Restaurants closed, hallways echoed with emptiness, and services that were normally available were quickly shuttered. Purses slammed shut, layoffs ensued, and customer sentiment plummeted. Scary indeed.

At the time, I wrote furiously about the lessons to be learned, but I’ve found the industry has a short memory. Then I was optimistic leaders could learn from the downturn. Sadly, not. We are in worse shape today than in 2007.

Reflection

In 2007, I also recalled the 2001 downturn. In 2001, I was a Gartner analyst, and the global market reaction to Cisco’s $2.25 billion write-off of equipment sourced to support the rise of e-commerce, but not needed when the e-commerce market cooled, was a big story. GASP!

My fingers quickly typed the stories of how the crisis exposed major weaknesses in supply chain information sharing, inventory management, and planning. Leaders struggling with the 2007 downturn had largely forgotten the Cisco write-offs and the market downturn that followed the e-commerce bubble burst. A major downturn has been almost two decades. Companies do not have downturn prep in their organizational memories. It is easier to power a supply chain through growth than a market downturn.

In 2007, the average company took six months to sense market shifts and adapt its supply chain. Figure 1 is based on qualitative interviews with thirty manufacturers in 2008. My estimate, based on work with clients, is that if a downturn happened today, the average company would take 20-30% more time to adjust than in 2007. Let me explain.

Figure 1. Time to Sense and Adjust Supply Chain Operations in the 2007 Economic Downturn

Current State

Over the last two decades, technology spending in supply chain management has become more insular, with tighter integration into transaction systems. Systems are now more aligned by function, and the gaps between functions are larger. The metrics are functional and misaligned with balance sheet performance. Placing agents on top of traditional supply chain architectures makes a company more reactive.

Based on my research, fewer companies are innovators. Most believe that historic processes are best practices.

Network visibility is an issue. Over the decade, network capabilities consolidated, but have been slow to innovate. The transportation visibility sector shows promise— the architectures are newer and the business problems clearer—but companies still don’t use transportation data well in planning. Using the data requires an architecture redesign.

Transportation Data is a Market Health Predictor

In discussions with supply chain leaders, most believe that the global stock market is overheated with AI spending. Just as we have forgotten the lessons of the pandemic, few leaders remember the political drama of large stock market drops on the supply chain.

Supply chain architectures aren’t built to plan easily when there are knowns and unknowns. The traditional focus is optimization of known inputs to known outputs. As a result, when the market dips, chaos ensues.

One of the best sources of demand data comes from transportation reporting. Here are some current trends.

• Ocean Capacity. Ships sitting in the Strait of Hormuz tie up global capacity. The rise of global shipping volumes amid geopolitical friction—especially in Asia—has created trade imbalances. Ocean shipping reliability now hovers around 60-65%, down from pre-pandemic norms of 70-80%. Roughly 8% of global ocean container ship capacity is now unavailable due to shipping inefficiencies. As inefficiencies rise, costs escalate and lead times are more variable. Ocean container equipment could easily become a constraint.
• Downward Trend of Shipments. The Cass Freight Index shows a twenty-eight-month decline in North American Shipments. Shipments are falling — consumers are buying less. The North American markets are slowing, and the US/Canadian trade talks are problematic. Note the downward trend and compare it to the 2007 and 2020 data.

Today, the public markets are buoyed by investments in war machinery and armaments and data centers to support AI. Is this reminiscent of the ecommerce bubble of 2001? No one knows, but it is possible. Companies need to remember that history — neither order history nor shipment history — is a good predictor of future demand. But a trend like this is a wake-up call.

Figure 1. North America Shipments: Cass Freight Index

Steps to Take

The first step is to align your supply chain with market data — from the customer’s customer to the supplier’s supplier. Investigate your organization’s transportation data sources and start projects with your data science teams to understand your goods’ shipment history against market drivers.

Step 1: Focus on latency reduction. Chart the latency in your supply chain based on demand flows using the definitions in Figure 2. (A demand stream is a distinct pattern for groups of items as defined by frequency and quantity of items ordered, lifecycles, forecastability, and demand shaping programs.)

Figure 2. Recognizing and Defining Supply Chain Latency

Align Demand and Supply Cycles. While most supply chain leaders can easily grasp the concept of matching demand and supply volumes, more mature companies match demand and supply cycles. Let’s take an example. For one of my clients, market latency was four months for their top-selling item, demand latency was 10 weeks, process latency (time to decide) was six weeks, and the supply cycle was two weeks. Yet, the customer was forecasting using their shipment data. YOWZA, I thought when I explained to the customer that they were making a supply chain planning decision twenty-eight weeks after a shift in consumer buying patterns. Too little, too late.

Simplify Demand Shaping Programs. The client’s demand and process latency issues were exacerbated by the number of demand-shaping programs. Unraveling the impact of the unchecked complexity in demand shaping (promotions and rebates) adds to process latency.

Drive alignment. Get clear on terms. The goal should not be real-time decisions. (You may have streaming data that is near real-time, but the data input pattern needs to match the goal of each planning horizon.) Focus on how to make decisions within each time horizon — frequency, granularity, and horizon. And importantly, define what makes a good decision. Focus on making decisions at the speed of business in each planning horizon.

Figure 3. Time Horizon Discipline

Define how long it takes the organization to make a decision today. Clarify who should make which decisions, and focus on streamlining the process. For example, for one client I work with, when a business leader asks a planner a question, it takes a couple of days to receive an answer to a minor question. But if the question involves decisions on capacity or inventory allocation, the decision can take weeks. The larger the organization, the greater the issue. So, focus on process improvement to help business leaders get to the information they need to run the organization.

Shift the focus from “integration” to “interoperability” and “orchestration.”

• Integration: Specific interfaces to move data between systems in a meaningful format.
• Interoperability: Exchange of data and process semantics without custom interfaces, based on common standards, protocols, and formats.
• Orchestration: Coordinated process workflows across and between functions and processes.

