What do I mean? Let me explain.

I find that the market moves slowly along traditional technology lines. This bothers me. I want it to bother you as well.

AI Here. AI There. But, no Meaningful AI Anywhere

As I wrap up this spring conference speaking tour, I feel a sense of emptiness. Over the past month, I attended the Kinexions conference hosted by Kinaxis, the P44 Velocity Conference by Project 44, and the Opticon conference by Optilogic. The conferences were very different: each was a good conference in its own right. Each technology company demonstrated tangible improvements in its own product portfolios. Bravo! (Side-step the AI hype shuffle and move forward.)

So, net: net. The conferences were better than most I have attended over the years, and I am a tough critic. Each conference was forward-looking, acknowledging that the current state of technology is insufficient. (But, I hope to never return to Vegas.)

As an attendee, I was struck by the differences in how the term Artificial Intelligence was used, often overused, and also by the lack of clear definitions. Buyer beware. We are in a spin cycle. Technologists, for some reason, hate straight talk. Clear definitions are seldom on the dance card.

Optilogic gets my vote for Fun!, networking, and engagement. P44’s event gets my vote for polish, professionalism, and content. And, Kinaxis gets my vote for grit, determination,  and tenacity. (Can you believe that a company that turned over most of its executive committee has continued to drive forward motion in the way that they have?) The new Kinaxis is just starting. I find that exciting.

My concern is that each conference focused on redefining the host’s solutions through an AI vision, but no company detailed a roadmap to drive interoperability between solutions and unleash unparalleled value. Or a vision on how to redefine work. The discussions were largely tech for tech’s sake. So, I ask: did the companies meet the market’s needs? I think not. If I were a client attending all three events and asked how to build a technology solution that would redefine work processes by using all three together, I would scratch my head. It would not be obvious.

I was also struck by the fact that no company focused on redefining work. The largest constraint in driving value through new technology is the gray matter between the ears of the supply chain professional. It is not the tech. We need to challenge existing paradigms. We don’t need faster horses. The opportunity is to help companies be more proactive, make better decisions, and improve value-based outcomes. This is not easy, and we are just beginning.

Steps to Take:

On many of my calls, clients ask me how to integrate supply chain planning and network design. My answer to him is, “With all due respect, please recognize that you are asking the wrong question.” The goal should not be integration. Instead, the focus should be on interoperability and improving work.

Integration Versus Interoperability. To understand my answer, let’s start with some definitions. While integration creates one-to-one data exchange through APIs, interoperability enables scalable collaboration across multiple organizations and technologies. One of the advantages of machine learning is data cleansing; one of the great benefits of agentics is workflow automation. The creation of a semantic reconciliation layer and rule-based ontological frameworks improves interoperability.

Interoperability requires synchronization and data harmonization. Think of integration as the plumbing, and synchronization as the flow of consistent information through it. Harmonization is the process of standardizing, reconciling, and aligning data from different sources so that it has a common meaning, structure, and format across an organization or ecosystem. I call this semantic reconciliation. I am firmly convinced that our AI journey needs to start with redefining our relationship with data. Instead of complaining about data quality, we need to focus on driving interoperability through a planning master data layer fueled by semantic reconciliation. I give Kinaxis kudos for starting us on this needed journey.  While there is much, much more work to do, at least Kinaxis is starting.  

When the client asks me about redefining work using AI, my answer, based on attending these three conferences, would be:

1. Redefine the Use of Network Design Technologies. The network design analyst usually sits at a low level in the organization, focusing on projects that drive functional outcomes, such as logistics or distribution. You might also find them doing projects in the Supply Chain Center of Excellence. My advice? Redefine the role. Push to use the network design technologies systemically; analyze the trade-offs of make, source, and deliver at the same cadence as your S&OP process. Don’t waste your time on periodic ad-hoc functional projects.
2. Build A Plan of Plans.  Pair the network design analyst with the financial strategic planner in the finance organization, and embrace the concept of the plan of plans. Produce multiple plans in the strategic time horizon (maybe 20-40) and present these at the S&OP meetings. Consume the network design plans into the tactical planning horizon using tools like Kinaxis, and reduce the plans to the most probable (maybe 4-5 plans). In the S&OP execution environment, consume plans informed by market drivers and narrow the options for the executional role. Use the probabilities and ranges from the plans in the tactical horizon to drive aggregate buying plans and design network strategies. Pull the levers on contracts and Purchase Orders as the market becomes clearer across time horizons. Net/net: Don’t consume data. Instead, consume plans enabling a planning environment where the plans can learn from each other.
3. Shift Planning Responsibilities to Line-of-Business Leaders. I was proud of the Optilogic team for building a digital assistant using their AI agent, Ada. (A platform named after Ada Lovelace, who is widely regarded as the world’s first computer programmer. Lovelace recognized the broader potential of computing long before modern computers existed, as Don Hicks, the Founder, states that Optilogic’s vision was to combine human expertise and advanced computing to solve complex supply chain problems.) The use of the digital assistant allows business leaders to run what-if analysis on their phones to understand outcomes. Sounds nice, right? The problem is that organizations are not aligned on the definition of supply chain excellence. For example, today, a decision made by a logistics leader to reduce costs could massively affect customer service. I was impressed by Ada and encourage you to ask the Optilogic team for a demo.
4. Train Your Organization on the Importance of Lead Time. The importance of lead time permeates supply chain planning decisions—affecting replenishment lead time for Vendor Managed Inventory (VMI), Just-in-Time (JIT), and Distribution Planning (DRP), promise dates for ATP, the determination of safety stock requirements, and raw material buying decisions for procurement. Many companies have invested in visibility solutions for transportation/logistics primarily to predict on-time delivery, but the data is not widely used to inform decisions. Instead, in most organizations, lead time is treated as a constant—a set-and-forget element—in supply chain planning. If I attended all three conferences, I would have a laser focus on bringing signals from companies like FourKites and P44 into a planning master data layer, as Razat Guarav, CEO of Kinaxis, describes in his opening presentation. Embrace lead time as a variable and educate the team on how the shifts in lead time change safety stock values, purchase plans, and go-to-market planning with customers. Shift the paradigm from a static, set-it-and-forget-it approach to a dynamic, proactive planning approach to help the organization move forward.
5. Clearly Define Value. Build a balanced scorecard and a clear definition of supply chain excellence, and pair a network design analyst with a partner in finance to drive strategy. This is very different from the current organizational design in many organizations, where the network design analyst performs ad hoc assessments in response to functional requests.  

I hope this helps you to get from 1+1+1 to equal unparalleled outcomes. Please do not AI-stupid by sprinkling AI all over existing planning taxonomies. Instead, lift the discussion to embrace the power of the technology evolution. In the process, side-step the spin cycle. The goal is not faster decisions; instead, it is about driving better outcomes.

Launch of Dynamic Benchmarking

So, in closing, I want to share some big news.

I have worked for fifteen years on the Supply Chains to Admire methodology. The benchmarking is independent and trusted: it is not a beauty contest of underperformers. The 2026 report will be published on June 23^rd. The report takes three months to build. We have done this for fourteen years. It is my annual gift to the supply chain community.

In 2024, the methodology was validated through two years of work with a Georgia Tech IYSE team led by Dave Goldsman. This month, I am proud to share the methodology with #supplychainleaders everywhere by launching the Dynamic Benchmarking solution through my LLM Ask Lora.

What is Dynamic Benchmarking? Wael ABDELMALEK and the Uthereal team have built agentics on top of my  LLM, drawing on my decade of research, to help you understand the gap between your current state and that of a Supply Chains to Admire Award Winner in 8-10 minutes. Start by answering 20 questions and receive a detailed plan of action that adapts to the market. Financial and maturity benchmarking for all based on Y-Chart data. Over time, the model learns to give greater insights.

Watch for the announcement, and join the launch podcasts. Then get your own orbit charts, and build your own river of demand to help your team gain insights into possibilities, while benchmarking to top performers defined by the Supply Chains to Admire. For kicks, grins, and giggles, you can also compare your performance to the Gartner Top 25 Performers. Gotta love it!

 As we wrap up two rounds of testing, I give thanks to the people in my network who have made this possible. The testers are currently going through their second wave of testing before release. I give thanks to Alex Pradhan, Bram Dresmet, Christine Barnhart, Christian Kroschl (and the E&Y team), Dave Winstone, Laura Koxholt, Lukasz Zieba, Margo Cohen, Mathew Spooner, Nicole Miara, and Peter Bolstorff for their help and for giving us the gift of time through the testing process. I don’t think that I could have had a deeper and more diverse group to push us over the finish line. #proud, #thankful

Thank-you Scott for hosting us on your Supply Chain Now podcast.

