• Bilal Memon

AI Adoption in the Supply Chain Doesn’t Happen from Day 1.

“As the pace of supply chain innovation escalates, so does the price of inaction. Leaders will outpace their competitors faster than ever. Firms that focus on the fundamentals of the Supply Chain DCI and put AI to use as soon as possible will put themselves at a significant advantage. Those who start sooner, rather than later, will reap big rewards now and even bigger ones in the future.” (George Prest, CEO of MHI)

The benefits of Adopting AI into the supply chain are immense but one thing to clarify is that AI adoption doesn’t happen in a day or so. There are steps that organizations need to take advantage of before implementing any sort of Artificial Intelligence.

I’ve outlined some of the major steps below:

1) Centralizing all relevant data. In the case of demand forecasting, that could be sales data, inventory data, product data, and any other relevant data that impacts demand planning. For many organizations, unfortunately, all of this data is in different systems or even spreadsheets so they aren’t really speaking to each other. Connectivity is absolutely essential and will serve as the foundation for accurate demand forecasting.

2) Automation of reporting + processes. When it comes to reporting, employees have to go through several repetitive tasks for reporting, etc. By automating repetitive tasks, your saving employees a ton of time so they can focus on higher impact tasks.

3) Applying machine learning and predictive analytics. All the relevant data from Point 1 is fed into the machine learning model for processing and analysis. The model should be flexible enough so that if new sources of data are added into the centralized database, it incorporate and synthesizes this data proactively.

Once you apply machine learning & predictive analytics, the output can be a custom dashboard that displays exactly when to replenish existing stock for Product X, in what specific quantities to replenish product X in 1 week, 2 weeks, 8 weeks from now, etc. Also the dashboard can have information on what products are likely to have excess inventory in 1 week, 2 weeks, or 6 weeks from now so you can proactively reduce excess inventory. These are just some of the outputs a custom dashboard could have. The beauty about applying machine learning is that it gets smarter through time and is significantly more accurate than traditional inventory management or ERP solutions. You can rely on the data a lot more for purchase and inventory decisions. Perhaps your current ERP or inventory management solution consistently reports a 15–20% inventory margin of error. With machine learning and predictive analytics, you can bet that the margin of error will be significantly smaller which can result in millions of dollars in cost savings.

4) Alerts. Within the dashboard, you can create alerts that’ll notify the relevant teams on issues that have risen in real-time or are likely to arise, which enables a proactive as opposed to a reactive approach to solving issues.

All of these steps are critical to empower teams to make intelligent decisions so they can avoid stock-outs, reduce holding costs, meet lead times to increase customer satisfaction, and ensure there are minimal hiccups in the Supply Chain.

If you’re a manufacturer, retailer, e-commerce brand, or wholesaler and want to learn more about how to solve your inventory issues in 90 days or less using the power of machine learning + predictive analytics, you can book a call here:

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