According to BRP's 2018 Integrated Planning and Inventory Management Survey, most retailers (67%) are not leveraging advanced analytics to improve their planning decisions and optimize inventory. The importance of enhanced data and analytics is not lost on retailers, however, there are further opportunities to optimize their planning and inventory. While 67% of retailers are not using advanced analytics for merchandise planning, only 39% of retailers identified improved analytics as a top priority. This is a disconnect. As technological capabilities continue to advance, investing more resources into data utilization needs to be a critical objective for retailers.
Advanced analytics, or predictive analytics, offers retailers the ability to predict outcomes based on sophisticated algorithms and historical data. This requires human interaction to query data, validate patterns, create and then test use cases and assumptions. Now, with artificial intelligence (AI), also known as machine learning, planning systems can reassess models and reevaluate the data, all without the intervention of a human. AI is able to test and retest data to predict every possible customer-product match, at a speed and capability no human, or team of humans, could possibly achieve. The result is far more accurate decisions.
“Analytics serve as an important tool in assisting retailers to find and interpret meaningful patterns in customer and inventory data to support decision-making,” said Robert Cuthbertson, vice president at BRP Consulting. “Insight into customer demand, product adjacencies, price sensitivity, reaction to promotions, demographics and more are key to drive merchandise plans and actions that maximize profitability. This is especially critical in an omni-channel environment, as understanding the preferences of disparate customer groups across different channels becomes more complicated.”
“The technology for AI tools has advanced dramtically in the past five years. Innovative new technologies can even predict where customers will be in the next hour or next day based on historical patterns,” said Ken Morris, principal at BRP Consulting. “AI is also helping retailers make better decisions on which store should fulfill an online order. While traditional logic would select the product from the closest store to the consumer, with machine learning techniques, retailers can assess the value of inventory in each store to make smarter fulfillment decisions. For example, if they can identify/predict that the item in inventory at the closest store will likely sell at full price, but the same item at a different store location will likely result in overstock and markdowns, the retailer can ship from the further store and maximize total profits.”
According to the 2018 Integrated Planning and Inventory Management Survey retailers’ current and planned usage of advanced analytics by planning area includes:
• Merchandise Planning – 33% of retailers currently use advanced analytics for merchandise planning and another 48% plan to within three years.
• Assortment Planning – 30% of retailers currently use advanced analytics for assortment planning and another 49% plan to within three years.
• Demand Planning – 31% of retailers currently use advanced analytics for demand planning and another 54% plan to within three years.
• Product Lifecycle Management (PLM) – 22% of retailers currently use advanced analytics for PLM and another 33% plan to within three years.
BRP conducted the 2018 Integrated Planning and Inventory Management Survey to explore the current state of retail planning and to identify and understand retailers’ priorities as they strive to meet the needs and demands of today’s consumers.