During the pandemic, Blue Diamond Growers, like many companies, was sitting on significant volumes of inventory that couldn’t be moved. The world’s largest almond processing cooperative comprised of nearly 3,000 growers. The situation required the company to manage a series of complex challenges with the goal of shipping its farmers’ almonds.

It was more than just a logistical challenge. To an outside observer, the idea of holding so much inventory without the ability to move it might be the stuff of CFO nightmares.

For decades, the supply chain and logistics functions at Blue Diamond were quite manual and, prior to the 2020 pandemic, disjointed from financial planning. The disconnect hit a crisis point during the pandemic for the company that produces its own branded nut products and sells ingredients to other consumer packaged goods companies. Customers stockpiled food. Truck drivers were scarce. And worst of all, there was unprecedented congestion at the Port of Oakland. Three-week waits to book a container on a ship extended to five months.

But even amid the challenges of the pandemic, Blue Diamond had something going for it. It had started down the path of investing in new cloud-based systems that would not only boost its supply chain management capabilities but offered the opportunity to tear down the data and functional walls between its supply chain management and financial planning functions.

The main driver for this work? Data silos. Over the years, the company had accumulated separate data systems across sales, supply chain (both customer demand and supplier shipments), and financials, explains Steve Birgfeld, Blue Diamond’s vice president of information technology and services.

“It was difficult to reconcile, especially across our two main lines of business,” says Birgfeld. “What sales indicated as sold was different than what supply chain indicated had shipped, which were difficult to align with what the financial plan was indicating.”

The company had already invested in a new automated supply chain planning system in 2017. In 2018, it implemented a cloud-based business intelligence and collaborative planning tool on the same platform. That would help the company move toward what’s called xP&A. The xP&A acronym refers to an approach that extends the principles of financial planning and analysis (FP&A)—the planning, forecasting, budgeting, and analytical activities that support a company’s decision-making and overall financial health—to other organizational areas.

Applying FP&A capabilities like forecasting, continuous planning, advanced analytics, and performance monitoring across other aspects of the organization, an xP&A approach breaks down silos between departments so leaders can align planning efforts across the company. Cloud-based systems support this movement by unifying the views of data for people across core operational areas—not only supply chain and finance, but also HR, sales, and marketing.

For companies with the determination to overcome organizational inertia and support these advances, not only efficiency but also an order-of-magnitude improvement in responsiveness to the market awaits.

The bad old days and a wake-up call

We’ve come a long way since the 1990s, when ERP systems—a Gartner-coined term—kick-started manufacturers’ attempts to combine data across finance and production departments, which were working in completely separate applications. In most companies’ procurement and order-to-cash processes, data silos have persisted, for cultural as well as technical reasons. Rather than do the costly heavy lifting of systems integration and strong data governance, organizations chose to leave finance, manufacturing, and supply chain operations working on separate cadences toward their own agendas.

As it was for Blue Diamond, “COVID was an awakening ,” for many companies, says David Vallejo, vice president , and global head of digital supply chain for SAP. “Supplier shortages, labor shortages, factories closing down: the financial implications of all of this were massive. Companies suddenly needed finance and supply chain to plan ahead.”

Automotive OEMs, for example, had suppliers unable to fulfill orders of major components. They were left wondering what that would mean for their inventory and their revenues. Could they still sell some cars? Would they have to redesign them? Maybe ship them without entertainment systems? “Decisions had to be made on the spot,” Vallejo says.

Around the same time, cloud-based technologies were maturing to offer the means to determine the financial and business effects of a factory closure and possible workarounds. Extending financial planning and analysis to other functions makes that kind of scenario planning possible.

Starting with supply chain

For product-centric companies like Blue Diamond, beginning by harmonizing supply chain data with finance is the most logical and effective place to start. After all, a significant percentage of a manufacturers’ cost of goods sold accrues within the supply chain.

Bringing together all the data helps ensure that finance has visibility into the supply chain so they can create more accurate and reliable financial forecasts.

