Demand forecasting is a well-established and important field in retail, but accuracy and speed are becoming challenges in a fast-changing and ever evolving market. Category managers need advanced support to stay ahead in modern retail.
This is according to Linda Mandyu, client success manager at the Argility Technology Group, who says most category managers still attempt to manage demand forecasting on complex Excel workbooks, while some trailblazers are trying to push the envelope by integrating statistical analysis and BI tools.
“It remains onerous and highly specialised, and requires very skilled statistical analytics, commercial and retail subject matter expert teams. These skills are hard to find, and when you do find them, they are generally experienced with industry and vertical-specific tools,” he adds. “You might need a team of ‘unicorns’ to deliver what modern retail needs.”
Argility combines mathematical and statistical expertise with the most advanced predictive and prescriptive modelling as well as leveraging modern cloud technologies, complete with a skills pool with over 40 years of retail experience.
“We’ve taken that deep experience combined it with scientific, technical skills and built a product that is fit-for-the purpose of demand forecasting directly on the world class Google Cloud platform with the ability to analyse petabyte-scale data. Then we apply machine learning tools and AI to supercharge those capabilities,” he says.
Mandyu confirms that it is essentially “demand forecasting as a service” that allows retailers to firstly improve stock holding and distribution, and then to support price optimisation initiatives, thereby driving an increase sales and retention of customers. “It’s like a secret weapon for category and brand managers alike, that arms them with highly valuable intelligence to inform precise decision-making.”
She explains that Argility’s Predict Retail services deep dive into enterprise data to uncover valuable insights. “For example, if they want to grow market share in a particular category, we look at what competitors have done, matched with their product-price elasticity boundaries, then give them high level intelligence on where they are over or under priced.
“We also focus on recommended order levels so they are not over or understocked and can optimise their supply chains and better meet customer demand pre-emptively. This gives value out of the box much faster. It also frees up their skilled resources to do higher value human-specialised analysis that may fall out of the capability of artificial intelligence or machine learning efforts. They don’t have to do the number crunching but rather pass it to a machine to do the heavy lifting of the incredibly large data sets stretching back over longer time series’.”