Understand what is possible and feasible. Accept the reality that the industry potential (average performance) of sectors at the intersection of operating margin and inventory turns is lower today than pre-pandemic in 80% of manufacturing industries.

Wrap-up

These steps help minimize supply chain market shock waves, but more importantly, they make good business sense. What do you think? Reach out if you have any questions.

Sources:

Sea-Intelligence – July Global Schedule Reliability Drops to Lowest 2026 Level

This morning I sighed at the announcement of a planning event in Shanghai on LinkedIn by a supply chain planning leader touting their solution as the answer for end-to-end planning. This is the same vendor that did an 180 on building outside-in capabilities and is now firmly entrenched in perpetuating traditional thinking. My email has also been full of complaints from employees from various planning technologies, pushing back, “How could I say that their solutions were not effective end-to-end?”

So, you might say, is this worth the trip, Lora?

I think so. 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 supply chain technology leaders perpetuating the myth of integrated planning. I dusted off the manuscript on Saturday. I think that we have a new chapter.

For clarity, my goal is not to throw mud at the industry — supply chain planning — where I work. I am grateful for my readership and support, but I struggle to perpetuate the current lie of planning technologists of end-to-end supply chain planning.

The Dialogue by Contrarian(s)

Many might call me a contrarian. I am ok with this label. So, if you are buying software or attending the unnamed vendor’s conference in Shanghai, here is my warning.

As the industry becomes more enamored with agents and agentics, my internal struggle intensifies. Most of the vendor briefings that I see day-to-day fall in the category of “AI Stupid.” This is a world where technologists are layering cool new technologies on top of old planning taxonomies and marketing the platforms as end-to-end planning orchestration. Yes, indeed, a new buzzword has entered our vocabulary. And we have many conferences where supply chain planning leaders tout the benefit of end-to-end supply chain planning, while their results on inventory and operating margin rank them as supply chain laggards. The voices of laggards with large egos perpetuate most conferences. And the unknowing nod in agreement.

The industry lacks a moral compass. We need to erase stupid.

Maybe it is just me, but I struggle to watch technologists get rich on a lie while supply chain business leaders struggle to make their systems work effectively. Technology can now answer many questions and provide insights on how to solve problems. Many business leaders do not realize that their support of traditional thinking is a barrier to progress.

In my email streams following my post, I also got three emails from leaders I respect. I found each comment useful. I hope you do as well. I share them to help spur a meaningful dialogue amongst those feeling the pain of the broken promise of supply chain technology providers:

After 20 years implementing planning across Asia Pacific, I’d push the diagnosis one step further: the silos survive every integration project because all planning layers aggregate.  The moment you aggregate, you destroy the rules that actually run a supply chain: MOQ per supplier, minimum remaining shelf life per customer, lead time variations linked to ship schedule, delivery held until fill rate, SKU-location replenishment policies, etc. Those rules only exist at the transaction level. So the end-to-end promise is structurally broken before the first connector is built. No common data model can restore what aggregation already deleted. The conclusion we were pushed into: keep the entire decision chain at order level, even for S&OP/IBP. In practice, that means simulating what the ERP will do in the future, forecasted order by forecasted order, with the chain’s logic intact all the way through. That’s the engineering problem we’ve been working on for years with SIMCEL. It’s hard, and we deliberately don’t call it end-to-end management. It’s conceptually one decision layer with no data break.

Julien Brun, CEO & Founder, SIMCEL | Augmented Decisions

The reason end-to-end planning does not exist is not that vendors are lazy or that the math is missing. It is structural. The connector is a transaction, not a decision. Order-to-cash and procure-to-pay are the load-bearing integrations in every supply chain stack on earth. They carry quantities, dates, and identifiers. What they cannot carry is why: the reasoning that produced the quantity, the bounds the decider was working inside, what they expected to happen, and how confident they were. Most IT stacks are built for transactions, not streams, so the insight dies at the handoff unless someone manually carries it forward.
That manual handoff is the connector the stack never built.”

The vocabulary is different at every hop. Lora’s line is exact: “Each system has a different definition of location, item, events, order, and purchase orders.” A site in the TMS is a dock. A site in the planning system is a stocking point. A site in the ERP is a plant with a storage location under it. You cannot compose decisions across systems that disagree about what a site is, and
no amount of middleware turns three definitions into one.

Trevor Miles, Founder of Azirella

“End-to-end supply chain management” has been promised for decades, but most architectures still connect planning, execution, and record systems through transactional workarounds rather than through a common decision model. The missing layer is not another planning module or another AI agent. It is a governed way to answer, across functions and trading partners:

What decisions are being made?
Who has authority to make them?
What evidence is required?
What are the next valid actions?
What happened compared with what was expected?

Until those questions are encoded into a shared operating model, AI will mostly accelerate disconnected decisions inside old architectures.A unified data model matters, but on its own it is not enough. The real breakthrough is a common rules-based ontology that connects context, authority, workflow, evidence and feedback. That is where I believe the next generation of trade architecture has to move: from disconnected systems of insight and execution to governed trade flow.

