It can be tough for consumer product companies to get a clear understanding of consumer demand. Remember when COVID hit and everyone was panic-buying toilet paper and hand sanitizer? That was a prime example of demand planning challenges for consumer product firms. Sometimes, there's a disconnect between what people want and what's actually available in stores. And retailers often hold the sales and inventory data, while consumer product companies don't.
But what if supply chains ran more like demand chains? Running a shopper-first supply chain can save money for retailers and consumer product companies and create better customer experiences. And the key is using real-time consumer data. Think about it. Consumers are the ones who know what they want and where they want it. They're using things like store locators, word of mouth, and social media to find what they need. By tapping into this information, companies can get real-time insights into consumer behavior and fill in data gaps left by retail partners.
And by combining this data with machine learning models, they can predict demand at the shelf level and make informed decisions about where and when consumers are likely to make a purchase. If someone is unable to find their favorite candy bar at their local grocery store, they might use a store locator tool on the candy website to find another nearby location that has it in stock. The data generated from this search can be used by their consumer product firm to understand the demand for their product in that specific location and time.
They can also gain more insight into their customers through direct-to-consumer models, giving them the option to buy directly on the website, add products to a shopping list through a retail partner, or set up a delivery. Consumer product firms can also use customer data platforms to bring all their data together, with retail partner data, to get a more complete picture of what their customers' needs are. If a company knows that one store has too much inventory and another store has too little, they can adjust their supply plan on the fly to avoid stockouts.
Crowdsource data, machine learning, and direct-to-consumer distribution options can all help consumer product firms create a more efficient and effective demand planning strategy, which means fewer stockouts and better sales.