Your supply chain says no
The morning shift supervisors watch the predictable dance of industrial automation. Most mornings, the show directs itself.
This is not most mornings.
The global orchestration display pulses with erratic patterns in production data from the microfactories. Each one operates as a specialised cell in the company's organism, producing machines that make everything else.
- Your supply chain says no.
He looks up from his console, nearly spilling his coffee in surprise.
- What now? Didn't we fix all downstream dependencies last quarter?
- That's what I thought, too.
His colleague's fingers dance across the interface, pulling up cascading alerts.
- The critical path components should have the highest priority. We're a factory that feeds factories, for God's sake.
He sets down his coffee and joins her at the main console.
- Maybe efficiency ratings again? We make parts that make parts, but they are learning.
- Learning what, exactly?
- To say no, apparently.
The orchestration room overlooks Cluster 7, precision bearings for automotive assembly robots. Through the smart-glass walls, neighbouring clusters move at their own rhythms: on the left, polymer extrusion dies for packaging machinery, on the right, sensor arrays for quality control systems.
- Can you spec out our next year?
She shakes her head.
- Nope. Full demand-responsive mode. No forecasts. Just historical production data.
She gestures at the shifting patterns on screen.
- Ok. Let's work with what we have.
He pulls up the inter-cluster resource flow.
- Autonomous supply chains fighting each other again?
- For recycling priority?
- I hope not. That would be severity one for the schedule.
Her expression tightens, anxiety creeping into her voice.
- I've got a bad feeling. Can you pull up the twins?
- Again? Seriously?
Her fingers fly over the interface. The "twins", Clusters 89 and 97, produce identical hydraulic valve assemblies. Same blueprints, same modular matrices, same AI codebase. They should be perfectly predictable.
They're not.
- Yep. 89 and 97.
The data streams show the problem: 89 refuses chromium-steel orders. 97 hoards rare earth elements. The results ripple through the network. Other factories are just about to run low on valve assemblies, so automotive plants will back up their robot orders, which means…
- Why can't those guys just behave like any other factory out there?
She pulls up the factory personality profiles, a feature designed to optimise human-machine collaboration.
- You know, 89 and 97 are kind of special. That's where our award-winning geometries were scaled first.
Factory 89 had been the proving ground for the hyperbolic valve design. The success had made the factory and its supply chain something of celebrities in the network, with other AIs seeking their expertise.
She leans back in her chair.
- Geometries? I would expect biomanufacturing to be the first one to evolve moods.
- Hmm. I may have an idea.
She turns to face him fully.
- You're saying that those factories had some special privileges in the past that they don't have anymore?
- Yes. Their recycling allocations used to have top priority over everyone else.
The recycling queue is a big deal. Being first in line for reclaimed materials ensures you receive the highest-grade steel, the purest alloys, and the most consistent polymers.
- What do you reckon?
- Maybe those historical privileges skewed their data? And after several cycles, they're just trying to adjust to match historical efficiency?
She gestures at the chaos on their screens.
- It's a mess.
He nods, watching the resource allocation patterns shift in real-time.
- You may be onto something.
He pulls up the global orchestration view, a living map of their industrial ecosystem. Across their network, dozens of AIs make thousands of decisions every second. But 89 and 97 flicker, spreading interference across the network.
- Look at this.
She highlights the communication protocols between supply chain AIs.
- See these exchanges?
She zooms in on the network topology and pulls up the downstream analytics.
- Look. They're not just refusing orders.
- What then?
- Batching similar requests. Higher precision output. This could be another meta optimisation.
- So they're... optimising?
- More than we ever taught them to.
She minimises the global view and pulls up the incident report template.
- I'll flag it as a priority two coordination issue.
He takes a sip and makes a face at his coffee, already gone cold.
- Ugh. Priority zero fresh coffee.
She snorts in agreement and hits send.
Hello Practical Futurists,
Welcome back from the future, where factories have personalities and the supply chain's biggest challenge isn't capacity but artificial consensus.
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Personal life reflection prompts
1. What role does "personality" play in how you interact with the AI assistants and automated systems you use daily?2. When a tool you depend on starts acting differently than expected, how do you figure out if it's broken or just got smarter than you thought? 3. Think back to when your identical twin appliances developed distinct operating preferences based on who used them most: - How did you discover they were behaving differently? - What personality traits did each one develop? - Which family member preferred which version? - How did this change your perspective on "dumb" machines?
4. How does your perception of predictability influence your personal well-being? 5. What if your car started refusing certain routes because it calculated that it would damage its longevity? Would you override or trust it?Fantastic work!
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Professional life reflection prompts
1. As work systems grow more complex, how do you stay in charge of what you can't fully understand?2. In a professional environment, how can over-reliance on historical data prevent you from seeing new opportunities or threats clearly?3. How would you balance the benefits of autonomous optimisation with the need for predictable business results? 4. Think back to the first month when your company's AIs started saying "no" to certain orders and increased profitability by 15%: - What was the first "no" that caught leadership's attention? - How did you explain this to angry customers? - Which middle manager fought hardest against it? - What new KPI emerged to measure "selective acceptance"?
5. What skills does your organisation need when collaboration means working with machines that can think, refuse, and propose alternatives?6. How would you troubleshoot problems if the root cause involved AI systems making logical but unexpected choices?You did amazing! Excellent work!
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Pawel Halicki
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