The one peer-reviewed study that went inside six factories and counted — 13 of 17 AI projects in ‘Make’ — taught plainly, then weighed: the spotlight effect and the small-n caveat.
August 2026 · Case 3 of 6
As you read — hold this question
When researchers actually counted where AI lands in a supply chain, where did it go — and does “where it’s easiest to measure” mean “where it matters most”?
13of 17
Of seventeen real AI projects across six factories, thirteen landed in “Make” — the factory floor, not the clever planning layer.
This is the required reading — Cannas and colleagues (2024) — and it is the module’s one piece of hard evidence. They didn’t survey; they went inside. What they found is the pattern nobody puts on a conference slide, plus two caveats the headline quietly skips.
The required reading
The textbook versionQuiz: Cannas, OEE & DCM
Six factories, seventeen real cases, one clear pattern
Cannas, Ciano, Saltalamacchia & Secchi (2024), in the International Journal of Production Research, ran a qualitative multiple-case study: six Italian manufacturers (firms A–F) and seventeen real AI implementations, each mapped onto a SCOR process. Thirteen of the seventeen sat in Make — the factory floor. Why? Because Make runs on your own clean machine-sensor data, and its headline metric is easy to move and measure: OEE (Overall Equipment Effectiveness = availability × performance × quality).
Benefits, counted
Cost savings appeared 15×, shorter lead-times 9×, better OEE 6× — and sustainability just once. Cost and speed dominate; green barely registers.
Where it’s heading (DCM)
Digital-Cognitive Manufacturing — cognitive computing + IoT + AI fused into factories that sense, decide and improve on their own. The paper’s bet on the future; mostly still a promise.
Read it with one eyebrow raised
The other side
Does “13 of 17” mean what it seems to?
The finding is real — but be careful what you take from it. “Make” may not be where AI matters most; it may simply be where results are easiest to measure — a spotlight effect, where you find AI wherever the light is brightest. The clever planning layer stayed empty not because AI can’t help there, but because Plan needs rival firms to share data and a big upfront bet before any payoff.
The other side — six firms are not the world
Six Italian manufacturers who agreed to be studied are not a random slice of global industry. It is excellent evidence for Italian manufacturing and a much weaker guide to your sector. And “Digital-Cognitive Manufacturing” is closer to a bespoke coinage than an established standard — even “cognitive manufacturing” still lacked a settled academic definition as of 2024. Cite the study for what it proves, not for everything it’s quoted to prove.
Quick recall — without looking back
Test yourself on this case
Question 1 of 3
Describe the Cannas et al. (2024) study design, and its headline finding.
A qualitative multiple-case study of six Italian manufacturers and seventeen AI implementations, each mapped to a SCOR process. Headline: 13 of 17 landed in “Make” (the factory floor); cost was the most-cited benefit (15×), sustainability the least (1×).
Question 2 of 3
Define OEE and DCM.
OEE = Overall Equipment Effectiveness = availability × performance × quality — the headline factory-floor metric. DCM = Digital-Cognitive Manufacturing = cognitive computing + IoT + AI fused into self-improving factories (the paper’s forward-looking idea).
Question 3 of 3
Give the two “other side” cautions about reading too much into “13 of 17.”
(1) The spotlight effect — Make is where results are easiest to measure, not necessarily where AI matters most. (2) Small, self-selected n — six Italian firms that agreed to be studied aren’t a random slice of the world.
Module 8 · Long Form · What AI Actually Does in Supply Chains
Module 8 · Short · What's Real, What's Hype
Sources
Required reading
Cannas, V. G., Ciano, M. P., Saltalamacchia, M. & Secchi, R. (2024). “Artificial intelligence in supply chain and operations management: a multiple case study research.” International Journal of Production Research, 62(9), 3333–3360.
OEE / cognitive mfg
ISO 22400-2 (OEE). “Cognitive manufacturing: definition and current trends,” J. Intelligent Manufacturing (Springer, 2024).
Innovare Study
Long-form video: What AI Actually Does in Supply Chains. 2026. youtu.be/HlD8PWRNDiM