Learning to Speak the Language of Demand

January 21, 2014
7:45 pm

New shoes feel awkward. Blisters appear. Feet hurt. The shoes are worn for short periods. Often we shelve them to allow our feet to recover. However, over time, they slowly feel comfortable. They become a part of our wardrobe.
Learning to speak a new language is similar. Conversations are strained. Mistakes are made. Pauses are awkward. Confusion reigns. Communication is stilted. It takes time. Slowly the words take definition in everyday speech.
Nine out of ten supply chains are stuck. Growth has slowed. Complexity has increased. Companies are stuck at the intersection of inventory turns and operating margin. They are unable to drive improvements in both. The secret to unsticking the supply chain is to redesign processes to be outside-in. The supply chain processes need to be designed from the market back.
This a step change, not an evolution. Why? Most companies have designed supply-centric processes from the inside-out. The first step to making the shift is learning a new language.

Step Up and Learn the Language of Demand

In companies, there is no standard model for demand processes. It is evolving. New forms of analytics make new capabilities possible. In the traditional organization, some demand processes are sales-driven. Others are marketing-driven. However, sales-driven and marketing-driven processes are quite different from market-driven processes. <In fact, so much so that I wrote a book about it.>
Unfortunately, companies have invested money in traditional forecasting processes believing that if they make the forecast better that corporate performance will improve. Improving forecasting is not sufficient. It is about much more than conventional forecasting. While we need forecasting and we need to improve the processes, we also need to teach teams how to use new forms of demand data and adopt demand processes.
Why is this important? Supply chain leaders are fluent in the language of supply. They don’t know the language of demand. To become demand driven (or market driven), they need to learn how to speak a new language. In this process, they slowly learn that the customer order is a poor representation of demand.
Tonight, I am stuck at a New York airport in a snow storm. I have been at client’s for the last two days helping them to make this transition. So tonight, instead of making snow angels, I thought I would help readers to get started in speaking the language of demand.

New Terms to Know

The concepts of demand driven are now vogue. Many supply chain consultants will quickly rattle off case studies and proof points, but the smart supply chain leader will ground the discussion with clear definitions. Let’s start with these:

Demand Sensing: The reduction of time to sense purchase and channel takeaway. Demand sensing is a process, automated by technology, that reduces demand latency.
Demand Latency: The latency of demand signal due to demand translation of a customer purchase through the supply chain to an order for a trading partner. The time is different in each supply chain based on product sales velocity and the technologies used. For example, in a hospital, it is the translation of usage in a procedure to hospital order to a distributor and the translation of that usage to an order for a manufacturer. This time lapse varies by product and by channel. For the purchase of Tide at Walmart to translate to an order at P&G, the time is 5-7 days. For the translation of a purchase of Aleve at a retail outlet store to Bayer, the manufacturer is 60 days. As the long tail (small orders shipped with low-frequency) of the supply chain grows, demand latency increases and there is a greater need for demand sensing technologies.
Independent Demand. The purchase of a product by a customer in the channel.
Dependent Demand. The translation of this demand signal from a channel demand signal to a manufacturer or a distributor through a bill of material or a transportation or manufacturing routing.
Demand Translation. The translation of demand by role within the organization. Each role–customer service, sales, procurement, manufacturing–have a different need/definition for the demand signal.
Demand Shaping. The use of demand tactics –price, sales incentives, marketing programs, new product launch, promotions, and assortment– to increase baseline forecasting.
Demand Shifting. The shifting of demand from one period to another (examples include pre-shipments at the end of the quarter, stuffing the channel to get rid of stock, or shipping early) increases supply chain costs and distorts the demand signal. Try to minimize demand shifting and maximize the value of demand shaping. Get clear on the difference.
Forecastability. The mathematical determination of ease of forecasting (the determination of the probability of demand). Many technologies include this in the base software package.
Forecast Value-Add (FVA): A methodology for continuous improvement of the demand plan where steps of the process are evaluated and the question is asked, “Did this change improve the forecast (bias and error) as compared to the naive forecast?” (For more on this topic check out the book, The Business Forecasting Deal.)
Naive Forecast. The historic forecast using prior month shipments.
Downstream Data: Use of channel data (Point of Sale (POS) and Warehouse Withdrawal) to sense channel demand.
Demand Synchronization. The demand signal must be connected from node to node in the supply chain and then synchronized and mapped. The most frequently mapped data elements are product hierarchies, time/calendars, and locations. In this mapping, the data granularity and frequency must be harmonized.
Demand Visibility. The translation of demand by role across the organization and across tiers and nodes of the supply chain.
Demand Consumption. The translation of the demand signal across planning horizons. In early planning products this was accomplished through rules-based consumption. New and more advanced technologies are using optimization and cognitive learning techniques to consume the forecast across planning horizons.
Integration. Close coupling of the data elements to use the data into software. Integration without synchronization and harmonization does little for the demand signal.
Harmonization. Data harmonization enables data of differing granularity and data structures to be harmonized into a common database.

Conclusion

Did I miss any? Just let me know.

And, please let me know if you have any great tips to share for the application of these concepts.

Also, if you want to practice speaking the language of demand face-to-face, it looks like I may be at the Marriott Airport Hotel in LaGuardia for a loooong time. Flights are canceled for at least 48 hours. Look for me at the bar….

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