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

Theory and Practice of Supply Chain Synchronization

In this dissertation, we develop strategies to synchronize component procurement in assemble-to-order (ATO) production and overhaul operations. We focus on the high-tech and mass customization industries which are not only considered to be very important to create or keep U.S. manufacturing jobs, but also suffer most from component inventory burden. In the second chapter, we address the deterministic joint replenishment inventory problem with batch size constraints (JRPB). We characterize system regeneration points, derive a closed-form expression of the average product inventory, and formulate the problem of finding the optimal joint reorder interval to minimize inventory and ordering costs per unit of time. Thereafter, we discuss exact solution approaches and the case of variable reorder intervals. Computational examples demonstrate the power of our methodology. In the third chapter, we incorporate stochastic demand to the JRPB. We propose a joint part replenishment policy that balances inventory and ordering costs while providing a desired service level. A case study and guided computational experiments show the magnitudes of savings that are possible using our methodology. In the fourth chapter, we show how lack of synchronization in assembly systems with long and highly variable component supply lead times can rapidly deteriorate system performance. We develop a full synchronization strategy through time buffering of component orders, which not only guarantees meeting planned production dates but also drastically reduces inventory holding costs. A case study has been carried out to prove the practical relevance, assess potential risks, and evaluate phased implementation policies. The fifth chapter explores the use of condition information from a large number of distributed working units in the field to improve the management of the inventory of spare parts required to maintain those units. Synchronization is again paramount here since spare part inventory needs to adapt to the condition of the engine fleet. All needed parts must be available to complete the overhaul of a unit. We develop a complex simulation environment to assess the performance of different inventory policies and the value of health monitoring. The sixth chapter concludes this dissertation and outlines future research plans as well as opportunities

Impact of business forecasting on demand planning. A strategy for improving business forecasting and reducing inventories throughout the supply chain for fast moving consumer goods in the Middle East market.

Poor quality of information and forecasting create a number of problems for manufacturing companies, such as poor planning of products and insufficient service levels, which leads to increased inventory and stock holding or stockouts and increased total costs. Cussons (UK) Limited is experiencing precisely these problems. Apart from these problems normally associated with forecasting demand for fast moving consumer goods there is an additional problem of reconciling the Western calendar with the Muslim calendar, and a recognition of the effects that Muslim religious holidays, as opposed to Christian religious holidays, have on demand. Muslim religious holidays rotate backwards with regard to the Western calendar, but in fact they occur at known dates and therefore the effect they have on demand for products can be taken into consideration when attempting to forecast demand. An additional problem that influences Cussons' sales in the market is the seasonal pattern of demand. Due to this, there is an increase in demand for Cussons' products during summer months. From the analysis of both data sets it was identified that the warehouse movement data is less variable and more reliable for business forecasting than order data. In this thesis, these forecasting problems are examined as a case study, focusing on these particular problems. To overcome these problems and to improve business forecasting of Cussons' products in the Middle East market, a forecasting strategy has been suggested which will enable Cusson's to reduce the inventories throughout the supply chain and to improve their customer's service.Ministry of Education, Government of Pakistan, Cussons (UK)Limited

An evaluation of the economic cost impacts of classical forecast errors

Evidence from literature suggests that there is no shortage of studies concerned with the supply chain risk management and the associated performance by the individual echelons and functional business areas or through coordinated efforts. Literature has also demonstrated strong association between the performance of supply chain inventory management and control policies and profitability. Thus, integration of operational policies with financial decisions has been seen as an avenue to improve and to better corporate strategic financial objectives in supply chain sector organisations through optimal inventory investment. This is quite important since measures to improve financial performance implicitly influence and restrict operational performance including the management of inventory. However, on the modelling of inventory and finance and in measuring the impact of one on the other, traditional approaches tend to think of one as the input into the other without due consideration for the interconnections between the two over time. In particular, the traditional inventory cost model appears to present a disconnect between operational choices and financial decisions. This thesis models both and their interconnections explicitly and simultaneously. Supposing a periodic review inventory policy with finite horizon and single perishable product, this study proposes a simple easy to understand solution. Specifically, in evaluating the economic consequences of classical forecast error metrics on inventory control system, study improves the current approach by creating a versatile consolidative costs evaluation function that aligns both operational and financial decisions as well as captures the business contextual considerations. The research study results revealed that we can easily utilise the proposed robust costs structure at the right scale (of demand uncertainty) and in the right scope (of financial capacity) to reveal the real and correct cost effects that facilitates users to produce practically feasible plans for their businesses

Data Science in Supply Chain Management: Data-Related Influences on Demand Planning