However, when it comes to supply chain excellence, companies spend a lot of money on improvements, yet I find that most are unclear on definitions and how to conduct supply chain health checks. I dedicate this blog to those trying to improve supply chain performance.

An Orbit Chart as a Supply Chain Health Check

I find an orbit chart to be a powerful tool for understanding the “health” of a supply chain and its potential for improvement. The supply chain is a complex, non-linear system with trade-offs. The relationship between trade-offs varies by industry, region, and size. The orbit chart is a diagnostic we use in the Supply Chains to Admire work. (Stay tuned, this year’s report is on target to publish on June 23rd.)

In developing the Supply Chains to Admire analysis, we start with a well-defined peer group. The reason? Each industry has a very different potential. Market factors shape the potential over time. The goal of the Supply Chain to Admire report is to beat the industry peer group while driving improvement. Sounds easy, right? Not so fast. Year over year, only 6-8% of companies meet this standard.

The analysis is based on reporting in public markets. We use a syndicated data feed — Y charts — to account for differences in reporting, currency, restatements, and aggregation across markets. (For example, Unilever reports in twenty-four public markets.) We start pulling this data in March and gradually eliminate outliers. It takes us a couple of months.

The analysis is for a ten-year period. The reason is simple. In my experience, it takes a lot of time and energy to turn a big ship. A transformation of a small company might take three to four years, but the transition of a large multinational can take six to ten.

What Is An Orbit Chart?

Definition: An orbit chart is a visual representation of relationships, influence, or movement around a central point. The term is used in several fields, but the core idea is the same: objects “orbit” around a central entity.

Using the Orbit Chart Technique to Better Understand Supply Chain Excellence

Let me use the pharmaceutical industry to illustrate the concept. In the period of 2016-2024, the pharmaceutical industry, as shown in Figure 1, averaged 24% margin and 2.75 inventory turns. (Note: this is the highest operating margin of any of the supply chain sectors.) (With these margins, isn’t it ironic that most of the pharmaceutical companies focus on reducing cost? And, are hamstrung by conventional thinking? And that no leader has built value networks to improve visibility and provenance?)

Table 1: Industry Sector Averages for Process-Based Industries for the Period of 2016-2025

My first insight is that the averages do not tell the story. Market turbulence and industry potential are best represented by an orbit chart. In Figure 1, I share the pharmaceutical industry orbit chart for 2016-2025.

Figure 1. Pharmaceutical Orbit Chart at the Intersection of Operating Margin and Inventory Turns

The winner of the Supply Chains to Admire for the pharmaceutical industry is AbbVie, with 36% margin and 4.33 inventory turns. Founded as a spin-off from Abbott Laboratories in 2012, the company has a newer take on supply chain.

The Company successfully navigated the massive Humira patent cliff while simultaneously scaling and launching newer therapies such as Skyrizi and Rinvoq. Those two biologic products now drive much of AbbVie’s growth. The supply chain was built on the principles of physician-patient networks, manufacturing redundancy, and the use of outside-in data.

Figure 2. Orbit Chart of AbbVie Comparison to the Pharmaceutical Industry for the period of 2016-2025

Now let’s take a look at Pfizer. In Gartner’s 2024 Life Sciences Supply Chain Top 25 research, Pfizer was ranked No. 20 in the global Top 25, achieving its fourth consecutive year in the ranking.

Both AbbVie and Pfizer manufacture biologics. Pfizer’s average operating margin is 25% with 2.48 inventory turns. They are less profitable than AbbVie and require more cash, but this is not the important story.

The magic of the technique lies in pattern recognition. Do you see how out of control Pfizer is compared to the AbbVie pattern? The large swings and gyrations are a reaction to variability in orders. (Order latency for a pharmaceutical company can be 30-90 days relative to consumption.) The Company’s supply chain thinking is old-school. During the period, Pfizer invested heavily in manufacturing analytics, advanced planning systems, AI-enabled forecasting, and control tower visibility.

The popular belief is that Pfizer is resilient. The large swings of this orbit chart tell me that the company is not. AbbVie is more resilient than Pfizer.

Figure 2. Orbit Chart of Pfizer Comparison to the Pharmaceutical Industry for the period of 2016-2025

Now let’s take a look at Bristol Myers Squibb (BMS). The Company operates at a 24% operating margin and 2.75 inventory turns, but YOWZA, take a look at the swings. The Company specializes in oncology and cell therapy and is pushing to become more dominant in biologics. BMS has steadily invested in digital manufacturing, serialization, AI-enabled forecasting, and integrated planning systems. The BMS definitions are traditional, focusing on functional optimization. BMS is less resilient than Pfizer.

Figure 2. Orbit Chart of BMS Comparison to the Pharmaceutical Industry for the period of 2016-2025

BMS and Pfizer are not outliers. I see the same pattern in AstraZeneca, Eli Lilly, J&J, Merck, Novartis, Novo Nordisk, etc. The lack of resiliency in the pharma sector at the intersection of operating margin and inventory turns, and growth and Return on Capital Employed (ROCE), permeates the industry. The industry is slow and reactive in a market that needs a patient-centric, responsive supply chain.

I am firmly convinced that the answer is not more traditional ERP and APS deployments. The demand latency and market characteristics require a redefinition that is only possible today through new AI techniques and redefining the first-principles redefinition of planning. A company steeped in tradition needs to question what are widely heralded as best practices to drive improvement.

I think it requires recognizing flow patterns across product categories, redefining demand management to focus on flow, designing networks for those flows, using market data to drive a balanced scorecard, and implementing bi-directional orchestration. It starts with redefining our relationship with data to build the semantic layer and to use market data in the planning master data layer. I explain some of these concepts in greater detail in my Handbook of Outside-in Planning.

So you might say, “What is the value?” Why should we change? These large swings are indicative of a reactive, not a responsive company. A focus on functional optimization and supply-centric strategies drives this pattern.

Summary

In closing, I want to say a prayer for all of my friends in flood-torn and fire-afflicted areas. It is raining on the East Coast, as fires rage on the West Coast. As I write this, I am finishing up 2 years of living in a hotel. What started as a fire in my house in August 2024 exposed the inadequacies of the US insurance system to me firsthand. You think that you are insured until you find out you are not.

The US home insurance system has been hijacked by financial markets and PE firms to prioritize balance-sheet performance over the delivery of promised outcomes.

I am fearful that this is also the case with Big Pharma. While I know many wonderful and caring people in this industry, the executive teams’ understanding of the requirements of a responsive supply chain to drive patient outcomes is lacking. Many executive teams think that the most effective supply chain is the efficient supply chain operating at the lowest cost. There is a strong belief that if you save money in the back office and pump it into the front office, a company will drive profitable growth. (I often think of my conversations with many executive teams.) Companies genuinely believe that functional excellence drives top performance. Risk management is focused on supply when the largest risk is in sensing and translating demand.

As I hear consultants talk about the autonomous supply chain, I shudder. The image below of a Waymo car zooming headfirst into a flood is apt. Please don’t WAYMO my pharmaceutical supply chain. Too many lives are at stake. We need to focus on responsiveness and flow alignment of cycles first.

Next week, I will be at Kinexions reporting on the focused work to redefine the semantic layer of planning, and attending Opticon, discussing the promise of AI-native network design technologies. If you are attending either of these events, I encourage you to reach out so we can share insights and explore potential collaborations. I am sure we will have a great conversation, but be forewarned, don’t try to WAYMO my supply chain.

For additional reading: Orbit charts, and why you should use them – Michel Baudin’s Blog

We have automated enterprise supply chains not value networks. Many times a well intended consultant will generalize and minimize the importance of industry-specific functionality. Please note that any methodology that puts all companies into a spreadsheet — while losing the industry nuance — should be thrown in the nearest trash can.

The goal of this post is to focus the reader first align on the differences and potential of each industry and then make a plea for supply chain leaders to build value networks.

Learning From The Supply Chains to Admire Methodology

I am deep into the Supply Chains to Admire Analysis. The report takes me three weeks to write (after Regina spends 18 weeks pulling and analyzing the data). It is always interesting for me to see the emergence of industry patterns.

This year’s analysis is particularly interesting because it spans 2016 to 2025. (We have been looking at ten-year comparisons of the industry since 2013.) The reason? It takes companies an average of four years to drive substantial supply chain improvement. Three years pre-COVID and three years post-COVID yields valuable insights on resilience.

The typical manufacturing company grew 2.8% annually before COVID, but the discrete industries rebounded post-COVID due to war, shifts in technology platforms, and the growth of Artificial Intelligence (AI). Process-based companies, with the exception of pharma, were stalled.