Blue Diamond, for example, couldn’t afford to spend weeks putting together reports and a response plan while its billions in almond inventory were just sitting there. But with centralized finance and supply chain data, company leaders determined it could move more product by rail (due to diminished road capacity), for example, and bleed off some inventory without resulting significant financial challenges. “Without xP&A, it would have been very hard to wake up Monday morning to make those decisions,” says Vallejo.

Blue Diamond now has a consolidated view of data across the entire planning and logistics process. Scenario planning to match supply and demand, previously a monthly exercise, can happen weekly or even daily. Blue Diamond’s planning for promotional programs to increase sales and attract new customers now takes 20 minutes instead of six hours. Previously, it had to be performed months in advance to ensure supply and transportation availability.

With its newfound ability to manage both the supply chain and financial aspects of their plans in a unified view, the company has also been able to:

• Reduce supplies on hand by 20%
• Expand order fill rates to 99%
• Save $1 million in logistics costs: order processing, inventory control, warehousing, packaging, and transportation

The unified data and integrated planning efforts have also minimized customer wait times and reduced stress on the customer service team, as noted in a Forbes article about Blue Diamond’s efforts.

A growing relevance for cross-functional data sharing

The challenge of data silos is especially acute when a crisis like the pandemic hits, but it's an ever-present issue. To get different functions to collaborate, they need to share data and cloud-based planning, analytics , and visualization tools better communicate about conditions in the business and collaborate on what to do about them.

Gartner first coined the term xP&A in 2020, to describe expanding financial planning and analysis beyond finance to supply chain management, sales, marketing, workforce management, and more.

Around the same time, cloud platforms emerged to help enterprise systems connect and synchronize plans across the company.

With a unified view of data, companies can connect budget expectations and annual operating plans with current inventory and sales figures and supply or demand forecasts. They can determine how what’s happening in the supply chain affects revenue expectations and make decisions accordingly.

Old habits and processes die hard: Overcoming challenges and resistance

So why wasn’t everyone doing this already? Well, first it’s a significant change to the status quo, a combination of good old-fashioned territorialism and possible incentives for data hoarding (versus data sharing).

“Real-time visibility is a scary endeavor,” says Vallejo. “There is perhaps the phenomenon of squirrels hiding their nuts.” Finance might not necessarily prioritize the investment in the systems and process changes required. What’s more, supply chain (or whatever other functions may be involved) might want to get a chance to improve their KPIs before sharing them more broadly.

Bringing the two different groups to collaborate on the same playing field requires a new mindset. “They think they know it better. Those working in supply chain planning environment just make logistics decisions and ship quantities and, if they’re behind on a delivery, will just expedite it with no good view of what that means from financial perspective,” says Vallejo. When a company introduces a new decision-making framework that is expressed in financial terms, they can make decisions that are better for the whole company.”

David Imbert experienced the finance-supply chain disconnect firsthand when he was managing financial planning at the Barry Callebaut Group, a Swiss Belgian cocoa processor and chocolate manufacturer.

“I could see that our supply chain was impacting the finance budget I was responsible for, but there were two different worlds,” says Imbert, who is now head of finance product marketing at SAP. “It’s commonplace for this kind of work to be happening in two different silos to the detriment of the company. It was management by experience, not management by data.”

The company was buying cocoa from farming cooperatives in Ivory Coast, but finance didn’t know how much inventory might sit at a co-op, or what the weather and production patterns were, or at what rates the cocoa was being harvested.

Imbert was keen to explore the opportunities of xP&A, and uniting financial planning data and supply chain planning data in a cloud platform seemed a good place to start “because supply chain carries so much risk and value,” Imbert says. Now Imbert helps other companies do the same.

Vallejo says that introducing a unified planning and analysis approach is not straightforward. “It will take time to overcome organizational objections to introduce a real-time view into separate worlds and wire different data models together and be comfortable with that,” he says.

It’s no small task to align financial statements with supply chain management. One is a ledger of accounts and the other a ledger of products, suppliers, and locations. People, process, and technology have to change. “And the people and process side is pretty intense. It’s not like implementing an expense management tool,” Vallejo says. There needs to be some psychological safety in place to ease, say, supply chain leaders into sharing any disruption or surprises they’re trying to manage with others in the organization when they haven’t done so in the past.