Pat Byrne, Founder and Chairman of IndEco Systems Pty. Limited

Thoughts on Getting Started

So, what do you do when you are stuck in an organization that firmly believes in the direction of the misguided gold diggers? Dig firmly into what drives business results. Redefine your relationship with data and build processes that matter. Measure the benefits and encourage the team to think differently. Here are some examples:

Rethink the Role of EDI. On September 16th, I am speaking at the 50th anniversary of the Book Industry Study Group. The topic of the speech is “Building the Supply Chain that the Book Industry Deserves.” (I love the title.) One of the study group’s recommendations is to discontinue the use of Electronic Data Interchange (EDI). They will be surprised when I share that the EDI data stream is an underused but valuable asset. Yes, I know the issues. EDI is expensive, there is data latency, and not everyone can use EDI, but this logic does not reflect the changes in the market. Today, EDI latency is dramatically less, and the costs per document have declined. The time to send messages used to be a nightly batch, but now it is hours and minutes, and the EDI messaging systems have the only effective use of standards for interchange in value networks that we have been able to build. After twenty years of trying to support the concepts of Supply Chain Operating Networks and Value Added Networks, I am rethinking my position. The industry has largely failed to build effective value networks. So, in this variable world, why don’t we embrace the predictive analytics and proactive alerting evolving from EDI networks as an outside-in signal for lead times, out-of-stocks, and alerts? The problem is that the EDI groups sit largely in the belly of the IT organization. The technology is not sexy, and the group often feels underappreciated and misunderstood. One of my recommendations for the BISG group facing shortages of paper and substrates is to rethink their relationship with EDI.

Define Demand Management As a FLOW, Not a Series of Time-Phased Data Outputs. One of the most important principles of outside-in planning is to use market signals (utilize advancements in technologies to use multiple inputs) to sense and define demand streams, and then apply the right models and optimizers based on characteristics and flow. The concept is that markets continually shift, and that the supply chain has many flows, not just one. The second step is to measure the Forecast Value Added by demand stream and help the organization understand the value of a demand process that drives intrinsic value. Push the numbers through a network planning optimizer to help the organization understand the value of the shift in demand thinking. Map these flows into tactical supply planning for S&OP and then apply the appropriate supply techniques. For example, if the item is not forecastable by any demand technique, should it be a make-to-stock item? If a contract manufacturer’s lead time is outside the requirements of a responsive supply chain (one that needs to respond quickly to market shifts), should the item be manufactured within the organization despite the negative hit on OEE? What is the impact of unchecked complexity and long-tail product portfolios on forecastability? Measure the impact. Are you shifting or shaping demand? If you are shifting demand from one period to another without a positive benefit to baseline lift, what is the impact on margin? Align with the data science team to educate the team to think differently.

Redefine Transportation Planning Outside-in. Use data from transportation visibility technologies to measure and predict lead times, and use this data to inform a planning master data layer that updates lead time predictions with each batch run of supply chain planning. Lead times matter. There are over fifteen systems within the organization that use lead time as a parameter. Don’t believe me, count them– Safety Stock and Material Buffers, Distribution Requirements Planning (DRP), Enterprise Requirements Planning (ERP), Vendor Managed Inventory (VMI), Manufacturing Planning (MPS), Transportation Planning (TMS), Just-in-Time Management (JIT), Available to Promise (ATP), Material Requirements Planning (MRP), Routing and Scheduling, and Procurement (SRM). Each runs with a different parameter that is not in sync with the other systems, but more importantly, not aligned with market reality. Most are set-and-forget values that are not in concert with market reality. Lead times are longer, but more importantly, more variable. Most systems are running on outdated data. Measure the impact and help the organization understand how this variability affects costs and order reliability.

Many projects today, like supply risk management and control towers, are knee-jerk reactions — largely reactive to symptoms — because we are not aligning the data with new technology capabilities to drive better outcomes. Be a change agent. Don’t fall prey to the narrative of the Misguided Gold Diggers.

I welcome your thoughts.

Our goal was to implement end-to-end planning for our client. We tried, but failed. After sitting on our beds and poring over manuals while eating takeout for hours, we couldn’t figure it out. So, we asked the larger Manugistics organization for help and discovered that, at that time, the only possible connection between tactical supply planning and transportation planning was order management. So, we mapped TMS to order management and order management to forecasting/distribution planning.

On the same day, we attempted to map planned orders in tactical planning to transportation requirements and freight constraints back into S&OP; the company issued a press release on end-to-end planning. We laughed.

We gave up trying to do more than map supply chain planning into ERP and then back out to TMS. Today, when I see Ellen at a conference, we still laugh. Thirty-five years later, not much has changed.

Manugistics assets were sold to JDA in 2006, which, through a series of revs, became a part of Blue Yonder. Blue Yonder’s tagline is Frictionless Outcomes. The Company claims to deliver end-to-end supply chain management. The reality is there is no such thing as end-to-end supply chain management.

My goal is not to pick on Blue Yonder, but to give you an example. Browse through any of the supply chain planning or supply management solution websites, and you will see the term end-to-end supply chain management in multiple places. The problem is that end-to-end supply chain management does not exist.

We should be using the new capabilities within AI frameworks to define:

• What decisions should be made?
• Who should make the decision?
• At what frequency should the decision be made?
• What defines a good decision?

In general, we are not. Instead, we are hanging agents and agentics on old-fashioned architectures like icicles on a holiday tree.

Straight Talk

I am known for straight talk.

Supply chain planning, supply management, supply chain execution, network design, and transportation/logistics management operate in silos. Not much has changed since Ellen and I worked together in 1992. The connections flow back through transactional systems: order-to-cash and procure-to-pay. Planning connects through an ERP bill of material to drive MRP (or DDMRP), but there is still no process flow to map planned orders from tactical supply planning (S&OP) to supply management (another name for procurement) for aggregate buying functionality or transportation planning. In short, the Systems of Insights for Supply Chain Management are disjointed. The language is packed with nuance, and the term “AI” is slathered everywhere. The technology promises sound good, but the reality is disconnected flows.

Newer solutions are emerging at the fringes of today’s technologies, but they do not fit into traditional taxonomies. There is no proven method to connect supply chain visibility flows — think vendors like Four Kites, Project 44, and Shippeo —into supply chain planning processes. This is the case despite valuable insights. Risk management technologies search for anomalies and provide early alerting, but there is no common data model, consistent definition of events, or workflows.

Traditional software players focus primarily on retail and make-to-stock flows. The discrete industries are underserved. The flows between Product Lifecycle Management and supply chain planning are underdeveloped. Software solutions like Pelico are automating clear-to-build for make-to-order and configure-to-order manufacturers, while Lean DNA tracks supplier conformance for discrete manufacturers.

