Imagine a business that runs better. What would that look like in practice? What would be its defining features? It would look like Integrated Business Planning—a harmonious framework in which sales, finance, and supply chain operations combine long-term profit goals and short-term demand planning efforts for a balanced, unified trajectory.
“Harmonious” is not how many brands would describe the last 12 months, which made up a year peppered with challenges such as semi-regular supply chain disruptions and inventory shortages. To combat constant change, brands are turning to the kinds of demand planning technologies that make their lives easier. Easier means faster processes, faster knowledge, and faster results. Easier means a demand planning process that's collaborative and visible, precise and proactive.
But “easier,” of course, is easier said than done. That’s why we’ve whittled down an ideal IBP process into 7 high-level characteristics. Together, they hold the key to producing a strong, confident wind behind the sails of an effective demand planning process.
Some call it flexibility; others call it adaptability or quickness to recovery. We call it agility. Whatever you may deem it, these words appeal to the same concept: a company’s need to be amply prepared to roll with the punches.
Agility is all about making sure your business can proactively plan, manage, and quickly pivot around inevitable market and supply chain fluctuations.
For example, most CPG brands were put to the test at the start of the Covid-19 pandemic perhaps more than ever before, especially when it came to demand planning. But consumer behavior and demand patterns are inherently volatile, pandemic or otherwise. What matters is how quickly you’re able to manage change.
Agility is made possible by adopting forward-looking technology, such as dynamic AI that increases in accuracy the more its models iterate. And, with the right technology, you can engage in the kind of collaborative functionality that enables you to input ground-level human insights at any time, and then automatically factor them into forecasts. This grants brands the ability to prepare for and respond to everything from an unexpected lift/drop in sales to crisis-level outlier events. Keeping up with forward-looking consumption metrics that illustrate the present and future—not just the past—is at the heart of agility.
Whether they’re built for sales, distribution, inventory, or manufacturing, highly accurate demand forecasts are a primary element of managing an effective IBP process. Let’s take a look at how accuracy can be an incredible money- and time-saving asset for CPG brands, producing an incredible domino effect across the supply chain:
In the context of IBP, actionability doesn’t refer to simply possessing the right data. Actionability refers to data that's dynamic, that illuminates a path forward so you can start to move in one direction with a clear vision. Good data tells a story, making it so that your next business move can be carried out confidently—not through guesswork.
By way of example, ask yourself these straightforward and eminently important questions:
Now, imagine being able to trust a system in which you receive these data-centric answers efficiently, clearly and—most importantly—consistently. In this case, actionability refers to two coexistent factors: AI, plus a way to bring its fruits to life.
The first, which we'll review below, is having the technology—artificial intelligence—that can identify baseline as well as dynamic relationships between various datasets and time horizons related to your business.
The second is having access to a system or platform that enables you to easily identify and analyze the answers to the aforementioned questions (plus more) at the click of a button. This can look like a sales driver or contributing factor dashboard, or a way to benchmark performance.
Actionability can look like priority rankings of profit margins across SKUs, which can be used to optimize manufacturing output and warehouse supply levels. Or, it can look like recommendations on how to transfer or reposition inventory based on stock and demand levels. Insights like these are actionable. And actionable data can help realize supply chain efficiencies quickly.
Automation makes certain obstacles obsolete, the most significant being the hours your team members need to put into culling, organizing, and then analyzing data. It's a step-killer—a capability that can transform a multi-step, multi-layered process into one. Automation means no more tedious, manual number-crunching. And when forecasts are built with unified data across all departments, automation means faster alignment and fewer silos, giving people back their precious time.
Effective automation simplifies the complicated and streamlines the complex.
Consider demand planners, who commonly have to wrangle—and then unite—data from various teams, often using disparate systems or organizational methods. Then comes the spreadsheets, alignment meetings, adjustments and more.
Typically, it would take hours to analyze the factors that contributed to forecast error, as well as how much each one impacted the performance gap between actuals vs. forecasted. And this is to say nothing of the "simpler" data, such as vital inventory data (QOH, WOS), sales data (distribution gains/losses), trade spend, line capacities, and more. Ideally, automation does the heavy lifting for you, in which algorithmic activity becomes hours recouped on your end—all of a sudden, that’s a ton of work you don’t have to do.
Instead of being in the weeds with numbers, automation lets you invest your time and energy into steering your business instead. You can turn your attention to the qualitative aspects of your business—the parts that need a human at the helm.
Artificial intelligence can sometimes feel like the buzzword of the moment—and for good reason. Essentially, AI is a series of specialized algorithms that identify dynamic relationships across datasets and time horizons both faster and more effectively than a human ever could. The more the models run, the more accurate they can become, effectively “learning” through digital neural networks in a similar way the human brain might.
