Get what you need on time and do what is not necessary for a while. No, this is not cleaning guru Marie Kondo, but Pipple. They helped Angro, a legwear wholesaler and wrestler with warehouse space, with handy up-to-date overviews to be able to supply smarter. Marie can learn something from that.

Angro’s collection consists of more than twelve thousand different products. They sell them in twenty countries. Their customers range from a boutique on the corner of the street to chains with 2500 stores. They get twenty to three hundred orders daily. They are picked in their central warehouse.

Pallets full of tights

Only a fraction of the stock fits in this central warehouse. The lion’s share of the socks, slippers, rain boots and related items is therefore stored in two other warehouses, one fifteen kilometers and the other five kilometers away. There they are packed in pallets. There are no facilities for splitting or unpacking the pallets at these locations. So are the pickers in the central warehouse short of three tights with stripes in size 38 for the order of Stefans Sokkencorner? Then they have to have a whole pallet of those tights pulled on from one of the other warehouses.

Eric Serrarens, one of the directors and owners, had had it all. ‘Mishandling products in the central warehouse drives up our delivery time and causes transport costs and hassle.’ His Business Intelligence tool could not provide the solution for this. ‘I can endlessly match and filter the load of data in it, but the system does not make intelligent calculations for me. But my accountant knew some smart guys who knew how to deal with that.’ Enter Pipple.

Exceptions rule

Pipple made a model for Angro. The result: an application that spits out an overview several times a day which pallets need to be brought to the central warehouse to complete the orders for a few days in advance. Pipple advised Eric to also do the opposite: transfer stock in the central warehouse that has not been necessary for a while to the decentralized warehouses. That would provide welcome space. A good idea, Eric thought, and so that side of the coin was also incorporated into the model.

A nice handle, Eric calls the model. He does not yet know exactly how much it has yielded for Angro so far. “I’ll calculate that later this year, we’re still tweaking. But I see that it has at least brought peace to the workplace.’ Ideally, he would like to be able to forecast the required stock in the longer term. ‘That’s ambitious, I know. With fads and weather conditions as crucial factors, creating a reliable forecasting model is difficult. Pipple did make a start with the historical data that were available, but the practice is indeed unruly. Exceptions rule our business.’ He thinks Pipple’s aftercare is neat. ‘When I call them to say that an assumption turns out to be wrong, they immediately dive in and adjust the model if necessary.’

Pleasant flattening

In addition to transforming data from a BI tool into practically applicable information, Pipple is excellent at pricking and thinking along soberly, according to Eric. “Working with them was refreshing. I always see our company in all its complexity. That makes me feel like I’m thinking about processes. But the people at Pipple think in models. They can flatten things out for you, regardless of emotions. ‘But why can’t it?’ is a very good Pipple question.’

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