Supply chain planning (SCP) is essential for businesses across all verticals. An intelligent SCP is fruitful for revenue and customer service models. Businesses thrive when their SCP is well revised and for the same numerous efforts are put. Machine learning is one technology being embedded in SCP applications for injecting intelligence to it. Apart from it, several other methodologies can be implemented to make SCP an intelligent one comprising both manual and automation for optimal results.
Data is the key to intelligent planning, and integrated frameworks allow enterprises to accumulate data from various sources and networks much easier way. Companies utilize multiple ERP systems for planning which are disintegrated from each other, but if an integrated framework is established for faster data sharing, SCP will enhance for better. More the data, better the output is the basic principle behind the concept of an integrated framework. Machine learning tools rely on the quality and quantity of data fed and integrated frameworks cater to tools with the same.
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Self-Healing Supply Chain
SCP system outputs depend on key supply chain parameters. Lead times is the most essential as longer the lead time greater the variability around it. This leads to more investments in stock safety. Such a supply chain is termed as a self-healing supply chain. Usually, businesses focus less on updating and verifying their lead times, but it is crucial to do so. Investing in comparing historical lead data with recent enterprise information will provide better insights to fill the gap and enhance supply chain intelligence.
Automation in Planning
Automation has become key to almost all business processes, and the same goes for SCP. Utilizing ML algorithms to predict stock keeping based on market demands. Replenishment and demand forecast are linked, and semi-automated planning also gains boost among the industries for SCP. Organizations can embrace machine learning technology as its algorithms hold the potential to cater the SCP with both semi-automated and fully automated planning system.