Measuring the variability in supply chains with the peakedness

This paper introduces a novel way to measure the variability of order flows in supply chains, the peakedness. The peakedness can be used to measure the variability assuming the order flow is a general point pro- cess. We show basic properties of the peakedness, and demonstrate its computation from real-time continuous demand processes, and cumulative demand collected at fixed time intervals as well. We also show that the peakedness can be used to characterize demand, forecast, and inventory variables, to effectively manage the variability. Our results hold for both single stage and multistage inventory systems, and can further be extended to a tree-structured supply chain with a single supplier and multiple retailers. Furthermore, the peakedness can be applied to study traditional inventory problems such as quantifying bullwhip effects and determining safety stock levels. Finally, a numerical study based on real life Belgian supermarket data verifies the effectiveness of the peakedness for measuring the order flow variability, as well as estimating the bullwhip effects.variability, peakedness, supply chain

The boomerang returns? Accounting for the impact of uncertainties on the dynamics of remanufacturing systems

Recent years have witnessed companies abandon traditional open-loop supply chain structures in favour of closed-loop variants, in a bid to mitigate environmental impacts and exploit economic opportunities. Central to the closed-loop paradigm is remanufacturing: the restoration of used products to useful life. While this operational model has huge potential to extend product life-cycles, the collection and recovery processes diminish the effectiveness of existing control mechanisms for open-loop systems. We systematically review the literature in the field of closed-loop supply chain dynamics, which explores the time-varying interactions of material and information flows in the different elements of remanufacturing supply chains. We supplement this with further reviews of what we call the three ‘pillars’ of such systems, i.e. forecasting, collection, and inventory and production control. This provides us with an interdisciplinary lens to investigate how a ‘boomerang’ effect (i.e. sale, consumption, and return processes) impacts on the behaviour of the closed-loop system and to understand how it can be controlled. To facilitate this, we contrast closed-loop supply chain dynamics research to the well-developed research in each pillar; explore how different disciplines have accommodated the supply, process, demand, and control uncertainties; and provide insights for future research on the dynamics of remanufacturing systems

Reducing Bullwhip Effect in Fresh Food Vegetable Supply Chain Management: A strategic approach for Inclusive Growth

Inclusive growth is the mantra for a countrys growth. Inclusive growth itself demands inclusive support from all the sectors of industry and agriculture. But economy of industry and agriculture growth depends on proper supply of goods and food items to the ultimate consumers at right place, right time, right quantity with right price based on effective prediction or judgement of demand. The failure to predict proper demand by a company leads to fluctuation of demand between supply chain stages. This extends to bullwhip effect, which is a threat for economic growth. Nowadays Indian retailing industry is booming with more opportunities and has got increased contribution to the growth of economy. Due to the impact of globalization, Indian retailing formats are seeing metamorphosis. Retailing is getting transformed like India from unorganized to semi organized and organized retailing. Retailing in fresh food vegetable supply is slowly gaining importance in the agricultural based economy. Reaching the fresh food to vast country like India without proper supply chain and infrastructure is a daunting task. Balancing the demand and supply between semi-organized fresh food vegetable (SOFFV) retailers and fresh food suppliers amongst the supply chain activities is a challenging job. Using conjoint analysis this research focuses on different levels of combination of attributes preferred by the semi organized vegetables retailers, based on demand to identify fresh food delivery package with best utility rate. This article helps to understand the efficiency of information sharing to reduce the bullwhip effect

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 bullwhip effect: Progress, trends and directions

This is the final version. Available on open access from Elsevier via the DOI in this recordThe bullwhip effect refers to the phenomenon where order variability increases as the orders move upstream in the supply chain. This paper provides a review of the bullwhip literature which adopts empirical, experimental and analytical methodologies. Early econometric evidence of bullwhip is highlighted. Findings from empirical and experimental research are compared with analytical and simulation results. Assumptions and approximations for modelling the bullwhip effect in terms of demand, forecast, delay, replenishment policy, and coordination strategy are considered. We identify recent research trends and future research directions concerned with supply chain structure, product type, price, competition and sustainability

