Demand planning seems straightforward: At the end of the day, all you need to do is make sure your brand can fulfill a customer order—that's all.
But that isn't "all." In the scope of the global supply chain, there are lots of moving parts that can impact the number of finished goods you'll need, and where. With so many different logistics questions to juggle, it can be difficult for a supply chain manager or operations lead to stay of top of it all.
Zone in and get back to basics. Enter: your supply chain management cheat sheet.
Demand forecasts have traditionally been calculated on a spreadsheet using historical shipment data. But historical sales data is just one piece of a much larger puzzle; it by no means represents the full scope of factors that impact a purchase order (and sales at large).
The most thorough forecasting techniques includes customer demand-driven data. In other words, forecasts should take into account consumer purchase behavior in addition to retailer purchase patterns.
Make no mistake: Historical data is vital to calculating demand. But demand planning and forecasting for CPG requires a more robust approach to the kinds of information that’s available. Those massive data sets extend well beyond supply-driven demand signals and require tools that are much more capable than spreadsheets alone (more on this in Step 3).
Factors that impact brand consumption and forecast accuracy include:
Even though the aforementioned data sets are readily available, most CPG brands don’t know how to correctly utilize this information and incorporate them into their logistics strategy and forecasting process.
The data sets listed in Step 1 must be sought out, as most of it comes from external sources. So what should CPG brands do once they’ve collected and tabulated internal* data related to historical shipments? Here are two ideas:
The number of data points that can affect actual demand is both diverse and massive. Ingesting and analyzing these data pools is better suited to machine-learning models run on enterprise-grade servers that can handle the sheer enormity of data — rather than, say, an Excel model run by a supply chain manager. Unfortunately, most CPG brands do not have the technological infrastructure necessary for this type of demand forecasting process; at most, they likely only have an ERP system, e.g. Microsoft Dynamics. Rightly so, they opt to use external demand planning platforms that are intuitive and offer a high degree of automation.
(A note on internal data sets: Historical shipment data, for example, is often incomplete and imperfect due to a number of factors, including data siloing, an inconsistent SKU strategy, or the use of insufficient inventory management software. As a result, it’s critical for brands to be able to track goods and shipments across the entire supply chain and have 360-degree visibility in real time, especially when demand planning. A data science team with experience in CPG is critical to organizing the data correctly.)
Once all data has been ingested and organized, it’s time to find dynamic relationships between various sets of data. To accomplish this, a team of data scientists will create customized mathematical models that overlay both internal and external data points in search of relationships that would help to accurately forecast sales of finished goods. Then, a data science team iterates these algorithms, molding and shaping and re-jiggering them until a predictive model is created that meets and/or exceeds predetermined benchmarks (such as forecast accuracy or forecast bias). Ideally, these machine learning models become so accurate that they resemble Artificial Intelligence in their predictive capabilities.
Having machine learning algorithms that yield accurate demand predictions at the SKU-level is only part of the solution. The analytics these models produce should be paired with intuitive, easy-to-use software that’s accessible across the entire organization.
At this point, you’ve got it all. The right data. The right technology. The right people. The right software. Most of all, the right product! So how do you leverage these demand forecasts to better shape your supply chain operations? How can your company, and the many areas of your supply chain, benefit from them? Here are few benefits:
Unioncrate is an AI-powered Supply Chain Planning Platform that gives CPG brands the technology they need to compete and win in a rapidly changing consumer landscape. Our automated demand and supply forecasts deliver unmatched accuracy, collaborative visibility, and actionable intelligence, simplifying a manual-heavy process and slashing hours from your week.