Data-driven decisions have become an important aspect of supply chain management. Demand planners are tasked with analyzing volumes of data that are being collected at a torrential pace from myriad sources in order to translate them into actionable business intelligence. In particular, demand volatilities and planning are vital for effective and efficient decisions. Yet, the accuracy of these metrics is dependent on the proper specification and parameterization of models and measurements. Thus, demand planners need to step away from a black box approach to supply chain data science. Utilizing paired weekly point-of-sale (POS) and order data collected at retail distribution centers, this dissertation attempts to resolve three conflicts in supply chain data science. First, a hierarchical linear model is used to empirically investigate the conflicting observation of the magnitude and prevalence of demand distortion in supply chains. Results corroborate with the theoretical literature and find that data aggregation obscure the true underlying magnitude of demand distortion while seasonality dampens it. Second, a quasi-experiment in forecasting is performed to analyze the effect of temporal aggregation on forecast accuracy using two different sources of demand signals. Results suggest that while temporal aggregation can be used to mitigate demand distortion\u27s harmful effect on forecast accuracy in lieu of shared downstream demand signal, its overall effect is governed by the autocorrelation factor of the forecast input. Lastly, a demand forecast competition is used to investigate the complex interaction among demand distortion, signal and characteristics on seasonal forecasting model selection as well as accuracy. The third essay finds that demand distortion and demand characteristics are important drivers for both signal and model selection. In particular, contrary to conventional wisdom, the multiplicative seasonal model is often outperformed by the additive model. Altogether, this dissertation advances both theory and practice in data science in supply chain management by peeking into the black box to identify several levers that managers may control to improve demand planning. Having greater awareness over model and parameter specifications offers greater control over their influence on statistical outcomes and data-driven decision

Maintenance spare parts planning and control : a framework for control and agenda for future research

This paper presents a framework for planning and control of the spare parts supply chain in organizations that use and maintain high-value capital assets. Decisions in the framework are decomposed hierarchically and interfaces are described. We provide relevant literature to aid decision making and identify open research topics. The framework can be used to increase the e??ciency, consistency and sustainability of decisions on how to plan and control a spare parts supply chain. Applicability of the framework in di??erent environments is investigated

Modeling inventory and responsiveness costs in a supply chain

Evaluation of supply chain performance is often complicated by the various interrelationships that exist within the network of suppliers. Currently many supply chain metrics cannot be analytically determined. Instead, metrics are derived from monitoring historical data, which is commonly referred to as Supply Chain Analytics. With these analytics it is possible to answer questions such as: What is the inventory cost distribution across the chain? What is the actual inventory turnover ratio? What is the cost of demand changes to individual suppliers? However, this approach requires a significant amount of historical data which must be continuously extracted from the associated Enterprise Resources Planning (ERP) system. In this dissertation models are developed for evaluating two Supply Chain metrics, as an alternative to the use of Supply Chain Analytics. First, inventory costs are estimated by supplier in a deterministic (Q , R, δ )2 supply chain. In this arrangement each part has two sequential reorder (R) inventory locations: (i) on the output side of the seller and (ii) on the input side of the buyer. In most cases the inventory policies are not synchronized and as a result the inventory behavior is not easily characterized and tends to exhibit long cycles. This is primarily due to the difference in production rates ( δ), production batch sizes, and the selection of supply order quantities (Q) for logistics convenience. The (Q , R, δ )2 model that is developed is an extension of the joint economic lot size (JELS) model first proposed by Banerjee (1986). JELS is derived as a compromise between the seller\u27s and the buyer\u27s economic lot sizes and therefore attempts to synchronize the supply policy. The (Q , R, δ )2 model is an approximation since it approximates the average inventory behavior across a range of supply cycles. Several supply relationships are considered by capturing the inventory behavior for each supplier in that relationship. For several case studies the joint inventory cost for a supply pair tends to be a stepped convex function. Second, a measure is derived for responsiveness of a supply chain as a function of the expected annual cost of making inventory and production capacity adjustments to account for a series of significant demand change events. Modern supply chains are expected to use changes in production capacity (as opposed to inventory) to react to significant demand changes. Significant demand changes are defined as shifts in market conditions that cannot be buffered by finished product inventory alone and require adjustments in the supply policy. These changes could involve a ± 25% change in the uniform demand level. The research question is what these costs are and how they are being shared within the network of suppliers. The developed measure is applicable in a multi-product supply chain and considers both demand correlations and resource commonality. Finally, the behavior of the two developed metrics is studied as a function of key supply chain parameters (e.g., reorder levels, batch sizes, and demand rate changes). A deterministic simulation model and program was developed for this purpose