Intelligent Supply Chain Demand Forecasting and Inventory Management Strategies

Shijie Luo

doi:10.62051/ejb42e55

Authors

Shijie Luo

DOI:

https://doi.org/10.62051/ejb42e55

Keywords:

Intelligent supply chain, demand forecasting, inventory management, big data analytics, artificial intelligence, machine learning, real-time forecasting, automation tools

Abstract

With the acceleration of globalization and the increasingly fierce competition in the market, demand forecasting and inventory management in supply chain management have become particularly important. Traditional supply chain management methods are difficult to cope with the complex and changing market demand, resulting in the frequent occurrence of inaccurate forecasts, excessive or insufficient inventory, and other problems. This paper aims to explore the application of intelligent technology in supply chain demand forecasting and inventory management, focusing on analyzing how technologies such as big data, artificial intelligence, and machine learning can improve forecasting accuracy and inventory management efficiency. Through the analysis of actual cases, this paper demonstrates the implementation effect of intelligent demand forecasting technology and proposes corresponding inventory management optimization strategies. The study shows that intelligent supply chain management can not only effectively reduce operating costs, but also improve service levels, thus enhancing the competitiveness of enterprises in a dynamic market environment

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References

[1] Gérard P. Cachon.Managing Supply Chain Demand Variability with Scheduled Ordering Policies[J].Management Science, 1999, 45(6):843-856.DOI:10.1287/mnsc.45.6.843.

[2] Downing M , Chipulu M , Ojiako U ,et al.Forecasting in airforce supply chains[J].International Journal of Logistics Management, 2011, 22(1):p.127-144.DOI:10.11080/9574091111127589.

[3] Narayanan V G , Raman A , Singh J .Agency Costs in a Supply Chain with Demand Uncertainty and Price Competition[J].Management Science, 2005, 51(1):120-132.DOI:10.1287/mnsc.1040.0211.

[4] Neubert G , Ouzrout Y , Bouras A .Collaboration and integration through information technologies in supply chains[J].International Journal of Technology Management, 2004, 28(2):259-273.DOI:10.1504/IJTM.2004.005065.

[5] Ali M M , Boylan J E .On the effect of non-optimal forecasting methods on supply chain downstream demand[J].Ima Journal of Management Mathematics, 2012, 23(1):81-98(18).DOI:10.1093/imaman/dpr005.

[6] Fildes R , Kingsman B .Incorporating demand uncertainty and forecasting in supply chain planning models[J].Journal of the Operational Research Society, 2011, 62(3):483-500.DOI:10.1057/jors.2010.40.

[7] Ali M M , Boylan J E , Syntetos A A .Forecast errors and inventory performance under forecast information sharing[J].International Journal of Forecasting, 2012, 28(4):830-841.DOI:10.1016/j.ijforecast.2010.08.003.

[8] Monroe R W .Supply Chain Management: A Global Perspective (review)[J].Transportation Journal, 2012, 51(4):506-508.DOI:10.1353/tnp.2012.0031.

[9] Azevedo S G , Carvalho H , Duarte S ,et al.Influence of Green and Lean Upstream Supply Chain Management Practices on Business Sustainability[J].IEEE Transactions on Engineering Management, 2012, 59(4):753-765.DOI:10.1109/TEM.2012.2189108.

[10] Thomas,J,Goldsby,et al.MODELING LEAN, AGILE, AND LEAGILE SUPPLY CHAIN STRATEGIES[J].Journal of Business Logistics, 2011, 27(1):57-80.DOI:10.1002/j.2158-1592.2006.tb00241.x.