Consumer goods: Identifying the demand signal

Consumer goods: Identifying the demand signal

Over the last few weeks, a lot of the conversations I’ve had with businesses in the consumer packaged goods (CPG) sector have been focused on a number of core challenges. As these companies look to navigate their way into ‘the new normal’ caused by the COVID-19 pandemic, it’s becoming increasingly difficult for supply chain and logistics teams to get an accurate feel for what’s happening in terms of supply and demand.

A major issue for CPGs currently is the fluctuation in the demand signal. The ever-changing political and economic landscape has made it almost impossible for businesses to make the “right decisions,” with over resourcing and buffer stock now more apparent than ever.

Common conversations I’ve had have been centered around product availability; some businesses aren’t producing enough stock to fill the retailer’s shelves, struggling with products, resource and raw materials. Others are struggling for enough logistics capacity, while others have capacity but are overstocking – leading to unnecessary wastage.

Solving the problem

So, how do you solve a problem like fluctuating demand? Well, before I go any further, I’d really recommend checking out the on-demand webinar hosted by my Peak colleague, data scientist Dr Simon Spavound. For me, he hits the nail on the head when it comes to the role of forecasting in the CPG supply chain.

Simon’s advice is for businesses to stop chasing the perfect forecast – it doesn’t exist, and you’re never going to be in a position to forecast with perfect, 100% accuracy. There’s only so far a good forecast can get you; in our view, what you need to be doing is becoming more comfortable with short term, reactive decision making in response to uncertainty. What’s coming through the door? What’s the best action you could possibly take right now?

The power of data

For businesses, being able to react optimally to fluctuations in demand has never been so important. However, it’s easier said than done. How can you possibly spot the demand signal in all of that noise? And what is the ‘optimal’ reaction?

The truth is that scenario planning is very, very difficult – and it’s something that us mere humans haven’t quite mastered just yet. “If we were to X, how would this affect Y?” may sound like a simple question, but the answer is often hard to decipher.

Let’s look at a practical example. Imagine you’re a crisps manufacturer and, completely out of the blue, you receive an astronomical order from a leading supermarket chain (who are, naturally, a very important customer!)

Your business would be faced with a difficult decision to make. Do you fulfill the order and deal with its knock-on effect on other customers and suppliers? Or, do you tell them no, and risk harming an important relationship and missing your OTIF targets?

The answer, we believe, can be found in your business’ data. If you think about the sheer amount of data you have access to across your supply chain and logistics network, do you think that you could be driving more value from it, and using it to make smarter decisions?

This data could place this customer order in clearer context, in terms of factors such as recent behavior, other customers’ recent orders, their forecasts, and your ability to supply. This highly insightful context allows you to find the signal amongst the noise, and reduces the likelihood of chasing false promises created by common business-alert methodologies. This insight allows for an AI-generated recommendation on fulfilling this order and replenishing stock to ensure global service is met in the near future.

A unified, holistic view of your data can help you and your business decide what your next move should be. When this data is leveraged with the power of artificial intelligence (AI), you can draw otherwise-hidden insights that a human would never have spotted, in order to identify the right demand signal and make quicker, reactive decisions to cope with the current fluctuating market.

By connecting traditionally siloed sources of data, such as sales data and supply chain data, for example, your business can improve connectivity across the entire value chain. In summary, it can help you to find that perfect balance between fulfilling demand, planning ahead as best you can, and keeping your customers happy.

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