In the report, I separate process-based manufacturing companies from discrete ones. The difference? Process-based companies predominantly focus on make-to-stock processes, while the discrete industries are more focused on configure-to-order or make-to-order processes. In the past decade, technologists have focused more on automating process-based manufacturing and underserved the discrete industry. This is despite the larger market potential in the discrete industries.

The Supply Chains to Admire report analyzes companies within an industry and rewards companies that drive improvement faster than the peer group at the intersection of operating margin and inventory turns while outperforming their peer group on growth, inventory turns, operating margin, and Return on Capital Employed. Why these metrics? Based on 10 years of research, first with Arizona State University and more recently at Georgia Tech, these metrics, when combined, can predict value as measured by market capitalization/employee. I want to help companies move from a cost-based agenda to one driven by value. This is my goal.

Measuring Industry Performance

In Tables 1 and 2, note that each industry has a different pattern. The largest companies by both revenue and market capitalization/employee are in the beverage, household durable, medical device, pharmaceutical, and semiconductor industries.

The interesting thing to me about this analysis is that for the past two decades, we have not achieved the supply chain economy of scale. As you will see from the report’s release on June 23rd, small companies that are more focused on innovation in a well-defined peer group consistently outperform larger companies. For example, Church & Dwight outperforms P&G; Monster Beverages outperforms AB InBev, Coca-Cola, or PepsiCo; and Ecolabs outperforms BASF or Dow. This is despite the industry’s commonly held beliefs that these larger companies are the market leaders.

What did I learn from the analysis so far? Here I share early insights:

• Improvement and Performance are Different Patterns. When companies start their journeys to drive performance it is easier to drive a difference. As companies mature, it is more difficult. Top performers like L’Oreal, Nike, and Somnigroup outperform in their sectors, but struggle to sustain improvement. When you reach higher levels of performance, it takes a step change to continue to drive improvement.
• Functional Metrics Erode Performance. Companies with deep investments in manufacturing scheduling underperform because the strong focus on manufacturing efficiency throws the supply chain out of balance, increasing inventory levels and reducing operating margins. A strong reminder that the supply chain is a complex, non-linear system. When companies optimize for a single function, they reduce value as measured by market capitalization per employee.
• Perception is Not Reality. The public perception of top performers could not be more wrong. Most industry conference keynotes are large companies that are underperforming.
• A Need to Rethink First Principles. There is no new normal. Post-COVID, the name of the game is not process improvement based on yesterday’s technology or process definitions. The business requirements changed at a first-principle level. Now, the goal is redefining processes based on new operating models using market data.

Table 1: Process-Based Manufacturers

Table 2: Discrete Manufacturers

Leadership Vacuum

I find it interesting that no company with an operating margin above 15% and a dominant position in its value chain has pushed the industry build a value chain and crack the code on building network interoperability. The supply chain, sadly, after four decades, largely operates on spreadsheets and EDI. The efforts at digital transformation failed because the focus was on automating historical practices, ignoring that each industry has shifted to a fundamentally different operating model. There is a glaring need for value-chain leadership. I am fearful that we will put AI on top of a system that requires change.

To understand the dilemma, check out the pattern of the Medical Device industry with 19.1% margin. The industry performance deteriated from 2016 to 2023, but started to rebound in 2023. The inbound supply chain of procurement to manufacturing is still largely manual. Linneage and quality of conformance are opportunities. Now would be a good time to rethink supply chain fundamentals and build meaningful networks.

Figure 1. Orbit Chart for the Medical Device Industry 2016-2025

But, as I look at the current waving of hands of putting agents on top of APS or ERP by technology marketers to drive the autonomous supply chains, I know that teams are fighting an uphill battle. My goal is to put an arrow in the quiver of supply chain leaders to drive home the point that the change is necessary.

The narrative goes like this: a strategic consultant with the term supply chain in their title comes into the boardroom, looks at your Company’s balance sheet, and, with a wave of the hand, convinces the Chief Financial Officer (CFO) that a new deployment of a tech solution can radically improve cash-to-cash. The focus is on inventory reduction.

When you are brought into the discussion, the deal is made. You are asked to manage a project that is doomed from the start.

Few companies actually step back to understand the role of inventory in the context of the established strategy or to analyze industry potential. Here, I explain both in case you are the unwilling protagonist in this well-established narrative.

Understanding Industry Potential

In my last post, I shared that the industry potential for inventory turns in the food industry fell by 30% (after a 28% decline in the prior decade), and in the chemical sector by 20% during the period of 2016-2020 (after a 20% decline in the prior decade). In a similar manner, as shown in Figure 1, the Container and Packaging Inventory Turns Fell 15% over 2016-2025, after a prior decline of 10% over the prior ten-year period. A mistake many organizations make is letting a misinformed consultant set unrealistic goals. My recommendation is to use the Supply Chains to Admire report series to set reasonable recommendations by peer group.

In the upcoming Supply Chains to Admire report, this pattern is omnipresent in the patterns of Aerospace & Defense, Apparel Manufacturing, Automotive, Automotive Aftermarket, B2B Technology, Beverages, Chemicals, Consumer Packaged Goods, Food, Furniture, Medical Devices, Personal Products, Pharmaceuticals, Semiconductors, and Trucks & Heavy Equipment. Only a few retail sectors improved inventory turns.

This contradicts the claims that the investment in supply chain processes and technology radically improves the company’s ability to lower inventory levels. The issue? Industry potential has declined in all industries except Diversified Industries. As a result, history is not a good predictor of the future. In addition, setting targets based on results in another industry is not effective. (For example, Unilever needs a different operating strategy than Dell.)

Figure 1. Orbit Chart for the Container and Packaging Industry for the Period of 2016-2025

Shifts in the Operating Model

The answer to effective inventory management lies at the heart of understanding the changing operating model. What most uneducated consultants see as a process improvement project is actually the redefinition of the role of inventory in an emerging operating model. The paradigm shift is major. Effective inventory management can no longer be seen as an implementation of a better technology engine for safety stock or process improvement from the improvement of demand error.

Table 1. Understanding the Form & Function of Inventory

Let’s start with the fundamentals.

Inventory is both the most important buffer and the largest source of waste. The key to driving improvement is to understand the difference between the form & function of inventory by demand stream. The form & function of inventory is explained in Table 1. This analysis is best accomplished through the use of network design technologies like Gains, Lyric or Optilogic.

In contrast, demand Stream Analysis plots forecastability, demand latency (time from channel purchase to order visibility), and volume. The goal is to align each demand stream with the right inventory tactics.

Inventory potential has declined across all industry sectors. Use the data to make a convincing argument. Compare your results to YOUR peer group within an industry sector.

Navigating the Change

To make improvement, do the analysis to understand what is required for form and function of inventory by demand stream and then help the organization understand how choices affect inventory levels. Examples include:

• Growth In-transit Inventory. With more inventory on ships and at ports, in-transit inventories rose. As lead times grew, in-transit inventories ballooned. Yet, the primary focus of most organizations remained on safety stock only. Most companies attempt to improve safety stock the old-fashioned way by reducing demand error or the use of inventory optimization engines. Few focus on understanding the requirements of the shift on the form & function of inventory.
• Unchecked Complexity. The rise in product complexity decreases item forecastability, increasing the need for safety stock, and amplifies the bullwhip effect for raw materials. Complexity management remains an opportunity. Use network design tools to help the organization understand the impact.
• Dependency on Outsourcing. The longer and more unpredictable the lead time, the greater the need for inventory as a buffer. Is low volume, and unpredictable items are outsourced with longer lead times, expect higher inventories and lower customer service levels.

Bottomline

Don’t let a well-intending, but ill-informed consultant or technologist set an expectation that you cannot meet. No when wins when there is a check written that cannot be cashed. In this case, the consultant will move to the next account leaving you holding the bag. Fight back with a data-driven argument. Help the organization think about inventory more holistically.

Figure 1. Industry Potential: Food Industry Average Performance by Year from 2016 to 2025

The precipitous fall in supply chain performance was largely self-induced: industry veterans held on to what they believed were best practices from the “good old days,” like Linus clings to his blue blanket.

As the market shifted, the industry did not adapt. Employee turnover and leadership were major factors. Supply chain practices from the 1990s — reducing demand error, sweating manufacturing assets, and outsourcing new product launches — were mandated as best practices when the industry needed to adapt to changing customer preferences and increasing supply volatility.

A Look at Performance

For the period of 2016-2025, the industry average was 11% operating margin and 7.82 inventory turns, but note the 35% decline in industry inventory performance. Few companies were aware of or adapted to the shift in industry potential shown in Figure 1.