How can we align if process flows cannot map the systems of insights and workflows across functions? Each system has a different definition of location, item, events, order, and purchase orders.

Supply management and supply chain planning operate in separate silos. Logistics and distribution planning have very little in common. There is no common data model.

Steps to Take

The first step is to have technologists and consultants speak in clear English. (If not English, insert the language of your native tongue). Force the technologists to identify themselves on the taxonomy map in Figure 1 and map the flows between their technologies to the systems of insights, systems of record, systems of execution, and the resulting workflows.

The second step to take is to get clear on the use of AI. Throw away the hype. Clearly understand each technologist’s value proposition. Get clear on the “So What” and “Who Cares.”

As a team, build an image that helps to define terms and interconnections. Don’t confuse the systems of insight with the systems of record and the systems of execution. Try to keep the flows clean. My crude drawing from lunch with a client is shown in Figure 1.

Figure 1: Definition of Solutions Under Supply Chain Management

After identifying flows and establishing a common vision, build a unified data model across the solution stack and define the canonical, rules-based ontological frameworks and a planning master data layer. In your selection of technologies, focus on minimal latency, but cast off terms like real-time execution.

This will help the team align and speak a common language.

Then, and only then, start the discussion on redefining work and improving capabilities through AI. Identify your requirements and build value. Then call me. I want to write your case study.

I hope this helps. I look forward to your thoughts.

I struggle with the fact that in the Supply Chains to Admire work that we find that global multi-nationals struggle to scale performance. We are not able to achieve economies of scale. I think that one of the reasons is a lack of focus on supply chain fundamentals.

The Dance With Shiny Objects

Many “experts”, I find, dance in the aura of new shiny objects- pontificating on the promise of the autonomous supply chain and touting the power of agentics. I say, let them have it.

“Faster horses?” Here is a quote that I like.

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.

Drive Value: Rethink Fundamentals

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.

Step One: Align on First-Principle Thinking

In prior blog posts, I asked teams to rethink their processes using first principles and improve their AI readiness.

In essence, what is it that you to do anyway, spoken in plain language? To get started, read the first column and then think about the difference in the second column, and ask yourself if this delta is important in your organization. Once clarity is achieved, start a discussion with technologists. Get past the shiny-object bunk and focus on driving value.

Table 1. Supply Chain First Principles

Step 2. Redefine Your Relationship With Data

The second step is to take an inventory of the data you have and ideate possible use cases or opportunities to redefine data structures to reduce latency and improve relevance. Traditional processes have inherent process and data latency that we should no longer accept. I have written many blogs on the need for a planning master data layer and a unified data model in my market-driven research.

Step 3: Embrace and Understand Lead Time

In the supply chain, lead time is a gossamer thread. Lead time should connect and link planning, inventory management, procurement, manufacturing, and customer delivery; getting it right is key to driving supply chain reliability, but in most supply chains it is a broken gossamer.

Gossamer is a light, fine thread web. A broken gossamer is hard to see.

When supply chains become less predictable and difficult to control, supply chain entropy—the accumulation of uncertainty and inefficiency increases. Disorder reigns. Functions within a company are not aligned, and the systems are not interconnected with a consistent and accurate definition of lead time. Supply chain effectiveness requires order and alignment of parameters to actuals.

Whild companies try to put pat answers on the cunundrum like risk management or improving resilience, a lot of progress can be made by focusing on reducing disorder.

Entropy is a measure of uncertainty, disorder, or the number of possible ways a system can be arranged.

Supply chain entropy is related to, but distinct from, the Bullwhip Effect. Let’s not confuse the two. The bullwhip effect describes how small changes in customer demand become amplified and distorted as they move through the supply chain. Entropy is broader, encompassing all sources of increasing disorder. A major source of disorder is the lack of discipline around lead time management. The larger and more complex the organization, the greater the opportunity.

The Opportunity

As a composite metric, aggregate lead time is the total time to complete all cycles: order, replenishment, manufacturing, procurement, and logistics cycles. This varies by item, category, region, asset class, and time period. It should never be a set-it-and-forget-it value. While many leaders speak of dynamic lead times — where lead time values are updated based on actual supply chain data — this is seldom the reality. In this world of increasing variability, it is important to understand the average and variance to drive a predicted lead time. (See Table 2)

Role in Planning Cycles

Aggregate lead time defines planning horizons. As shown in Figure 1, planning processes vary and are defined by aggregate lead time. Getting clean on these definitions is a first step toward governance and discipline.

• Tactical Planning. In the tactical horizon—the focus of demand and supply planning and Sales and Operations (S&OP) planning—the emphasis is on planning beyond aggregate lead time.
• Operational Planning. In the operational time horizon, the planning focus is synchronizing and harmonizing data across source, make, and deliver to improve delivery reliability. The operational time horizon includes transportation load tendering, production scheduling, materials requirements planning, distribution requirements planning, and inventory allocation. Lead time also plays a critical role in JIT and VMI processes. Without it, materials never arrive on time.
• Executional Planning. The order cycle time defines the executional planning time horizon and drives the cadence for routing, warehouse management, and inventory deployment.

Figure 1. Lead Time Defines Planning Horizons

In traditional deployments, lead time was defined by a series of averages. With increased supply variability and the advent of new forms of math and solvers, probabilistic engines use both the average and the pattern of lead time to calculate the probability of lead time included in the aggregate lead time calculation. In Figure 2, we present an example of raw-material inbound lead time for an automotive distributor.

Figure 2. Managing a Supply Chain in a World of Increased Variability

Role in Safety Stock

Lead time actuals should be used in the calculation of safety stock. Unfortunately, when we interviewed thirty supply chain professionals, we found that no leader was updating lead time in safety stock calculations based on actuals or using demand variability in their calculations. As a result, many feel comfortable in their safety stock calculations, but they shouldn’t.

