Three Ways Artificial Intelligence Improves Supply Chain Management

    

With the rapid advancement of the digital technology, forty percent of retail and consumer products companies are using AI-powered solutions, and this number is expected to double within two years according to a new National Retail Federation survey.

Currently, the remaining 60% of the industry continues to operate and manage their company’s supply chain with insufficient data. Many DSD consumer goods companies have low data velocity and unreliable information. Thus, generating an accurate demand forecast feels impossible, especially for perishable food manufacturers who must balance eliminating out-of-stocks while maintaining product freshness.

As companies seek to increase revenues and reduce costs, many are incorporating advanced analytics and artificial intelligence into their current systems.

Improving Order Management and Accuracy
Let’s examine how a large bakery company using a DSD business model can benefit from AI. Most consumer goods companies do not know how many bags of chips or loaves of bread have been sold, or if the store shelves are empty. Without reliable data, forecasts are flawed due to incorrect information.

Companies must leverage AI to rapidly digest the massive volume of sales history, SKU and store data, then harness the information into actionable decisions.  As AI drives behaviors, it further increases forecast accuracy, enhancing revenue, improving margins, and reducing working capital needs.

AI enables the use of other variables/data points – such as return rate (in the absence of daily inventory data from the retailer) – as a proxy to auto-correct the demand model. For instance, spikes in a return rate may indicate an incorrect product is on the shelf, while a zero rate may mean out-of-stocks.

Generating an accurate forecast also improves downstream processes as AI analyzes its ecosystem and takes continual actions to maximize its success, learning at every step. That learning, when reinforced, leads to continuous improvement.

Reducing Product Returns
Continuing with our bakery DSD example, when AI was incorporated, management discovered that the retail store routes experienced optimal return rates. Not zero, which might indicate out-of-stock issues and potential loss of revenue, but instead 2 or 3 percent indicating a healthy supply chain.

The increase in accuracy also drove improvements in the procurement and production planning processes. Once the bread company improved the demand signals from the shelf, the impact was felt throughout the organization. All decisions from negotiating for ingredients and raw materials, to final production and transport of the products to stores, were linked to factual data – how many bread loaves were really sold at retail.

Frontline Improvements
Companies using AI-powered forecasting solutions have experienced as much as a 10 percent improvement in their forecasting accuracy. Using more accurate forecasts for supply chain management has a significant impact on P&L by improving margins, increasing revenue, and reducing the need for working capital.

The most significant benefit is felt by the people on the front lines, who are responsible for stocking shelves and placing orders. These team members have a substantial stake in having access to accurate forecasts since sales volume and return rates are their KPIs.

In the back office, managers spend less time reviewing full SKU lists knowing that past performance (Forecast versus Actual) is calculated via a scientifically generated forecast. This allows them to focus their energies on the most valuable 20% of the company’s SKUs knowing the AI-powered system will handle the rest. Companies can now streamline processes and focus their attention on exception management.

AI and machine learning in predicting demand can unlock substantial value for companies, especially consumer foods that use a DSD model. By improving the forecast accuracy, companies can significantly improve product freshness and on-shelf availability, while reducing return rates.

Conclusion
Companies must incorporate advanced analytics, artificial intelligence, and machine learning to remain competitive. These technologies are just scratching the surface with the potential to enhance the upstream and downstream supply chain processes for CPGs and relieve individuals of mundane data duties allowing them to focus on higher-value activities. The most important result being increased market share and improved profit margins.