Impact of Sharing Point of Sales Data and Inventory Information on Bullwhip Effect

Matharage, Sandun Tharaka; Hewage, Uthpalee; Perera, H. Niles

doi:10.1109/ieem45057.2020.9309733

Cited by 6 publications

(2 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The bullwhip generally comes from retailers, but it may also be traced back to those retailers' suppliers and manufacturers. However, exchanging inventory data and point of sale data can lessen the consequences of bullwhip, claims a 2020 study utilizing Excel macro and visual basic (Matharage et al, 2020) Due to uncoordinated ordering and production planning rules building up backlog over the disruption period, the ripple effect affects the bullwhip impact. A contingent production-inventory control policy is created to combat the ripple and bullwhip effects and presents evidence in support of information cooperation in SC disruption management.…”

Section: Literature Reviewmentioning

confidence: 99%

Strategies to Counter Supply Chain Disruptions for FMCG Brands during a Pandemic

Khayer¹,

Rahul²,

Chakraborty³

2022

10.5267/j.jfs

View full text Add to dashboard Cite

The FMCG sector in developing nations is still not prepared to withstand any disruption brought on by the worldwide pandemic. In order to adapt to the new normal, businesses must make both micro and broad changes to their supply chain strategy. The goal of this research is to create plans to minimize any disruptions caused by upcoming pandemics. To restore the broken supply chain, a number of new implications and adjustments to the current attributes were proposed in the areas of sourcing, manufacturing, and distribution. Finding the fundamental drivers that are frequently impacted by the disturbance is part of the technique. The afflicted locations were the focus of the models' development. The ideas work as preventative measures intended to thwart the disturbance when and if it happens. In order to assess the model's viability, the Key Performance Indicators (KPI) value was ultimately retrieved with the aid of 25 industry experts. These suggestions may result in improved transparency, real-time monitoring, cost effectiveness, and responsiveness, among other benefits. Our analysis indicates that the KPI scores for procurement, production, and distribution are 92.86%, 82.14%, and 87.50%, respectively. The models' total viability is 87.50%. The most recent Covid-19 pandemic has provided us with a vivid illustration of what could go wrong in such circumstances. In both pandemic and non-pandemic conditions, the adaptation of stated suggestions at the aspect of sourcing, production, and distribution might result in a significant shift to organization-wide activities.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Strategies to Counter Supply Chain Disruptions for FMCG Brands during a Pandemic

Khayer¹,

Rahul²,

Chakraborty³

2022

10.5267/j.jfs

View full text Add to dashboard Cite

show abstract

“…In addition, macroeconomic variables linked with consumer behaviour influence the demand forecasts (Tangjitprom, 2012). Hence a single traditional statistical forecasting technique comprises historical sales data which is inadequate to deliver proper forecasts where the impact of related sales data to mitigate the bullwhip effect in the supply chain is proven (Matharage, Hewage & Perera, 2020). With the prerequisite of a method to incorporate many variables with significantly improving data availability, the capability of ANN which is a part of Artificial Intelligence which creates sales forecasts with high accuracy integrated with many variables is to be assessed with the integration of macroeconomic variables based on model's accuracy.…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network Based Demand Forecasting Integrated With Federal Funds Rate

ARACHCHIGE

Sugathadasa²,

Herath³

et al. 2021

acs

View full text Add to dashboard Cite

Adverse effects of inaccurate demand forecasts; stockouts, overstocks, customer loss have led academia and the business world towards accurate demand forecasting methods. Artificial Neural Network (ANN) is capable of highly accurate forecasts integrated with many variables. The use of Price and Promotion variables have increased the accuracy while the addition of other relevant variables would decrease the occurrences of errors. The use of the Federal Funds Rate as an additional macroeconomic variable to ANN forecasting models has been discussed in this research by the means of the accuracy measuring method: Average Relative Mean Absolute Error.

show abstract

Strategy Adjustments for FMCG Supply Chains in Bangladesh to Counter Future Pandemic Disruptions

Khayer

Rahul

Chakraborty

2023

J. Inst. Eng. India Ser. C

View full text Add to dashboard Cite

Impact of Sharing Point of Sales Data and Inventory Information on Bullwhip Effect

Cited by 6 publications

References 21 publications

Strategies to Counter Supply Chain Disruptions for FMCG Brands during a Pandemic

Strategies to Counter Supply Chain Disruptions for FMCG Brands during a Pandemic

Artificial Neural Network Based Demand Forecasting Integrated With Federal Funds Rate

Strategy Adjustments for FMCG Supply Chains in Bangladesh to Counter Future Pandemic Disruptions

Contact Info

Product

Resources

About