A key characteristic of AI is that it doesn’t understand outlier events through an emotional lens. Its lack of human emotionality means it wouldn't see the Covid-19 pandemic as a once-in-a-generation crisis but, instead, as just another dataset (in a sea of many, many more).
For an IBP process to be as streamlined, collaborative, and forward-looking as it’s supposed to be, it has to leverage AI.
And yet, AI doesn’t know everything. After all, it’s not human intelligence with boots on the ground, so to speak. That’s why when AI combines forces with human intelligence, its dynamic computation lays the perfect foundation for teams to interpret AI’s insights and subsequently make decisions.
Ideally, CPG brands should implement software that also enables human insight to be factored into AI forecasts, providing them with a functionality that captures real-time insights (agility!), visibility, collaboration, and accountability.
Finally, there's a common misconception that integrating AI into existing systems would be a disruptive, time consuming, or lengthy process. But this should not be the case. CPG Brands can implement AI for demand planning purposes in a matter of months, and it should work seamlessly with—and actually augment—the systems and the people that are already in place.
If IBP could be boiled down to any of the A’s, it would be this one.
Essentially, alignment means nearly universal cross-departmental visibility. It means everybody is on the same page.
Instead of your teams suffering through the informational silos that come with the traditional supply chain planning process, real-time data links across the company can result in higher forecast accuracy, unified decision-making, and better business performance. Being able to access a granular view of data also makes way for alignment, since different data cuts—forecasts down to the SKU or ship-to, for example—can give you a deeper, denser picture of demand.
When various teams are working within the same system and have access to the same cross-departmental data, data silos will be erased and decision-making can be much more cross-functionally dynamic. For example, a sales team may sell into 1,000 more doors than planned for due to increased or unforeseen demand. On the trade marketing front, a retailer may approve, at the last-minute, a shipper display for a particular product or product line.
In both instances, this information is vital to supply chain and financial operations, as well as to future forecasts. It's also vital to supply teams who, with knowledge of new distribution gains, could make the recommendation that the uptick in distribution may need to be pushed further into the future until manufacturing can catch up with the pace of sales, due to a lack of labor or raws, for instance. Alternatively, salespeople may be able to negotiate larger volumes with buyers if they know that supply has additional capacity.
On the financial side, money can be shifted from other parts of the business to the supply side, so that sales teams can fulfill orders as promised. In addition to cash flow, finance teams must understand how this occurrence will affect financing of inventory and payment terms to manufacturers or customers, as well as labor and other hard costs. Understanding additional warehousing and logistics costs is vital, too. Increases in distribution may mean a need for larger trucks, higher repositioning costs, increased labor needs, or simply more space. This can have a significant impact on working capital.
The domino effect will happen regardless. Alignment helps to make the dominos fall harmoniously—in concert with one another.
With all of its cross-functional visibility, IBP ideally provides a collaborative demand planning framework that’s also uniquely predisposed to accountability.
In a sense, accountability is the gap between the effectiveness of a brand’s demand forecasting process and the operational execution behind it. Having full transparency into not only what, but also why, something has occurred across your supply chain is key to making strategic shifts in your demand planning process.
And the faster a brand can understand the contributing factors and reasoning for a forecasting error, the faster it can implement the changes that will improve efficiencies, margins, and more.
For instance, let’s say a forecast predicted a 5,000-unit order from a particular retailer, which a brand would ideally access on a performance accuracy page that exhibits just how much historical forecasts deviated from your actuals—plus the contributing factors of these deviations. However, that PO comes in from the customer as expected, but the aforementioned performance page shows that only 4,500 units shipped, indicating a 10% error in forecasted sales vs. actuals.
Perhaps manufacturing didn’t make enough units, or there was enough inventory but there were shipping issues as logistical operations lagged. Being able to quickly understand responsibility for the 10% error is incredibly useful. Alternatively, if a forecast predicts 5,000 units but the PO comes in for 6,000, it could be a forecasting issue, and the responsibility of that 20% error may fall on the shoulders of those involved in the forecasting process.
Another functionality that enables improved accountability is a cloud-based system that allows sales, marketing, supply, and finance teams to manually adjust AI-generated forecasts with inputs that may impact future sales—including, but not limited to, distribution changes, new marketing investments, supply constraints, and other buyer communication. It’s up to the people on the frontlines—teams speaking to retailers and brokers, for instance—to enter pertinent information, such as new distribution gains at 1,000 stores. If that information is entered by a salesperson incorrectly, such as entering the wrong amount of SKUs per store, it could signify a communication breakdown.
Adhering to these seven demand planning characteristics will undoubtedly lift your chances of finding continued—or renewed—success for your brand in 2021 and beyond. This list is battled-tested, through the inescapable ebb and flow of running a business in the CPG industry.
And because change is a constant, it's absolutely essential to possess the tools of the trade that will help you weather any situation. This begins by taking on the right perspective and ends when you implement the kind of demand planning technology enables your IBP process to run like a well-oiled machine.
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.