Multi-Echelon Inventory Optimization and Demand-Side Management: Models and Algorithms

Inventory management is a fudamental problem in supply chain management. It is widely used in practice, but it is also intrinsically hard to optimize, even for relatively simple inventory system structures. This challenge has also been heightened under the threat of supply disruptions. Whenever a supply source is disrupted, the inventory system is paralyzed, and tremenduous costs can occur as a consequence. Designing a reliable and robust inventory system that can withstand supply disruptions is vital for an inventory system\u27s performance.First we consider a basic type of inventory network, an assembly system, which produces a single end product from one or several components. A property called long-run balance allows an assembly system to be reduced to a serial system when disruptions are not present. We show that a modified version is still true under disruption risk. Based on this property, we propose a method for reducing the system into a serial system with extra inventory at certain stages that face supply disruptions. We also propose a heuristic for solving the reduced system. A numerical study shows that this heuristic performs very well, yielding significant cost savings when compared with the best-known algorithm.Next we study another basic inventory network structure, a distribution system. We study continuous-review, multi-echelon distribution systems subject to supply disruptions, with Poisson customer demands under a first-come, first-served allocation policy. We develop a recursive optimization heuristic, which applies a bottom-up approach that sequentially approximates the base-stock levels of all the locations. Our numerical study shows that it performs very well.Finally we consider a problem related to smart grids, an area where supply and demand are still decisive factors. Instead of matching supply with demand, as in the first two parts of the dissertation, now we concentrate on the interaction between supply and demand. We consider an electricity service provider that wishes to set prices for a large customer (user or aggregator) with flexible loads so that the resulting load profile matches a predetermined profile as closely as possible. We model the deterministic demand case as a bilevel problem in which the service provider sets price coefficients and the customer responds by shifting loads forward in time. We derive optimality conditions for the lower-level problem to obtain a single-level problem that can be solved efficiently. For the stochastic-demand case, we approximate the consumer\u27s best response function and use this approximation to calculate the service provider\u27s optimal strategy. Our numerical study shows the tractability of the new models for both the deterministic and stochastic cases, and that our pricing scheme is very effective for the service provider to shape consumer demand

Uncertainty and the price for crude oil reserves

Innovations in futures, options, and derivative instruments permit active trading, speculating and hedging - linking markets for physical petroleum products with financial markets. These derivative markets continuously value petroleum delivered today and for future dates, providing a market price for inventories. Underground petroleum reserves are also an inventory defined by exploration surveys and development drilling. Thus, observable market information can be used to value these reserves. Option - valuation models can be used to price reserves using observable markets, but are dependent on unexplained convenience yields revealed by the term structure of futures prices. The authors apply a general inventory pricing model to petroleum inventories and generate an empirical model of the returns to storage for petroleum markets. They examine the determinants of the crude oil convenience yield using a stochastic control model. They specify optimal production and inventory conditions using a third-order cost function and estimate them using monthly observations. Their inventory arbitrage condition embodies the Hotelling principle and Kaldor's convenience yield, and includes a premium on the dispersion in crude oil prices. The empirical results suggest that returns to storage contain both a cost-reducing component and often sizable premiums associated with the dispersion of petroleum prices. Their findings suggest that crude oil markets differentiated by quality and location provide similar premiums. The premiums associated with the dispersion of petroleum prices may account for persistent backwardation in crude oil prices. This finding may also explain the wide discrepancies between Hotelling values and transaction prices found in previous studies.Economic Theory&Research,Environmental Economics&Policies,Markets and Market Access,Labor Policies,Payment Systems&Infrastructure,Oil Refining&Gas Industry,Environmental Economics&Policies,Access to Markets,Markets and Market Access,Economic Theory&Research

Agribusiness supply chain risk management: A review of quantitative decision models

Supply chain risk management is a large and growing field of research. However, within this field, mathematical models for agricultural products have received relatively little attention. This is somewhat surprising as risk management is even more important for agricultural supply chains due to challenges associated with seasonality, supply spikes, long supply lead-times, and perishability. This paper carries out a thorough review of the relatively limited literature on quantitative risk management models for agricultural supply chains. Specifically, we identify robustness and resilience as two key techniques for managing risk. Since these terms are not used consistently in the literature, we propose clear definitions and metrics for these terms; we then use these definitions to classify the agricultural supply chain risk management literature. Implications are given for both practice and future research on agricultural supply chain risk management

Ordering policies for a dual sourcing supply chain with disruption risks

Purpose: The main purpose of this article is to explore the trade-off between ordering policies and disruption risks in a dual-sourcing network under specific (or not) service level constraints, assuming that both supply channels are susceptible to disruption risks. Design/methodology/approach: Stochastic newsvendor models are presented under both the unconstrained and fill rate constraint cases. The models can be applicable for different types of disruptions related among others to the supply of raw materials, the production process, and the distribution system, as well as security breaches and natural disasters. Findings: Through the model, we obtain some important managerial insights and evaluate the value of contingency strategies in managing uncertain supply chains. Originality/value: This paper attempts to combine explicitly disruption management with risk aversion issues for a two-stage supply chain with two unreliable suppliers.Peer Reviewe

