You're reading this right now because you likely know there's something you could improve about your demand planning and forecasting process. In the end, it's all about gaining the know-how and adopting the perspective you need in order to level up. We're here to provide you with a set of tools, insights, step-by-step techniques, and executable strategies that will help you:
Without further ado, here's a list—we like to call it definitive—of the 21 best practices for demand planning and forecasting professionals in the CPG industry (and beyond).
This is always a solid place to begin any assessment and learning endeavor: What are your goals? Perhaps you:
Whatever it is, write it down, record it, share it. Seek advice. And rest assured, you've come to the right place.
It's vital to understand what's impacting the daily lives of people around the world, even on the most basic levels. What's the day-to-day like for consumers around the world—economically, socially, professionally, personally? How have recent events changed their lives, and what impact do they likely have on their future? How have consumer purchasing patterns changed given the circumstances? Exploring and following these informational trails is key.
Consumption-driven demand forecasts and the algorithms that support them should be informed by a deep knowledge and analysis of these variables.
The aforementioned questions don’t need to be answered this very second. Rather, the most important thing to consider is the fact that Covid-19 has changed the way the world, and therefore the consumer, functions—perhaps permanently. At the very least, the pandemic has accelerated changes that were already in motion, such as the solid emergence of DTC-native brands and widespread adoption of e-commerce channels, each with its own unique fulfillment practice and typical buyer journey.
For example, three models in the customer purchasing journey in the digital grocery e-commerce landscape include:
The lesson here is that consumer behavior and demand patterns are inherently volatile—perhaps more than ever before. Demand can drastically change in an instant, and it's your job to help your organization keep up as much as possible by staying on the pulse of consumer purchasing patterns.
Your sense of urgency should stem from this very fact. When the variables that impact consumption change, how quickly can your company adapt to new, in-motion realities? What technologies or processes do you have in place that enable such agility?
If you’re not asking these questions now, your sense of urgency needs to be dialed up.
Now it's time to take a good look in the mirror, so to speak. As much as time and resources allow, execute an internal (or external) assessment of your current demand planning and forecasting process, from top to bottom. Ask questions—lots of 'em.
These questions should set you off on the right path of exploration. We'll dive deeper into each of them below and examine plenty more, too.
Asking key questions is just the start; you also need to do the work to answer them.
Once you've had the chance to thoroughly examine your demand planning and forecasting process, it's important to identify the areas that need the most attention and begin to consider potential solutions.
In practice, a thought exercise might look like this:
Don't stop until you've determined your most pressing needs. Then:
In our guide "The New Rules of Demand Planning," we detail why a fresh approach to demand planning is vital in today's CPG industry. Below is a breakdown of the ways in which you can take on this new perspective—a list of effective demand planning commandments for 2020 and beyond.
To understand the future, you must embrace the present. No longer should you treat historical shipments, or supply, as the end-all-be-all of your forecasting and demand planning strategy. An effective forecast should be driven by the consumers and the variables that influence demand.
Data is the foundation of an accurate demand forecast. Your data should possess two basic qualities: cleanliness and robustness—and in that order, too. "Bad" or "dirty" data describes information that's tainted by error: incomplete and inaccurate. While you may have all the data your servers can handle, some of it may be unusable because it contains mistakes. Fix these first.
Dynamic data and insights do not just appear; they must be scientifically ascertained. The analysis of two or more datasets, as outlined in our guide, The New Rules of Demand Planning and Forecasting, can yield transformational insights that can be acted on. They include:
Automation is all about letting a process work for you—and for your team—in the background. It's like putting your business forecasting on cruise control. While the technology does the work, you and your teams can focus on other important tasks and goals. As they say, time is money.
As an example, learn how automating demand forecasts has been incredibly beneficial to The Fishin' Company, the largest importer of tilapia in the world and one of the largest importers of frozen fish. Automation has helped Fishin' Co. streamline their S&OP process, making it more efficient. They've also been able to increase in accuracy using Demand Planning AI, helping them to save millions in yearly inventory holding costs.
Simply put, CPG companies must arm themselves with the tools that enable them to quickly and adeptly maneuver their business through supply disruptions and drastic changes to consumer behavior. Now more than ever, organizations should be looking to incorporate the kinds of technological advancements available to them in order to prepare for unforeseen or obstacles that may arise and throw off commercial plans at a moment's notice.
Demand Planning AI is a tool that was engineered to meet this moment—specifically for CPG companies. Machine Learning models are programmed to deeply and continuously analyze the complexities and ever-evolving dynamics of the CPG industry. Statistically speaking, these dynamics represent countless internal and external datasets, including historical shipments, point-of-sale data, and other variables that impact demand (and, therefore, a demand plan). Obtaining this amount of information is tough enough, to say nothing of cleaning it and analyzing it. This is the job of an AI, not a human being (or even an army of them).
Additionally, Demand Planning AI will automatically adapt (rather than react). What does this mean and why is it a vital element of an effective and efficient demand planning process?
Well, take Covid-19, for example, which brought about fast, unprecedented changes to consumer purchasing patterns. In March and April of 2020 especially, many CPG companies struggled to produce and/or hold enough inventory and meet unprecedented spikes in consumer demand, resulting in lost sales. Furthermore, in some cases, by the time some companies caught up, demand unexpectedly ceded back to normal (or less elevated) levels, causing capital to be tied up in excess inventory and warehousing costs.
