Jon W. Hansen, FCIPS · Procurement Insights | Hansen Models™ · 17 October 2026
Most organizations will study this week the way they study every disruption: afterward. There will be after-action reviews, lessons-learned documents and slide decks explaining what happened. They are worth doing. But relying on a review written after the outcome is like a general preparing to fight the last war. By the time it is finished, the next one has already started.
This post starts from a different premise. An event is not the thing to track. A strand never ends. It is a continuous flow, and it does not move in a straight line. It moves in waves, rising and falling, and those waves are always crossing and connecting with the waves of other strands. Most of those connections are small and pass unnoticed. An event is the moment when several strands meet at the same time and place with enough force to be felt, and the ripple that spreads outward from that meeting.
From 8 October, before landfall. On the left, the supply map: one route and fixed handoffs. On the right, the operating reality: each strand moves in waves, and the faint lines behind each one are its connections to the others. Illustrative, not measured data.
Hurricane Isaias was such a meeting point: a storm, a fuel market already stretched by war, a sanctions law with a deadline and an election calendar came together in the same week. The storm passed. The strands did not, and they are already moving toward their next crossings.
That premise is what sets ARA™ RAM 2025™ running on SLAP OS™ apart as an operating system for the AI age. It follows strands, not isolated events. Strands exhibit tendencies and rhythms, and their intersections can recur within recognizable operating bands. The system identifies those patterns and their potential impact points before the outcome is known. It then follows what happens, compares the outcome with its prior understanding, and feeds the difference back through the loopback learning process. Each crossing becomes evidence that confirms, challenges or changes the system’s explanation, and a decision can be judged by how it was made, not only by how it turned out.
What is in motion
On 8 October, before Hurricane Isaias made landfall, the Isaias Strand Lab named policy as one of the strands the storm would cross. The storm did not create that strand. It was already moving, driven by the global fuel shortage from the war in Iran and by the approach of the midterm elections. Isaias crossed it, and the strand kept moving after the storm had passed.
In brief: a sanctions law signed on 18 September set 18 October as the date from which goods from the largest buyers of Russian oil and gas could face tariffs of up to 100 percent. On 9 October, hours before landfall, the administration announced that Russia would make more than 300,000 metric tons of diesel immediately available to U.S. and global markets, with further shipments expected. On 16 October, two days before the deadline, an executive order waived the law’s sanctions and tariffs, with no fixed expiry. A bill to block the diesel deal was introduced the same day.
Most coverage has treated these as political and foreign-policy stories. They are also supply chain stories, and they reach far beyond diesel.
Where it reaches
Sourcing from China and India. Both are among the largest buyers of Russian energy. The tariff that could have applied to their goods from 18 October has been waived, not removed. Electronics, components, textiles and generic pharmaceuticals all sit inside that exposure, and the waiver can be lifted whenever the administration decides further action is appropriate.
Inputs that come from Russia itself. The waived tariff on Russian goods also covered commodities the United States still imports, such as fertilizer and certain metals. Fertilizer reaches food prices on the same slow clock as diesel: farm costs first, shelf prices months later.
Fuel and freight. Analysts expect the announced diesel volumes to do little to global prices. Any movement in diesel this autumn will have several possible causes at once: the war in Iran, the deal, the storm passing, the season.
Shipping, insurance and compliance. Some Russia sanctions have been waived and others remain in force under earlier orders. Every bank, insurer, carrier and port screening these flows now has to tell the difference, and honest mistakes become more likely when the rules are mixed.
The consequences nobody intended
Organizations that prepared may appear to have been penalized, and those that waited may appear to have been rewarded. Companies that spent money preparing for 18 October may now see those costs described as wasted. Companies that waited may appear to have made the better decision. Neither conclusion follows from the waiver alone. But the lesson many will draw is to wait next time, and repeated across thousands of organizations, that lesson weakens everyone’s readiness for the next deadline that does hold.
