The Thing She Cannot Name

Essay 4 of 5, Decision-Native 2040 series. Draft 1, 2026-04-23.

At 5:04 on a Wednesday morning, Asha Iyer is at Terminal 2 of Zurich Airport, waiting for the red-eye to Coimbatore.

She has packed light. One overnight bag. The cardigan her daughter bought her two years ago, now faded. A copy of a trade paper she has been meaning to read for months. She orders a black coffee. She does not open the laptop.

The plant she is flying to is a tier-two supplier of precision-machined components. Her firm has used them for nine years. The firm’s supplier-risk platform has rated them green for the last fifteen quarters. The scorecard updates weekly from financial filings, customs data, trade-press mentions, and ESG disclosures. The scorecard is, by every metric available to it, correct.

She could have stayed home.

Twenty weeks a year, Asha flies to places like Coimbatore. Her peers in the procurement function travel three weeks a year. Most of them have stopped travelling entirely since the scorecard went firm-wide in 2031. She has been asked twice in the last year to rationalise her travel budget. She has been asked once, politely, whether her team should pilot the scorecard-only operating model. She has said, each time, that she will think about it.

What the scorecard sees

The scorecard is built on everything you can pay for in scale. Financial filings. Export and customs data. Workforce headcount from public filings. Trade press. Five layers of ESG disclosures. A satellite feed that pings plant parking lots once a week. A news-sentiment stream that flags shifts in language around a given supplier. The platform processes 18,000 suppliers for her firm. One analyst can manage the flags generated by about 400 of those. Asha’s team of six manages 140.

The scorecard is correct on most things. Suppliers that are about to go insolvent mostly hit it first. Quality drift at scale shows up in the customs returns eventually. A factory that gets caught in a regulatory sweep lights up on the news-sentiment stream within hours.

What the scorecard does not see is the small things. It does not see a worker who looks terrified when the owner walks past. It does not see a pallet of packaging in a language the company’s export licence does not cover. It does not see a stack of reject parts that have been re-bagged with different serial numbers and placed back into inventory. It does not see the owner’s tone when he is asked about his largest customer.

Nobody decided to blind the scorecard. The scorecard was built to run across eighteen thousand suppliers at once, and running across that many required choosing the signals it could measure cheaply and at distance. The small things are expensive. A person has to be there. And the person has to have been there before, enough times, to know what they are looking at.

What she finds in Coimbatore

Nothing dramatic, this visit. Asha walks the plant for three hours with the plant manager, who has been there eleven years. She eats with the shop-floor lead who has been there sixteen. She notices that the packaging line is running a shift shorter than usual. She asks. The plant manager says they are running down inventory for a large order. She notices that the large order is for a customer she does not recognise. She writes the customer name in her notebook.

She notices that the reject bin is smaller than usual. She asks why. The shop-floor lead laughs and says they have gotten better at sorting earlier.

She notices that the walls are newly painted on the side visible from the main road.

Nothing of this goes into a report. Nothing of it is scored. The visit will be logged in the system as quarterly visit, no anomalies flagged. That log is what the scorecard will see.

But Asha, flying home on Thursday afternoon, will write the customer name and the painted wall and the smaller reject bin in her own notebook. She keeps the notebook in the drawer of her desk. It is a running inventory of small things she does not yet understand.

The inventory of small things

Three months later, the customs data will show that the plant’s largest customer has gone elsewhere. The plant will not have declared this on the scorecard update, and the scorecard will not know because the customer reference in the export records is routed through a trading company. The plant’s revenue will dip two percent, which will read on the scorecard as within normal volatility.

Six months later, a regional regulatory shift will require additional traceability on serial numbers. The plant’s QC system will flag a small number of inconsistencies that, on investigation, will turn out to be the re-bagged rejects.

Nine months later, the plant will be placed on the scorecard’s amber list. By then, Asha’s team will have already recommended a divestment. They will have recommended it six months ago, on the basis of a report that did not cite any of the small things directly, but said workforce and inventory signals suggest this supplier is under unusual pressure.

The recommendation will be unpopular with the procurement-optimisation function, which will have cited the scorecard’s green rating. It will be actioned reluctantly. By the time the amber flag arrives, the divestment will already be complete, and the cost of replacement will have been lower than it would have been in six months.

Asha could not name the signal she was reading when she recommended the divestment. She could not have produced a single scored field to support it. If she were asked to defend it in a compliance review, she could say only I have been there twelve times, and the signals of distress this time are different from the signals of ordinary volatility. That is not a defensible argument in the current regime. It is the argument she keeps making anyway.

In the room

In late 2039, Asha sits in the budget review for the procurement function. Across the table is Marcus Brandt, the procurement-optimisation MD, and the CEO. Brandt has prepared a slide titled “Operating model options for the supplier-risk function.” Option B is “Scorecard-led, head-count rationalised by 60%.” Beside it, in a separate column, sits Asha’s team’s annual travel budget.

