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  1. Aior

    Stage-gate vs lean: structuring product development without strangling it

    Two opposite failure modes Product development goes wrong in two characteristic ways. Either the company has too little structure — every project is a hero's journey, schedules slip, learnings don't transfer — or it has too much structure, projects spend more time on gate reviews than on actual...
  2. Aior

    Observability with Prometheus, Loki, Tempo, Grafana: building a stack that survives growth

    Three signals, one stack Modern observability is metrics + logs + traces. Each tells you something the others can't, and the value compounds when they're cross-linked. The Prometheus + Loki + Tempo + Grafana ("PLTG") stack has emerged as the open-source default in 2026, and for good reason —...
  3. Aior

    Predictive vs preventive vs reactive: structuring an industrial maintenance programme

    The maintenance hierarchy Three modes of maintenance, none of them universally correct: Reactive — fix it when it breaks. Cheapest per intervention, most expensive per year for critical equipment. Preventive — fixed schedule based on hours / cycles / calendar. Cheap to design, often...
  4. Aior

    Test & measurement bench design: from one-off rigs to repeatable validation

    Why test rigs grow into projects The pattern: someone needs to test a thing, builds a rig in a week, the rig works, the company keeps using it. Two years later the rig is a single point of failure for the whole product validation pipeline, run on a laptop with a Python 2 environment that nobody...
  5. Aior

    Terraform, Pulumi, Ansible: choosing infrastructure-as-code without the religious wars

    The IaC question, untangled "What infrastructure-as-code should we use" is asked as one question but it's actually three: which provisioning tool (creates resources), which configuration tool (configures already-created resources), and which orchestration tool (sequences both). The wars over...
  6. Aior

    Traceability without rebuilding the entire factory: practical genealogy patterns

    Why traceability projects are hard The customer-facing version of traceability sounds simple: "given a finished serial number, tell me what raw lots, what equipment, what operators, and what process parameters were involved". The data model is a directed acyclic graph. The math is trivial. The...
  7. Aior

    Picking a CAD/CAM/CAE stack: the questions that decide more than the tools do

    The toolchain conversation we keep having[/HEADING> "What CAD should I use" is a perennial question. It's also one where the right answer depends on inputs that engineers under-specify: the team's existing skill base, what the supplier ecosystem expects, what the customer mandates, and how the...
  8. Aior

    CI/CD pipelines that survive: GitHub Actions, GitLab CI, Jenkins, and the lessons that don't change

    The CI/CD tool changes; the lessons don't Every team has a story about migrating CI from one tool to another, with the same problems showing up on the new tool within a year. The patterns that survive across tools, after seeing a fair number of pipelines mature and rot, are below. The tool...
  9. Aior

    SPC that operators actually trust: control charts beyond the textbook

    Why most SPC implementations are ignored Statistical process control is in every quality engineer's training. It's also, in our experience, the single most under-implemented part of factory quality systems. Operators don't trust the charts; engineers respect the theory but don't use the output...
  10. Aior

    Electrical schematics that survive the field: from harness design to as-built reality

    The schematic is a contract The schematic is the document an electrician reads at 3 AM in a cabinet that's been on the line for 8 years. It needs to be unambiguous, readable, and consistent with reality. Most schematics aren't. The patterns below are what we've converged on after re-drawing too...
  11. Aior

    Docker, Podman, and Kubernetes: when to step up the abstraction, and when to stop

    The container abstraction ladder The "should we use Kubernetes" question gets asked in absolutes. In practice, it's a ladder, and most teams are happiest one or two rungs below where the architecture diagrams suggest. Below are the actual rungs and the questions that pick between them. Rung 1...
  12. Aior

    MES that actually gets used: integrating with the line, not just the database

    Most MES projects fail at the floor The pattern we keep inheriting: a customer bought an MES, configured it, integrated it with their ERP, and three months later the operators are running the line on paper because the MES screens are slower than what they replaced. This isn't an MES product...
  13. Aior

    Design for manufacturing: the rules that survive contact with a real production line

    Why DfM rules are usually broken Design-for-manufacturing as taught in university is a list of rules: minimum wall thicknesses, draft angles, hole-to-edge distances. Each rule is correct. The way they get applied in practice — at the end of design, by a CAD operator who isn't the original...
  14. Aior

    AWS, Azure, GCP, Hetzner, OVH: picking a cloud for the actual workload, not the brand

    The cloud question, asked properly "Should we be on AWS / Azure / GCP / Hetzner / OVH" is a question that gets asked at a level too abstract to answer. The right question is: "given our workload's compute / storage / network / managed-services profile, latency requirements, compliance...
  15. Aior

    Field commissioning notes: the things you only learn by being in the cabinet

    Office work doesn't teach you this Every commissioning we've done has included at least one "huh, didn't expect that" moment that wasn't in any document. After enough cells, those moments form a pattern. Below are the things we now build into our checklists, written for the engineer who hasn't...
  16. Aior

    IoT project patterns that survive deployment: lessons from a few hundred devices in the field

    The shape of an IoT project that survives[/HEADING> Below are the patterns we've extracted from IoT projects that worked, vs the ones that quietly died after the pilot. None of these are exotic; all of them are skipped on first projects, and skipping any of them is the most common cause of "the...
  17. Aior

    Servo, stepper, or VFD: matching the drive to the motion, not the catalog

    The drive sizing question, asked properly "What drive should I use?" doesn't have a single answer because "drive" covers three quite different technology families. Servo, stepper, and VFD overlap in capability bands and are not interchangeable in the bands where they don't. Picking by catalog...
  18. Aior

    Self-hosted vs AWS IoT vs Azure IoT vs ThingsBoard: picking an IoT platform for the next 5 years

    The platform pick is mostly about who runs operations The "best IoT platform" question is less a feature comparison and more a question of operational ownership. Cloud-managed platforms transfer ops cost from the customer to the cloud vendor for a per-device fee. Self-hosted platforms keep ops...
  19. Aior

    ABB, KUKA, FANUC, UR — picking a robot brand for a project, not for a love affair

    Robot brand choice is a 15-year decision A robot bought in 2026 will likely still be in production in 2041. The brand decision determines the maintenance team you need, the spare-parts logistics, the IDE your team writes programs in, the cobot story when the customer wants one — and, two...
  20. Aior

    The IoT gateway: where most security and reliability problems get fixed (or shipped)

    Why the gateway is the critical layer Sensors generate data. The cloud receives data. Between them sits the gateway, and the gateway is where 90 % of the operational and security work happens. Get the gateway right and the rest of the IoT system is mostly tractable. Get it wrong and you're...
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