There’s perhaps never been a more exciting time to be a part of the CPG industry.
The sharp spikes/ebbs in demand that occurred across nearly every category this year tell an important story about the Consumer Packaged Goods industry. More than anything, this was a year of change. But it was the rapid speed with which the change occurred that made 2020 both dazzling and tough to reconcile—especially for those involved in the demand planning process.
How to make sense of this data? Of a year packed with such wild, momentous swings in consumer buying behavior and supply chain disruptions? Here are four lessons for 2021.
1. Adapt, don’t react.
The pandemic exposed an ever-pressing need for organizations to be capable of recovering rapidly when faced with circumstances that cause drastic, unexpected changes in sales and inventory. Being proactive means having the right demand planning technology and adaptive S&OP process in place before the storm.
2. Leverage the power of artificial intelligence.
In the spring, when demand for many items skyrocketed, it would have been safe to assume that those numbers would remain consistent for a period of time. Fear of the unknown was a factor for consumers, and it was not illogical to think that emerging trends in buying behavior, such as curbside pickup, were here to stay. However, this wasn’t always the case.
Pantry-loading behavior in March and April threw many supply planners into a whirlwind, prompting them to over-predict demand in the coming months and therefore make and store more inventory than usual. An AI-driven solution, on the other hand, would have continued to grasp point-of-sale data and consumer trends data that may have shown that certain buying behaviors were temporary, and that some type of normalization would soon occur.
Though its impact was (and remains) massive, Covid-19 is essentially an outlier event. In other words, while it’s a vital dataset, it is but one of countless variables that make up a complete picture of demand that can help businesses drive more informed supply chain decisions.
3. Harness the power of data.
The number of data points that can impact a picture of future demand is both diverse and massive.Seek to obtain all available data—via your own records (historical), partnerships (point-of-sale data, e.g.), and other external sources that can offer rich insights. Ingesting, cleaning, and analyzing these datasets is better suited to automated, machine-learning models run on enterprise-grade servers—which can handle the sheer enormity of data—rather than, say, an Excel model run by an analyst.
4. Then... Unleash it.
The bridge between high-quality data and commercial benefits is the process of understanding how this data can translate into dynamic insights about your supply chain, your business model, and the CPG industry at large.
In order to open the "black box" on demand and eliminate blind spots, it’s vital to be able to understand the factors influencing past and predicted sales. To do this, companies must identify the dynamic relationships between datasets across various time horizons—including historical sales, inventory, and seasonality; product transitions, obsolescence, and discontinued products; new products and distribution data; and marketing investment data, to name a few.
Then it’s time to take action. For example, when companies can accurately predict sales at the customer, brand, and SKU levels, as well as predict inventory levels across all of their warehouses based on regional demand patterns, they can optimize working capital across the supply chain and allocate resources accordingly. And it waterfalls from there. Logistical efficiencies are put into place, manufacturing can focus on higher-margin SKUs, marketing spend can be better utilized, and so on.
By following these key action items, CPG brands and their partners can improve the efficiency of theirS&OP process and effectively navigate future fluctuations in consumer behavior, like the ones we experienced in 2020.