One of our customers, a top craft brewer, faced a challenging problem. With the exception of a few direct outlets for their beer, their restaurants, they had no way of determining product demand for seasonal brews and their base products.
Interestingly, they are not alone with this issue as producers of alcoholic beverages in the US sell to distributors. And distributors do just that, distribute to retailers, restaurants and bars. This separation of markets makes it very difficult for brewers to determine real-time market demand and react to that demand. Not being able to know directly what the end consumer was demanding was causing our customer to lose sales on in demand seasonal brews and having to guess at demand in general causing overstock on inventory with limited shelf life. Because no one likes a stale beer. This was costing our customer $3.5 million in opportunity costs, retooling and unsold inventory per year.
Alcoholic beverages in the US in 2012 had a market size of 9.4 billion gallons and $197.8 billion in retail sales dollars. Beer dominates the sector on a volume share basis at 87% of consumption and approximately 49% of the revenue. US law dictates that manufacturers sell to distributors and distributors sell to retailers, restaurants, bars and other outlets. This creates a vast disconnect between the producer of the product and the outlet for their product. Thus creating the conundrum of: how they can know their market, without knowing their market.
Fortunately, the beverage industry, like many other industries, has an array of market intelligence data available. In the case of the alcoholic beverage industry, a valuable source of this data comes from Vermont Information Processing (VIP). VIP captures point-of-sale data for a majority of the retail alcoholic beverage market. To make this data actionable, our customer needed to combine that end consumption information using predictive analytics to known production information. They turned to Halo to provide that unique capability.
Using our capabilities our customer is now able to detect sales trends in near real-time and better schedule the use of equipment, plan inventory and meet market needs. The increased revenue and decreased costs resulted in a gain of $2.9 million on the bottom line. When you don’t have direct access to the market, it’s exceptionally hard to know your market. By effectively incorporating internal production data, external consumption data and using predictive analytics companies can be more effective in delivering the right product at the right time in the right quantities resulting in lower costs and greater profitability.