Supply chain uncertainty:a review and theoretical foundation for future research

Supply-chain uncertainty is an issue with which every practising manager wrestles, deriving from the increasing complexity of global supply networks. Taking a broad view of supply-chain uncertainty (incorporating supply-chain risk), this paper seeks to review the literature in this area and develop a theoretical foundation for future research. The literature review identifies a comprehensive list of 14 sources of uncertainty, including those that have received much research attention, such as the bullwhip effect, and those more recently described, such as parallel interaction. Approaches to managing these sources of uncertainty are classified into: 10 approaches that seek to reduce uncertainty at its source; and, 11 approaches that seek to cope with it, thereby minimising its impact on performance. Manufacturing strategy theory, including the concepts of alignment and contingency, is then used to develop a model of supply-chain uncertainty, which is populated using the literature review to show alignment between uncertainty sources and management strategies. Future research proposed includes more empirical research in order to further investigate: which uncertainties occur in particular industrial contexts; the impact of appropriate sources/management strategy alignment on performance; and the complex interplay between management strategies and multiple sources of uncertainty (positive or negative)

Behavioral analyses of retailers’ ordering decisions

The main objective I pursue in this thesis is to better understand how different factors may independently and in combination influence retailers' ordering decisions under different supply chain structures (single agent and multi agent), different demand uncertainty (deterministic and stochastic), and different interaction among retailers (no interaction, competition and cooperation). I developed three different studies where I build on different formal management model and then run multiple behavioral studies to better understand subjects’ behavior. The first study analyzes order amplification in a single-supplier single-retailer supply chain. I used a behavioral experiment to test retailers’ orders under different ordering delays and different times to build supplier’s capacity. Results provide (i) a better understanding of the endogenous dynamics leading to retailers’ ordering amplification, and (ii) a description of subjects’ biases and deviation from optimal trajectories; despite subjects have full information about the system structure. The second study analyzes how order amplification can also take place when there is fierce retailer competition and limited supplier capacity. I study how different factors (different time to build supplier capacity, different levels of competition among retailers, different magnitudes of supply shortage and different allocation mechanisms) may independently and in combination influence retailers’ order in a system with two retailers under supply competition. Results show that (i) the bullwhip effect persists even when subjects do not have incentives to deviate, (ii) subjects amplify their orders in an attempt to build an unnecessary safety stock to respond to potential deviations from the other retailers, and (iii) retailers’ underperformance varies with the allocation mechanism used by the supplier. In the last study, I analyze retailers’ orders in a system where there is uncertainty in the final customer demand. I experimentally explore the effect of transshipments among retailers in a single-supplier multi-retailer supply chain. Specifically, I explore retailers’ orders under different profit and communication conditions. In addition, I integrate analytical and behavioral models to improve supply chain performance. Results show that (i) the persistence of common biases in a newsvendor problem (pull-to-center, demand chasing, loss aversion, psychological disutility), (ii) communication could improve coordination and may reduce demand chasing behavior, (iii) supply chain performance increases with the use of behavioral strategies embedded within a traditional optimization model, and (iv) dynamic heuristics improve overall coordination, outperforming a simple Nash Equilibrium strategy

Dynamic Analysis of Healthcare Service Delivery: Application of Lean and Agile Concepts

Hospitals are looking to industry for proven tools to manage increasingly complex operations and reduce costs simultaneously with improving quality of care. Currently, €˜lean€™ is the preferred system redesign paradigm, which focuses on removing process waste and variation. However, the high level of complexity and uncertainty inherent to healthcare make it incredibly challenging to remove variability and achieve the stable process rates necessary for lean redesign efforts to be effective. This research explores the use of an alternative redesign paradigm €“ €˜agile€™ €“ which was developed in manufacturing to optimize product delivery in volatile demand environments with highly variable customer requirements. €˜Agile€™ redesign focuses on increasing system responsiveness to customers through improved resource coordination and flexibility. System dynamics simulation and empirical case study are used to explore the impact of following an agile redesign approach in healthcare on service access, care quality, and cost; determine the comparative effectiveness of individual agile redesign strategies; and identify opportunities where lean methods can contribute to the creation of responsive, agile enterprises by analyzing hybrid lean-agile approaches. This dissertation contributes to the emerging literature on applying supply chain management concepts in healthcare, and opens a new path for designing healthcare systems that provide the right care, at the right time, to the right patient, at the lowest price

