I wrote my first report on Sales and Operations Planning (S&OP) while sitting on the floor in the Atlanta airport in 2005 when I was an AMR Research analyst. When my flight was canceled, I was en route to the annual AMR conference in Phoenix. The floor was the only place that I could find to write for what turned out to be a ten-hour flight delay. (I wrote many reports on airport floors in those days–electrical plugs were just too scarce.) The model in Figure 1 became the foundational model for the Gartner S&OP model. Forty interviews and two quantitative studies helped me build the model in my mind.

Figure 1. Sales and Operations Maturity Model from 2005-2008

Two decades later, I now write less and listen more.

At the time, I was fascinated by how the most mature teams bucked the system. Let me explain. One of the most advanced teams was a division of DuPont. (At the time, DuPont, now acquired by DOW, boasted externally on internal adherence to SAP standardization for decision support. However, this mature team within DuPont found the technology insufficient. They gave lip service to the need for IT standardization and ran their process on a custom-built model that enabled a reverse bill of material, and profitability analysis. They were ahead of their time on simulation analysis and cross-functional orchestration.)

In 2022, I frequently ask companies to draw their river of demand. The activity is designed to challenge new thinking–to rethink demand as a flow, to identify rocks (barriers), and define process latency (the time to make a decision). Nine times out of ten, the finance group is portrayed as a dam, a barrier or a ferris wheel in the drawings. In Figures 2 and 3, note the portrayal as a dam. The tight integration of the budget in the redefinition of S&OP to IBP is a strong deterrent to building a responsive value network.

Figures 2 and 3. Drawings from Two Very Different Businesses Portraying the River of Demand

My take? Over the last fifteen years, Sales and Operations Planning (S&OP) enjoyed a renaissance. The process designed to help companies manage constraints outside of lead time took on a life of its own. Here I argue that we made five fundamental mistakes. I also share data to try to convince you to join a guiding coalition to think bigger.

While the research in 2005 showed that 13% of companies were attempting to orchestrate demand and supply, today, based on my recent research, I find that it is a much smaller number of 4%. (Orchestration enables companies to effectively manage trade-offs between source, make, deliver and sell.) Due to organizational alignment issues and the increase in demand and supply variability, S&OP effectiveness regressed over the last decade.

The process mistakes include:

Mistake #1. If you want to know about future sales, ask sales. Fact. Sales data has the greatest bias and error due to bonus incentives. Don’t believe me? Measure it.

Mistake #2. Tight coupling of the supply chain forecast to the financial forecast will improve value. Fact. Companies tightly coupling the budget to S&OP have significantly higher inventories and lower growth than their peer group. For validation, listen to the ongoing Q1 and Q2 earnings calls.

Mistake #3. The order represents true demand. Fact. Demand latency is two-eight weeks delayed from consumption purchase to translate to an order. My challenge? Measure it.

Mistake #4. Deployment of deeper statistical engines for inventory management with a focus on safety stock will improve inventory levels. Fact. Industries carried on average 32 days more inventory in 2020 than in 2007. (I give you this evidence in this blog.)

Mistake #5. Organizations can align to drive value despite the allegiance to functional metrics. Fact. Organizational alignment in manufacturing organizations is worse between operations and finance teams today than in 2013. (Read on to see the facts.)

There are many consultants touting S&OP–both process consulting and technology deployment–but, the facts support that traditional S&OP processes are insufficient. I believe that we need to make five changes that I list at the bottom.

Reflection

When I was at AMR, I had a client named Peter. Every time I published a report, he would call me and express his concern that I did not fully understand the issues of the global supply chain organization. I would ask, “Why?” Unfortunately, I never got a straight answer from Peter.

I better understand Peter’s pain in my recent work with companies to draw the river of demand. The issue is that we have focused on process and technology but have not attacked the problems of the larger organization and how we do work. A global manufacturer’s organizational dynamics are starkly different from a regional supply chain.

I am shocked to see how the lack of governance–clarity on who and how to make decisions–derails well-intended processes. Now I know that I was naive when I wrote the reports at AMR. I missed the impact of regional/divisional gaming to make bonus incentives. While I wrote about the need to clearly define the role of the global organization, the regions, and the divisions, I never conceived of the degree of under and over-forecasting to make bonus targets.

Without clear governance, customer data never sees the light of day. Companies tout being customer-centric, but this is anything but the case. In the words of one of the students in a recent class, “When finance and sales hold hands, the blinds close on the ability of the supply chain organization to use customer or sell-through data.”

I also did not understand that even if the forecast was wrong–even bad– the company would religiously cling to the number as an alignment value between functions. Even when I proved that the process was degrading the forecast by 35-60%, companies still clung to the wrong number. When I realized this, I scratched my head. Why would an organization tightly tether to an alarming number? Sadly, I learned that even a bad number aligns the organization cross-functionally. My insight? Even after knowing that the company is going down the wrong path, leaders hold hands to walk it together.

I also had a client at the time by the name of John. His company had 90% of market data shared weekly with minimal latency. I kept pressuring this client to use their channel data locked into the Excel files in their sales account teams. One day, John called me and said, “Lora, you don’t understand. We are a big organization. We will not use data unless it is perfect. The data is dirty and incomplete. Even though you keep preaching about the issues with demand latency, we think that we are ok. Demand variability continues to be low. We will never get the data perfect, so stop your preaching. We are ok.” I smiled this week when John’s prior employer reported Q1 revenue shortfalls due to a lack of inventory to investors due to the pandemic. The Company’s CFO talked about the need for resilience, but this company is among the last to adopt new technology. The industry considers the company a leader, but I know the dark side that internally, the IT processes squash innovation. John moved on touting the superiority of his prior employer’s approach through consulting to other companies. Bad behaviors perpetuate. If it weren’t sad, it would be laughable.

