Revamping legacy systems and human judgment with data-focused strategies: Though traditional forecasting techniques and predictive models based on human judgment have limitations, and tightly coupled legacy systems make it difficult to adopt AI/ML without rethinking and strategizing the tech spend, it is still unrealistic to replace these legacy systems in one swoop.Historical sales data, combined with external sources such as weather, holidays, etc., will help predict and recommend the ideal planogram assortment by uncovering past sales patterns. The most important consideration, therefore, is to focus on building a centralized data repository to make data available in a public cloud platform, along with other historical sales signal data such as promotions, discounts, and demographics, among others. Deriving insights from data: Historical sales performance data is a key element in building an AI/ML-based planogram recommendation.How do we ensure the successful adoption of the ML-based planogram?ĭo we have historical sales data available in an accessible state to adopt AI / ML?īefore 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 an AI/ML-based planogram?.Do we have historical sales data available in an accessible state to adopt for AI/ML?.The three key aspects that help to build a successful ML-based planogram assortment are: 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. 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. 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. A blog post by Chida Sadayappan, lead specialist, cloud, data, and machine learning, Deloitte Consulting LLP and Dinesh Kumar, principal machine learning and deep learning engineer, Deloitte Consulting LLPĪ planogram is a model that specifies exactly how much product should be displayed on store shelves to maximize sales and enhance the customer experience.
0 kommentar(er)