The impact of supply chain performance measurement systems on dynamic behaviour in supply chains

The amplification of demand variation up a supply chain widely termed ‘the Bullwhip Effect’ is disruptive, costly and something that supply chain management generally seeks to minimise. Originally attributed to poor system design; deficiencies in policies, organisation structure and delays in material and information flow all lead to sub-optimal reorder point calculation. It has since been attributed to exogenous random factors such as: uncertainties in demand, supply and distribution lead time but these causes are not exclusive as academic and operational studies since have shown that orders and/or inventories can exhibit significant variability even if customer demand and lead time are deterministic. This increase in the range of possible causes of dynamic behaviour indicates that our understanding of the phenomenon is far from complete. One possible, yet previously unexplored, factor that may influence dynamic behaviour in supply chains is the application and operation of supply chain performance measures. Organisations monitoring and responding to their adopted key performance metrics will make operational changes and this action may influence the level of dynamics within the supply chain, possibly degrading the performance of the very system they were intended to measure. In order to explore this a plausible abstraction of the operational responses to the Supply Chain Council’s SCOR® (Supply Chain Operations Reference) model was incorporated into a classic Beer Game distribution representation, using the dynamic discrete event simulation software Simul8. During the simulation the five SCOR Supply Chain Performance Attributes: Reliability, Responsiveness, Flexibility, Cost and Utilisation were continuously monitored and compared to established targets. Operational adjustments to the; reorder point, transportation modes and production capacity (where appropriate) for three independent supply chain roles were made and the degree of dynamic behaviour in the Supply Chain measured, using the ratio of the standard deviation of upstream demand relative to the standard deviation of the downstream demand. Factors employed to build the detailed model include: variable retail demand, order transmission, transportation delays, production delays, capacity constraints demand multipliers and demand averaging periods. Five dimensions of supply chain performance were monitored independently in three autonomous supply chain roles and operational settings adjusted accordingly. Uniqueness of this research stems from the application of the five SCOR performance attributes with modelled operational responses in a dynamic discrete event simulation model. This project makes its primary contribution to knowledge by measuring the impact, on supply chain dynamics, of applying a representative performance measurement system

Mitigating the Bullwhip Effect and Enhancing Supply Chain Performance through Demand Information Sharing: An ARENA Simulation Study

The supply chain is a network of organizations that collaborate and leverage their resources to deliver products or services to end-customers. In today's globalized and competitive market, organizations must specialize and form partnerships to gain a competitive edge. To thrive in their respective industries, organizations need to prioritize supply chain coordination, as it is integral to their business processes.   Supply chain management focuses on the collaboration of organizations within the supply chain. However, when each echelon member optimizes their goals without considering the network's impact, it leads to suboptimal performance and inefficiencies. This phenomenon is known as the Bullwhip effect, where order variability increases as it moves upstream in the supply chain. The lack of coordination, unincorporated material and information flows, and absence of ordering rules contribute to poor supply chain dynamics. To improve supply chain performance, it is crucial to align organizational activities. Previous research has proposed solutions to mitigate the Bullwhip effect, which has been a topic of intense study for many decades. This research aims to investigate the causes and mitigations of the Bullwhip effect based on existing research. Additionally, the paper utilizes ARENA simulation to examine the impact of sharing end-customer demand information. As far as we are aware, no study has been conducted to deeply simulate the bullwhip effect using the ARENA simulation. Previous studies have investigated this phenomenon, but without delving into its intricacies. The simulation results offer potential strategies to mitigate the Bullwhip effect through demand information sharing. Keywords: Supply Chain Management, Bullwhip effect, Inventory management, ARENA simulation, Information sharing, forecasting technique, Demand variability. DOI: 10.7176/JESD/14-14-07 Publication date:August 31st 202

The System Dynamics Model in Electronic Products Closed-Loop Supply Chain Distribution Network with Three-Way Recovery and the Old-for-New Policy

