Emily Newton

Artificial intelligence has seen rapid growth across many industries. As supply chain leaders seek to become more efficient and resilient, they can no longer afford to overlook this growth. AI forecasting can give distributors the edge they need in this rapidly evolving market.

Data-based decision-making is already a critical driver of success for distributors. Supply chain operations generate considerable amounts of data, providing important insight if organizations can harness it. AI can take this advantage further if distributors learn how to implement it effectively.

How AI Forecasting Helps Distributors

If distributors hope to capitalize on AI fully, they must first understand where it is most useful. With that in mind, here are three areas where AI forecasting can help distributors make smarter decisions.

1 – Predicting Demand

Demand forecasting is the most obvious application of this technology for distributors. Machine learning algorithms can analyze past demand fluctuations to predict similar shifts in the future. Distributors can then adjust inventories in response to prevent stock-outs or surpluses as clients’ demands change.

Carrying costs often account for as much as 30% of total inventory costs, so preventing surplus amid dwindling demand is crucial. However, shortages carry similar gravity, as they can lead to lost business. The key to both issues is adapting to demand changes before they occur — AI provides the necessary insight to enable that adaptation.

Machine learning algorithms excel at spotting subtle patterns in data humans may miss. Consequently, they can accurately identify signs of demand shift before they are noticeable to human analysts. Distributors can then increase stocks of items that will be in demand soon and decrease those that will see less demand.

2 – Optimizing Inventories

Similarly, AI forecasting can help distributors optimize their inventories for greater supply chain efficiency and resiliency. These changes go beyond adjusting stock levels in response to incoming demand. AI can also determine the best inventory storage methods and layouts.

Just as AI tools analyze client ordering data to predict demand shifts, they can analyze warehouse workflows to identify inefficiencies. Algorithms may detect which products see the most demand, or the ones warehouse workers must travel the most to pick and pack. Adjusting to these insights lets distributors move inventory faster and with fewer errors.

A distributor may need to place some products closer to loading bays or reorganize their inventories to make more products easily reachable for workers. Like client demands, ideal layouts may change over time, and AI can predict and suggest these changes, too.

3 – Streamlining Supply Chain Operations

Distributors can use AI forecasting to apply similar benefits to their supply chains as a whole. AI-powered management platforms can integrate with over 100 different apps and services, providing more insight into how each supply chain operation affects the others.

Vendor and 3PL management is one of the most beneficial examples of this analysis. AI can analyze financial data and past timelines from logistics partners and vendors to determine which ones offer the best rates or highest efficiency. With this information, distributors can change 3PLs or vendors to streamline their expenses or shipping times.

As machine learning models analyze more data about past disruptions, they can forecast future challenges, too. That way, AI tools can alert distributors when to expect delays, shortages, or similar obstacles so they can adapt accordingly to mitigate the impact.

AI Forecasting Best Practices

Despite this potential, it is important to remember AI forecasting is just a tool. The extent to which distributors will experience these benefits depends on how well they can use this technology. Consequently, distribution leaders must keep some AI best practices in mind.

First, distributors must recognize AI requires vast amounts of data to work accurately. Consequently, using Internet of Things systems and similar technologies to provide data from across the supply chain is a crucial prerequisite for AI implementation. Because poor-quality information costs organizations $12.9 million annually, distributors must also clean their data before feeding it to AI algorithms.

Distributors must also recognize between 60% to 80% of AI projects fail, most often because of a lack of focus. Before investing in AI forecasting, businesses must identify a specific use case, then determine what data they need to enable that application. Taking a smaller, more focused approach to AI will minimize related expenses and boost the project’s chances of success.

Similarly, distributors should start by using AI in just one area before using it to inform larger decisions across more workflows. Starting small and expanding slowly will help them learn first-hand how to use AI effectively, leading to better returns on investment.

Modern Distributors Need AI Forecasting

The supply chain sector is facing rising obstacles. In the face of increased competition and quickly evolving demands, distributors must become as cost-efficient and agile as possible. To do that, they must capitalize on AI forecasting.

Effective AI implementation will soon become a differentiating factor between top-performing distributors and all the rest. Learning about this technology and how to use it sooner rather than later is critical to future success in the industry.

About the author

Emily Newton is an industrial writer reporting on how technology disrupts industrial sectors. She’s also the editor-in-chief of Revolutionized, covering innovations in industry, construction, and more.

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