To help the reader, here we share one formula used to calculate safety stock (there are many):

Safety Stock = Z × √[(LT × σd²) + (D² × σLT²)]

Where:

• D = average demand per day
• σd = standard deviation of demand
• LT = average lead time
• σLT = standard deviation of lead time

This accounts for variability in both demand and supplier performance.

The So What and Who Cares

So, as you play with shiny objects and talk about the promise of AI, remember, that your current architectures were compromised by the limitations of technologies fifty years ago. As you redefine the promise of planning with new technologies, start with first principles, redefine your relationship with data (to use all forms of data), and put some discipline around the use of lead times. Your supply chain will thank-you.

And if you want to calculate the impact of your current supply chain entropy, in the appendix, I share some formulas. To monetize the affect of entropy on your supply chain, push the insights into your network design technologies to run simulations to understand the impact of entropy on cost and customer service.

Then call your CFO and COO to have a different discussion.

Start with clear fundamentals before you chase shiny objects.

Appendix: Calculating Supply Chain Entropy

Start by using Table 2 to calculate your aggregate lead time. Do the analysis on the elements that are important by product, asset and value classifications. <I think that the analysis will surprise you.>

Table 2: Aggregate Lead Time Discovery

I believe that you will find that the procurement and transportation/logistics cycles contribute the most to high entropy. The greater the outsourcing the larger the predicted entropy.

Focus on the difference between actuals and current values, and understand the variability. H=−i=1∑n​pi​ln(pi​)

where:

• $p_i$pi​ = probability of observing lead time $i$i.

Interpretation: Low entropy means lead times are highly predictable, whereas high entropy spreads lead times across many values, indicating greater uncertainty. While entropy captures unpredictability, it does not reflect the magnitude of delays. Two suppliers could have the same entropy, even though one has much longer lead times.

A useful composite metric is:$$SE = H \times CV$$SE=H×CV

where$$CV=\frac{\sigma}{\mu}$$CV=μσ​

• $\mu$μ = mean lead time
• $\sigma$σ = standard deviation of lead time

This metric increases when lead times are both unpredictable and highly variable. Which is our unfortunate reality.

In the face of market uncertainty, fulfilling the brand promise requires consistent, reliable order delivery, product quality, and adherence to the promised date.

Performance and improvement are intertwined but separate. Here I share some insights. Let me start with a sports analogy.

Reflection

Over the course of my life, I have had many personal trainers. I know it sounds illogical to hire someone to force me to work out regularly, but it is my reality. I have good intentions, but scheduling sessions with a trainer forces me to go to the gym.

On the first day of work with a professional athletic trainer, there is an assessment of potential: stamina, balance, flexibility, and health limitations. The capabilities and testing methods for determining body mass index, lean muscle mass, and cardio fitness have dramatically improved over my lifetime. I find it amazing to see heart rate, strain, and cardio load on my devices. When I am in the fitness groove, I love the tracking.

The assessment is usually followed by the question of, “What is your goal?” Fitness, weight loss, running a marathon, competing in a triathalon, or participation in a special event? Each goal requires a different plan.

Over the years, I have had many trainers. Unfortunately, a lot of starts and stops. I am back at it.

At the start of a fitness journey, it is easy to see progress. You lift heavier, do more reps, and run faster. You may tone and lose weight. However, as your body gets fitter, improvement is tougher. Staying on track requires discipline — in sleep, diet, and regular workouts. There are many obstacles to staying on track — work, travel, family, and social commitments. The relationship between performance and improvement changes as you age.

Driving Supply Chain Improvement

I find the processes of supply chain improvement to be analogous to the journey of physical fitness.

Many years ago, I managed a continuous improvement program for a large manufacturer. Reporting through the manufacturing organization, I focused vigorously on improving OEE (Overall Equipment Effectiveness). My focus was factory automation. In my work, I never spoke to my logistics teams or understood the impact of my zealous efforts on inventory. They were in different organizations. I now realize the error of my ways.

My focus was on improvement. I never considered that the company did not have unlimited opportunity, or that improvement would get harder as the organization matured. I was naive.

I see many companies making the same mistakes I made as a practitioner forty-five years ago.

The problem is that the impact is now much worse. This is due to the lengthening of the product portfolio (increasing demand variability and demand latency), the evolution of e-commerce with shorter order cycles, and the inventory repercussions on cash-to-cash cycles.

I shudder at the thought of putting improvement efforts on steroids with AI technologies without understanding the impacts holistically.

Here is what I wish I knew then:

1. Supply chain improvement efforts do not have unlimited potential. Understanding the interrelationships between functions and the connection to operating margin is critical.
2. The goal of lowering manufacturing costs can actually increase total cost and degrade corporate value. We see this in the best deployments of factory scheduling in process-based industries.
3. In my prior organization, there was a continuous improvement program in manufacturing, procurement, and logistics. Our efforts were aligned to our functions, but not aligned to a corporate strategy. In fact, we did not know what the corporate strategy was. (Our research shows only 15% of supply chain managers are clear on strategy.) We falsely believed that we could work effectively through an independent functional focus. As a result, my savings in manufacturing actually increased logistics and procurement costs. We were not able to see total costs easily, so balance was an almost impossible task.
4. The goal of efficiency was being undermined by unchecked complexity. As product portfolios and market efforts proliferated, the potential to reduce operating margins declined. We were not managing complexity. The company was drunk on growth efforts with unchecked discipline on baseline demand.
5. Market headwinds greatly impacted the bill of materials and labor inputs. We did not account for market shifts to understand the realistic potential of our supply chain.
6. Our benchmarks were not realistic. We let leadership set unreasonable goals.
7. We focused on improving operating efficiency. The efficient supply chain may not be the most effective. In the process, we unknowingly gave up capabilities for responsiveness, flexibility, and agility. In the face of unchecked complexity, and increasing issues with item forecastability, the corporation needed increased flexibility, but it was not valued in bonus incentives.