Today, the shifts are faster as consumers trade down to cheaper brands and retail private label gains market share. Major inflationary spikes in protein, especially beef and eggs, due to supply shortages and disease-related disruptions in 2025, continue the never-ending ride in commodity volatility.

The industry is not adapting. No company is using point-of-sale data well, and there is a lack of understanding of how to redefine processes with AI with market data to drive improvement. Instead, companies are layering new and promising technologies on top of historic processes, which will only degrade performance faster.

Figure 2. Food Industry Performance for 2016-2025

What Can We Learn?

Big Brands and Large Companies Underperformed. Take a look at the list. Pilgrim’s Pride is a manufacturer of chicken. The Company outperformed the industry giants of Campbell’s, General Mills, Hershey, Kraft/Heinz, McCormick, Mondelez, and Nestle. It is not because Pilgrim’s Pride is such a strong performer; in fact, the lack of understanding of historic supply chain practices may actually be a boon. The need to fix traditional supply chain processes in the food industry is greater in larger companies, but there is no momentum to act.

Industry Consolidators Believed that the Best Supply Chain Was the Efficient Supply Chain. Kraft/Heinz and Mondolez fell victim to industry consolidation, with financial ownership driving an efficiency or lower-cost agenda amid declining industry potential, and consumers’ focus on healthy eating and regionalization. Only about 15% of the volume could be managed efficiently, yet the consolidators drove a knife into the company’s heart by mandating cost-cutting measures. Most of the volume, due to a rise in complexity, required an agile supply chain response. The merger mania resulted in employee turnover and a loss of supply chain domain expertise.

Rise of Unchecked Complexity. Companies focused on a marketing-driven, not a market-driven, strategy. As a result, item complexity grew unchecked, the long tail of the supply chain expanded, making many products unforecastable, and intensifying the bullwhip effect. Freight market volatility and rising commodity prices increased costs and risk in a market where the company was focused inside-out rather than outside-in. Procurement constraints increased, but the signal to procurement was poor because traditional processes focus on reducing manufacturing constraints rather than on the bi-directional orchestration of manufacturing/procurement trade-offs.

Inside-out. Not Outside-in. As the long tail of the supply chain grew, with increasing complexity, order latency (the time from shelf take-away to order) increased by 15-20%, making the organization more insular in a volatile world. No company is actively measuring demand shaping versus shifting, or effectively using point-of-sale/channel data.

Focus on Functional Metrics. Manufacturing-centric organizations like Kellogg shifted demand — failed attempts at demand shaping — and pushed factories to their limits, focusing on OEE. As a result, costs increased, volumes sold were flat, and cash-to-cash took a nose dive. The end result? Kellogg was sold at a discount after several leadership changes.

Summary

I remember when the Campbell’s, General Mills, and Kraft teams were strong in driving supply chain innovation. Not so today. The fall of the food industry in delivering supply chain performance is sad, and another warning of why we should not be talking about the autonomous supply chain and the automation of practices that reduce shareholder value.

Let me end with a footnote. When I visit a private equity company, I see many new food products with premium ingredients targeted at regional markets. The candy on the counter from Unreal looks much like a Hershey’s Reese’s Cup, but tastes better, and the snacks, looking much like a Frito-Lay bag of tortilla chips and salsa, taste fresher. The food industry has, over the years, focused on cheaper ingredients rather than eliminating supply chain waste associated with traditional processes. I think it is time to adapt the supply chain to support a more agile product portfolio. Don’t you?

1. Nonsense.
2. Silly talk or writing; nonsense.

Phooey /foo͞′ē/: interjection

1. Used to express disgust, disbelief, or contempt.
2. An expression of disgust, rejection, or disappointment.

For the last three weeks, I have been quiet. This post aims to shed light on a dark, automated world where supply chain leaders believe they have answers and are busy automating outdated processes, but not driving value.

The Prologue

I watched as supply chain retreads shuffled through the halls at the Gartner event, posting celebratory pictures on LinkedIn, and toasting their great accomplishments and group dinners. As I watch, I am struggling with Hooey: Phooey. Think of it as a diatribe of nonsense followed by my internal disgust.

My struggle? When you go to the home of an executive in the field of supply chain, opulence abounds. Lucrative compensation packages drive technology companies to close deals, but there is no accountability to drive value for the business client.

In parallel, the industry waxes eloquent about the benefits of AI, focusing on making current solutions faster or hands-free (less labor), but there is no clear industry definition of what drives value. Without a clear definition, the autonomous supply chain is just Hooey: Phoey.

What Is Value?

For the past decade, I have attempted to correlate choices made within the supply chain to the delivery of value. Each year, I write the Supply Chains to Admire report. As I watch all the nonsense on LinkedIn, I am deep in the analysis for the 2026 report.

One of my observations is that the generally held beliefs about top performers are false. My second observation is that the manufacturing companies touted as top supply chain performers and technologists who advocate for their solutions drive unsubstantiated promises.

To make my argument, let’s take a look at the chemical industry for the period of 2016-2025. The imbalance between supply growth and weaker global demand was a structural challenge by the mid-2020s that few companies navigated well. The industry went through three distinct periods of change:

• 2016-2019: Globalization and promise of digital transformation. Rise in focus on sustainability.
• 2019-2022: Faltering demand. Shifts in global consumption. Logistics constraints.
• 2022-2015: China’s expansion of chemical production capacity in petrochemicals, polyethylene, polyurethane, methanol, and commodity chemicals, creating downward pressure on prices and margins.

As shown in Figure 1, the investments prior to COVID — primarily in efficiency and inside-out processes — were deleterious to the companies to survive the shocks through and post COVID. The downward slide in margin potential at the intersection of operating margin and inventory turns with post-COVID capacity caught most by surprise.

Figure 1. Chemical Industry Orbit Chart 2016-2026

Digital transformation — over-hyped and under-delivered — by consultants and technologists lacked a common definition and value proposition. For many, it was a reason to continue spending on ERP and APS initiatives, assuming that historical definitions of supply chain excellence were sufficient. The focus was on manufacturing efficiency and improving internal processes to make data more available. These were faster and more capable inside-out processes, assuming that the order represented demand. Companies with the strongest focus on digital transformation underperformed.

When you ask CHATGPT, which chemical companies are top performers, the answer is BASF and Dow, but this answer could not be further from the truth, as shown in Table 1.

Table 1. Supply Chains to Admire Analysis for 2016-2025

Here are my thoughts.

• First, look at growth. The market slump post-COVID caught most by surprise. Remember that supply chain teams do better when they are peddling uphill than downhill. A traditional focus on cost and manufacturing efficiency is a recipe for disaster when the industry enters a growth slump. The inside-out processes created a false sense of safety in a rapidly changing market post-COVID. Large companies were slower to respond than smaller companies.
• The larger the company, the more interconnected and the more automated it was, the poorer the performance. There was no substitute for leadership and functional alignment within the organization to drive performance.
• The industry’s perception of leaders and their actual performance are misaligned. BASF and Dow were not leaders during this period; yet, when you ask ChatGPT for the leaders in the chemical industry, they are rated highly. Each will likely make the Gartner Top 25 list based on popular opinion. Smaller players focused less on technology and more on organizational alignment through better management of feedstocks and go-to-market plans through S&OP, and delivered superior results.
• The fall of Eastman Chemical is particularly interesting to me. In 2015, Eastman was a winner of the Supply Chain to Admire Award. Today, the Company is a strong laggard, like P&G; it spends a lot of money not to grow. Eastman is the best deployment of OMP that I have witnessed, but the focus was on OEE and manufacturing efficiency with little interest in outside-in sensing. The company worked with Deloitte for many years on supply chain archetypes, but the driver was internal process improvement rather than market sensing or alignment. Let me ask, who cares if you can manage inventory if you cannot drive growth in a volatile market? The chart is a testimony to how spending a lot of money on traditional supply chain processes can lead to failure for shareholders, employees, and customers.

Can We Dispense with the Hooey?

Supply chains, to me, are serious business. As I watch friends at DOW be laid off, I cringe. I remember the Accenture ads at airports highlighting their success. You don’t see these same ads now.

When I see OMP touting the success at Eastman Chemical, I have to refocus and talk to the inner Lora not to write a diatribe about the dangers of inside-out processes in a volatile world. The sad thing is that most supply chain leaders believe that traditional processes are sufficient. Or that traditional demand planning is the key to success. Failure should be the reason to change, but the rich reward systems for technologists drive industry resistance.

