Bridging the Gap: How to Prepare for the Future of Demand Planning
Retailers’ bearings for demand forecasting are now better calibrated than they were just a couple of years ago, although shockwaves from supply chain and distribution disruptions persist in some areas. These challenges create a disconnect between what retailers think is the "new normal" vs. what their actual "new normal" likely is.
This dichotomy hinders their ability to prepare for a new future of demand planning. This is no easy feat for grocers, considering other factors outside of their control that they must also account for in this era. That includes matters such as unpredictable supply chains as well as shoppers who want products faster than ever before and from the channel that best fits their lifestyle. That’s why grocers need to rethink demand planning and forecasting in order to meet consumers where they shop — whether it’s online, in-store, or both.
The Times Are (Always) Changing
As retailing has evolved over the years, so have its various foundational operations. Every few years, new trends force retailers to rethink critical business processes. The industry is now at another such inflection point.
The rapid pace of change in shopper, competitor and market behaviors means that history provides fewer and fewer useful demand forecasting baselines. The answer lies in supplementing historical purchasing data with current, relevant data from a variety of sources — and leveraging artificial intelligence (AI) technology that can accurately leverage that data to more accurately predict what items shoppers want and in which channels and stores.
Innovative retailers factor in not only historical data, but also current contextual data, such as information on promotions, stock-outs, and seasonality. For many grocery retailers, however, existing tools aren’t capable of meaningfully analyzing and leveraging the range and quantity of data available.
Finding the Right Solution
All the above point to reasons why the time is now for retailers that aren’t there yet to consider how to not only use the right data, but use it meaningfully to embrace the future and abandon increasingly inadequate existing statistical approaches to demand planning. That means they must adopt the use of predictive capabilities that can learn from historical information and apply it to the present as well as offering advice on the future.
AI-based data analysis also helps predict the future with much higher accuracy and much more rapidly than statistical methods supplemented with a team of analysts. Naturally, predictive systems still need humans to interpret and apply the insights, but it would be impossible to keep pace with today’s demand by doing business as it’s always been done. The amount of time and number of people needed to do things the old way are not only resource-intensive, but will likely generate inaccurate forecasts or, at best, outdated ones.
Most modern solutions are also cloud-based, allowing everyone involved to review suggestions and foster a collaborative environment spanning the entire spectrum of demand planning functions, while also providing affordability and scalability as data volumes proliferate.
Bridge the Gap
Any product's sales can soar — or stagnate — at any moment for reasons ranging from a viral social media post to a worldwide pandemic. It’s par for the course in the ever-evolving state of demand planning, but it poses forecasting and supply planning issues that need to be resolved at speeds that lagging systems can’t keep up with.
These demand complexities can cause grocers to fall behind in adapting to this new era. This rate of change, the multitude of factors influencing demand across different channels, and the external competitive climate all point to one thing: to think differently about demand planning and what will be necessary to succeed in the future, grocers must break with convention and the technology and methods of the past and adopt modern technology that effectively tackles today’s retail realities.
Troy Prothero is senior vice president of product management, supply chain solutions at SymphonyAI Retail CPG, a provider of enterprise AI SaaS solutions for category planning and supply chain optimization.
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With more than 20 years of experience as a supply chain leader for both retailers and retail technology companies, Troy has extensive experience applying dynamic data analytics to managing and leading all aspects of supply chain optimization. Prior to joining SymphonyAI Retail CPG, Troy held leadership positions related to the supply chain at SAS, Retail Solutions Inc., and Delhaize Group.