Recently, I published “Supply Chains to Admire for 2026.” Over the last two weeks, two thousand readers downloaded the report. My goal is a type of informal coaching. I want to help others to avoid the issues I faced in attempting to drive continuous improvement.

The design of the report is to help companies understand what is possible, and share performance against peer group. For success, it is critical to understand industry potential and set realistic targets.

I loved some of the comments on LinkedIn:

• Winners understand their industry frontier, manage complexity, align functions, sense changes in demand and supply, and orchestrate trade-offs over time. Orbit charts show how the system moves. Evidence explains why it moves. The control point analysis identifies where value can be captured. AI accelerates the synthesis, but the structured context is the asset.
• One takeaway I hadn’t considered before was the idea that continuous improvement isn’t unlimited. Every industry has constraints and an “effective frontier.” The goal isn’t chasing perfection. It’s understanding what’s realistically achievable within your operating environment and improving from there.
• I never realized how large the gap was between perceived supply chain leadership and actual results.
• The regression finding is the part that deserves more attention than it typically gets. Forty percent of market capitalization explained by four metrics — revenue growth, operating margin, inventory turns, and ROCE is a strong empirical case for why supply chain planning should be anchored to shareholder value, not functional cost. The practical problem I have seen is that most supply chain teams are measured on cost and service levels because those metrics are visible within a quarter. Operating margin and ROCE play out over longer cycles and get attributed to commercial or finance decisions rather than supply chain ones. So even when the analytics are clear, the incentive structure pulls planning teams back toward the wrong optimization target.

Coaching

If these comments resonate with you, start by:

1. Getting clear on the goal. What is supply chain excellence? What is the right role for demand planning? Inventory management?
2. Understanding potential. As you face increasing market headwinds, what is the market potential to drive improvement in your supply chain?
3. Building capabilities. How do you define efficiency? Balance? Agility? What is improvement?
4. Aligning incentives. Actions need to align to reward systems. How do you realign to drive improvement?
5. Measuring effectiveness. Are your planning systems adding value? Measure Forecast Value Added (FVA). Inventory health and the bullwhip by demand stream.
6. Avoiding shiny objects. In this world, where everyone is espousing AI like snake oil focus on value. Side step buzz word bingo.

And, consider hiring a coach. A trainer can help your organization stay on track amid firefighting, technology and consulting overpromises, and organizational shifts.

Let me know if I can help. I love this role, and also know some good resources.

In the meantime, check out my new resource, Ask Lora for benchmarking insights..

See Me Speak

I am speaking in the fall at the Cleo Connect Conference in Chicago, Illinois on September 2nd, and the Keller Logistics Summit on September 23rd in Defiance Ohio. If you are there, let’s continue the dialogue.

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.)

What is Artificial Intelligence?

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 believe that companies need to start the journey by redefining their relationship with data — embracing and loving semantics and different data types. When you realize the potential of redefining your relationship with data, it is freeing.We no longer need data lakes or the goal of clean, pristine data.

There are many “experts.” I deliberately put the term expert in quotes. Most are technologists or consultants. Building the tech is easy. Driving value requires a redefinition of how we work.

A Framework to Drive Your Journey

Here, I share a framework to help you understand your AI readiness to unleash improvements in your supply chain.  

Knowledge. 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. The first step for your team is education. Training is essential to driving outcomes. The second is clarity of definitions. When your organization speaks about the power of AI, is the discussion clear? Are they aligned on the technique to be deployed and the desired outcome? Is there a clear link to value?

In the process, sidestep vague discussions and ensure clarity. The more mature your company is in critical thinking,  systems thinking,  and design thinking, the faster the progress. If your organization is mired in politics and chasing shiny objects, sound the alarm:  you are not AI-ready.

A second pitfall is when teams get caught in a hype cycle — failing to define terms and outcomes — falling prey to buzzword bingo. Alas, pretty words and pictures do not drive successful projects. Chasing shiny objects is a recipe for failure. Clarity on business objectives, definitions, and outcomes is essential.

The most successful teams are driven by a clear goal and fueled by a curious, diverse team. The greatest challenge for the supply chain team is to challenge traditional paradigms embedded in the gray matter between supply chain professionals’ ears.

Successful projects avoid vague terms such as control tower, digital twin, continuous planning, and real-time decision-making. Instead, step back and ask yourself, “What is a good decision? How do I know if the technology is recommending a good and feasible plan? Who should make a decision? What is the right cadence of a decision? How do I drive learning to improve the next decision?” These answers are not easy.

Curiosity and Innovation Culture. If your company is an early adopter, there is a higher likelihood of success. If the company is a late adopter, mired in technologies that do not work, and is primarily using spreadsheets to make decisions, sound the alarm. You are not AI ready. If this is you, focus on why the organization is dependent on spreadsheets and fix the issues before moving forward.

The pace of technological change in our personal lives, driven by digital transformation and artificial intelligence, is fast and furious. As hard as business leaders try, this shift cannot be duplicated in enterprise applications. The gaps are growing. The issue? Foundational supply chain architectures are a barrier to redefining work to unleash new levels of value. Just like a house cannot be built on a bad foundation, our current definitions of planning and execution architectures need to change to take advantage of new capabilities.

Organizational Capabilities. Organizations with a high level of readiness can easily access and trust data within enterprise systems. The organizations are aligned to drive outcomes. Few are.

In planning systems, there is clear governance over which decisions need to be made and who should make them. Companies are also clear on what makes a good decision.

When AI initiatives are reported by business leaders with a focus on clear outcomes, there is a higher chance of success. Diverse teams with business leaders, IT teams, data scientists, and finance representatives are more likely to succeed.