From supply chains to demand networks. Agents in retailing: the electrical bazaar

A paradigm shift is taking place in logistics. The focus is changing from operational effectiveness to adaptation. Supply Chains will develop into networks that will adapt to consumer demand in almost real time. Time to market, capacity of adaptation and enrichment of customer experience seem to be the key elements of this new paradigm. In this environment emerging technologies like RFID (Radio Frequency ID), Intelligent Products and the Internet, are triggering a reconsideration of methods, procedures and goals. We present a Multiagent System framework specialized in retail that addresses these changes with the use of rational agents and takes advantages of the new market opportunities. Like in an old bazaar, agents able to learn, cooperate, take advantage of gossip and distinguish between collaborators and competitors, have the ability to adapt, learn and react to a changing environment better than any other structure. Keywords: Supply Chains, Distributed Artificial Intelligence, Multiagent System.Postprint (published version

Antecedents of Quality Information Sharing in the FMCG Industry

Information sharing in a retail supply chain presents challenges of mapping information flow in terms of collection and transfer capabilities from one point to other internal and external users. Efficient mapping information flow seems to be dependent on information availability, velocity and the level of volatility. This would strengthen partnerships between the upstream and downstream sites of a supply chain in terms of information capturing, transformation and exchange between both internal and external supply chain users. This study examines the relative magnitude of advance economic information sharing in optimizing integrated supply chain activities in the consumer goods industry. It further analyses the challenges of bullwhip effect from the perspective of electronically-enabled supply chain management (eSCM) systems and information sharing in the fast moving consumer goods (FMCG) industry. The study finds that information sharing is related to supply chain performance targets in the FMCG industry in terms of a higher order fulfillment rate and achieving shorter order cycle time through integrated e-SCM systems. The managerial implications of this study are that integrated IT infrastructure capability and top management support (in terms of visible involvement, commitment and participation of executives and the allocation of the necessary resources) are significant antecedents of the quality of shared information

The influence of promotional activity on supply chain stability: a fast moving consumer goods (FMCG) perspective.

Master of Commerce. University of KwaZulu-Natal, Pietermaritzburg, 2014.Today, most sales are stimulated at the point of purchase, so sales promotions are becoming a crucial element of any marketing campaign. The consequence of these promotions is the creation of unpredictable demand. The resultant instability has been termed the “Bullwhip Effect” (BWE). The BWE has a negative effect on business performance as it creates information distortions that cause excessive inventory holdings, higher overall costs, poor customer service and lost sales. An important strategy to achieve a smooth flowing supply chain is to mitigate or preferably eliminate the BWE. The aim of this research was to monitor the stock levels of a high value product flowing through the supply chain to determine whether marketing activities, such as promotions, contribute to increased instability in the chain. The study followed a case study approach and analysed the business activities of consumer packaged goods company (CPGC) “X” promoting their product “X”, an item of high value, with retailer “X”. The promotion was monitored in three phases. The phases included pre-promotion planning, execution of the promotion and post promotion analysis. The researcher employed both qualitative and quantitative research methods. The research established that the ROI on the promotion was greater than the target and that the retailer made an additional profit. However, when the assessment of ROI included more of the supply chain, there was a negative operating profit due to excess upstream inventory. The study confirmed that promotional activities contribute to the BWE and that this effect may be more pronounced with products of higher value. The phenomenon worsened as the distance of supply chain nodes from the real demand increased. This caused a major shift in ordering patterns and an altered total inventory pipe fill in the chain. The recommendations arising from this study are that the CPGC and retailer should implement a true scorecard and a joint business plan for those brands that have products of high value. Subsequently, a vendor managed inventory (VMI) system should be implemented. This will remove the retailer’s need to forecast and may prevent unstable ordering and delays due to cost avoidance. Shrinkage will be reduced as the CPGC would directly own, control and supply stock in the retailer’s DCs

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