Role of AI in Optimizing Inventory and Demand Forecasting to Ensure a Resilient Supply Chain

Organizations need to implement various measures for transforming the supply chain into a resilient one.

The current pandemic has witnessed huge disruptions in the supply chain all over the world in the form of a sudden drop in demand as well as interruptions in supply chains. This disruption of an unprecedented scale has impacted almost every organization.

Modern day organizations have always aimed for lean operations with zero inventory by establishing a balanced flow of supply and avoiding maintenance of buffer stocks to absorb uncertainties. When people do not have to face the uncertainties for a long period of time, it becomes difficult for them to appreciate the need of resiliency over efficiency. However, this pandemic has brought a fresh focus on resiliency of supply chains with high amount of urgency due to its scope and scale.

The supply chain risk and resilient processes work at various levels of the entire value chain on both the supply and demand side including the manufacturing operations. However, the ultimate success depends on the availability of the inventory at right place, at right time and in right quantity without creating inventory buffers and compromising efficiency.

Organizations need to implement various measures for transforming the supply chain into a resilient one, however adopting Artificial Intelligence (AI) and powering the processes with better decision making is one of the most effective measures.

Gartner predicted in March 2021 that by 2024, 50% of supply chain organizations will invest in applications that support artificial intelligence and advanced analytics capabilities. AI’s application in demand forecasting, predictive maintenance, predictive process quality, predicting lead time, propensity of order cancellation, predicting employee absenteeism, predicting commodity prices, etc. will help in improving the resiliency.

AI increases Accuracy of Predictions

AI powered Demand forecast considers trend, seasonality, cyclicity and helps to estimate the demand accurately. The accuracy can be improved by including data representing all the factors influencing the demand. The demand can be predicted at various levels for each SKU at each hierarchical level of the distribution network.

AI helps pre-empting impact of external factors

There are external factors which may impact the demand or supply lead time. We can include all those external factors possible in the prediction models to incorporate impact of those factors. External data representing economic indicators, geo-political situations, commodity pricing, currency exchange rates, weather data, competition intelligence, market size, Industry reports, News, Social Media posts, etc. can be included in the models.

Weather over ‘What If’ situations

Once the data pipelines are setup to acquire real time data, frequent simulation runs can predict the demand and supply parameters. Any recent change in status of any significant information will change the demand or supply situation. The organization can then react immediately for the remedy. The predictive AI presents to the decision makers on what may happen; however, the prescriptive AI will help to simulate various scenarios to find the most optimum remedy to handle the situation.

It is very important for any organization to have an end-to-end visibility with reference to optimizing inventory across the entire value chain and incorporate all significant data points in the AI model to accurately predict the outcome. The data enriched and AI powered approach with ability to simulate to get predictive and prescriptive outcomes help organizations to strike right balance between redundancy and efficiency to achieve inventory optimization to achieve resiliency in supply chain.