“You need executive level support to prepare the organization to share bad news and create a process of trust and accountability and align on common objectives to govern joint decisions,” says Vallejo.

At Blue Diamond, Birgfield says leaders were convinced of the value of combining demand, supply, logistics, manufacturing, and financial data to enable enhanced decision - making. To date, the company has connected supply chain data and financial planning and analysis data. It is mapping out its trade promotion data and will follow suit with logistics and manufacturing data. “We knew it would be a journey,” says Birgfield, “but the strategy made a lot of sense for our business.”

“The biggest challenge, without a doubt, was aligning the master data across primary elements,” Birgfield says.

Companies will often have customized definitions of terms and data requirements, department by department and heterogeneous  and even conflicting data stores (resulting from M&A growth, for example). Some of the information Blue Diamond wanted to access was not even in the latest version of the company’s ERP platform. There were also issues of taxonomy and nomenclature to iron out among the groups. “This was the long pole in the tent,” Birgfield says.

The shared planning and analysis platform can help overcome this challenge by using data models related to customer, product , and other important planning dimensions, Vallejo says.

A manufacturer’s move to share planning data

The effort involved in breaking down silos is not easy, but the payoff is real. Take the experience of Freudenberg Home and Cleaning Solutions.

The cleaning product company had highly complex forecasting projects with thousands of planning objects (the individual elements that are analyzed and tracked for planning, such as individual products, product lines, departments, regions, and customer segments, inventory levels at specific warehouses, specific suppliers).

Labor-intensive manual report preparation was slowing down its planning activities. There were varied planning approaches across different locations , making a unified organization-wide view impossible. And distinct finance and supply chain planning resulted in variances between finance and production plans.

Freudenberg wanted to not only standardize and consolidate planning and forecasting across its regions, but also to connect financial and supply chain data to run simulations and better understand the profit-and-loss results of different planning scenarios.

The company implemented a cross-functional planning and analysis foundation using its existing data warehouse to bring together financial KPIs and sales data from its ERP application , together with volume forecasts from its cloud-based supply chain module , to perform scenario planning in the cloud.

Standardizing forecasting processes across the company improved planning accuracy and visibility across all divisions and regions. Plus, the forecasts are now more granular because they combine operational and financial data (product volumes, raw materials required, cost of goods sold, and sales values, for example).

This means the company can perform its planning process 10 times faster , despite larger data volumes and greater granularity. Planners have the information they need to respond to fluctuating demand and manage Freudenberg’s complex and often disrupted supply chain. The production department benefits from detailed production bill-of-materials breakdowns and can now create an initial top-down production plan in just a couple of days . Sales, finance, and marketing also use in-depth forecasting data.

Busting planning silos across the organization

Bringing together financial and supply chain planning is an obvious place for product companies to start, but there are opportunities to expand this approach to any function with a business plan: sales, marketing, workforce management, and procurement. Any willing operational stakeholder can be part of a motion toward more continuous planning.

At Freudenberg, the successful collaboration between finance and supply chain prompted other business functions, like marketing and customer promotions, to request the same approach.

“If you do this well, this principle will scale to include other functions in the company,” Vallejo says.

At Blue Diamond, having volume analyses and financial analyses linked creates a strong foundation for performing real-time planning. “We can run scenarios across the two lines of business. We can assess impact by crop variety and quality to ensure we have the proper supply mix. We have improved insights for days of supply volume across our distribution,” says Birgfield.

The company is continuing to assess other areas of opportunities. “For example, we are exploring how demand sensing and retailer data can help improve our planning and sales analyses,” Birgfield says. “We want to understand how we can further leverage AI within our xP&A data to gather additional insights.”

Those potential insights depend on a company’s circumstances, but experts point to opportunities that sharing common planning and analysis data could create opportunities for new revenue models, such as direct-to-consumer sales.

The approach is worth exploring for any company, argues Blue Diamond’s Birgfield, who has advice for others heading down this path: “Understand it is a journey, and there will always be continuous optimization opportunities. Know that master data is the critical component, and don't underestimate the need to capture that. Don't overengineer the solution. Know your desired end game but consider a phased approach to get there.”

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