How it started

The owners of the company invited me with two questions:

1. They couldn't understand what was happening. They could have a very positive month and then two in losses, or vice versa, while nothing seemed to change in the business from month to month.
2. They had obvious problems with the warehouse. They tried automation and made the warehouse the CEO's top priority, but nothing changed.

Let's take the financial story and the warehouse story separately.

The financial story

I took their reporting and found that, while they really had very stable revenue, their management reports were built on cash basis accounting instead of accrual basis accounting. Cost attribution was also done wrong. On top of that, their P&Ls were not supported by balance sheets on a monthly cadence. All of this made management signals messy and hardly supported by the numbers.

In plain terms: the reports showed when money came in and went out, not when the business actually earned or spent it. In this business those two moments were far apart. Payments from the supermarket chains didn't come on a stable schedule. Promos and seasonal peaks changed volumes, so the gap between selling and getting paid kept moving. On top of that came supplier rebates, fines from the chains deducted from later payments, and returns of unsold product credited back long after the sale. A “good” month was often just a month when a big payment or a rebate landed. A “bad” one was paying for the fines and returns of earlier months.

We put every sale, payment, rebate, fine and return back into the month it belonged to, and built a model that decided how to attribute costs to each contract. It took us 3 months to set up new reporting and make sure the numbers were aligned with reality. After that, several problems became obvious:

1. Logistics costs could be cut by 20% by introducing real competition and bringing new vendors on board.
2. One of the contracts they considered top priority because of its volume was in fact generating losses. For a distributor, a “contract” is the whole chain: buying from the supplier and delivering to supermarkets under our own contracts. This one brought a big share of revenue, but revenue doesn't mean profit. We only saw it once the cost attribution model was in place.
3. The order pickup rate was extremely low, so low that it started to block sales from bringing new products to clients.

In the following months we:

1. Onboarded 3 logistics companies, which led to a 25% decrease in logistics costs.
2. Negotiated with the supplier behind the loss-making contract. The supplier decided to quit, but for the 8-month transition period we agreed better conditions. That brought the contract slightly above zero and won time for the sales department to replace it.
3. Fixed the warehouse (see below).

The warehouse story

It was a dairy distributor with quite an old warehouse facility, no WMS, and goods mostly stored on the floor. Products usually had several expiry dates. The CEO tried at the same time to adapt the software to manage expiry dates and to introduce this exact accounting in the warehouse. So the company was overwhelmed with high software expenses, poorly thought-out changes to warehouse processes, and new technology with plenty of bugs on top of the existing mess.

What we did: we rolled back all the automation and instead adopted elements of the Toyota Production System in warehouse operations, using a mostly visual approach that was clear to warehouse staff from the very beginning and quite simple to implement.

For each product we measured how many expiry dates it usually has, drew areas on the floor and ordered the expiry dates from left to right. Picking happened at night, and stockers stopped picking exact dates. Instead, for each client they got an instruction: most fresh first, most old first, or middle if possible. For some long shelf life products we also drew stock level lines to show the moment we need to order them.

That stopped stockers from hunting for exact dates, made processing faster and reduced mistakes. Order picking accuracy went up by 15%. As a result, logistics could drive out earlier with fewer traffic jams and deliver products early in the morning to almost all clients.

The result

In 8 months we brought the company into positive numbers. Later, this company became a huge success story in its market.