Right now, all CPG companies should be planning an honest review of their internal demand planning efforts. The status quo is no longer a viable option; assess and implement the right changes, or risk being left behind.
Using the lessons of recent events as guidance, organizations should proactively put into place the tools and processes that will enable them to remain competitive for years to come, as unforeseen variables will continue to drastically impact consumption and the global supply chain.
The first step begins with your outlook. Adopting a fresh perspective on demand planning means placing the utmost importance on data and your organization’s capabilities to actively utilize it.
It’s one thing to possess rich data; it’s another to be able to handle it, ingest it, clean it, analyze it, gain actionable insights from it, and then realize benefits across the business.
The cherry on top is to be able to automate this entire process, from start to finish, which will have a significant impact on the efficiency and efficacy on both the demand planning and S&OP processes.
Now, let's dig in.
To help CPG companies think about integrating a more efficient, agile, and accurate demand forecasting process, we’ll review the following topics:
The sheer number of variables that can affect demand is both diverse and incredibly massive. But there should be no barrier between the data a CPG company possesses and its transformation of internal and external data into a gold mine (in the form of efficiencies across the business).
In the past, some organizations have been hindered by the amount of data they’re capable of handling and processing. Nowadays, with vital datasets widely available, organizations should not feel overwhelmed by the size of its data pool, nor their appropriately lofty goals of deepening it. Rather, CPG companies should possess the technological infrastructure needed to ingest and analyze scores of datasets—or partner with a demand planning firm that does.
The most powerfully capable demand forecasting models should ingest a wide variety of datasets for a more complete and accurate picture of future demand. But vital, historical sales alone (shipments at the SKU-level by customer, ship-to, and ship-from locations) cannot account for rapidly changing consumer behavior. As a result, forecasting models should constantly analyze not only supply data but also consumption-level data. This includes:
All CPG companies possess large amounts of data—some of them vast—but quantity is not a mark of richness or usability.
Simply put, demand forecasts are only as good as the data used to calculate them: 4,000,000,000 incomplete or mismatched data points is exactly that. As they say, “garbage in, garbage out.”
In short, it’s all about quality. So when data is “dirty,” it must be cleaned. And the more data there is, the higher the likelihood of errors existing in the data pool, which in turn can negatively impact the data's usability (to say nothing of forecast accuracy or its benefits).
It deserves repeating: Every bit of data that’s used in a forecasting model should be “clean.” Clean data refers to all internal and external data—SKU numbers, BOM quantities, warehouse addresses, historical shipments, market trends, POS data, and any other piece of information essential to your supply chain and S&OP process—that’s organized, timely, precise, complete, consistent, and generally error-free.
It would take one human being (let alone a team of them) a very, very long time—a lifetime, even!—to sift through a company’s data and check, double-check, and triple-check the viability of every single piece of data. It’s a human impossibility, and this is before any sort of analysis is completed or insights are drawn. So what’s the solution?
Clean, rich data sets the stage for machine-learning analysis, which in turn drives actionable insights. As a result, CPG companies should possess substantial data computing capabilities that scientifically clean and process raw daily sales data. They can automate this process by applying cleaning algorithms that use a data mapping process to identify and correct various trends in error, such as human entry mistakes or those made by a third-party vendor whose data quality is not up to par with your needs and standards.
And at a time when data exists in near-infinite amounts, artificial intelligence is a vital tool for organizations in the CPG industry. When specialized data is incorporated into machine-learning models, the more they learn and the more accurate they become. These models form a neural network whose speed, dynamism, and computing capabilities trump any human’s ability to ascertain such complex relationships between datasets. Eventually, these trained algorithms behave like a powerful demand planning AI tool.
Behind the scenes of automation, predictive analytics models are calculating dynamic relationships between humongous datasets. Expert data engineers tweak and iterate models in search of locating clear-cut demand drivers, based on consumer behavior and market signals, while giving weight to top-selling SKUs.