So, can we dispense with the Hooey? Can you join me in the Phooey? Let me explain what I mean. Last month, Knut Alicke vibe coded to produce S&OP software in 30 hours. Madhav Durbha postulated on three layers of Context Engineering. Do we really care? What is the value to business leaders?

The problem is that existing process definitions and understandings are not sufficient. It does not matter that we can build code quickly if it is not used. I would like to see us start with a deeper understanding of user personas and gaps to explain why the Excel Spreadsheet is the number one supply chain planning technology.

Figure 2. Use of the Excel Spreadsheet in Supply Chain Planning

When KettieQ posts that planning is no longer a function, but a Continuous Decision System, I have a violent reaction. The blog post postulates that planning is moving from static outputs to continuous, agent-driven decisioning. It is no longer about creating a plan. It is about running a system that adapts in real time.

My fingers want to type violently, ENOUGH! I want to ask what decision intelligence is. While it is used frequently in marketing messages today, the buyer needs to ask. Is decision intelligence insight? Prediction? Recommendation? Decision? Course Correction?

When business leaders speak of the autonomous supply chain using traditional supply chain views, I envision a WAYMO car falling off the cliff, like BASF, Dow, and Eastman in the analysis above. Trust me, these were not dumb and uncaring teams. Instead, the focus was on the painful implementation of today’s processes, but it did not help, because inside-out processes are inadequate in a volatile world environment.

Can someone build an app quickly? Sure. But is it scalable? What about security? Future roadmap and product development? Software maintenance? Does your team really have the deep domain knowledge and technical expertise? Will the team use it? But, most importantly, will it drive value? Hooey, I say to all. My simple brain is trying to unravel the logic behind:

• Why are we trying to make today’s planning processes faster when they don’t work well? Why do 94% of companies with a supply chain planning system have a strong dependency on using Excel as their primary technology for planning?
• What good is software if it is not used to drive value? Why would we vibe code existing applications? A focus on cost degrades value.
• How can we run a system that adapts in real time if we are not clear on value-based outcomes? And, how to make the trade-offs across source, make, and deliver bidirectionally? Why are we not measuring the impact of waste and the bullwhip?
• Planning should never be real-time. Anyone who talks about real-time planning does not understand planning.

Instead, my focus is to prototype models that can create value. Here are my top four areas of focus. I want to sidestep HOOEY/Phoey by building prototypes with innovators:

• A Plan of Plans. My vision is that, as the ease of use and speed of network design tools rise, we can run different market scenarios before each S&OP review and then consume plans (not data, as is conventional thinking) as the market becomes clearer. Like a hurricane hitting Florida, in the strategic planning horizon, there would be 10-15 plans, each with a different probability. As the tactical horizon becomes the focus, the organization would work with the probabilities of different scenarios in the operational and executional environment. The focus is not on a single plan or probabilistic outcome of an engine; instead, the driver is tracking and analyzing different market scenarios.
• Demand Stream Management. Traditional demand management processes are flawed. Most assume that all items are forecastable, and there is no distinction in the alignment of demand and supply by stream. In each organization, there is an efficient product flow — that is, very predictable and manageable by conventional thinking — and the need for a responsive supply chain (short cycles) and an agile supply chain that is lumpy and unpredictable. The focus on FVA by stream and the inclusion of plans uses the probabilities of market potential in each scenario.
• Bi-directional Orchestration. Each industry needs to make trade-offs amongst make, source, and deliver to a balanced scorecard. Conventional processes focus on linear flow, recognizing manufacturing only as a constraint, and prioritize functional outcomes. This is problematic for many reasons.
• A Network of Networks. We need to leverage AI advancements to build a network of networks. Companies today lack interoperability among networks. Investments in control towers showed no value because most were insufficient in scope.

What Is Value?

Thanks for reading my blog.

I hope that you will join me in questioning traditional approaches to supply chain management that I believe are woefully inadequate for the global multinational. Instead of hyping the false promise of using Artificial Intelligence (AI) to automate traditional planning processes, I hope you will join me in pushing to use new technologies to redefine the promise of supply chain planning and drive value.

Enough Hooey: Phooey, I say. Supply chain excellence should be serious work to drive value.

In the first quarter of 2026, the value of the companies offering Software-as-a-Service (SaaS) fell by 25-30%. The sell-off occurred as investors worried that Artificial Intelligence (AI) would replicate capabilities, undercutting the brand promise of packaged software.

The fall sent shockwaves through the supply chain community and affected the evaluations of public companies in the software sector, such as E2open, Kinaxis, Palantir, and ServiceNow, along with the private evaluations of companies like Anaplan, Celonis, Coupa, Gains Systems, Everstream Analytics, Fourkites, Lyric, Relex, o9 Solutions, OMP, Optilogic, ToolsGroup, and P44.

In this world, where AI is everywhere yet still emerging, investors and software buyers are confused. I am answering questions like, “Do I EVEN need to buy supply chain software again with the evolution of technologies like Claude and Gemini?” I think that it is a case where two things can be true. Yes, it is easier to build software, but it is harder to make it successful. Domain expertise is the constraint. Too few understand the requirements for a successful supply chain planning deployment.

Should I Build Supply Chain Planning Software Myself?

The largest competitors for most technology companies in the supply chain planning market are DO NOTHING or BUILD IT YOURSELF.

Build-it-yourself strategies are risky, no matter how smart the developer and the evolution of AI tools. AI makes this easier. My answer is to look past the initial code build to think through the delivery of value. I believe the greatest value comes from building the right relationship with the provider of the code. (And, remember, I have never seen a consulting partner deliver code successfully. Consulting and software are different business models.)

A build-itself model is only a valid option for a short-term, limited-use deployment. You may have a bright individual or team in your organization that wants to build a solution themselves. And, when you compare the cost of the build to the cost of packaged software, this may seem appealing. My caution is to think beyond the initial deployment to the technology’s evolution and use. Focus on the long-term view of usage and value. In this blog, I give you considerations for building a relationship.

With all the upheaval and mounting cost pressures within companies, many buyers are taking a wait-and-see attitude before making a purchase. However, there are many compelling reasons that a company cannot wait. If this is you, consider this guidance. Evaluate these criteria against a five-year time frame. Why five years? The market is very fluid.

Building a relationship requires work. My advice? Flip the script. Focus less on implementation and more on delivering value through usage and evolution. The traditional software deployment focuses 80% on the initial install and 20% on software usage through evolution. I believe that to drive value, you need to shift the focus with 80% of the effort on building the relationship.

Steer clear of third-party assessments that are often manipulated  (Gartner reviews and Magic Quadrants) and do your own homework. The references used by technologists in a sales cycle are usually positive. Seek honest and unabridged assessments. The best way is networking. I usually do this by standing at the water cooler at the user conference and sparking conversations. Be a sponge.

Here, I share my reasoning.

Driving Improved Outcomes

What is SaaS? By definition, a SaaS platform has a cloud-hosted delivery, and operates on a subscription/recurring-revenue model, provides centralized updates to subscribers, uses a multi-tenant or cloud-native architecture, and is scalable. The evolution of the SaaS deployment model was a step change from the licensing model two decades ago.

Artificial Intelligence is now changing the model again. With the evolution of artificial intelligence and the introduction of AI-native platforms, SaaS technologies are being embedded into platforms and agentic solutions, and pricing models are shifting from a seat-based/user model to a value-per-outcome model.

As you search for a solution, throw away your rose colored glasses.

There is no perfect solution in the market today, and the offerings come in all shapes and varieties. As a result, it is critical to clarify the business requirements for defining success and focus on building a relationship with a software provider.

In this process, avoid vague and squishy terms and focus on a clear definition of measurable outcomes. A bad statement would be “the selection of an end-to-end supply chain planning solution to improve visibility.” The reason? The terms end-to-end planning and visibility mean many things to different people.

Instead, I find it useful to examine why companies use spreadsheets. If your organization has a supply chain planning solution yet remains extremely dependent on spreadsheets, press for an explanation of why. List the use cases. Understand the reasons.

I find it fascinating that, on the one hand, business leaders want to AI everything, and, on the other, they don’t know why their teams, despite millions of dollars invested in planning solutions, still rely primarily on spreadsheets, with planning technologies primarily used as a system of record. Press to understand the root cause.

Driving Usage

The process of planning does not come naturally to an operations leader who rewards action. Many don’t even value planning.

Many mistake reactivity with responsiveness. A reactive supply chain constantly throws the supply chain out of balance creating waste whereas a responsive supply chain senses, evaluates alternatives, and then responds. In this world of rising uncertainty, they may question the value of planning.