With the technological developments and rapid changes in demand pattern, diverse varieties of electronic products are entering into the market with reduced lifecycle which leads to the environmental problems. The awareness of electronic products take-back and recovery has been increasing in electronic products supply chains. In this paper, we build a system dynamics model for electronic products closed-loop supply chain distribution network with the old-for-new policy and three electronic products recovery ways, namely, electronic products remanufacturing, electronic component reuse and remanufacturing, and electronic raw material recovery. In the simulation study, we investigate the significance of various factors including the old-for-new policy, collection and remanufacturing, their interactions and the type of their impact on bullwhip, and profitability through sensitivity analysis. Our results instruct that the old-for-new policy and three electronic products recovery ways can reduce the bullwhip effect in the retailers and the distributors and increases the profitability in the closed-loop supply chain distribution network

Taming the Business Cycles in Commercial Aviation: Trade-space analysis of strategic alternatives using simulation modeling

We investigate the effectiveness of strategic alternatives that are designed to dampen the cyclicality manifest in the commercial aviation related industries. The constituent enterprises of the commercial aviation system exhibit managerial and operational independence and have diverse value functions that often viewed the enterprises to view their competition as a zero-sum game. We argue that this need not always be the case; in the commercial aviation system both airline and airframe manufacturers constituents would benefit from a steadier influx of aircraft that counters the current situation that is characterized by relatively stable demand growth rate for air travel while airline profitability and aircraft ordering fluctuate intensely. In order to identify and evaluate the symbiotic potential, we use a system dynamics model of commercial aviation. After testing several individual strategic alternatives, we find that capacity management is key to cycle moderation for non-collusive strategies. Comparing faster aircraft deliveries to semi-fixed production schedules among other alternatives shows only the latter alternative to be Pareto efficient

Order Stability in Supply Chains: Coordination Risk and the Role of Coordination Stock

The bullwhip effect describes the tendency for the variance of orders in supply chains to increase as one moves upstream from consumer demand. We report on a set of laboratory experiments with a serial supply chain that tests behavioral causes of this phenomenon, in particular the possible influence of coordination risk. Coordination risk exists when individuals' decisions contribute to a collective outcome and the decision rules followed by each individual are not known with certainty, for example, where managers cannot be sure how their supply chain partners will behave. We conjecture that the existence of coordination risk may contribute to bullwhip behavior. We test this conjecture by controlling for environmental factors that lead to coordination risk and find these controls lead to a significant reduction in order oscillations and amplification. Next, we investigate a managerial intervention to reduce the bullwhip effect, inspired by our conjecture that coordination risk contributes to bullwhip behavior. Although the intervention, holding additional on-hand inventory, does not change the existence of coordination risk, it reduces order oscillation and amplification by providing a buffer against the endogenous risk of coordination failure. We conclude that the magnitude of the bullwhip can be mitigated, but that its behavioral causes appear robust.National Science Foundation (U.S.) (Grant SES-0214337)Mary Jean and Frank P. Smeal College of Business Administration (Center for Supply Chain Research)Sloan School of Management (Project on Innovation in Markets and Organizations

A Critical Evaluation Of Empirical Non-Linear Control System And System Dynamics Modeling Theories For Mitigating Risks Arising From Bullwhip Effect

Bullwhip effect is a threat observed in multi-echelon supply chains, which is one of the prominent indicators of inefficiencies in a supply chain. Primarily, bullwhip effect occurs as a result of disruptions in information and materials flow, lead-time delays, lack of coordination, and panic stocking amidst visibility into local risk factors. When bullwhip effect occurs, the demand variations entering the supply chain from the customer end amplifies gradually as it flows upstream towards the supplier ends. This may cause unused inventory and may later lead to wastage and obsolescence. Bullwhip effect can be curbed through many approaches. This study has focused on control theory approach that promotes small-scale control behaviors throughout the supply chain to dampen the bullwhip tidal waves. The approach investigated in this research is a combination of control system modeling and systems dynamics modeling, which is not researched adequately by bullwhip academics. Based on the investigations, a six-step approach for reducing Bullwhip effect is proposed in this research and illustrated with examples. The six-step approach comprises of first-level multi-echelon survey to derive the initial system dynamics model, second-level survey to collect primary data for all the variables and relationships formed, principal component analysis and Cronbach Alpha / split-half testing for reliability, verification, and validity testing and exploring the best optimal construct using structural equation modeling, and finally, applying controllers to the optimal systems dynamics model through interpretive analysis of the model