Methodologies for performance enhancement in decentralized supply chains

Ph.DDOCTOR OF PHILOSOPH

Engineering patterns of supply chain optimization to manage oscillation effect

The cascading order variability from downstream trumping up the upstream site of the supply chain network indicates the deleterious effect to the performance of the fast moving consumer goods industry. The fundamental likelihood to optimization in this industry requires dexterous flows of quasi-real-time information, as well as reliable product availability. In this context, this study analyzes the challenges of bullwhip effect on the perspective of ingenious optimization strategies, and further contemplates to establish the engineering patterns of interrelationships on the magnitude of pooling the resources to advance supply chain capabilities. The suppression of bullwhip effect on underlying optimization strategies is sought to elevate accelerated responsiveness, improve network demand visibility and reduce volatility in frequencies to inventory replenishment. A rigorous and disciplined quantitative approach afforded the tentatively development of pattern of interrelated supply chain dimensions. The factor analysis method was used on 448 responses and insightful findings were produced from the compelling purposive sampling technique. The findings indicate that the magnitude of better ameliorating bullwhip effect, the value of competitive economic information and strength of selected optimization strategies depend on the model of unified engineering patterns. This paper provides insights to FMCG industry on using innovative strategies and modern technology to enhance supply chain visibility through integrated systems networks

Impact of communication delay on distributed load frequency control (dis-LFC) in multi-area power system (MAPS)

In this paper, impact of communication delay on distributed load frequency control (dis-LFC) of multi-area interconnected power system (MAIPS) is investigated. Load frequency control (LFC), as one of ancillary services, is aimed at maintaining system frequency and inter-area tie-line power close to the scheduled values, by load reference set-point manipulation and consideration of the system constraints. Centralized LFC (cen-LFC) requires inherent communication bandwidth limitations, stability and computational complexity, as such, it is not a good technique for the control of large-scale and geographically wide power systems. To decrease the system dimensionality and increase performance efficiency, distributed and decentralized control techniques are adopted. In distributed LFC (dis-LFC) of MAIPS, each control area (CA) is equipped with a local controller and are made to exchange their control actions by communication with controllers in the neighboring areas. The delay in this communication can affect the performance of the LFC scheme and in a worst case deteriorates power system stability. To investigate the impact of this delay, model predictive controller (MPC) is employed in the presence of constraints and external disturbances to serve as LFC tracking control. The scheme discretizes the system and solves an on-line optimization at each time sample. The system is subjected to communication delay between the CAs, and the response to the step load perturbation with and without the delay. Time-based simulations were used on a three-area MAIPS in MATLAB/SIMULINK environment to verify the investigations. The overshoot and settling time in the results reveals deterioration of the control performance with delay. Also, the dis-LFC led to zero steady states errors for frequency deviations and enhanced the MAIPS’ performance. With this achievement, MPC proved its constraints handling capability, online rolling optimization and ability to predict future behavior of systems

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

Exploring the Relationship between Supply Network Configuration, Interorganizational Information Sharing and Performance

ABSTRACT EXPLORING THE RELATIONSHIP BETWEEN SUPPLY NETWORK CONFIGURATION, INTER-ORGANIZATIONAL INFORMATION SHARING AND PERFORMANCE By MARCIA DALEY August 2008 Committee Chair: Dr. Subhashish Samaddar Major Department: Decision Science Critical to the success of a firm is the ability of managers to coordinate the complex network of business relationships that can exist between business partners in the supply network. However many managers are unsure on how best to leverage their resources to capitalize on the information sharing opportunities that are available in such networks. Although there is significant research on information sharing, the area of inter-organizational information sharing (IIS) is still evolving and there is limited research on IIS in relation to systemic factors within supply networks. To help fill this gap in the literature, a primary focus of this dissertation is on the relationship between the design of the supply network and IIS. The design of the supply network is characterized by the supply network configuration which is comprised of (1) the network pattern, (2) the number of stages in the supply network, and (3) where the firm is located in that supply network. Four different types of IIS are investigated, herein. These types of IIS are a function of the frequency with which information is shared and the scope of information shared. Type 1 (Type 2) IIS is the low (high) frequency state where only operational information is shared. Similarly, Type 3 (Type 4) is the low (high) frequency state where strategic information is shared. The argument is that the type of IIS varies depending on the configuration of the supply network and that this relationship is influenced by the coordination structure established between firms in the network. The second focus of this dissertation deals with the relationship between IIS and performance. Research findings on the benefits to be gained from IIS have been ambiguous, with some researchers claiming reduced cost in the supply network with IIS, and others finding minimal or no benefits. To add clarity to these findings, the role that uncertainty plays in the relationship between IIS and performance is examined. The thesis presented is that the positive relationship between IIS types and the performance of the supply network is impacted by process uncertainty (i.e. the variability in process outcomes and production times), and partner uncertainty. Social network theory and transaction cost economics provide the theoretical lens for this dissertation. A model is developed and will be empirically validated in a cross-sectional setting, utilizing a sampling frame randomly selected and comprised of supply management executives from various industries within the United States