minimization of total investment costs under constraints on both the aggregate mean number of backorders per capital good type and the aggregate mean fraction of repairs that are expedited per repair resource. For this non-linear non-convex integer programming problem, we develop a greedy heuristic and an algorithm based on decomposition and column generation. Both solution approaches perform very well with average optimality gaps of 1.56 and 0.23 percent, respectively, across a large test bed of industrial size. Based on a case study at Netherlands Railways, we show how managers can significantly reduce the investment in repairable spare parts when dynamic repair policies are leveraged to prioritize repair of parts whose inventory is critically low

Rapid deployment of oil-drilling tools utilizing distribution network and inventory strategies

Thesis (M. Eng. in Logistics)--Massachusetts Institute of Technology, Engineering Systems Division, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 73-76).DTS is an oil and gas services company that delivers drilling tools to six major customer districts in the continental U.S. After the tools are used at a rig, they are transported to the closest repair and maintenance (MTC) facility in either Colorado (CO) or Oklahoma (OK) where they are disassembled and reconditioned for use on a future job. The tools are modular and require custom assembly and programming, depending on the requirements of the well. On occasion, DTS receives urgent orders for drilling tools to replace failed tools or to cater to unexpected demand. These urgent orders are expected to be delivered to customer sites in less than 24 hours from when an order is received. DTS wants to analyze the supply chain impact of consolidating MTC activities to a single facility for operational efficiencies. The rationalization of MTC activities to the CO facility affects DTS's ability to deliver tools within 24 hours due to the longer transportation times to customer districts. How can this longer transportation time be mitigated? Our research shows that using the OK facility as a postponement and distribution hub allows DTS to continue servicing expedited orders within 24 hours and results in a 28% logistics cost savings over a direct shipment method. The postponement strategy entails staging reconditioned inventory at both the OK and CO facility where they can be configured for use within 4 hours of receiving an order. By simulating the movement of inventory around the closed inventory loop, we determined that the total number of tools in the network and the MTC capacity are two important levers of control that affect the availability of reconditioned inventory to service demand. We found that we were able to fulfill a target item fill rate by calculating capital inventory required using an "order up to" inventory policy and setting facility capacity at one standard deviation above average demand.by Ryan Rahim.M.Eng.in Logistic

Containing Risk when Maximizing Supply-Chain Performance

The objective of this dissertation is to develop and test an approach that will quantify the level of risk in the supply chain, evaluate the cost and impact of risk mitigation strategies, validate event management protocols pre-implementation, and optimize across a portfolio of risk mitigation strategies. The research integrates a Mixed Integer Linear Programming (MILP) model and a Discrete Event Simulation model to investigate a production-inventory-transportation problem subject to risk. The MILP model calculates the optimal Net Profit Contribution of the supply chain in the absence of risk. Deviation risks are introduced as volatility in final demand and lead times, with lead time volatility affecting raw material lead times from suppliers to manufacturing plants and finished goods lead times from manufacturing plants to the warehouses. Disruption risks are modelled as temporarily impeding production at the manufacturing plants, in-bound distribution of raw materials from suppliers to the manufacturing plants, and out-bound distribution of finished goods from the manufacturing plants to warehouses. Computational experiments are run to examine the impact of risk on the supply chain. Further experiments explore the consequences of three risk mitigation strategies (inventory placement, expediting, and production flexibility) on supply chain performance in the presence of risk with the aim of discovering whether one strategy dominates or whether a portfolio approach to risk mitigation performs best. In sum, this research seeks to develop a framework that can inform efforts in understanding, planning for and controlling risk in the supply chain