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

A Simulation Approach to Modeling Contingency Strategies for Managing Electronic Part Supply Chain Disruptions

Due to the nature of the manufacturing and support activities associated with long life cycle products, parts need to be dependably and consistently available. However, the parts that comprise long life cycle products are susceptible to a variety of supply chain disruptions. In order to minimize the impact of these unavoidable disruptions to product production and support, manufacturers can implement proactive mitigation strategies. Careful selection of the mitigation strategy (second sourcing and/or buffering) is key, as it can dramatically impact the part total cost of ownership. This thesis developed a simulation model that performs tradeoff analyses and identifies a near-optimal combination of second sourcing and buffering for specific part and product scenarios. In addition, this thesis explores the effectiveness of traditional analytical models when compared to a simulation-based approach for the selection of an effective optimal disruption mitigation strategy. Several case studies were performed that: 1) tested the impact of popular analytical limiting assumptions, and 2) implemented realistic disruption data in the context of real part management. The first set of case studies demonstrated that the simulation model is capable of overcoming significant scenario restrictions prevalent within traditional analytical models: finite horizon (including non-zero WACC), fixed support costs, and unreliable backup suppliers are essential components for determining the effective optimal disruption mitigation strategy for a given disruption scenario. The second set of case studies demonstrates the importance of proper mitigation strategy selection in real electronic part supply chain scenarios. The results from the case studies not only justified the need for a simulation-based approach to disruption modeling, but also helped to cement the simulation model as an effective decision making tool for electronic part distributors

Global dual-sourcing strategy: is it effective in mitigating supply disruption?

Most firms are still failing to think strategically and systematically about managing supply disruption risk and most of the supply chain management efforts are focused on reducing supply chain operation costs rather than managing disruption. Some innovative firms have taken steps to implement supply chain risk management (SCRM). Inventory management is part of SCRM because supply disruptions negatively affect the reliability of deliveries from suppliers and the costs associated with the ordering process. The complexity of existing inventory models makes it challenging to combine the management of the supply process and inventory in a single model due, for example, to the difficulty of including the characteristics of the disruption process in the supply chain network structure. Therefore, there is a need for a simple flexible model that can incorporate the key elements of supply disruption in an inventory model. This thesis presents a series of models that investigate the importance of information on disruption discovery and recovery for a firm’s supply and inventory management. A simple two-echelon supply chain with one firm and two suppliers (i.e., referred to as the onshore and offshore suppliers) in a single product/component setting has been considered in this thesis for the purpose of experimental analyses. The sourcing decisions that the firm faces during periods of supply disruption are examined leading to an assessment of how information about the risk and length of disruption and recovery can be used to facilitate the firm’s sourcing decisions and monitor the performance of stock control during the disruption. The first part of this thesis analyses basic ordering models (Model 1 and Model 2 respectively) without the risk of supply disruption and with the risk of supply disruption. The second part analyses the value of supply disruption information, using a model with advance information on the length of disruption (Model 3) and a model with learning about the length of disruption (Model 4). The third part explores a quantitative recovery model and the analyses in this part consider of three models. Model 5 assumes a basic phased recovery model, Model 6 assumes advance information about the phased recovery process and Model 7 assumes learning about the phased recovery process. The last part of this thesis investigates the order pressure scenario that exists in the firm’s supply chain. Under this scenario, disruption to one part of the supply chain network increases demand on the remainder resulting in a lower service levels than normal. This scenario is applied to all the previous models apart from Model 1. The models in this thesis are examined under finite and infinite planning horizons and with constant and stochastic demand. The objective of the models is to minimise the expected inventory cost and optimise the order quantity from the suppliers given the different assumptions with respect to the length of supply disruption and information about the recovery process. The models have been developed using the discrete time Markov decision process (DTMDP) technique and implemented using the Java programming language. The findings of this thesis could be used to help a firm that is facing the risk supply disruption to develop its SCRM program. The findings highlight the importance of considering quantitative measures of the disruption and recovery processes, something which is still not popular within SCRM in some organisations

Finite Alphabet Control of Logistic Networks with Discrete Uncertainty

We consider logistic networks in which the control and disturbance inputs take values in finite sets. We derive a necessary and sufficient condition for the existence of robustly control invariant (hyperbox) sets. We show that a stronger version of this condition is sufficient to guarantee robust global attractivity, and we construct a counterexample demonstrating that it is not necessary. Being constructive, our proofs of sufficiency allow us to extract the corresponding robust control laws and to establish the invariance of certain sets. Finally, we highlight parallels between our results and existing results in the literature, and we conclude our study with two simple illustrative examples