AI/ML for Retail Planograms – Deloitte On Cloud Blog | Deloitte US has been saved
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A planogram is a model that specifies exactly how much product should be displayed on store shelves to maximize sales and enhance the customer experience. Traditionally, the retail and consumer product industries relied heavily on their past statistics for forecasting and used heuristic methods and human judgment to perform a planogram product assortment, resulting in lost sales due to out-of-stock product(s), generating greater waste due to decayed product(s), and entailing high service levels in stocking up merchandise. Retail and consumer product companies have begun to realize that the traditional approach has its limitations and needs to be reimagined in a rapidly evolving and highly competitive market. That said, artificial intelligence (AI) and machine learning (ML) have started playing a critical role in the planogram assortment by helping to rank and recommend the products to maximize sales.
The three key aspects that help to build a successful ML-based planogram assortment are:
Do we have historical sales data available in an accessible state to adopt AI / ML?
Before taking the journey toward implementing AI/ML on a large scale for a planogram assortment recommendation, retail and consumer product companies need to consider two things:
Should we build or buy?
After overcoming initial hurdles and deciding to move ahead with an AI/ML-based planogram solution, it’s time to answer the million-dollar question: build or buy?
Build: Although there are commercial off-the-shelf AI/ML-based planogram solutions available in the market, it is important to evaluate the solution and make the right assessment before deciding. Key drivers to help decide may include:
If it is a consumer products company or a manufacturer is looking for the ML based planogram assortment solution, then custom-built solutions may work better because of the constraints and limitations they will have to deal with at physical store locations.
Buy: For small and midsize retailers, it is wise to buy a commercial planogram solution instead of building, as these applications will deliver quick value and can take advantage of pretrained models built by the product vendor using real-world data, resulting in higher accuracy. No time is wasted by investing in hiring and training the right talent and supporting infrastructure. Even if small and midsize retailers choose to build their own AI with open-source tools, it will entail significant monetary investment and may take months to train ML algorithms to do what most vendors have already achieved.
Commercial off-the-shelf AI planogram solutions provide advantages by helping understand localized customer behavior patterns and operational processes, thus enabling a data-driven decision-making process. They also help reduce time spent on cleansing data, smoothing production issues, and avoiding reinventing the wheel while deploying models daily or building in documentation and best practices to enable process automation.
How do we help ensure successful adoption of an ML-based planogram?
All the excitement of implementing an AI/ML-based planogram will be short-lived if companies do not establish proper evaluation criteria, consult subject-matter experts for their knowledge, train field staff to adopt and use the ML recommendations, and—most importantly—integrate the output of the planogram solution with the consumption system or application.
We can help ensure readiness for adoption of AI/ML-based planograms by:
To summarize, AI/ML has come a long way to help retail and consumer product companies gain data-driven insights, enhance sales, stay ahead of their competitors, and, more importantly, better understand customer behavior, leading to better customer satisfaction.
As the chief cloud strategy officer for Deloitte Consulting LLP, David is responsible for building innovative technologies that help clients operate more efficiently while delivering strategies that enable them to disrupt their markets. David is widely respected as a visionary in cloud computing—he was recently named the number one cloud influencer in a report by Apollo Research. For more than 20 years, he has inspired corporations and start-ups to innovate and use resources more productively. As the author of more than 13 books and 5,000 articles, David’s thought leadership has appeared in InfoWorld, Wall Street Journal, Forbes, NPR, Gigaom, and Lynda.com. Prior to joining Deloitte, David served as senior vice president at Cloud Technology Partners, where he grew the practice into a major force in the cloud computing market. Previously, he led Blue Mountain Labs, helping organizations find value in cloud and other emerging technologies. He is a graduate of George Mason University.