Brandt walks the room through the maths. The scorecard’s coverage has improved each of the last six quarters. False-positive rates have fallen. Suppliers flagged amber by the scorecard have a ninety-three percent concordance with later-stage QC inspection. The budget impact of moving to scorecard-only is modelled at 4.2 million Swiss francs over three years. It is the kind of slide that wins.

The CEO turns to Asha and asks if anything in the slide is wrong.

Asha sits with the question a beat longer than is comfortable. The numbers are right, she says. She would not have known about Coimbatore from the dashboard. The supplier had been green for fifteen quarters; three months after her visit, their largest customer moved their volume; the scorecard did not catch the shift for another nine months; her team had divested by month three.

Brandt is polite. He says you cannot run an 18,000-supplier function on instinct, that the Coimbatore call could have been luck.

She does not answer. She does not have a defence she can put on a slide.

The CEO does not commit either way. The meeting ends. Brandt’s slides remain in circulation. The travel budget is renewed for one more year, marked for review.

She leaves the room knowing she is now visible.

What it costs

Twenty weeks a year on the road is a material cost to her career. Her cohort in the firm is promoted at a slower rate than the scorecard-native cohort. Her children are older now. Her team’s budget is flat while the scorecard-native teams are expanding. The firm does not punish her practice. It does not reward it either. It accepts it, in the tolerant way that firms accept things they cannot yet explain the value of.

There are visits where she finds nothing. She walks a plant for three hours, has a decent dinner with the plant manager, flies home with an empty notebook. On those evenings, in the taxi from the airport, she wonders whether the younger procurement MDs have it right. She does not wonder long. But she wonders.

She is not in a position where she would win a presentation. The presentation rewards legible signals. Her signals are not legible. They are slow, embodied, cumulative, and they live in her notebook and her head. In a procurement-function review, the story she can tell about a supplier sounds anecdotal. The story the scorecard can tell sounds systematic. The anecdotal story is more often correct. This is not something she has said out loud to her CEO. She thinks her CEO already knows, and she thinks her CEO could not defend it in a budget review any better than she could.

Why she keeps doing it

Early in her career, Asha was the junior on a supplier-network project that collapsed in a way the scorecard-predecessors had rated as unlikely. The scorecard had been green for six quarters. She had been to the plants. She had flagged, in a note to her then-director, that one of the plants felt thin. She could not defend the word. The flag was not ignored, exactly. It was discussed and set aside. When the network collapsed, the then-director retired early and the junior, Asha, was asked in the postmortem what she had noticed and why.

She could not name the signal then either. But she learned that the signal existed, that her flag had been correct, and that the correct flag had been silent in the system. She decided, after the postmortem, that she would keep visiting plants, whether or not the system required it, and that she would trust flags she could not defend.

Twenty years later, her CEO calls her when something shifts in a region. Not because she has data the scorecard does not. Because she has been in the region. Repeatedly. Slowly. For a long time. The encounter inventory, which epistemologists would call her tacit dimension, is what the CEO is actually borrowing from.

The tail event

The procurement-optimisation function, which is currently led by someone ten years younger than Asha, will at some point propose a scorecard-only operating model for her team. The proposal will be defensible. Her team’s 140-supplier throughput is costly. The scorecard-native teams can handle three times as many suppliers per head. The savings will be significant. The case against her practice will be made in her own organisation, by people she likes, using metrics that have been reliable for a decade.

If the transition happens before a regional tail event, nothing visible will change. The suppliers will mostly remain green. The cost savings will show up in the function’s budget. Asha’s team will be reabsorbed or retired. The notebook in the desk drawer will be archived and not read again. Her successor, a competent procurement MD, will not have time to do what Asha did, because the metric the successor will be measured against will be suppliers processed per head-count per quarter.

If a regional tail event happens before the transition, Asha will still be in place, and she will advise the CEO, and the advice will be valuable, and the practice will survive another budget cycle.

The order in which those two things happen is largely luck.

A quiet one

Asha is not a hero. She is not particularly political. She is not trying to save her firm. She is a competent, somewhat stubborn professional who discovered, early, that a specific cognitive act was worth preserving, and who has continued to perform it at mild personal cost because she has not been able to think of a reason to stop.

She is running a practice that used to be the profession and is now an individual quirk.

The firm does not know what it owes her. She does not know what she is owed. When she retires, nobody will replace the specific thing she does, because the specific thing she does is illegible to the replacement process. The decision to not replace her will be a budget item. The cost will show up somewhere later, attributed to something else.

She will be at Terminal 2 again in three weeks. The flight is already booked. The notebook in the desk drawer has two new entries she has not yet understood.

She could have stayed home. She did not. In 2040, that is what practice looks like.

A note on this essay. Asha Iyer is a composite, not a real person. The supplier-risk scoring described here is a reasonable projection of current industrial-scale vendor monitoring tools (Everstream Analytics, Resilinc, riskmethods) into the 2040 frame. The “encounter inventory” phenomenon she embodies has a name in epistemology: Michael Polanyi’s tacit dimension, described in The Tacit Dimension (1966). Research on the erosion of tacit knowledge inside procurement functions is scarce, which is part of the essay’s point.