Using an ERP? Here's How to Scale Your Demand Planning and S&OP Process

Sophie Guimaraes, Kunal Kohli & Jonathan Zalman

6 minutes

In this article we examine...

  • How CPG brands can scale their demand planning process and the efficiency of S&OP meetings.
  • The impact and importance of automating your supply chain processes, such as using an ERP in supply chain management and an AI platform for demand planning and forecasting.
  • Why brands should adopt an independent demand planning solution that can "sit on top of" and augment their ERPs.
  • Why most demand planning tools are typically too rudimentary for the needs of today’s complex supply chain challenges and ever-changing consumer behaviors. 
  • Why AI is the primary choice for finding relationships and patterns among complex datasets, plus how AI can be augmented with human intelligence.

Introduction: ERP, easy as 1-2-3

Whether you’re a rapidly growing brand with a surging presence or you’ve already got hundreds of SKUs available at retailers nationwide, you know that using an ERP is vital to managing and scaling your operations. A majority of CPG brands have turned away from spreadsheets and adopted an ERP solution to improve their orders and inventory management, become EDI compliant, and manage their accounting and financials. Plain and simple, ERPs make life easier—and adopting one is a clear sign of success. 

In other words, adopting an ERP isn’t a starting point—it is just the beginning; a growth milestone that hopefully marks the first of many more. But many brands do not take the same approach they do with an ERP system to scale their demand planning and forecasting, which is puzzling. Why continue to rely on spreadsheets to continually execute one of your most essential processes?

So you’ve ditched spreadsheets for a supply chain ERP. Now leave the manual forecasting methods behind, too.

If they’re leveling up elsewhere, then why do so many brands continue to rely on spreadsheets for their demand planning needs? Maybe it’s about comfort with a “tried-and-true” approach, a belief that spreadsheets are working “well enough.” Maybe it’s about onboarding, adopting a new system, or relying on computers to learn (from) the nuances of your business. For one reason or another, the logic does not track, and brands are paying the price for it.

But contrary to popular belief among CPG-ers, an ERP platform is not a one-size-fits-all solution, with one exception being its forecasting capabilities. In that sense, it falls on the shoulders of brands to level up their demand planning solution just as they have with their supply chain operations—through automation.

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Why Certain Demand Planning Methods Fall Short

1.  Spreadsheets are not scalable

Just as ERPs automate and centralize key operational and financial processes, the best demand planning solutions generate similar efficiencies. And as your company grows, so too does your data: 20 SKUs swells to 50, plus a new category. Local distribution becomes regional which in turn becomes nationwide. You’ve gone from 5 customers to 50 and are dealing with multiple distribution centers. The list goes on… and remember, these are good growing pains!

As the data piles up, the complexities of demand planning grow as well. At a certain point, processing sales orders and inventory management using spreadsheets is simply too slow, too siloed, and unscalable. As a result, the need for better forecasting infrastructure becomes ever-pressing. But spreadsheets are not built to generate the kind of efficiencies that help brands scale:

  • Spreadsheets rely on slow, manual processes to get the job done. 
  • Spreadsheets often lead to silos, as various teams may come to S&OP meetings with their own view of the business. This can lead to misalignment, wasted time, operational silos, and increased working capital costs. 
  • Spreadsheets lack the kind of machine-learning capabilities that can identify dynamic relationships and patterns that consistently raise accuracy levels, generate actionable insights, and increase efficiencies.

[Related Reading: 3 Essential Demand Planning Analytics That Every CPG Brand Should Automate]

2.  Forecasting With the Wrong Tools

ERPs aren’t typically built for the kind of sophisticated, modern forecasting solutions that CPG brands need to keep pace with, and get ahead of, demand. While ERPs are great for managing your supply chain operations, there are a number of demand forecasting potential pitfalls you should be aware, including the following areas where ERPs fall short:

  • One-dimensional linear regression models that only analyze historical sales
  • Lack of granularity, especially down to the ship-from, ship-to, and SKU levels
  • Strictly a business intelligence (BI) tool without any AI to calculate and learn from dynamic patterns across datasets
  • Inability to unite human and artificial intelligence to reflect distribution changes, last-minute promotions, and supply constraints in AI forecasts
  • No lock-in periods or supply chain lead times taken into consideration
  • Accuracy levels falling short
  • Silos and lack of visibility across all departments, despite the ERP’s best efforts

Sound familiar? It’s what drove our founder to start Unioncrate, after all. But there’s a silver lining: an opportunity is created to adopt an independent demand planning solution that can sit on top of your ERP software and level up your demand planning process.

The Next Evolution of Supply Chain Planning [Unioncrate]

How to Level Up Your Demand Forecasting & S&OP Process

1. Gain visibility, simplicity, and granularity—via automation

Granularity is key to understanding a full picture of demand. Forecasts broken down by SKU, ship-to, and ship-from help you better understand your business as a whole. And when this information is automatically unified and available across the entire company, departments shift from operating in silos to working together cross-functionally. With this level of understanding, demand planners can optimize key cost centers, align forecasts with purchasing and manufacturing plans, and optimize logistics, working capital, margins, and more. Most CPG brands with ERPs are likely still using spreadsheets to do their forecasting, after all.

2. Get accurate, really accurate, and reap the rewards

With that granularity comes the ability to reach newfound heights of accuracy, which can lead to incredible money and time savings. The domino effect of high-accuracy forecasts can be felt across the supply chain and business at large. Among the benefits of accurate sales and distribution forecasts are:

  • Better discussions and negotiations with distributors
  • Reduced/optimized working capital/cash flow, including warehousing and labor costs
  • Optimized marketing investments
  • Fewer stockouts
  • Optimized lead times and DC splits
  • Reduced COGs and increased margins, plus improved negotiations for volume cost breaks for raw materials, components, and finished goods
  • Greater ability for distribution teams to help sales teams, and increased visibility for sales teams into customer inventory levels—allowing them to incrementally upsell the buying and planning teams

3. Get AI-driven insights and forecasts

Between good, clean data and commercial benefits lies 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 gain visibility into 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 that’s trained inside and out on the nuances of demand for your products within the consumer goods industry: to analyze data and find dynamic relationships between datasets across various time horizons—quickly. 

A business's demand sensing capabilities should be versatile and ever-aware. Luckily, AI is never static. Its algorithms are trained to tell us something we don’t know (due to either time or capability constraints, for example) by powering through a jungle of complex data with every passing millisecond. Even better, since AI “learns” as would a human brain (hence the “intelligence” in “artificial intelligence”), it’s self-correcting or self-learning, so to speak, and can increase in accuracy every time it runs while yielding dynamic data-driven insights.

4. Get agile and collaborative—across teams and between HI and AI

Powerful, precise, and agile as it may be, AI does not know everything. The key to fully integrated supply chain agility is collaboration—specifically between artificial and human intelligence. How could it be? One-off circumstances pop up in the real world that AI, without human input, doesn’t have insight into, such as a last-minute shipper approval.

Having the right demand planning platform that allows for manual input (human intelligence) allows you to unite AI forecasts with important events in real time, like large non-seasonal orders. Uniting the forces of AI and HI permits team members to share information only they may be privy to, boosting visibility and flexibility for AI-generated demand forecasts. Balancing data with boots on the ground enables the kind of quick pivots you need to make to optimize production, and more.

Concluding Thoughts

It bears repeating: If you’ve leveled up your operational processes with an ERP, you should apply the same logic to your demand planning needs. It's the next step to achieving something greater: scalable growth. And the sky’s the limit.

About Unioncrate

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.

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