A note on the legal considerations in decision science models of collaboration, information sharing and integration in supply chains

2015 ◽  
Vol 14 (1) ◽  
pp. 1
Author(s):  
Karen A. Reardon
Keyword(s):  
Supply Chains ◽  
Information Sharing ◽  
Decision Science ◽  
Science Models

Information sharing in decentralised supply chains with partial collaboration

2021 ◽  
Author(s):  
Roberto Dominguez ◽  
Salvatore Cannella ◽  
Borja Ponte ◽  
Jose M. Framinan
Keyword(s):  
Supply Chains ◽  
Information Sharing

scholarly journals Revisiting the value of information sharing in two-stage supply chains

2018 ◽  
Vol 270 (3) ◽  
pp. 1044-1052 ◽  
Author(s):  
Ruud H. Teunter ◽  
M. Zied Babai ◽  
Jos A.C. Bokhorst ◽  
Aris A. Syntetos
Keyword(s):  
Supply Chains ◽  
Information Sharing ◽  
Value Of Information ◽  
Two Stage

Anticipation of Demand in Supply Chains

2019 ◽  
pp. 1-45
Author(s):  
Youssef Tliche ◽  
Atour Taghipour ◽  
Béatrice Canel-Depitre
Keyword(s):  
Supply Chains ◽  
Information Sharing ◽  
Performance Metrics ◽  
Mean Squared Error ◽  
Moving Average ◽  
Demand Management ◽  
Inventory Level ◽  
Squared Error ◽  
Interfirm Collaboration ◽  
Overall Performance

The main objective of studying decentralized supply chains is to demonstrate that a better interfirm collaboration can lead to a better overall performance of the system. Many researchers studied a phenomenon called downstream demand inference (DDI), which presents an effective demand management strategy to deal with forecast problems. DDI allows the upstream actor to infer the demand received by the downstream one without information sharing. Recent study showed that DDI is possible with simple moving average (SMA) forecast method and was verified especially for an autoregressive AR(1) demand process. This chapter extends the strategy's results by developing mean squared error and average inventory level expressions for causal invertible ARMA(p,q) demand under DDI strategy, no information sharing (NIS), and forecast information sharing (FIS) strategies. The authors analyze the sensibility of the performance metrics in respect with lead-time, SMA, and ARMA(p,q) parameters, and compare DDI results with the NIS and FIS strategies' results.

Ensuring Food Supply and Security During Localised Lockdowns

2021 ◽  
pp. 160-193
Author(s):  
Iman Hussain ◽  
Chloë Allen-Ede ◽  
Lukas Jaks ◽  
Herbert Daly
Keyword(s):  
Supply Chains ◽  
Information Sharing ◽  
Food Supply ◽  
Third Sector ◽  
Food Supply Chains ◽  
Emergency Situations ◽  
Blockchain Technology ◽  
Stable Conditions ◽  
Third Sector Organisations ◽  
Great Pressure

A pandemic crisis inevitably puts great pressure on different aspects of societal and commercial infrastructure. Paths for information and goods designed and optimised for stable conditions may fail to meet the needs of emergency situations, whether suddenly imposed or planned. This chapter discusses the effects of the 2020 pandemic on food supply chains. These issues are considered as problems of information sharing and systemic behaviour with implications for both people and technology. Based on work in Wolverhampton, UK, the effect of the 2020 lockdown period on local businesses and charities is considered. In response to these observations, the design and development of Lupe, a prototype application to support the distribution and trading of food during periods of lockdown, is described. The aim of the system is to integrate the needs of consumers, businesses, and third sector organisations. The use of blockchain technology in the Lupe system to provide appropriate functionality and security for data is explored. Initial evaluations of the prototype by stakeholders are also included.

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