Getting good at planning in these organizations is not easy; it may require driving a “tipping point” or pushing past the threshold where the change becomes self-sustaining. The goal is to drive adoption, which becomes like water suddenly freezing once conditions cross a boundary. In practice, you don’t force a tipping point directly—you shape the conditions so it becomes inevitable. Measure the waste created by a bad plan.

Tipping points aren’t just about amplification—they’re about alignment between value, network, and context. It’s not about convincing everyone—it’s about reaching a critical mass within the right network or segment. Once that threshold is crossed, spread accelerates naturally. The question for the implementor is, “How do you shape the work processes to need planning?”

One important reality: you can do all of this and still not hit a tipping point if the underlying idea doesn’t solve a real problem or the timing is off. Tipping points aren’t just about amplification—they’re about alignment between value, network, and context.

Redefining the Value of Software in the Emerging World of AI

The speed and accuracy of code development are accelerating with technologies such as Cursor, GitHub Copilot, Claude Code, and chat models like Claude and Gemini. The business user’s relationship with data is changing with the shift from relational databases, where data had to be clean and pristine under schema-on-write architectures, to using NoSQL/Python to work with unstructured data in schema-on-read architectures. The shift in the data layer enables the definition of schema-ready architectures that enable more insight into data sources, attributes, and usage information. For example, an item can now include attributes, provenance, grade, use-by date, packaging, etc.

As the industry is being redefined, the basics of how software is built, sold, and priced are changing simultaneously. Each reader of this blog is living through a period where the business user’s relationship with data and code development is being redefined. In parallel, the traditional model of data stripped of semantics and written into fixed rows and columns is driving innovation in the solution redefinition. The solutions to planning that we know today will look quite different in a year.

As a result, business users need to be open to the outcome to rethink processes and use cases as new solutions enter the market. While generative AI models are also a great help in generating Requests for Proposal (RFPs and RFIs), now is not the time to buy based on a feature-function analysis. Put the RFP/RFI approach on the back burner.

Instead, think hard about the relationship that you need and want to build with a technology provider. The relationship elements include implementation partner ecosystems, industry templates, user enhancements, industry share groups, benchmarking, training, expert coaching, and events. Think beyond the purchase to define what you need in the software’s evolution to deliver value. 

Get Clear. What Do You Need in a Software Relationship?

No relationship is ever perfect. In your search for a software relationship, make a list of what your company needs. Build this into the governance model for decision-making. Consider the following elements:

• Geographic Presence. Understand the dependencies on data centers and third-party software. Evaluate the risk of war, grid failure, and security.
• Financial Viability. Select a company that will be around and viable throughout its lifecycle. If the company is a start-up, ask about planned liquidity events and focus on selecting a company with a low risk of a change in state during the consideration lifecycle. Remember that the only people who win in an acquisition or a buy-out are the investors. Market churn usually destroys and redefines the customer/provider relationships.
• Work with Data Scientists. In this emerging AI world, enabling data scientists within your organization to work directly with those in the provider’s organization becomes paramount. Determine the availability of resources for data scientist support. Give extra points for the presence of a Foundry or a Test & Learn Innovation Lab.
• Industry Templates. Does the company understand the needs and requirements of your industry? Look for companies like you. Ask to see the industry templates and review the product development roadmap, looking for innovations that fit your industry.
• Training. With M&A and employee turnover, packaged training is an essential. Find out what is readily available and the potential for customization. Give extra points if there is a certification process for third-party implementors.
• Scalability. Define the number of users, items, and locations, and the time requirements for batch processing. The solutions range in scalability around these dimensions. Test the solution in a third-party lab to assess performance. For example, there is a solution on the market with great production planning, but the scalability and the time it takes for a demand planner to get data are deadly; use this insight in the solution consideration.
• Assessment and Benchmarking. Online assessments and periodic tune-ups/evaluations are key. Gain an understanding of what is available.
• User-Based Enhancements. Gain an understanding of how user enhancements are initiated, evaluated, and implemented. Get clear on the user group’s governance.
• Push for Interoperability. Focus on the need for interoperability, and clearly understand the difference between integration and interoperability. When business leaders say that they want to drive integration, they usually mean interoperability. In your search, get clear. Integration is about linking systems together so they function as one. It usually involves building specific connections—such as APIs, middleware, or custom code—so that data flows between systems in a controlled manner. These connections are often tightly coupled, meaning that if one system changes, the integration might break or need updates. Interoperability, in contrast, is defined by a capability for different systems to communicate and use each other’s data without special, one-off connections. It relies on shared standards, formats, or protocols. The difference is a semantic reconciliation layer. Explore this difference in your discussions.

Conclusion

The limiting factor in supply chain planning deployment is usually domain expertise and clarity on the planning mission to drive value. Despite the rise of technology to make do-it-yourself modeling easier, side-step the do-it-yourself option and build a relationship with the technology provider. I look forward to seeing your comments.

See You There?

I am facilitating a Virtual Roundtable event this Friday with Don Hicks, Founder of Optilogic, to discuss the evolution of network design technologies and the use of quicker, easier-to-use technology approaches in the redefinition of work. This roundtable is limited to 25 business leaders. Sign up. It is sure to be fun.

If you would like to discuss these topics further, please reach out. I will be speaking at the ASCM chapter event in Rochester, New York, on May 21st on the power of AI in redefining supply chain outcomes. This presentation is offered for both in-person and virtual attendees.

I will also be speaking on the executive tracks at Kinexions 26 and Opticon 2026. I hope to see you in my travels.

The supply chain is knotted. It is unruly. It is complex. It is growing more so.

Current supply chain planning deployments often degrade value. This is not by design, but by the lack of focus on value-based delivery.

Companies are unclear about what drives value and what to measure. Most are stuck in traditional paradigms as the world of new and promising technologies swirls around us.

Over the last six months, multiple technologists briefed me with a similar message: “Let’s improve reaction time.” With excitement, the voices on the phone share how they used agents and agentics to streamline an insight. The conversation grows painfully quiet when I ask, “What is a good outcome? How do you define a good plan?” I call this AI stupid. My goal is to AI smart. I want you to join my coalition to use technology better.

Reflection: Use Performance Data to Build a Guiding Coalition

I find many companies unclear about the role of planning. Many are surrounded by math geeks. Effective supply chain planning is more than a math problem to be solved; it is a systems dynamic opportunity to be harnessed.

If you accept this mission, the first stop on the supply chain leader’s journey is the Chief Financial Officer. The goal is to align the financial team with the mission of delivering a reliable supply to drive growth amid uncertainty.

The important discussion for the supply chain leader in the CFO’s office is to convince the financial team that the efficient supply chain—the attempt to run the supply chain at the lowest cost— is not the most effective approach in the face of rising uncertainty. Based on the analysis of demand and supply variability, on average, only 20% of product volume can be managed effectively with a focus on the lowest cost. (My analysis of 80 companies, where effectiveness is defined as consistent cost, quality, and customer service.) (Using supply chain modeling technologies like Lyric or Optilogic, do your own analysis of your demand and supply variability to determine what percentage of your flows can be managed effectively with a focus on the lowest cost. )

Historically, companies focused on labor efficiency. Advancements in technology made this possible. Traditional Advanced Planning Models (APS) focus on improving manufacturing efficiency and maximizing throughput through constraint-based theory. This thinking drove dramatic improvements prior to 2020. As shown in Table 1, this is no longer the case.

Table 1. Revenue/Employee of Manufacturing Companies

The second stop to build the guiding coalition is the Chief Operating Officer. The goal of this discussion is to align on the supply chain’s role in delivering value. This conversation is not trivial.

Many companies use the cost of goods as a proxy for supply chain excellence. This logic is flawed. Let me explain. The cost of goods measurement is the direct costs associated with producing the goods a company sells, excluding indirect expenses such as distribution and sales costs. In contrast, a company’s operating margin is the amount it earns on every dollar of sales after deducting variable costs but before accounting for interest or taxes. In contrast, operating margin is calculated as operating income divided by net sales.

The question for the COO is, “How do we best manage costs?” Traditional APS approaches do not allow companies to make trade-offs between make, source, and deliver. With rising demand variability and the lack of product forecastability, traditional planning processes push and amplify the bullwhip, increasing waste. The COGS (Cost of Goods) is rising due to several factors: misalignment between operations and commercial teams, a traditional focus on functional metrics, unabated complexity, and a lack of a clear strategy to deliver value. Forcing the discussion of what we are doing today is not working is not easy, but it is necessary. A shift from COGS to operating margin better aligns commercial and operations teams.

Table 2. Analysis of the Rising Cost of Goods for Manufacturing Industries

To deliver results, push the organization to move from a focus on transactional efficiency, past the theory of constraints, to embrace the world of flow and supply chain dynamics. Question the first principles of existing systems. Ask the COO not to accept the status quo, but to also side-step the hype.

