How To Generate Accurate Long-Term Demand Forecasts and Add Value to FP&A

Nari Viswanathan & Stephanie Buck
Nari Viswanathan & Stephanie Buck

Nari Viswanathan is currently Sr. Director of Product Segment Marketing at Coupa, where he helps bring products to markets in the areas of Supply Chain Design and Planning. Over the past 20 years, Nari has held VP and Director of Product Management, Research and Marketing roles at Aberdeen Group, River Logic, Steelwedge and E2open. He has significant experience building products from the ground up and managing the P&L for a product suite. He is a proven B2B marketer with expertise in content marketing, competitive intelligence, and positioning. He has published numerous thought leadership articles, whitepapers, blogs and delivered dozens of webinars during his career. Nari Viswanathan is a five times SDCExec Supply Chain Pro to Know award winner. Nari holds a master’s degree in Manufacturing Systems Engineering at the University of Wisconsin-Madison and a bachelor’s degree in Mechanical Engineering at the Indian Institute of Technology, Chennai.

Stephanie Buck is passionate about storytelling and helping leaders, businesses, and organizations transform the way they connect with their customers, prospects, and others. At Coupa, she leads storytelling and content production efforts for supply chain. She brings over a decade of experience supporting marketing and communications with impact-oriented enterprises and mission-driven organizations. She earned her Master's degree from the London School of Economics and her Bachelor's degree from Texas Christian University. She grew up in the Chicago area, but currently calls Washington, D.C. home.

Read time: 5 mins
How To Generate Accurate Long-Term Demand Forecasts and Add Value to FP&A

How can you predict future revenue in a world that’s changing at a faster pace than ever before? How will inflation affect future customer demand? How would a recession impact demand for your products? What investments do you need to make now to meet future demand? Should you ramp up production now? Or do you wait?

We hear these questions a lot from supply chain and finance leaders as they grapple with today’s realities. If demand predictions are right, you’ll save money and increase business growth. If these predictions are wrong, growth could stagnate, you could lose out financially, and the company’s footprint could shrink.

In the past, people in the financial world relied on spreadsheets and prior experience to predict long-term market demand. Supply chain organizations also relied on spreadsheets to plan for working capital and operating expense spend. In practice, these organizations have relied on different datasets. For example, the supply chain organization often looked at volumes, and finance looked at cash flow and other financial indicators. Even when they were working from the same spreadsheets, there would be a disconnect.

Good ol' spreadsheets — The outdated demand forecasting method

We all know macroeconomic issues affect consumer demand, but it’s hard to know exactly how, when, and where. For example, as inflation causes prices to rise, people might spend less in other areas. But in which areas? How can you predict how these macroeconomic factors will impact demand for your products and inform the decisions you need to make to keep the supply flowing (and not overflowing) both now and years down the road?

Typically, one way to respond to these questions is to forecast demand. But the solutions that have existed up to this point tend to focus on the short term, limiting longer-term projections required for sustainability and growth. Plus, demand today is much more volatile than it’s been in previous decades, which makes it tough to figure out what to put in your production lines. And while market trend reports exist, it can be unclear how these reports apply to specific customers.

In short, CSCOs and CFOs want to make data-driven decisions, but there have been obstacles.

For example, a building materials company saw demand for roofing tiles shoot up at the beginning of the pandemic, and that demand has continued to grow. But there have also been supply constraints around asphalt, a key component of roofing tiles. And even though demand has been high, how could they know for sure what macroeconomic factors were influencing that demand, and what would influence it in the future? Human biases hadn’t been able to get it right so far, so they knew they needed to try something different.

Using Coupa’s Demand Modeler, this company was able to create a 10-year market forecast, examining everything from mortgage rates, to consumer spending, to population growth, GDP per capita, employment, existing home sales, and more. By weaving all of these factors into a machine-learning model, they got the information they needed to start planning investment strategies for new production plants. They also gained insight into when to take advantage of decreases in raw material prices and when to use pre-built roof tiles to optimize production.

Timely and accurate demand forecasts are crucial

Demand forecasting is increasingly an area where the CFO and CSCO need to be working closely together to both meet demand and financial goals. Each impacts the other and can either drive growth or drive a business into the ground.

So what impacts can better demand forecasting have?

First, it can help you determine how well you’re positioned to capitalize on existing opportunities, build a resilient supply chain, and ensure business continuity. With a clear picture of long-term demand and data-driven insights, you can make sure any investments you make in future production won’t eat into profits in other areas.

Second, it can boost your confidence that you’re making the right decisions and help you avoid making potentially detrimental decisions. You can build in a strategic piece of the puzzle that’s typically missing and protect the long-term future of your company.

Use machine learning in demand forecasting to improve supply chain planning

Here’s what demand forecasting combined with intelligent market sensing can do for you:

  1. Gain visibility to make better long-term decisions. While we all love a good spreadsheet, spreadsheet analysis is an expensive and time-consuming way to validate data, pre-process and input the data, run algorithms, etc. On top of that, data patterns change, and sometimes humans miss these changes. This is where machine learning comes in. Machine learning forecasts can save your company a lot of time and money by removing a significant amount of manual data processing and management. 
  2. Get insights that are tailored to your needs. Not every macroeconomic trend will impact every business in a particular sector the same way. But with Demand Modeler, you get data that’s specific to your organization and, combined with macroeconomic trends and historical analysis, takes the guesswork out of translating these trends and forecasts into your own business projections. 
  3. Once you have a forecast, embrace the network (design). Even the best forecasts will fall short if it’s not combined with a solid supply chain network design that integrates across apps and processes you’re already using.

Coupa’s Demand Modeler bridges the gap between FP&A and supply chain predictions

Coupa’s user-friendly Demand Modeler solution saves time and improves short- and long-term demand forecasting and predictions. It empowers supply chain and finance organizations with information that helps them make better decisions together.

Discover what makes Demand Modeler a unique solution to your needs.

Discover Demand Modeler