Accuracy and Time Savings Through AI-Driven Forecasting

Tags: Retail, Supply Chain Management, Technology , Fulfillment

Despite a wealth of available solutions and methods, retailers still struggle to produce accurate and timely demand forecasts. Existing challenges are further complicated by the idea of unified commerce, as shoppers choose from a variety of more convenient fulfillment options. There are adverse effects throughout the supply chain resulting from inaccurate forecasts, but today’s retailers are becoming better equipped to forecast in the face of retail disruption. How? Through artificial intelligence and machine learning.

Challenged by data, fresh and external influences

AI is quickly gaining ground in supply chain management, and we’re seeing it applied to demand forecasting to overcome many obstacles. One of them is the sheer volume of data that must be cleansed to be of any use to the retailer. Even with the best forecasting system, bad data always leads to poor results, and Gartner estimates that 30 to 60% of orders require human intervention because of inaccurate forecasting. Next, AI is used to understand the impact of events, whether a promotion, competitor activity, new product introduction, change in weather or other seasonal shift. Finally, AI can drive the nuanced strategies required for fresh and ultra-fresh produce—with variable demand and shorter shelf lives, fresh categories can eat into a retailer’s bottom line if not forecasted properly. AI alleviates inconsistent inventory buys, overstocks (and resulting markdowns), and out-of-stocks, all while optimizing the supply chain to avoid waste.

The people impact of a smart supply chain

There is no “one size fits all” solution for supply chain challenges, but the beauty of adopting machine learning is that the system learns and improves over time, depending on the retailer’s needs. Algorithms used in AI are constantly learning, enabling supply chain managers to respond to sudden fluctuations in demand through real-time visibility and daily and intra-day forecasts.

AI-based demand forecasting makes use of machine learning and is based on the idea that when we submit data to machines, they can learn for themselves. For forecasting, this means that the machine learning algorithms automatically detects patterns and makes connections in huge batches of data that would be impossible, or take too long, for humans to recognize. Deep learning is AI’s way of reasoning and understanding the total picture, so the system can give you recommendations on the steps to take. Machine learning tells you why it happened and deep learning tells you what you should do about it. So, a smarter supply chain is one that frees up supply chain professionals from manual intervention, which can lead to errors, and enables them to take on more strategic work.

AI turns data into a competitive advantage

With channel convergence and the rise of new formats, understanding what customers need and forecasting for each physical or digital location is critical—and because many of today’s systems are outdated and incapable of understanding today’s complex consumer behavior, it’s important that retailers adopt technology to process the data most effectively. The AI system serves as an automated data scientist, with new levels of information, alerts and insights, so retailers have no need to employ an expensive team of data scientists. Because machine learning algorithms are automated, they can analyze all the data—not just part of it—at scale, unlocking an enormous amount of business intelligence. According to Ensemble IQ, 52% of retail supply chain executives say they spend too much time on data crunching; AI and machine learning solutions overcome this.

AI-based forecasting with machine learning is now a practical reality for retailers of all sizes, especially for those grocery retailers stocking ultra-fresh foods and prepared meals. Poor inventory planning profoundly impacts a retailer’s logistics and multi-channel retailing muddies the waters even more. An end-to-end view of the supply chain is important for timely replenishment and efficiency, and AI can analyze varied demand patterns to provide that perspective.

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