Demand planning software—more specifically, Demand Planning AI—is effective because of its ability to proactively treat Covid-19, and other abnormal events, for what it is at the most basic level: data. The pandemic is an outlier event that brought about unprecedented changes to consumer purchasing behavior and the CPG industry at large. And its impact will continue to reverberate for the foreseeable future. Ultimately, however, the pandemic is a small dataset in a pool of variables.
As a result, Demand Planning AI can help CPG companies to recover quickly from unprecedented events like a global pandemic. Read here to learn how Demand Planning AI corrected itself during the peak of Covid-19 to help a category leader with its inventory optimization, allowing it to meet demand in just a three-week turnaround.
The "assess" portion of this best practice is actually a two-step process. First, you should determine the data you're actually using for demand forecasting. If it's internal data, determine exactly what you're working with. How granular of an understanding do you have into your historical shipments? How far back do your numbers go? How much data do you have? And so on.
The second part of this process is to determine which data you wish you could include in your calculations. Ask yourself: What's missing? Some datasets you might be well aware of but don't have access to (or the time or technological capabilities to obtain it); others you simply might not know about.
The size of your organization's data pool should not present an obstacle to any part of your demand forecasting process. In the past, this may have been a hindrance to companies, but this is no longer the case at all. This sort of technological infrastructure (enterprise-grade servers, e.g.) is widely available. In short, bring on the data! The more the merrier.
Consider the adage, Garbage in, garbage out. If you're inputting "dirty" data into your demand forecast calculations, you're likely to end where you began—on the wrong foot.
When your data isn't clean—meaning that it's incomplete, inaccurate, not up-to-date, or mistake-laden—then your demand forecast is relying on erroneous data to begin with. This is a recipe for disaster.
Whether your company has 5 SKUs, 2 warehouses, and 20 ship-to locations, or you distribute 10,000 SKUs worldwide, the data-cleaning process is much, much easier using Machine Learning algorithms than human power. For instance, data engineers can create and apply mapping algorithms to large sets of raw data, thereby automating the cleaning process and completing it in a fraction of the time it would take a human to accomplish similar tasks. Manual data analysis is incredibly time-consuming and heavy on process. This may include labor-intensive tasks like "sourcing, scrubbing, collating, and formatting activities that leave little time for actual analysis." ("Machine Learning in Supply Chain Planning-When Art & Science Converge," Journal of Business Forecasting, Spring 2019)
Taking a fresh perspective on demand planning and forecasting means not relying solely on historical sales data (shipments at the SKU-level by customer, ship-to, and ship-from locations). This data alone cannot account for rapid shifts in consumer behavior and other microeconomic indicators.
And in today's economic landscape, where eCommerce is booming and DTC-native brands are gaining ground on market share from legacy brands, it's vital to have a complete picture of the variables that can drive demand. Major datasets include:
External datasets, including some of the above, are widely available, sometimes for a fee and sometimes not. A few free online repositories are: Google's Public Data Explorer directory, Data.gov, Google Trends, the U.S. Census, GitHub (for developers), FiveThirtyEight, Kaggle's datasets, and this compendium from Eastern Michigan University. And, of course, there are free resources from market intelligence firms like IRI Worldwide (its Covid-19 economic indicators page is well worth a visit), SPINS, and Nielsen.
Data science has become one of the most sought-after applications of the 21st Century—a time when there is more ascertainable data produced than ever before. Simply put, data science is the framework through which robust analytics can be executed.
The discipline itself is a riveting cross-section of expertises, combining some level of mathematics and statistical aptitude, computer science (knowledge of database languages like SQL or Python; data governance and infrastructure), and certain domains of professional knowledge, such as criminology, sociology, and political science—or, in this case, the Consumer Packaged Goods industry and supply chain. But it's where these three disciplines overlap and are applied that truly illustrates the impact data science can have on a CPG company. This includes data engineering, Machine Learning, and research and analysis.
The work of data scientists is incredibly interdisciplinary, which is part of what makes it so effective.
Over time, statistical forecasting methodologies have of course changed. While many forecasting fundamentals remain, technological advancements and the modernization/digitization of commerce and the supply chain have completely changed the demand planning game.
It's essential for any CPG company to be able to implement, test, iterate, and hone a number of algorithms that can efficiently and effectively analyze data. A consumption-based forecasting model should be able to predict future demand with high accuracy across a number of time horizons; measure for forecast error (WMAPE); and forecast sales at the SKU level by customer, warehouse, ship-from, and ship-to locations.
An ideal demand planning system with AI technology goes beyond insights, however, providing recommendations that pinpoint exactly where and how companies can realize efficiencies across the supply chain and business at large. For instance, the best AI platforms should have the ability to transform raw data into actionable insights, which themselves become benefits. This includes:
And, this entire process should be automated—zero-touch forecasts available at the click of a button.
As we outline in our Ultimate Demand Planning guide, the process of streamlining S&OP meetings is all about realizing the benefits of automation and forecast accuracy. While the right demand planning process varies for each company, the collaborative goal of reaching a consensus and planning according to projections remains relatively the same. Automating your demand planning process, as well as implementing a consumption-driven Artificial Intelligence tool to achieve high levels of accuracy, can help you realize some of the following benefits:
And there you have it. Twenty-one of the very best demand planning and forecasting practices. Now, though we do cover a ton of ground, this is not totally exhaustive—we'd need a library for that. And yet, for executives, demand planners, or supply planners who strive for continuous improvement—want to make headway in terms of understanding how they can use cutting-edge technology to realize supply chain efficiencies—this guide will keep them plenty busy.
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.