Structuring operational progress
How can you make operational improvement repeatable without automating decisions that have not been sufficiently verified?
3500 € per session, excl. VAT
In short
Over two days, totalling 14 hours excluding breaks, you design a prototype operational improvement system supported by generative AI. You structure feedback from the shop floor, prioritise problems, prepare standards and test their quality. You leave with a measurable pilot dossier, without claiming to implement full industrialisation during the training.
Why this training?
Local improvement initiatives lose momentum when problems are described differently, responsibilities are fragmented and actions are closed without evidence. A common system must preserve the specific characteristics of each workshop while enabling reliable comparison. Generative AI can help rephrase incidents, suggest classifications and prepare standards. Its contribution remains subject to evidence from the shop floor: a plausible cause is not a proven cause, and a convincing synthesis does not validate an action. Running case study: Sarnove Assemblage, a fictitious organisation, experiences component shortages and rework at goods receipt. Its workshops use incompatible incident forms. Using fictitious data, you design a common workflow linking reporting, analysis, corrective action and validation by purchasing and production. Transfer to the workplace: Propose a pilot to the operations manager, limited to one workshop and one purchasing category; compare the results with the initial baseline before any expansion.
Objectives of this training
- Mapping the deviation management workflow
- Defining a common incident data model
- Prototyping an AI assistance chain with human validation
- Testing output quality on held-out incidents
- Building a pilot plan with indicators and stopping conditions
Who is it for and what are the prerequisites?
Roles and tasks concerned
- Operations, procurement and operational excellence managers
- Process leads responsible for coordinating multiple teams
Prior skills
- Have practical experience of problem analysis and tracking corrective actions.
- Be able to use a generative assistant and verify its responses.
- Be able to work with operational tracking tables.
What you will work on
Defining the system’s scope
Objective: Choose a manageable scope. Purchasing–production flow, recurring pain points and responsibility boundaries.
Standardising reports
Objective: Make incidents comparable. Taxonomy, mandatory fields, confidentiality and traceability.
Establishing the baseline
Objective: Measure before making changes. Recurrence, time to closure, reopenings and denominators.
Prioritising without false causality
Objective: Select the problems to investigate. Severity, frequency, effort; distinction between correlation and causality.
Designing the assistance chain
Objective: Define human checks. Inputs, instructions, expected outputs, exceptions and escalation.
Building the prototype
Objective: Produce a reproducible processing workflow. Version-controlled instructions and structured formats.
Preparing the standards
Objective: Provide a framework for corrective actions. Owner, deadline, evidence of effectiveness and approval.
Testing difficult cases
Objective: Detect unacceptable outputs. Held-out test set, acceptance criteria, contradictions and missing information.
Correcting and governing
Objective: Control changes to the system. Version control, access permissions, maintenance and manual processing.
Deciding on the pilot
Objective: Make the case for a limited trial. Workload, milestones, adoption, stopping thresholds and conditions for expansion.
What you take away
- Target architecture and responsibility matrix
- Document-based prototype: register, AI instructions and validation points
- Test protocol and deviation log
- Pilot dossier: indicators, milestones and expansion decision
Method and assessment
- Short instructional inputs followed by immediate practice on the running case study.
- Individual work followed by work in pairs, with peer review of the outputs.
- Facilitated group review: what works, what needs correcting and what remains a human decision.
- Preparation: Prepare a simplified process map and anonymised incident records; a fictitious case study is available. Check which tools the organisation permits and the rules for transferring data.
Observable criteria: Each incident processed retains its source and a record of missing information. ; Each recommendation distinguishes between facts, hypotheses and expected evidence. ; Tests document errors, omissions and human corrections. ; Each indicator specifies its formula, reference, frequency and owner. ; The pilot includes acceptance thresholds and a procedure for reverting to manual processing..
Frequently asked questions
Will the solution be deployed in two days?
No: the training produces a tested prototype and a pilot plan.
Is programming required?
No, the workflow is simulated using documents and tables.
Can we use our own incidents?
Only in anonymised form, within authorised tools; otherwise, use the fictitious case study.
Does AI validate the root causes?
No, validating them is the responsibility of the relevant business managers, based on evidence from the field.
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