Enhancing the Supply Chain Performance by Integrating Simulated and Physical Agents into Organizational Information Systems

As the business environment gets more complicated, organizations must be able to respond to the business changes and adjust themselves quickly to gain their competitive advantages. This study proposes an integrated agent system, called SPA, which coordinates simulated and physical agents to provide an efficient way for organizations to meet the challenges in managing supply chains. In the integrated framework, physical agents coordinate with inter-organizations\' physical agents to form workable business processes and detect the variations occurring in the outside world, whereas simulated agents model and analyze the what-if scenarios to support physical agents in making decisions. This study uses a supply chain that produces digital still cameras as an example to demonstrate how the SPA works. In this example, individual information systems of the involved companies equip with the SPA and the entire supply chain is modeled as a hierarchical object oriented Petri nets. The SPA here applies the modified AGNES data clustering technique and the moving average approach to help each firm generalize customers\' past demand patterns and forecast their future demands. The amplitude of forecasting errors caused by bullwhip effects is used as a metric to evaluate the degree that the SPA affects the supply chain performance. The experimental results show that the SPA benefits the entire supply chain by reducing the bullwhip effects and forecasting errors in a dynamic environment.Supply Chain Performance Enhancement; Bullwhip Effects; Simulated Agents; Physical Agents; Dynamic Customer Demand Pattern Discovery

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

Measuring and Analyzing the Bullwhip Effect in a Two-Product and Two Echelon Supply Chain Using Control Theory Approach

Coordination is very important in supply chain management and it is one of the main factors in supply chain profitability. Bullwhip effect is one of the basic obstacles to achieve coordination in supply chains and reduction of this phenomenon has an important role in supply chain harmony. The other side, costs of supply chain can be mitigated and customer service level can be increased by reducing of bullwhip effect. Because measurement of bullwhip effect is very important in analysing and controlling of it, providing equations to investigate bullwhip effect behaviour based on real world supply chain conditions is necessary. The previous studies mostly concentrate on single product supply chain and few studies have been done on supply chains with more than one product. Here we quantify and investigate the bullwhip effect in a two-echelon supply chain with two products using control theory approach. Due to the relationship between demands of two products in our proposed supply chain, first order vector auto regressive model is used as demand process of the products. We also apply moving average method for lead-time demand forecasting within the "order up to" replenishment policy. We derive a closed form bullwhip measure and then bullwhip effect in a two-product supply chain is discussed and illustrated through a numerical example

Forecasting Demand for Optimal Inventory with Long Lead Times: An Automotive Aftermarket Case Study

Accuracy in predicting customer demand is essential to building an economic inventory policy under periodic review, long lead-time, and a target fill rate. This study uses inventory and customer service level as a stock control metric to evaluate the forecast accuracy of different simple to more complex predictive analytical techniques. We show how traditional forecast error measures are inappropriate for inventory control, despite their consistent usage in many studies, by evaluating demand forecast performance dynamically with customer service level as a stock control metric that includes inventory holdings costs, stock out costs, and fill rate service levels. A second contribution includes evaluating the utility of introducing more complexity into the forecasting process for an automotive aftermarket parts manufacturer and the superior inventory control results using the Prais-Winsten, an econometric method, for non-intermittent demand forecasting with long-lead times. This study will add to the limited case study research on demand forecasting under long lead times using stock control metrics, dynamic model updating, and the Prais-Winsten method for inventory control

The impact of demand parameter uncertainty on the bullwhip effect

The bullwhip effect is a very important issue for supply chains, impacting on costs and effectiveness. Academic researchers have studied this phenomenon and modelled it analytically, showing that it affects many real world industries. The analytical models generally assume that the final demand process and its parameters are known. This paper studies a two-echelon single-product supply chain with final demand distributed according to a known AR(1) process but with unknown parameters. The results show that the bullwhip effect is affected by unknown parameters and is influenced by the frequency with which parameter estimates are updated. For unknown parameters, the strength of the bullwhip effect is also influenced by the number of demand observations available to estimate the parameters. Furthermore, a negative autoregressive parameter does not always imply an anti-bullwhip effect when the parameters are unknown. An analytical approximation is proposed to mitigate the poor accuracy of existing models when the parameters of an AR(1) process are unknown, forecasts are updated but parameter estimates remain unchanged

Centralised demand information sharing - A Thesis submitted for the degree of Doctor of Philosophy