Deadlines may now look negotiable. A legislated date was waived two days before it arrived. Future deadlines may carry less credibility with the organizations and countries that plan around them, which could change behavior well beyond this law.
Flexibility may disappear next time. A Congress that sees a waiver used this way may write the next law without one. Today’s flexibility could become tomorrow’s rigidity, in a crisis where flexibility is exactly what is needed.
Results will be credited to the wrong cause. When diesel and freight costs move this autumn, organizations will credit whichever explanation fits their plans. Without checking what else changed in the same period, they will learn the wrong lesson from their own results.
What to do now
Trace back from your potential failure points, not forward from the event. Start with what could fail: a line that stops, a product you cannot ship, a contract you cannot honor at the price you agreed. Then trace backward to the strands that could cause it, including suppliers and sub-tier suppliers in the countries that are the largest buyers of Russian energy, and inputs in your products that originate in Russia. Most organizations know their first tier; few know where their second and third tiers sit, and a forward-looking map rarely reaches them.
Treat the waiver as a live condition, not a resolved one. Build a scenario for its reversal. Check whether your contracts say who pays if a tariff appears with little notice. Avoid long commitments that only make sense if the waiver lasts.
Know your dates. The election on 3 November, after which the political strand behind this policy changes. The diesel license, which runs to 7 April 2027. The bill to block the deal. The status of the energy ceasefire.
Before crediting any price movement, ask what else changed. A lower freight bill this autumn may have nothing to do with anything your team did.
Decide now who can stop a sourcing decision if the condition changes, and what evidence they will act on. That decision is much harder to make in the week a tariff returns.
Don’t punish the teams that prepared. If your organization spent money getting ready for 18 October, record why it was the right decision on the evidence available at the time. Otherwise you are teaching your people to wait.
What we are watching
We are recording dated expectations for this strand before the outcomes are known: whether the waiver survives the election and the next 90 days, whether the announced diesel volumes are actually delivered, whether the deal moves diesel prices materially, whether India’s and China’s purchases of Russian energy change, and whether the energy ceasefire holds. We will check them on 4 November, 16 November and 15 January, and report what we got right and what we got wrong.
A storm, a law or a deal is a single event. The strands it touches keep moving, crossing into other markets and other organizations’ supply chains. That is Strand Commonality™: parts of an operation that seem unrelated are connected, so a change in one can affect another that was working. The organizations that see the crossing before the consequence arrives are the ones with room to act.
The takeaway
Hansen follows strands continuously, and has done so since RAM 1998, based on the theory we developed called Strand Commonality™. In 1998, we began by following three or four strands at once, and over about a year and a half expanded that to four to six that we could follow reliably and simultaneously. With ARA™ RAM 2025™ running on SLAP OS™, we can now follow 10 to 12 strands simultaneously, and our goal is to expand that to 20 by 2030.
The results are on the record. In 1998, RAM 1998 followed a focused set of strands around the procurement of MRO parts supporting a military’s IT infrastructure. Within three months, next-day delivery rose from 51 percent to 97.3 percent, and it held there for seven years. Over the past six months, ARA™ RAM 2025™ running on SLAP OS™ has averaged 91 percent output accuracy, assessed against the evidence in our documented case labs.
RAM 1998 demonstrated the process. Individual orders varied, but the strands underlying them did not behave randomly. Service technicians repeatedly held their part orders until late afternoon; sourcing and transportation options repeatedly narrowed within the same time band; and next-day delivery repeatedly failed where those strands met. Recognizing that rhythm made intervention possible. Feeding each outcome back into the system kept improving both the understanding and the operating response.
ARA™ RAM 2025™ is not a prediction tool. It is a system of intelligent operational recognition. It recognizes recurring strand tendencies and intersection points, records that understanding before the outcome is known, and tests it against what subsequently occurs. Through loopback learning, every confirmed pattern, contradiction and unexpected consequence changes what the system looks for next. The dated expectations in this post are not bets placed to be proven right; they are part of that loop. Prediction asks what will happen next. Strand recognition identifies what is happening again, and loopback learning determines what the system should understand differently because of it. Hansen strives to get it right rather than be right, on a continuous basis.