Between good data and commercial benefits is the process of understanding how data translates into dynamic insights about your supply chain, business model, and the CPG industry at large. In order to open the "black box" on demand and eliminate blind spots, it’s vital to be able to reveal the factors influencing past and predicted sales. This is the job of artificial intelligence: to analyze data and find dynamic relationships between datasets across various time horizons. (Even better is a platform that presents these insights to you in a centralized and ranked fashion, available immediately at the click of a button. That’s the definition of truly accessible automation.)
It bears repeating: Only artificial intelligence designed specifically for the nuances of CPG can get the job done right.
What underlies data that drives growth and efficiencies is the ability of demand planning AI to translate data into CPG- and supply-chain-related insights. It’s the work of a team of data engineers to build and iterate predictive models to ascertain these company-specific insights and segment them in a way so that they can be applied across the business.
The value of demand planning AI extends well beyond its ability to ingest, handle, and analyze multitudes of data. If recent events are any indication, organizations must be able to rapidly recover from an outlier event (like Covid-19), which can quickly throw off their commercial plans and disrupt supply chain operations (let alone an entire industry). Artificial intelligence enables demand planners to automatically account for new market variables and regain accuracy levels. It adjusts—or, rather, corrects itself—with each passing millisecond; that’s the power of automation. Demand planning AI is also a tool that’s trained to be agile and extremely efficient.
The best zero-touch demand planning AI platforms also offer highly accurate predictions. They provide actionable SKU-level insights that identify where risks and opportunities may lie in the future as well as strategic recommendations for immediately realizing efficiencies across the supply chain. Benefits include working capital improvements (such as a reduction in safety stock levels), improved service levels, reduced transfer costs by moving inventory from warehouses with lower demand, and reductions in logistics overhead. And that’s only the beginning. The following are some cross-functional benefits of forecast accuracy:
Though no two S&OP processes are the same, the end goal is clear: to formulate a plan that maximizes sales and profits. But the process of integrating demand, supply, sales, marketing, product, distribution, and financial teams can become bogged down by the amount of time, resources, and overhead it takes to align on a demand forecast. This process is further complicated when team benchmarks are misaligned data, perhaps tied to departmental KPIs rather than total company; when data is siloed amongst teams; or when systems are varied and individualized. This can cause confusion and informational discrepancies, not to mention integration challenges. But it doesn’t have to be this way.
When demand forecasts are automated, and sales data is visible across an organization, S&OP meetings can begin from a place of alignment rather than difference. One source of truth, if you will: a singular, 360-degree view of business across all areas of the supply chain in real time. Furthermore, forecasts are produced with the click of a button (rather than the grind of a spreadsheet and alignment meetings). Here are some ways automation can benefit the S&OP process:
It's no secret that the CPG industry is experiencing a once-in-a-generation shift. The impact of Covid-19, for example, has touched nearly every facet of the CPG supply chain while accelerating drastic changes to consumer behavior, including further adoption of eCommerce across the board.
An entire generation of online-native shoppers with enormous logistical expectations wasn’t created out of a vacuum. By all accounts, a consumer evolution was inevitable; Covid-19 sped it up.
For many organizations, it was mayhem. Retailers, distributors, e-Commerce platforms, logistics companies, warehousing firms—you name it—were more or less shooting in the dark. For many manufacturers, inventory simply fell short and countless companies missed out on sales; demand predictions were typically way off, inaccurate to a fault. And as service levels dropped, stress levels rose—from the production floor to S&OP meetings, where forecasting demand accurately was a serious challenge.
The pandemic laid bare just how quickly consumer behavior can change, further exposing a pressing need for organizations to be able to recover rapidly and adapt accordingly when faced with unforeseen circumstances and supply chain disruption.
In short, when the future meant more than ever, it was increasingly difficult to sift through a jungle of data, analyze it, and accurately forecast sales. During the most challenging times it's vital to have the tool in place to overcome obstacles like "too much data to handle" let alone an inability to gain actionable insights from it.
As you navigate these new and often unprecedented market realities, it’s important to have that perspective, and we hope that this guide will continue to provide answers and illuminate an actionable path forward for years to come. That path is paved by automation and artificial intelligence built specifically for CPG. Both are essential to an effective demand planning and S&OP process—now and for the foreseeable future.
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.