Industry Trends – Beer Distribution and Improving Profit Performance

Beer Distribution is an interesting business: High margin, protected by regulation that has traditionally limited most forms of competition, which leads to an overall lack of incentive to innovate technologically.

Nevertheless, despite the lack of innovation incentives there are some activities occurring that signal the status quo may be changing a little bit; for example, the recent foray of Berkshire tossed into the mix through the purchase of a couple of distributors.

If the current dynamic were to change, for whatever reason, forecasting would be one area that would allow distributors to rapidly improve – even advance – their bottom line performance outcomes.  Currently, on average, there is not a lot of focus on forecasting. Basic practices involve sales people “working” their on- and off-premise customers, while the inventory people make sure they keep enough stock on hand to ensure customer order fulfillment is met. Inventory managers look for opportunities to take advantage of strategically ordering from suppliers that game prices increases, etc.

A heightened focus on improving forecasting and ordering would allow distributors to lower working capital invested in inventory, while maintaining and/or improving customer service.

Customer service could improve in a number of ways; better order fulfillment being the most basic upgrade. On the more advanced side of the equation; distributors could work together with bars and liquor stores to make sure the products stocked, or on offer, respond and adapt to seasonal changes, trends, pricing, promotions, and holidays – making the distributor a value-added supplier.

In turn, the end merchant will become an even more valued customer by providing a more accurate forecast to their suppliers. This helps distributors and their supply chains become more efficient. Ultimately, this virtuous cycle helps set the distributor apart as a better supply partner – making it one that beer manufacturers will want to work with and which has the capacity to make a product successful in a new market.  This allows the distributor to negotiate more favorable terms with suppliers, thereby increasing margin performance. Everyone benefits.

Improving the forecast model will require improvements in technology and process systems – something that owners will have to support. Since distribution sales people are singularly focused on driving volume and taking care of their customers, they do not take kindly to activities such as supply chain forecasting. But their input is critical in order to achieve a “big picture” point of view that will help the entire company. When forecasting is tied directly to how it will help sales people earn more money (working for a higher margin distributor), a critical component of improving forecasting will be realized.

It’s time to raise the bar for forecasting and demand planning outcomes

 Raising the Bar for Demand Planning Outcomes

I recently happened across this post on

Few days back I was interviewing candidates for Demand Planning position. I asked one of the candidates to share his greatest frustration as a demand planner. What he shared was quite shocking. He had been given a target on forecast accuracy that he missed completely due to uncertainty of tender business, which contributed about 15-20% of the total business. Though the company could book the sales and sales team earned handsome incentives, the poor guy lost his annual bonus.

A missing component here? The salespeople had no incentive for forecast accuracy. A customer behavior-related miss is something that can be avoided with the proper input from sales — and it should be expected that salespeople have detailed knowledge of transactions such as tender, and that they input that information into the forecast by whatever mechanism provided by your process. Any company that’s serious about forecasting should have sales contributing to the forecast. It was fair to hold the demand planner to the numbers, but not fair in that nobody had incentive to help — and that management failed to build and support a holistic process. However, the demand planner should have also built the case that sales is equally responsible for the forecast, and at the very least, should have actively engaged the sales team for the necessary information — with the help of his manager, if necessary.

Many companies have a utopian belief that by having a dedicated demand planner and / or a sophisticated tool, any demand could be forecasted with an accuracy that should touch 90% or more. Such obsession leads to frustration and demoralizes the entire supply chain staff. The fact of the matter is that one should forecast what is forecast-able and not forecast what is not forecast-able.

There is no such thing as “non-forecast-able.” The minute you label something as such, you’ve just issued an organization-wide “Get Out of Responsibility Free” card. Ultimately, your supply chain people will have to deal with all the “non-forecast-able” variables that nobody wants to think about. So there will be a comprehensive forecast, just not one that leadership wants control of or responsibility for — which is basically unprofessional.

While I agree that there is no utopia around 90% — it’s just a number, after all — the indisputable fact is that more accurate forecasts equal more effective supply chains and a higher EBITDA in turn. This is backed by all the research, including AMR/Gartner.  So anything that can be done to increase forecast accuracy at the execution level (detailed level) is well worth it.  It is an issue of getting the forecast as accurate as possible with a continuous improvement cycle. There is nothing more important to bottom line performance over time for your supply chain than forecast accuracy.

Mendiratta goes on later in the post to work through a hypothetical demand management problem that I would not characterize as difficult. In fact, our customers manage problems like this and far worse all the time. Your company’s forecast system should allow multiple aggregations of forecast detail — SKU, SKU by customer, by location, by business (B2B vs. B2C, e.g.) — so that the detail variations (differences in ordering patters) can be quickly identified. This is not difficult and I would argue that the entire last half of the article is the type of work that should be going on 24/7 as part of the demand planning and forecasting process.  Otherwise, as suggested before, you are just throwing the problem over the wall and telling the supply chain to deal with it. That isn’t right. It means your company is underserved and therefore missing out on profitability and competitive advantage because of the approach expressed in this article.

Here’s a question that any supply chain pro should be asking themselves right now: “What if I could reduce my forecast error by a minimum of 25% at the execution level? What would that mean to my supply chain performance? My EBITDA?”

Want to find out? You know where to find me.