The disruptions caused by market forces such as multiple sales channels, always-on shoppers and agile competitors are not confined to retailers alone. There is an increasing need for manufacturers to leverage advanced technologies such as machine learning and AI to gain visibility into consumer interest at scale and down to the micro-market level. In order to succeed and stand out from the competition, manufacturers are tasked with building end-to-end agility across the entire supply chain.
Manufacturers need to harness the power of real-time data to not only be responsive to market signals but also build a pre-emptive approach to manufacturing—and the ongoing coronavirus pandemic has underlined the importance of this. For example, consumer interest in yeast increased by over 6,000% over a period of two weeks before US states implemented lockdowns due to coronavirus, according to Centricity—this was weeks before the product went out of stock at multiple stores.
CPG manufacturers have embraced real-time data to a very large extent. Some 66% of CPG manufacturers use real-time data to drive their business, and 25% expect to leverage real-time data by mid-2021, according to a December 2019 survey by American trade publication IndustryWeek in partnership with Oracle.
Although the overwhelming majority of CPG manufacturers leverages real-time data to drive their business, it needs to be combined with technologies such as AI and machine learning to harness its true power. The same survey revealed that the overall adoption of advanced technologies such as AI and machine learning is currently relatively low: Only 26% of respondents cited using such technologies.
Decision-making has continued to become increasingly challenging for retailers. Both retailers and their suppliers understand the importance of joint business planning and increased collaboration, yet there are many obstacles, including misaligned goals. Furthermore, lack of trust and transparency remains a key challenge between retailers and suppliers, which often translates into limited data sharing and misaligned key performance indicators. The outcome is that retail category managers often rely on their instincts when considering new products with near-zero differentiation for their stores.
In such an environment of isolated planning, category managers may not be able to forecast market trends accurately, which may prevent them from reaching the ultimate goal of shopper centricity.
More importantly, “bad innovation” or an incorrect decision has a ripple effect on the overall business of both retailers and their suppliers.
Retailers are under increasing pressure to bring in the right innovation at the right store and at the right time. This means that they must correctly align their assortments with shopper demand.
Retail category managers’ decisions have a ripple effect on their organizations. For example, if a category manager decides to carry a new product, there is an immediate impact on supply chain processes such as inventory management and warehousing. Most decisions by category managers impact the store associates for manual tasks such as planogram generation and assortment planning. In addition, discontinuation of old products involves marking down the old products—at a loss in some cases.
Category managers therefore substantially impact store operations and, in turn, overall profitability. In fact, increased profitability (42%), higher efficiencies in operations (41%) and better customer service (39%) surfaced as the top benefits of effective retail category management, according to our March 2020 survey.
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