As every manufacturer knows, demand forecasting is critical to operational success. Accurately predicting future sales allows you to set the right targets for procurement and production. Overestimate, and you’ll be trying to explain to management why shelves are full of obsolete inventory no one can sell. Underestimate, and you’ll be trying to mend fences with customers who can’t get what they need quickly enough, or trying to juggle schedules and shift priorities at the last minute, only to alienate other customers.
The problem is conventional enterprise resource planning-based (ERP) forecasting methods just aren’t good enough. They often rely too heavily on data from a static past to predict a future where variables are ever-evolving. But new technologies are changing the game. They’re enabling manufacturers to understand future demand better than ever before. And that means meeting customer expectations and effectively growing the business.
What demand forecasting can and can’t do
Forecasting starts with ERPs. Executives rely on ERPs to make important decisions about allocating resources to meet demand. These systems are a storehouse for information on past production and shipments, supply chain figures, financial statements, and signed contracts, as well as data about labor and machinery. You can’t live without them. But sometimes it’s tough to live with them.
In a typical ERP setup, your ERP feeds into your demand planning software, which analyzes historical trends and seasonal patterns to produce a forecast. But that forecast is only as good as the data on which it’s based.
The data they contain is very complex – and systems vary widely in their ability to connect and share information. Many manufacturers have several ERPs, or even dozens, tacked on over the years as the business expanded, upgraded, or made acquisitions. Older systems may not integrate with newer ones. Some systems don’t provide full data to mobile devices. Others reach a point where the vendor no longer provides support, making it difficult or impossible for IT to fix problems.
In a typical ERP setup, your ERP feeds into your demand planning software, which analyzes historical trends and seasonal patterns to produce a forecast. But that forecast is only as good as the data on which it’s based. And with different systems producing data of varying quality and inclusiveness, accuracy can be a challenge. More than eight in 10 manufacturers say inaccessible data, legacy tools, and cloistered teams impede their forecasting process.
There is no ERP holy grail. To some degree, life’s messy changes will always get in the way of forecasting based on past demand.
However, that’s not the only issue with ERPs. Information gathered over months and years is essential for uncovering trends. But demand is a moving target. You may have sold 100,000 widgets at this time last year and the four years before that. Yet, customer tastes and markets can change quickly. So historical data doesn’t always hit the mark, particularly when conditions take a sudden turn, as they did during the pandemic.
Manufacturers are more eager than ever for a better solution. But the truth is, there is no ERP holy grail. To some degree, life’s messy changes will always get in the way of forecasting based on past demand.
It makes sense that many manufacturers said they need new approaches and new tools to do accurate forecasting. More than three-quarters agreed that “traditional forecasting has gone out the window.”
The challenge of getting the right information at the right time
To supplement their ERP data, manufacturing executives hold regular, high-level meetings with production and sales managers, who have more current information about supply and demand. But a variety of factors can interfere with obtaining and using this knowledge.
For one thing, sales managers commonly overestimate demand to ensure they’ll have enough product if their most optimistic predictions come true. If those predictions fall through, inventory piles up, raising costs.
Other information provided by sales managers lacks details. Their teams spend a lot of time listening to customers and learning why those customers want products, how and when they plan to use them, and whether they are likely to expand or close business lines. And all of these anecdotal, ear-to-the-ground insights are quantified into dollars-and-cents estimates of upcoming order value in sales notes. But those notes aren’t often formalized and shared with managers to report at executive meetings. So this global microcosm of demand indicators isn’t accounted for by forecasting systems.
How the cloud can help
The great hallmark of the cloud is its ability to connect disparate systems and data sets in a way that allows companies to make sense of them and profit from them.
Using a cloud-based system, you can collect data from all of your disjointed ERPs, organize it to provide a single source of truth, and put it to work. You’ll be able to instantly obtain demand levels overall or segregated by time, business unit, region, or dozens of other variables. The right system will present all this information in an easy-to-understand graphic form. It will also allow you to incorporate the latest, detailed demand information from sales notes. That may reveal previously invisible trends that add a whole new dimension to your forecasts.
77% of manufacturers who have moved to the cloud now automate most or all of their forecasting processes.
In recent years, manufacturers have doubled down on moving sales and operations planning to the cloud. In fact by 2023, almost half of all manufacturing software will be cloud-based. Seventy-seven percent of manufacturers who have moved to the cloud now automate most or all of their forecasting processes, enabling them to adapt demand predictions quickly to reflect market changes.
In short, the cloud’s scalability, security, collaboration, and automation capabilities create tremendous operational efficiencies which enable manufacturers to better match product capabilities to customer needs and improve forecasting.