As supply chains become more complex and customer demand moves toward individualized products, how can you best analyze vast amounts of data in order to improve demand forecasting?
Artificial intelligence (AI) tools, which are becoming increasingly prevalent in the industrial world, can combine large amounts of diverse data, allowing companies to make more accurate, proactive decisions. Consider the traditional method of demand forecasting: A seasoned staff member looked at historical sales and inventory data, along with vendor incentives or manufacturing capacity, to determine how much product would be required in the next month, quarter, or year. Using a bit of intuition and a lot of Excel spreadsheets, the worker could get a pretty good estimate of how much inventory would be needed to meet demand.
Fast-forward to today, when customer demand may shoot up — or down — overnight due to social media chatter. Plus, weather patterns, customer product reviews, and agile competitor moves make it virtually impossible to forecast demand using only historical methods.
AI-based forecasting tools are now able to combine data from multiple sources, allowing for quick demand estimates for a given product. These tools can combine data that currently exists in a company’s legacy systems to create comprehensive demand forecasts, far exceeding the capabilities of humans. Using vast amounts of data, complex algorithms, and advanced processing speeds, AI-based tools create forecasts that more accurately predict the amount of inventory, labor, and resources needed to meet demand.
AI-Based Demand Forecasting Tools at Work
Most people are most familiar with the use of AI tools at major companies like Amazon. But do these tools work for small or niche companies as well? One such tool, created by Llamasoft, aims to help clients in various industries better forecast demand.
In one case study, a retailer with 600 stores tested this software on two bulky inventory items — bikes and kayaks — that have seasonal demand. The company needed to be able to fulfill demand and still meet the company standards of 90% service levels. To simplify the project, they focused on “what-if” scenarios that would bring quick wins, including higher ROI and lower costs. The results were impressive, including 7% lower inventory requirements, a 4.5% reduction in inventory carrying costs, and a 284% ROI for bikes. Based on this pilot project, the company was able to move forward using the AI approach to manage demand for other products.
Another case study showed that AI tools are not just for inventory. In the case of a client providing printing services, their challenge was to predict on-demand services for their customers and overcome the current ad hoc approach. By scrubbing data from existing Excel spreadsheets and Access databases, the AI demand planning approach saved the company over $200,000 a week — nearly $11 million a year — by predicting and streamlining demand for services.
While these case studies are, of course, specific to these two companies’ experiences, they serve to illustrate the value offered by AI-based tools.
Setting Up AI-Based Demand Forecasting Tools
Before embarking on an AI initiative, remember that scrubbing data is one of the keys to creating an optimized solution from existing databases and legacy systems. One of the main hurdles in creating a great AI solution is ensuring that all inputs are clean, so scrubbing data prior to analysis is a crucial aspect of implementing a viable AI demand forecasting solution.
AI tools are proving their worth to companies of all sizes. It’s not just the Walmarts and Amazons of the world that can benefit. Any sized business can now leverage AI software to reduce costs, increase sales, and more effectively forecast demand.
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