There needs to be a focus on learning while accepting that failure is sometimes the best teacher. A fail-forward mentality penetrates the winning team. The team is naturally curious with a focus on first-principle thinking. Here I share a chart of first principles that I drafted over coffee a couple of months ago. Use it as a starting point.

Process Readiness. A successful team starts with a clear charter focused on process innovation success. The greatest benefit happens when the funding is not based on a fixed Return-on-Investment mentality. Instead, the funding is seen as an investment in innovation. Progression from concept through testing to adoption occurs through a stage-gate approval process.

Build an innovation fund and a governance board for teams to create and redefine process capabilities with AI. Use the packaged technologies on the market and test and learn. Side-step custom code.

Artificial Intelligence performs best when processes are defined and repeatable. Focus on building a solution against a goal, and ensure repeatability to improve outcomes. Define success before you start, and align outcomes with a balanced scorecard.

Technology.  A winning team knows the difference between integration and interoperability, and understands the value of synchronization and harmonization. Knowing that data will never be perfect, the group focuses on defining a semantic layer for demand and supply translation to minimize the bullwhip effect. Most companies discover their biggest AI challenge is not model development but connecting fragmented systems.

The teams work to gain benefits from technology partner advancements, but never hold themselves to fixed IT standards. Instead, the focus is on delivering business results.

Network and Supplier Relationships.  AI requires visibility beyond the four walls of the enterprise. Has the winning company built robust bidirectional canonicals with major suppliers? How good is your forecast for suppliers? (Measure it using FVA analysis.) They use supplier development programs for supplier onboarding and network design/usage.

After you do this assessment, take the quiz.

First question. Are you clear on what defines supply chain excellence? If the answer is no, stop and define it before taking the quiz. After a clear definition, as a team using the criteria above, rate your readiness.

Rate each category from 1 (just starting) to five (perfect)

If your score is low (6-10), focus on knowledge and cultural elements. Educate the leadership team on their role in driving success.

If you have a mid-level score of 15-20, start your journey by forming the cross-functional team and a governance board with stage gates and learning. Train the organization to fail forward.

If your score is above 20, form teams to focus and co-develop with existing technologists. Focus a few projects driven by business outcomes.

Remember that no one knows the future. The potential of technology to improve supply chain processes is evolving before us. Stay centered in the core principles of driving value, building teams, and questioning the status quo.

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? Using my Supply Chains to Admire research, I worked with the Uthereal team to put 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. Companies 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. The model even assesses your AI readiness to your peer group.

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. I hope to see you there.

Other Articles to Consider

The Courage to Jump – Supply Chain Shaman

Zac, Don’t Waymo My Supply Chain – Supply Chain Shaman

The Courage to Jump – Supply Chain Shaman

The Beat Goes On – Supply Chain ShamanCase Study.  Franklin Sports: A Scrappy Demand Management Approach – Supply Chain Shaman

Every now and then, a blog troll appears in my feed. What is a blog troll? A blog troll is a person who intentionally posts provocative, inflammatory, misleading, or disruptive comments in online discussions to provoke emotional reactions, start arguments, derail conversations, or attract attention. My troll is André Martin.

He wants me to endorse the concepts of Flowcasting. I cannot. I frequently get emails from him telling me how stupid my approach is. Some of this comes with the territory, but I find that misunderstandings abound in supply chain management. Andre feels that he is right. I cannot endorse the technique.

Let me explain. The concept was developed by André, along with Mike Doherty and Jeff Harrop, in their book Flowcasting the Retail Supply Chain. Flowcasting is a retail supply chain planning methodology that starts with a forecast of consumer demand at the store/SKU level and then mathematically translates that demand into replenishment, distribution, purchasing, and manufacturing requirements across the entire supply chain. Instead of creating separate forecasts for each node (store, DC, supplier, factory), Flowcasting uses a single-demand forecast and calculates the rest.

My issues are many, but my primary issue is that demand is not a continuous flow. Companies consciously disrupt the flow—shifting from pull to push—through demand-shaping programs. Suppliers work with retailers to stimulate demand. This is the goal of the sales account team. The average consumer products company has forty sales account teams. To drive growth, demand-sensing and demand-shaping programs are essential. It is about understanding push and pull against the backdrop of baseline demand.

To all my flowcasting trolls, I would like to share the concept of the challenger demand forecast that I discovered while working with Franklin Sports. As you read it, think about the concept of flow.

I like this case study because it 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.

Franklin Sports Case Study

Background

Franklin Sports was founded in Brockton, MA, in 1946 by Irving Franklin and is now based in Stoughton, MA. Using discarded scrap leather from local shoe factories, he developed a line of footballs, boxing gloves, basketballs, baseball gloves, and more to provide athletes of all ages with quality athletic equipment. While much has changed since 1946, Franklin Sports continues to operate with one central belief: Sports Make Life Better.

In 1984, the Company made its indelible mark on the baseball equipment landscape by inventing the Pro Classic, the first-ever baseball batting glove. Designed with the help of Hall of Famer Mike Schmidt, the all-new batting glove quickly became a must-have accessory for hitters across Major League Baseball. To this day, the company continues to innovate and develop new PRT protective equipment and batting-glove technology for our players, as the official batting-glove partner of MLB.

Making sports better requires order reliability, and a focus on customer service. With a primary shipping center in Memphis, a Franklin Sports Asia office, and a network of vendor and retail partners in dozens of foreign countries, Franklin Sports operates a global supply chain that needs to be nimble and responsive. With partnership commitments to the MLB, NFL, NHL, NBA, NCAA, and MLS, time is of the essence for driving an effective response to fluctuating demand.

Meet Scott Kennedy: As Scrappy as The Batting Glove

In our effort to try to document the use of Artificial Intelligence in the supply chain, I asked TrueGradient to introduce us to Scott Kennedy. Scott is the Vice President of Digital Strategy & Analytics at Franklin Sports and one of those rare, holistic leaders who combine deep technical expertise with exceptional people skills. Scott was an early adopter of TrueGradient’s solution.

