GUEST COMMENT: When the demand forecast is wrong, what happens next

In an unpredictable world, Isabella Wayte, CEO of XDP, the UK’s only privately owned IDW (irregular dimension and weight) carrier, explores what agility and resilience really look like.

Competitive advantage in retail isn’t defined by who produces the most accurate forecast. It’s defined by who can adapt fastest when the forecast is wrong. After more than 20 years in logistics, that is something I have learned first-hand. And it’s happening more and more often.

Forecasting matters. Of course it does. Retailers need forecasts, warehouses need forecasts and carriers need them too. They help us plan people, vehicles, space and capacity.

But the reality is that forecasts are not always right. And sometimes they are a long way out.

Circumstances change – quickly

Take something as simple as a spell of hot weather. Suddenly portable air conditioning units are flying out of warehouses. A retailer might have forecast one level of demand and then twice that amount of freight turns up.

At that point, talking about why the forecast was wrong doesn’t get anything delivered.

The stock is there. The orders have been placed. The customer has already clicked buy. Somebody still has to move it. That is the bit of eCommerce I don’t think we talk about enough.

Retailers have become incredibly good at generating demand. Promotions can go live quickly, social media can make a product take off overnight and the weather can completely change what people are buying.

But every online order eventually becomes a physical operation. Someone has to pick it, move it through a network, put it on a vehicle and get it to the customer’s door on time and in one piece.

And when you are talking about furniture, garden equipment, gym equipment, appliances or air conditioning units, that is a different job to putting another small parcel through a conventional network. You can’t just keep pouring volume into a network and expect nothing else to change.

There are physical limits: vehicles, drivers, warehouse space, sortation time and delivery capacity. Most parcel networks know exactly what unexpected volume can do to service, which is why they have to protect their networks when volumes spike.

I work in the large, heavy and awkward freight market, where volatility is part of the job. Our customers don’t always know exactly what they are going to sell. Neither do we.

We can have all the forecasts and planning conversations in the world, but sometimes the reality on Monday morning is that considerably more freight has arrived than anybody expected.

Then you have a choice. You can point at the forecast and let the customer down. Or you can work out how to move it and fulfil the sale.

For me, that is what agility actually looks like.It isn’t pretending you can predict every eventuality. It is having an operation and a team that can react when reality doesn’t match the plan.

Sometimes that means changing resource. Sometimes it means creating additional capacity or moving things around the network. Quite often it means making decisions quickly with the information you have in front of you.

It isn’t always neat. That’s logistics

And the customer doesn’t care what was on the forecast. If I order an air conditioning unit in the middle of a heatwave, I want it because I’m hot now. If it arrives three weeks later, it isn’t much use to me. The same goes for furniture, gym equipment or anything else where demand can suddenly spike.

From the customer’s point of view, the purchase and the delivery are one experience. The retailer may have made the sale, but the delivery is still part of the promise.

That is why retailers need to have a different conversation with their logistics partners.

Don’t just ask: ‘Can you handle our forecast?’ Ask: ‘What happens if we’re wrong?’ Because that tells you much more about how resilient your supply chain really is.

We are going to keep getting demand wrong. Weather changes. Products go viral. Promotions perform better than expected. Consumer behaviour surprises us.

Better data and better forecasting will help, but I don’t think the answer is believing we can predict everything perfectly. I’ve been in this industry long enough to know we can’t.

For me, the real question isn’t whether your forecast will always be right. It won’t be. It’s what your operation does when it isn’t.

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