In the process, ask your executive team to have a more mature discussion on the role of inventory in the supply chain. Face the hard reality that inventory levels rose an average of 30% across manufacturing industries in the past two decades.

Most organizations focus myopically on managing safety stock in planning processes, when the increase in inventory is usually due to higher cycle stock resulting from a rise in product portfolio complexity, and to increases of in-transit inventory arising from outsourcing and sourcing/logistics choices. Every year when I do this chart, I laugh when I think about the discussion that I once had with a CFO who said, “If I had a penny for every dollar that I mistakenly spent on the management of inventory, I would be a rich man.” He would indeed.

Table 3. Analysis of Inventory by Industry

Ground the Discussion on Business Performance

In the process, push to redefine your organization’s relationship with data, build a semantic reconciliation layer that embraces all data forms to enable interoperability (not just integration) with market data. (For example, today, data used by the sales and marketing teams cannot be used effectively in supply chain decisions.) Push for organizational change to include data scientists in the supply chain center of excellence and to design and modify the supply chain response at the same cadence as Sales and Operations Planning (S&OP).

To understand the opportunity, study the progress of companies in each industry. Analyze the choices made and the results. (Spoiler Alert: The Supply Chains to Admire report for 2026 is a month away. I am heads down in analysis.)

Let me give you an example. The Household Non-Durables manufacturing sector struggles with growth. Each team and function sits in an isolated silo(s). Tens, sometimes hundreds, of consumer account-facing teams model demand and supply data for each account weekly and monthly. This data is not connected to mainstream supply chain planning data because there is no “ship-to” translation into “ship from” information. As a result, supply chain planners cannot use data that account teams see, which is 15-20 days earlier than their demand-planning view based on orders.

P&G is readily hailed as the market thought leader, but as shown in Figure 4, while the company has outperformed on operating margin and inventory turns, it has spent a lot of money to not grow. I define top supply chain performance as the ability to drive improvement, outperform their peer group on key metrics (growth, operating margin, inventory turns, capital investment), and deliver value.

Using this definition, the top performer in the Household Non-Durable industry is Church & Dwight. Why has Church & Dwight, and less than a tenth the size of P&G, outperformed its peers? The answer is largely leadership. The Company has shifted from a marketing-driven to a market-driven model, with a focus on rationalizing portfolios, redesigning demand processes, building strong design and modification capabilities, and driving functional alignment through metrics.

Why is Unilever underperforming? Inventory turns and operating margins are high, but growth lags the industry. The reason? Each region operates largely autonomously, and the organization lacks discipline in managing its product portfolios. The alignment of operations and commercial teams is an opportunity.

Similarly, Henkel and Reckitt are stalled in their attempts to drive improvement, and Colgate, with a myopic focus on cost, struggles to manage inventory. The key to driving market capitalization per employee is balance.

Table 4. Ten-Year View of Household Non-Durables

Which returns me to my core statement of how do companies drive reliable growth in an uncertain world? The answer, I am afraid, is not more of the same. The answer, I am afraid, is not buying into hype.

It requires a clear mission and alignment with a balanced scorecard. It also requires sidestepping the wonk-based discussions that permeate the supply chain world.

Fire the Wonks

Driving results requires a strong foundation in making better decisions based on a clear strategy. It requires side-stepping the many hype cycles that surround us today.

When I asked ChatGPT for a definition of a ” wonk”, I received the following definition:

A “wonk” is an informal term for someone who is deeply interested in and/or is knowledgeable about a specific subject, especially in a very detailed or technical way.

Most commonly, you’ll hear the term it in politics:

• A policy wonk is someone who studies laws, policies, and government programs in great detail.
• These people often focus on the fine print, data, and mechanics, rather than broad ideas or speeches.

Today, the supply chain WONKS come in many flavors. Stay rooted in business fundamentals, and side-step the wonks. Meet the supply chain WONKS:

• The Real-time Wonk. There is a wide variety of real-time wonks: the real-time S&OP wonk, the real-time planning wonk, and the real-time decisioning wonk. Planning should never be real-time. It needs to enable data at the speed of business with insights that have zero market latency. Planning options need to be incorporated into the operational and executional processes. Most real-time wonks confuse time horizons and focus on execution, wanting insights now to react. However, reactivity is not responsiveness. Over-reaction introduces nervousness into the complex system, reducing effectiveness.
• The Autonomous Spouting Wonk. Many don’t understand why we cannot put planning processes on autopilot. My retort? How do you define a good plan? And, if there is no response, I ask, if we are not clear on outcomes, how can we be autonomous?
• The AI is Everywhere, but Nowhere Wonk. After a lot of hand-waving, ask the speaker to define AI and draw a visual of how to use AI to improve business outcomes. Ground the discussion.
• Supply Chain is a Math Problem Wonk. Driving supply chain outcomes is more than better math. Focus on building an organization that can use math, but is aligned to outcomes. Build systems thinking.
• The Religious Wonks. You can count the different sects —DDMRP, flowcasting, lean — the problem? These are all tools in the supply chain toolbelt, versus the panacea.

Wrap-up

Driving supply chain excellence is hard work. Building a guiding coalition requires a clear definition of the mission and alignment with metrics.

If the mission is driving reliable growth in an uncertain world, focus on first-principle thinking, redefine your relationship with data, and fire the wonks. Then you can start having the important discussions of how to redefine supply chain outcomes in this new world of emerging Artificial Intelligence.

If you would like to discuss these topics further, please reach out. I will be speaking at the ASCM chapter event in Rochester, New York, on May 21st on the power of AI in redefining supply chain outcomes. This presentation is offered for both in-person and virtual attendees.

I will also be speaking on the executive tracks at Kinexions 26 and Opticon 2026. I hope to see you in my travels.

Background

“Complex selling” is a sales approach used when deals are high-value, involve multiple decision-makers, take longer to close, and require tailored solutions. The focus is on navigating multiple stakeholders to say “YES” in longer sales cycles. There is usually a political element, and the technique requires both Art and Science. In the deployment of complex selling techniques for supply chain planning, we often labeled the Information Technology (IT) group as “Dr. NO.” I taught the teams how to neutralize Information Technology Groups (IT) in the complex sale.

Today, if I were teaching complex selling to Native AI and the new generation of AI platform vendors, I would label the supply chain team and the supply chain planners as “Dr. No”. Sadly, I see that many of these teams, steeped in traditional thinking, have become the blockers, not enablers, to adopting new ways of work and embracing new forms of tech.

The Winding Path

The supply chain is a complex, non-linear, distributed system that must be adaptive to meet business needs in an uncertain world. Unfortunately, today, most deployed technologies amplify and distort the signal to improve reliability and drive growth in a world that is becoming more uncertain. Companies talk about the symptoms with a lot of handwaving and acronyms, but don’t know how to roll up their sleeves and embrace uncertainty.

Closing the performance gap is seldom a single factor. The answer is not a single technology or a simple change of approach. While business users are enamored with AI hype and experimenting with agents and agentics, embracing the full value of artificial intelligence requires an AI-enabled architecture, from data architecture definition to solution delivery. Most clients I work with do not know how to get started and fall prey to the AI-stupid pitches from technologists.

Most supply chains have multiple flows—an efficient flow that can be managed at the lowest cost, an agile flow that requires a focus on reliability of cost, quality, and customer service despite variability and uncertainty, and a responsive flow that requires a focus on short cycles. Prior solutions were unable to recognize and manage the flows and automate the rules for customer and product segmentation. As a result, there were many workarounds, black holes, and spreadsheet dependencies. The use of these new approaches recognizes flow and automates the rules. They close the gaps in today’s solutions that require custom code, workarounds, and spreadsheets.

While many social influencers push different narratives emphasizing  “autonomous,” “self-healing,” “real-time planning, “” continuous planning,” or “self-driving” planning, this is not me. I try to sidestep this hubris and hype. My goal is to help companies understand real and tangible use cases to redefine/improve work.

The World is Grayer and Less Certain. Companies Have Not Adapted.

Over time, in my role as an analyst, organizations have become more fragmented, with few companies having purchasing, distribution, and manufacturing reporting to a common leader. We find that reporting relationships matter in the analysis of the Supply Chains to Admire.

We find each leader purchasing systems for their own functional silos, creating a barrier to interoperability. The opportunity is to redefine the architecture to drive interoperability and drive holistic workflows, better aligning the organization with value-based delivery. (The measurement of functional objectives tied to bonus incentives creates waste. Companies need to align to drive reliability.)