In my recent class, I asked the attendees to rate how the shift in paradigms of using new forms of analytics changed their ability to think about the use of data in S&OP. I show the results in Figure 4. I would love to share Figure 4 with John and Pete, but neither are in their old jobs fifteen years later. Nor do I think that they would be open to rethinking their paradigms.

Figure 4. Shifts in Thinking on Planning Processes When Embracing New Forms of Analytics

What The Data Tells Me

So, you might say, this is provocative, but show us the data. Let’s start with inventory. While the press is full of articles on the lack of inventory, I allege that we need to rethink inventory. Why do we have 32 days more inventory by company in 2020 than in 2007? (This is a weighted average across all companies.)

Figure 5. Days of Inventory by Industry Sector Across Time Periods

1. Measurement. As long as companies are motivated by functional metrics, inventory management will be an enigma. When companies reward OEE in manufacturing and POV in procurement, companies will systemically make the wrong stuff. Ironically, the organization efficiently produces goods that will only sit in the warehouse.
2. Focus. Most companies focus on safety stock in S&OP and fail to reflect the plan’s impact on total inventories –seasonal, in-transit, and cycle inventories. Over the last decade, in-transit and cycle inventories increased. With the increase in in-transit lead time, more and more goods are stuck in transit. With the increase in complexity, manufacturing cycle inventories rise. Only 9% of companies actively design their networks as a part of S&OP to understand the role and function of inventory and the design of buffers.
3. Management of Complexity. The more complex the item master, the platform strategy, or the product portfolio, the greater the inventory increase. Complexity is like cholesterol–within an organization, there is good and bad complexity. Good complexity powers growth, while bad complexity drags the balance sheet. Less than 2% of companies actively manage complexity.

As I speak about the rise in inventory, I reminisce. I fondly remember a conversation with a disgruntled CFO in Geneva at the end of a long strategy session. His team wanted to implement an advanced optimization engine for Multi-Level Inventory Management (often referred to as MEIO in the market). As he spoke, he sighed and then raised his hands in disgust and remarked, “If I had the money invested in technology to reduce inventory, I would be a billionaire. I want to help the team, but I don’t think that the answer is rooted in buying another technology.” If only I were smart enough then to answer his question correctly. I would have liked to have replied, ” You are right. The answer is not technology, but you can make a difference by changing bonus metrics and implementing network design to ensure the organization meets the inventory targets based on the form and function of inventory.” Many of the organizational behaviors are rooted in the CFO’s understanding of supply chain management. For most, this is a gap.

Over the less decade, companies are less aligned. Inadequate work processes and functional incentives forged crevices between commercial and operations teams. It is fools play to think that a simple process like S&OP can close these gaps. To make the point, let me share data on alignment taken from similar surveys from 2013 and 2020.

Figures 6 & 7: Organizational Alignment in 2013 and at the Start of the Pandemic in 2019

Note that in 2013, the gap between commercial teams and operations was 58% and between manufacturing and procurement was 32%. In 2019, the gap between commercial and operations teams is still high, but the gap grows between finance and operations, manufacturing and procurement, and operations and IT. These gaps are statistically significant at an 80% confidence level.

What Should Organizations Do?

The answer is simple: the shift requires leadership. The journey starts by clearly defining supply chain excellence within the business. (A definition that steers clear of non-descript buzz words and shiny objects.) Based on this definition, design processes and align the organization to understand that the business response is based on flows in a complex non-linear system. In the design, measure the variability of cycles: lead times, conversion rates, and in-transit shipments. Use this data in the design of the process flows at least quarterly. (93% of companies have advanced technologies, but really use spreadsheets. Variability modeling in a spreadsheet is nearly impossible.)

Reward the organization based on a balanced scorecard of growth, margin, inventory turns, customer service, and asset utilization. Use this balanced scorecard for bonus incentives across divisions, regions, and functions. (The movement from functional cost-based incentives to margin is essential to drive alignment.) Transition functional metrics to focus on reliability. Examples include a shift from demand error to the measurement of Forecast Value Added (FVA), the movement from OEE in manufacturing to first-pass yield and schedule adherence, or a change in procurement from Purchase Price Variance to first-pass quality and on-time and in-full to the factories.

Wrap-up

When people speak to me about the benefits of S&OP, I smile. The concept had such promise but unleashing the potential resulted in a tangled mess of issues—such a quagmire for most organizations. The reasons are many, but most are rooted in the basics of leadership.

Let me end with a story. In 2013, I published a series of articles warning companies about the tight integration of S&OP to the budget in the newly minted concept of Integrated Business Planning (IBP). At the time, IBP was a new shiny object and a process fad.

The posts got a series of flaming comments from a consultant in Australia. I did not know him at the time, and he did not know me. Let’s call him Jan. Young and new to the industry, Jan wrote on the promise of tight budget integration to S&OP through IBP to drive results. His personal experience with two companies fueled his argument. Six months ago, he contacted me and apologized.

While it is clear to me that the promise of S&OP was stilted by the adoption of the processes of sales forecasting and tight integration of the budget to S&OP, Jan now agrees. I hope you do as well.