Retailers are embracing these technologies both on the front end – to engage consumers – and on the backend – to improve decision-making throughout the supply chain. With the amount of turnover in retail and the instability of international trade, retailers leveraging the power of AI and ML are armed with a wealth of data to analyze trends and inform future decisions. Learning how to analyze the data allows retailers to determine what works and what doesn’t in established processes, including those in sourcing and the supply chain, potentially improving shipping, sampling, fabrics and materials selection and more. AI’s ability to automate and learn from interactions, using historical data to predict future scenarios, makes it a crucial tool for retailers in the years ahead as trade rules and regulations continue to shift.
Automate, collaborate, innovate
From food service to lab testing, automation is on the rise in almost every industry, as companies strive to improve efficiency and consistency. In retail, automation can be implemented throughout the supply chain to ensure complete and accurate product orders are delivered to the right place at the right time. When combined with machine learning, intelligent automation can essentially run itself with minimal monitoring. The human element still matters; it comes into play when analyzing the subsequently gathered data to shape future business decisions.
By automating everyday processes, AI and ML make the retail supply chain more efficient and effective, giving retail professionals time back in their day to allocate toward more valuable tasks like collaboration and innovation. With a platform that integrates ML into every step of the process, retailers can make more informed decisions regarding industry partners, scanning supplier behavior for delivery accuracy, close collaboration and speed to create preferred supplier profiles. Just as Netflix recommends movies based on what you’ve recently watched, a retail platform with ML at its core can recommend new suppliers and other industry partners based on successful past relationships.
Predictive analytics don’t just benefit retailer relationships, but can also be applied to the product development process. It can be used to anticipate possible risk or supply chain disruption, and when applied with a “what if” costing approach, predictive analytics can determine alternative paths to avoid lost time or money. Predictive analytics can be used to forecast inventory needs using buying patterns over time – for example, when planning for seasonal products. By processing a large number of predictor variables and determining those that are significant to the planning, retailers can use machine learning to build a forecast model that leverages the company’s historical sales experience.
Adjust and save
These technologies also provide an opportunity for brands to meet consumers where they are, by predicting and addressing shopping needs. Using AI to analyze consumer shopping habits, retailers can determine what products – and what amount of inventory – will best serve its customer base. Using data analytics to adapt inventory orders allows retailers to stay agile, helping consumers find what they want when they want it and avoiding the risk of excess unsold inventory.
Example: With billions of dollars of clothing returned every year, retailers are forced to pick up the tab on shipping. And it’s not just the cost of shipping retailers need to consider, but the resources involved in inspecting, repackaging and returning the item to the warehouse for resale.
Artificial intelligence and machine learning help retailers meet consumer needs faster and more efficiently than ever before, providing a wealth of information for retailers and their industry partners to leverage toward the creation of the perfect product. From product design to delivery, these technologies showcase the potential cost savings and long-term business success retailers can achieve when embracing change.
Retailers need to combine machine learning with the human element of collaboration to forecast potential areas of risk. Read how in our whitepaper, “Mitigating retail risk and uncertainty through what-if, any-market costing.”