This thesis explores Centralised Demand Information Sharing (CDIS) in supply chains. CDIS is an information sharing approach where supply chain members forecast based on the downstream member’s demand. The Bullwhip Effect is a demand variance amplification phenomenon: as the demand moves upstream in supply chains, its variability increases. Many papers in the literature show that, if supply chain members forecast using the less variable downstream member’s demand, this amplification can be reduced leading to a reduction in inventory cost. These papers, using strict model assumptions, discuss three demand information sharing approaches: No Information Sharing (NIS), Downstream Demand Inference (DDI) and Demand Information Sharing (DIS). The mathematical analysis in this stream of research is restricted to the Minimum Mean Squared Error (MMSE) forecasting method. A major motivation for this PhD research is to improve the above approaches, and assess those using less restrictive supply chain assumptions. In this research, apart from using the MMSE forecasting method, we also utilise two non-optimal forecasting methods, Simple Moving Averages (SMA) and Single Exponential Smoothing (SES). The reason for their inclusion is the empirical evidence of their high usage, familiarity and satisfaction in practice. We first fill some gaps in the literature by extending results on upstream demand translation for ARMA (p, q) processes to SMA and SES. Then, by using less restrictive assumptions, we show that the DDI approach is not feasible, while the NIS and DIS approaches can be improved. The two new improved approaches are No Information Sharing – Estimation (NIS-Est) and Centralised Demand Information Sharing (CDIS). It is argued in this thesis that if the supply chain strategy is not to share demand information, NIS-Est results in less inventory cost than NIS for an Order Up To policy. On the other hand, if the strategy is to share demand information, the CDIS approach may be used, resulting in lower inventory cost than DIS. These new approaches are then compared to the traditional approaches on theoretically generated data. NIS-Est improves on NIS, while CDIS improves on the DIS approach in terms of the bullwhip ratio, forecast error (as measured by Mean Squared Error), inventory holding and inventory cost. The results of simulation show that the performance of CDIS is the best among all four approaches in terms of these performance metrics. Finally, the empirical validity of the new approaches is assessed on weekly sales data of a European superstore. Empirical findings and theoretical results are consistent regarding the performance of CDIS. Thus, this research concludes that the inventory cost of an upstream member is reduced when their forecasts are based on a Centralised Demand Information Sharing (CDIS) approach

Internet of Things and Modern Supply Chain Management

Information flow has a great influence over the flow of materials in the supply chain industry. The behavior by which the materials flow is highly affected by how the information flows throughout the organization in a smooth manner. To develop the supply chain performance and improve the efficiency of information sharing a lot of practices have been developed to achieve that target. However nowadays with the expansion of companies and having complicated structures of communication, ordinary practices cannot suffice any longer. Additionally, a lot of time is not utilized properly wasting a lot of time and lowering the efficiency of the organization. This research aim is to investigate the development of the internet of things and how when properly utilized it can make a huge impact on modern supply chain management. This research aim is to provide a theoretical basis on how companies can use internet of things to allow easier access for information throughout the organization with minimal effort. The research questions to be addressed in this research are (1), What is the impact of the internet of things on modern supply chain management (2) what are the possible improvements and future work that can be done with regards to the internet of things (3) is it easy to use. An application of internet of things in the supply chain management is developed based on literature findings. The applications aim is to take place to match between execution flexibility and information abundance. Information sharing aimed should be providing high quality information for the higher ups and management before making crucial and swift decisions. To improve the flexibility of the operations and improve the pace within the working environment information must be gathered in a swift manner. It was determined that there are several reasons behind the turbulent flow between materials flow and information flow. Numerous plan changes in response to demand changes, varying planning processes which would subsequently cause problems when designing a supply chain model to organize the information flow. Moreover, it was also found that another reason was insufficient data which resulted in the inability of sharing information between various departments

The impact of supply chain structures on performance.

La Tesis analiza el impacto que tiene la estructura de las redes de suministro sobre su rendimiento, concretamente sobre el “efecto látigo” o efecto bullwhip. Para ello se desarrolla una arquitectura basada en la metodología de los sistemas multi-agente, que permite el modelado de sistemas complejos. Dicha arquitectura es implementada en un software dando lugar a un simulador de redes de suministro llamado SCOPE, que permite el modelado y simulación de una amplia variedad de configuraciones de redes de suministro. SCOPE es utilizado para investigar una de las suposiciones más comunes en el campo del modelado de redes de suministro: el uso de estructuras muy sencillas en forma serial generalmente con muy pocas fases funcionales y pocos nodos. Para determinar el impacto de la estructura de la red sobre el efecto bullwhip se utiliza una estructura más compleja y más acorde con las estructuras de redes de suministro reales: la red divergente. Se realizan tres experimentos: (i) análisis comparativo del efecto bullwhip entre la red divergente y la serial; (ii) análisis comparativo de la eficacia de dos técnicas muy conocidas para la limitación del efecto bullwhip entre la red divergente y la serial; (iii) determinación de los parámetros estructurales de la red de suministro divergente y análisis estadístico para determinar si dichos parámetros estructurales impactan sobre el efecto bullwhip. Los resultados obtenidos revelan que todos los parámetros estructurales analizados impactan significativamente sobre efecto bullwhip. Además, en caso de un impulso inesperado en la demanda, el impacto de la red de suministro en el efecto bullwhip es mayor. Las técnicas para limitación del efecto bullwhip son también efectivas en redes de suministro divergentes, consiguiendo además un aumento de su robustez ante cambios bruscos inesperados en la demanda