Figure 1. Supply Chain Organizational Definition

Today, there are more unknowns than knowns. The traditional approaches to supply chain decision-making focused on known inputs, using known models to drive known outcomes. In the world of unknowns, generative AI, agent-based workflows, What-if Simulation, and What-if Optimization are growing in importance. The benefit is quicker, role-based insight, along with workflow collaboration, to answer questions, increase awareness, and drive action.

Traditional APS solutions focused on known inputs, known models, and known outputs. I laughed this week with the publication of the Gartner Magic Quadrant. The Reason? Fifty percent of the solutions listed as top performers with both vision and execution do not scale for the global multi-national. Most focus on optimization in an architecture that is only a good fit for smaller, regional teams. By and large, we are not asking the right questions.

A New Dr. No Is In Town

The buyer today for supply chain planning is more conservative. The leaders — Chief Supply Chain Officers — are hardened and conservative, with many becoming “Dr. Nos” during sales cycles while pushing traditional definitions of technology. They have not invested in reskilling to learn new concepts and sort reality from hype.

The reason? The deployment of new approaches requires the learning of a new language, rethinking first principles, and being open to new outcomes, all of which fly in the face of tradition. In Figure 2, when potential buyers of Supply Chain Planning were asked the question, “In your opinion, what is your company’s preference in the purchase of new technologies? Would you classify your company as an innovator, early adopter, mainstream adopter, late-stage adopter, or laggard?  The market shows marked change. The innovators and early adopters accounted for 42% of the market in 2016 and 24% in late 2025. When tested for demographic consistency, this result is significant at an 80% confidence level.

Don’t you find it interesting that innovation at scale is the new reality, but organizations are more cautious?

Figure 2. Shift in Innovators

So, how is it that answers at scale and relevancy are now available, and companies have become less innovative in using new technologies? It often comes down to the fact that companies assume historic practices are best practices, and they have never questioned the current state defined by technologies from four decades ago.

If you are a business user, look around your organization for the Dr. Nos. You will know them when they embrace historic practices as best practices, or when they spout hype-based word salad from their tongues. They are usually the members of the team who get excited about the use of agents in spreadsheets or are quick to buy based on the Gartner Magic Quadrant.

The Path Forward

As a business leader, in your talks with your analytics teams, the path forward is to redefine the architecture with the goal in mind from the data up, with a focus on value.

Measure and Understand Variability. To understand the world of possibilities, make a list of supply chain issues and data available–all forms structured, streaming, audio, text, images–and make a visit to your data science teams and explore the building of a semantic layer with a graph (for relationship and semantic reconciliation), along with an ontology to drive flexibility in decision support architectures. Layer this underneath your current architectures to gain insights into planning master data — inputs into planning systems that are variables but treated as constants. These include lead times, conversion rates, yields, price, run rates, etc. Use machine learning and pattern recognition techniques to understand the impact of these inputs on plan outputs.

Define and Measure a Good Plan. As you analyze your plan, focus on the balanced scorecard metrics of growth, operating margin (not cost), inventory turns, and Return on Capital Employed (ROCE). Then measure, with the help of your data science teams, the reliability of the plan by analyzing:

• Forecast Value Added (FVA). An analysis of the value of demand-stream forecasting based on forecastability and flow characteristics.
• Inventory Health by Form and Function of Inventory with an Analysis of Inventory Value Added (IVA).
• Raw Material Value Added. How much improvement did the plan make in the purchase and storage of raw materials? (RVA)
• Customer service: On-time and In-full measurements with reason codes.
• Schedule adherence. The adherence of the manufacturing teams to production plans.
• Bullwhip Effect.
• The Effectiveness of Demand Shaping Programs: Shaping versus Shifting.
• Asset Utilization

In Figure 3, I share a high-level architecture perspective as a starting point for the data science conversation.

Figure 3. High-level Architecture

Summary

My advice? Embrace innovation to define new opportunities, develop better process capabilities, and redefine work. Side-step the hype and focus on creating value for your firm. Traditional supply chain planning approaches are not sufficient, but the redesign requires crafting a solution with the goal in mind, from the data up to new solutions to unleash new forms of value.

“The man who moves a mountain begins by carrying away small stones.”

Confucius, born Kong Qiu, the revered Chinese philosopher

This quote isn’t about literal mountains—it’s about tackling challenges. Even the most daunting tasks are achievable when tackled gradually, one small effort at a time, turning patience and persistence into remarkable progress.

In this picture, we depict a forward path surrounded by small stones. Your journey as a supply chain leader is a journey of carrying small stones.

Climbing the Mountain

I am not patient, but I am persistent.

My goal is to help supply chain leaders get unstuck. My observation is that despite technological evolution and the promise of capabilities, we are having the same discussions, not realizing that most of the current dialogue centers on groupthink — focused largely on symptoms rather than fixes. Here, in the words of Confucius, I want us to focus on identifying and carrying small stones.

Let’s start by focusing on the mountain the leaders are trying to climb. Simply put, our systems do not know how to talk to each other. It is hard to put data to work without semantic reconciliation, a common data model, and an ontological framework.

Here are two examples. (I can list many. The current white paper that I am writing is capped at twenty-five, but I will not bore you here.)

DRP and TMS Lack a Common Data Model. Companies want to be customer-centric and share order-reliability data, but the lack of a common data model between Distribution Requirements Planning (DRP) and Transportation Management Solutions (TMS) is a barrier. In DRP, lead time is usually set to a constant value. (Time to travel from point A to point B on a lane or a route.)

In reality, lead time is a constantly changing variable in supply. Actual lead times should feed into Available-to-Promise, inventory safety stock calculations, and deployment logic, but it doesn’t. Transportation data stays in logistics, and shipment data stays in DRP. Moving data from one model to another requires a common data model and a planning master data layer. The two solutions are not aligned at a goal or a first-principle level.

Table 1. Shifts in First-Principle Thinking for Supply Chain Planning

As the global multinational evolved, the first-generation planning solutions focused on demand variability, assuming that supply variability would be low. It is not. The new requirement is to embrace the fact that both demand and supply are variable, and to use machine learning on transportation data to help map supply uncertainty.

–Mapping Ship to and Ship From Information. In mapping demand data, using consumption or point-of-sale data, a successful solution needs to bridge the ship-to information (customer distribution network logic) to the ship-from information (supplier distribution network). The answer lies in ontological modeling to map customer and supplier logic, drive semantic reconciliation of the item number and the definition of the saleable unit (selling unit of measure), understand dependent demand (items within a shipper or a package), and the shipping instructions. CRM data cannot be used in Supply Chain planning without translation.

While many use the terms agents, agentic, and agentics interchangeably. They are not the same. Agents are the actors; agentic acts are autonomous decision-makers; and agentics is the construction of systems with agents governed by system design. A focus on agents without resolving semantic reconciliation is a fool’s play.

Take a look at the architecture in Figure 1. This type of architecture is required for AI enablement. An investment in agents without semantic reconciliation has limited value.

Figure 1: Architecture for AI Enablement

Moving Mountains

So, if you are a business leader and feeling stuck (as most are in my discussions), side-step the discussion of agents and ask, “Is this a semantic reconciliation opportunity?” Followed by: “How are we aligning architecture requirements with shifts in first principles?”

Lead by asking the organization to build an architecture that enables artificial intelligence. Current architectures are insufficient. Solutions with our favorite acronyms — CRM, APS, ERP —evolved from schema-on-write architectures with a focus on transactional data.

As business requirements have changed, the answer is not integration, but interoperability. AI enablement requires investment in schema-on-read architectures that support the use of unstructured data and new forms of analytics.

As most of you know, I am a supply chain gal. I cut my teeth believing that the answer to supply chain problems lay in better math. At the beginning of my career, this was the promise of Advanced Planning (APS). Life has taught me to think more broadly. Supply chain professionals are laser-focused on better engines, but architectural interoperability remains a barrier for the industry.

So, how to carry your small stones? Educate your teams. Work with data scientists to understand new capabilities. Ask your lawyers to focus on the interoperability of data models in licensing. Build architectures to enable AI capabilities. Don’t fall prey to vendor hype of agents on top of outdated architectures.

Here Me Speak

If you want to know more and gain a greater understanding, I will be speaking at:

• P44 Executive Workshop on April 9th.
• ASCM Top Management Night in Rochester, NY on May 29th

Definitions

Agentics. Multiple agents working together through governance and workflow to drive autonomous process automation.

Synthetic Data: Synthetic data attempts to preserve the patterns and relationships of real data without exposing the original data.

Retrieval-Augmented Generation (RAG). A technique used in AI systems to make language models more accurate and up-to-date by retrieving external information before generating an answer.