The reality is that you can make the right decision and get the wrong result, or make the wrong decision and get the right result. An outcome on its own does not tell you which one happened. Through Strand Commonality™, we track the impact and the continuing consequences of both, so that organizations learn from how their decisions were made, not only from how they turned out.
Right Decision, Wrong Result: What a Hurricane, a Sanctions Law, and a Diesel Deal Mean for Every Supply Chain Going Forward
Posted on October 17, 2026
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Jon W. Hansen, FCIPS · Procurement Insights | Hansen Models™ · 17 October 2026
Most organizations will study this week the way they study every disruption: afterward. There will be after-action reviews, lessons-learned documents and slide decks explaining what happened. They are worth doing. But relying on a review written after the outcome is like a general preparing to fight the last war. By the time it is finished, the next one has already started.
This post starts from a different premise. An event is not the thing to track. A strand never ends. It is a continuous flow, and it does not move in a straight line. It moves in waves, rising and falling, and those waves are always crossing and connecting with the waves of other strands. Most of those connections are small and pass unnoticed. An event is the moment when several strands meet at the same time and place with enough force to be felt, and the ripple that spreads outward from that meeting.
From 8 October, before landfall. On the left, the supply map: one route and fixed handoffs. On the right, the operating reality: each strand moves in waves, and the faint lines behind each one are its connections to the others. Illustrative, not measured data.
Hurricane Isaias was such a meeting point: a storm, a fuel market already stretched by war, a sanctions law with a deadline and an election calendar came together in the same week. The storm passed. The strands did not, and they are already moving toward their next crossings.
That premise is what sets ARA™ RAM 2025™ running on SLAP OS™ apart as an operating system for the AI age. It follows strands, not isolated events. Strands exhibit tendencies and rhythms, and their intersections can recur within recognizable operating bands. The system identifies those patterns and their potential impact points before the outcome is known. It then follows what happens, compares the outcome with its prior understanding, and feeds the difference back through the loopback learning process. Each crossing becomes evidence that confirms, challenges or changes the system’s explanation, and a decision can be judged by how it was made, not only by how it turned out.
What is in motion
On 8 October, before Hurricane Isaias made landfall, the Isaias Strand Lab named policy as one of the strands the storm would cross. The storm did not create that strand. It was already moving, driven by the global fuel shortage from the war in Iran and by the approach of the midterm elections. Isaias crossed it, and the strand kept moving after the storm had passed.
In brief: a sanctions law signed on 18 September set 18 October as the date from which goods from the largest buyers of Russian oil and gas could face tariffs of up to 100 percent. On 9 October, hours before landfall, the administration announced that Russia would make more than 300,000 metric tons of diesel immediately available to U.S. and global markets, with further shipments expected. On 16 October, two days before the deadline, an executive order waived the law’s sanctions and tariffs, with no fixed expiry. A bill to block the diesel deal was introduced the same day.
Most coverage has treated these as political and foreign-policy stories. They are also supply chain stories, and they reach far beyond diesel.
Where it reaches
Sourcing from China and India. Both are among the largest buyers of Russian energy. The tariff that could have applied to their goods from 18 October has been waived, not removed. Electronics, components, textiles and generic pharmaceuticals all sit inside that exposure, and the waiver can be lifted whenever the administration decides further action is appropriate.
Inputs that come from Russia itself. The waived tariff on Russian goods also covered commodities the United States still imports, such as fertilizer and certain metals. Fertilizer reaches food prices on the same slow clock as diesel: farm costs first, shelf prices months later.
Fuel and freight. Analysts expect the announced diesel volumes to do little to global prices. Any movement in diesel this autumn will have several possible causes at once: the war in Iran, the deal, the storm passing, the season.
