Forecasting: Let’s Start by Asking the Right Questions

May 1, 2018
9:37 am

Ten calls. They are all starting to sound the same. Here is the storyline:

“We implemented SAP APO, and we now know its limitations. We have tried SAP IBP and are not impressed. Since SAP is not investing in improving the depth of their solutions for planning, we are looking for a new solution. Can you help us select a new approach? Which of these vendors do you suggest?” They then list a combination of Anaplan, Aera Technology, E2open, JDA, Kinaxis, Logility, o9, and OM Partners. Followed by a question of, “Which do you suggest?”

Then a young consultant from one of the large technology firms usually chimes in. “I have been mapping the process for end-to-end planning. We have been building an RFP. I can share it with you.”

I usually put the phone on mute and smile. RFPs and PowerPoint presentations are the worst way to buy decision support technologies like supply chain planning. Most consultants don’t know what they don’t know about planning. As a result, only 42% of companies rate their S&OP process as effective. This is especially true for forecasting. It is the flip of a coin.
I find that too many companies try to buy forecasting software through a Request For Proposal (RFP). The second mistake is treating it as the implementation of a technology. Instead, the selection process should start through data-driven discovery. The analysis should lead you to a small list of technology providers. In my experience, most companies ask the wrong questions and consequently have the wrong discussions.
Figure 1. The Effectiveness of Current S&OP Processes

What Do I Suggest?
Start your journey by understanding the problem. Begin a data-driven discovery.

Analyze your data. How forecastable is your product set?
Define the problem. What is the segmentation of demand patterns by intermittent demand, seasonal demand, phase-in/phase-out? Buy demand planning based on engine capabilities for the demand patterns.
What is your Forecast Value Add (FVA) by product segment? High volume products? Promotions? Are you adding value to your current process? What is possible?
How clean is your data? Can you easily get to three years of forecasts to backcast the data to analyze the forecasting engines? (Backcasting is the process of using prior year’s demand data to try to forecast the current year, e.g., 2015 and 2016 would be used to try to forecast 2017.)
What is required for “what-if” analysis?
How do you shape demand? What is required in the analysis for revenue management? What is the role of the demand process in this analysis?
What is needed as a system of record? As an engine? User interface?
What is your demand latency by product demand classification? What does this mean to your inventory buffer strategies?
Are you clear on the role of the technology for the planners, business executives and leaders?

Figure 2.

So, if you are one of the business leaders struggling with what to do next, start by helping yourself. Ask yourself the hard questions. Do not send the market into a tailspin with an RFP. (Most are just too vague to answer and waste everyone’s time.)
This type of rifle-shot of an RFP drill is a waste of time. These technology providers are very, very different. Invest to understand the differences before grouping them together for an analysis. This type of approach just demonstrates how little thought has gone into market intelligence.
Instead, roll up your sleeves and drive a data-driven discussion. Define the requirements. If unfamiliar with FVA, the SAS white paper is a good overview of the Forecast Value Add Methodology.
I hope this helps. Let me know if you have any questions.

Warning: Sidestep the Narrative of the Misguided Goldiggers

August 10, 2026 4:58 pm

Ten years ago, I started writing a book titled Stories of the Misguided Gold Diggers. The book was a collection of stories from two decades of stories of technology leaders perpetuating the myth of integrated end-to-end supply chain planning.

I dusted off the manuscript on Saturday. I think that we have a new chapter. Companies focused on putting Artificial Intelligence (AI) on top of existing architectures are putting AI Stupid on steroids.

The Myth of End-to-End Planning

July 31, 2026 9:14 pm

Supply chain planning, supply management, supply chain execution, network design, and transportation/logistics management operate in silos. Not much has changed over four decades. The connections flow back through transactional systems: order-to-cash and procure-to-pay. There is a myth that companies can buy an end-to-end supply chain management solution. This is largely a myth. Here we explain.

Lead Time: A Broken Gossamer

July 16, 2026 6:38 pm

If you are struggling with supply chain planning, dancing in the light of shiny objects, and scratching your head, please read on. My goal is to help you.

Please do not AI Stupid. What do I mean? AI Stupid is putting agents and agentics on top of existing architectures believing that making them faster and hands free add value. To me, this is fools play.

I love AI. I am excited about new technologies. To this end, I want to shine a light on how new technologies can help address the black holes and inconsistencies in today’s supply chain, which largely stem from the limitations of the first generation of supply chain planning and execution technologies. In this blog, I give you three places to start.

Do You Need a Supply Chain Coach?

July 9, 2026 5:27 pm

Supply chain is where the rubber hits the road. For a public company, over 40% of market capitalization is tied to the trade-offs between growth, operating margin, inventory management, and Return on Capital Employed.

The road for supply chain improvement is fraught with issues. Here we share some and offer some advice.

Is your Supply Chain AI Ready?

June 11, 2026 2:39 pm

A simple quiz to assess an organization’s AI readiness.

The pace of change is fast and furious. Every day, technology advances faster than we can digest. A great challenge to have.

Determining whether a supply chain is “AI-ready” is less about technology and more about the gray matter between the ears of supply chain leaders. Leadership, alignment, and clarity of goals matter.

Too few companies are clear on the definition of supply chain excellence. Measuring and rewarding functional metrics reduces the firm’s value. Putting agentics on top of today’s processes can make bad practices run faster, reducing value.

The toughest job for the supply chain leader is challenging existing supply chain paradigms that were defined by the limitations of decades of supply chain technologies. As the curtain lifts on the potential of new forms of technology, process redefinition is our opportunity, but only if we are clear on what drives value. (Here, I link to the Supply Chains to Admire reports to help you define value. The next report will be published on June 23rd, along with my Dynamic Benchmarking Product, to help you define value in the face of your AI readiness. More information about the launch is at the bottom of this blog.)

Case Study: A Scrappy Demand Management Approach

June 9, 2026 5:37 pm

This study of Franklin Sports shines a light on the work that needs to be done at the sales account level to challenge a retail forecast, and also highlights the importance of a new technique for a forecast engine — reinforcement learning.

Artificial intelligence comes in many forms — large language models, generative AI, machine learning, unstructured text mining, deep learning, neural networks, reinforcement learning, agents, and agentics. While the industry is wigging out about agentics, I think reinforcement learning is a great step forward in the journey of Artificial Intelligence.

Supply Chain Shaman