In our reference call, Scott took me through a no-nonsense approach. He has a burning desire to learn how to improve demand planning workflows using the power of Artificial Intelligence. With a history of helping his business colleagues make better decisions with Power BI, Scott was on a mission to determine how AI techniques could improve insights for his responsive supply chain when he met TrueGradient. (A responsive supply chain needs to operate effectively in a world of changing lifecycles and preferences with a short shipment cycle.)

The Business Challenge

When an NFL team goes to the Super Bowl, fans want that team’s gear in time for the game. The Super Bowl is a hot market. Serving the partnership requirements of our professional league partners is a fast-fashion supply chain.

When Scott first started at Franklin Sports, he built an analytical framework by pulling point-of-sale data and searching for a technology partner to help the company gain insights from channel data. The focus was to use techniques such as reinforcement learning to quickly generate predictive insights in a changing market. As markets shifted, Scott wanted to understand the market drivers and gain insights at the same speed as the market with minimal latency.

The Answer: Reinforcement Learning to Drive Probabilistic Account Forecasting at Scale

Using Reinforcement Learning, Scott worked with TrueGradient to build Challenger Models to improve his partnership with Amazon. In the challenger model, he uses Amazon’s point-of-sale forecast data to predict their purchase order requirements. The Amazon account team then uses Franklin Sports’ demand models to challenge Amazon’s forecasts with more informed insights. Through better insights from modeling, the sales account team has improved its relationship with Amazon and built a more collaborative relationship by minimizing stockouts.

By design, the company has a group of people more closely aligned with the sales division, who could be brought into conversations and trips to retailers, and a group who sit in operations and are more traditional demand planners. The goal is a responsive supply chain to drive growth. When it comes to talent, Scott’s demand planning team runs the gamut, with a broad range of skill sets, and certain people are more retailer-focused than others.

Within a month, Franklin Sports expanded the challenger model to other retail relationships. The start is building a probabilistic band of what could happen in the future. Then, in conjunction with the sales teams, the demand insights team collaborates with the retail account to align on future orders through probabilistic modeling using reinforcement learning.

Acceptance was easy. Franklin had been working with Amazon’s POS probabilistic models for years, so the concept was already familiar. When TrueGradient built a probabilistic forecast for Scott at the wholesale level, the team was already comfortable with the approach. The probabilistic forecast is used as the direction for what the sales teams want us to drive. For example, if we want to take a deeper position on the Chiefs for the Swifties and look at Taylor Swift’s attendance at a probabilistic level, we can run different scenarios to drive alignment, then consume a plan based on what we are seeing in the point-of-sale data.

Scott said, “Retailers are starting to provide automated procurement plans, but we are still having a gap with out-of-stocks. The use of reinforcement learning and the development of a predictive purchase order plan help us prevent out-of-stocks, drive growth, and improve customer relationships. Working with TrueGradient made this easier because of the founders’ experience in point-of-sale modeling. They were always there for me; I never got kicked down the organization to work with a junior analyst.”

The Approach

Working hand in hand with TrueGradient, Scott built the Amazon challenger model in a week. We are evolving, learning as we go. Retailer fulfillment is improving in line with our probabilistic forecast/plan. It is more of a tool that helps companies understand the future in a supply chain model. Working with a smaller technology partner provides agility to move quickly. It may sound daunting, but the flexibility of an innovator collaborating with a small technology firm drives innovation quickly.

In discussions with TrueGradient, I found that Scott is an early adopter. Franklin Sports is an innovator, having been among the very first customers to test the  Reinforcement Learning Agent and Customer Success Agent. The partnership has been invaluable in shaping the TrueGradient platform.

Wrap-up

Scott found TrueGradient through LinkedIn. The TrueGradient founders, with a deep understanding of demand and inventory analysis from their time with Antuit, moved quickly to seize the opportunity to test their solution with an innovator. The TrueGradient, a no-code model, is priced at 80% of the cost of a conventional demand management deployment, and the company is eager to gain market traction.

The Franklin Sports’ goal is to drive reliable growth in a fast-fashion market. The tech is an enabler to the goal. Even though less than 7% of the market is full of early adopters like Scott, he is a business leader who is not afraid to test and learn. Even though he sits in an analytics role, his focus is not tech for tech’s sake. His focus and zeal are on winning with customers. You can hear the drive to win in his voice.

As a multigenerational, family-owned business, Franklin Sports moved from concept to production at the speed of the business. As a result, this is a good case study of an innovator working with another innovator to use AI techniques to improve demand-planning engines. 

About Franklin Sports. Founded by Irving Franklin in 1946, Franklin Sports is a Stoughton, Massachusetts-based sporting goods brand that manufactures and sells over 10,000 products across many different categories. Franklin Sports is proud of its partnerships with Major League Baseball, Major League Soccer, National Basketball Association, National Football League, National Hockey League, Major League Volleyball, USA Pickleball, Franklin US Open Pickleball Championships, Women’s National Basketball Association, National Women’s Soccer League, Hasbro, and many others. Franklin is a multigenerational, family-owned business with a rich history and a trusted reputation as a quality sporting goods brand – from recreational sports to the professional level. At Franklin Sports, the team believes that sports make life better. For more information on Franklin Sports, please visit www.franklinsports.com or follow Franklin on Instagram @franklinsports, @franklinsportseqp, and @franklinpickleball, Facebook, and X @franklinsports.

About TrueGradient. TrueGradient is the AI-native Planning OS for modern consumer brands and retailers, replacing spreadsheets and fragmented tools across demand, inventory, pricing, assortment, personalization, and capacity planning. The platform empowers businesses to boost service levels while minimizing costs. Built by operators with real-world experience from Amazon, Walmart, Mondelēz, and IBM, they are bringing enterprise-grade AI to the mid-market and serving a growing customer base across North America, Europe, Australia, and India.

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.

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.