Shipping, insurance and compliance. Some Russia sanctions have been waived and others remain in force under earlier orders. Every bank, insurer, carrier and port screening these flows now has to tell the difference, and honest mistakes become more likely when the rules are mixed.
The consequences nobody intended
Organizations that prepared may appear to have been penalized, and those that waited may appear to have been rewarded. Companies that spent money preparing for 18 October may now see those costs described as wasted. Companies that waited may appear to have made the better decision. Neither conclusion follows from the waiver alone. But the lesson many will draw is to wait next time, and repeated across thousands of organizations, that lesson weakens everyone’s readiness for the next deadline that does hold.
Deadlines may now look negotiable. A legislated date was waived two days before it arrived. Future deadlines may carry less credibility with the organizations and countries that plan around them, which could change behavior well beyond this law.
Flexibility may disappear next time. A Congress that sees a waiver used this way may write the next law without one. Today’s flexibility could become tomorrow’s rigidity, in a crisis where flexibility is exactly what is needed.
Results will be credited to the wrong cause. When diesel and freight costs move this autumn, organizations will credit whichever explanation fits their plans. Without checking what else changed in the same period, they will learn the wrong lesson from their own results.
What to do now
What we are watching
We are recording dated expectations for this strand before the outcomes are known: whether the waiver survives the election and the next 90 days, whether the announced diesel volumes are actually delivered, whether the deal moves diesel prices materially, whether India’s and China’s purchases of Russian energy change, and whether the energy ceasefire holds. We will check them on 4 November, 16 November and 15 January, and report what we got right and what we got wrong.
A storm, a law or a deal is a single event. The strands it touches keep moving, crossing into other markets and other organizations’ supply chains. That is Strand Commonality™: parts of an operation that seem unrelated are connected, so a change in one can affect another that was working. The organizations that see the crossing before the consequence arrives are the ones with room to act.
The takeaway
Hansen follows strands continuously, and has done so since RAM 1998, based on the theory we developed called Strand Commonality™. In 1998, we began by following three or four strands at once, and over about a year and a half expanded that to four to six that we could follow reliably and simultaneously. With ARA™ RAM 2025™ running on SLAP OS™, we can now follow 10 to 12 strands simultaneously, and our goal is to expand that to 20 by 2030.
The results are on the record. In 1998, RAM 1998 followed a focused set of strands around the procurement of MRO parts supporting a military’s IT infrastructure. Within three months, next-day delivery rose from 51 percent to 97.3 percent, and it held there for seven years. Over the past six months, ARA™ RAM 2025™ running on SLAP OS™ has averaged 91 percent output accuracy, assessed against the evidence in our documented case labs.
RAM 1998 demonstrated the process. Individual orders varied, but the strands underlying them did not behave randomly. Service technicians repeatedly held their part orders until late afternoon; sourcing and transportation options repeatedly narrowed within the same time band; and next-day delivery repeatedly failed where those strands met. Recognizing that rhythm made intervention possible. Feeding each outcome back into the system kept improving both the understanding and the operating response.
ARA™ RAM 2025™ is not a prediction tool. It is a system of intelligent operational recognition. It recognizes recurring strand tendencies and intersection points, records that understanding before the outcome is known, and tests it against what subsequently occurs. Through loopback learning, every confirmed pattern, contradiction and unexpected consequence changes what the system looks for next. The dated expectations in this post are not bets placed to be proven right; they are part of that loop. Prediction asks what will happen next. Strand recognition identifies what is happening again, and loopback learning determines what the system should understand differently because of it. Hansen strives to get it right rather than be right, on a continuous basis.
The reality is that you can make the right decision and get the wrong result, or make the wrong decision and get the right result. An outcome on its own does not tell you which one happened. Through Strand Commonality™, we track the impact and the continuing consequences of both, so that organizations learn from how their decisions were made, not only from how they turned out.
Sources and related reading:
Truth Is Believing. Accuracy Is Knowing. Outcome Is Proof.™
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