Prioritise, test, improve

How can you turn procurement and operational failings into prioritised actions with verifiable effects?

1960 € per session, excl. VAT

In short

In one day of 7 hours, excluding breaks, you build a fact-based improvement framework: diagnosis, causes, priorities, owners and indicators. Generative AI helps structure analyses and challenge actions. You prepare a pilot, simulate its review and define the conditions for rollout.

Why this training?

Procurement and operations departments sometimes accumulate corrective actions without checking the causes of delays or rework. An overly broad portfolio spreads resources too thinly and makes trade-offs difficult. Generative AI can connect findings, suggest causes and challenge prioritisation. Its proposals remain hypotheses: decisions are based on process data, human checks and explicit criteria. Continuing case study: Derviane Robinetterie, a fictitious organisation, experiences delayed receipts and recurring urgent orders. A fictitious case file brings together order histories, discrepancies on receipt and internal reports. Participants must distinguish supplier-related causes from shortcomings in internal approval, then prepare a pilot for a procurement category. Transfer to the workplace: Within the following month, reuse the template within a scope approved by your manager, establish the baseline measurement and initiate a pilot review before any expansion.

Objectives of this training

  • Define the scope of a process and establish its baseline performance.
  • Distinguish symptoms, substantiated causes and hypotheses proposed by AI.
  • Prioritise actions according to expected impact, effort, risk and dependencies.
  • Build a roadmap with owners, deadlines and indicators.
  • Test a review protocol and formalise the conditions for rollout.

Who is it for and what are the prerequisites?

Roles and tasks concerned

  • Procurement, supply and operations managers tasked with reducing process failures.
  • Process owners and continuous improvement managers coordinating several departments.

Prior skills

  • Be familiar with a procurement or operations process and its main indicators.
  • Be able to formulate a structured request to a generative AI assistant.
  • Use a spreadsheet to check lead times, volumes and proportions.

What you will work on

  1. Define the scope of the operational problem

    Objective: Establish a measurable scope. Plan, do, check, act cycle; process boundaries; data confidentiality.

  2. Establish the baseline

    Objective: Establish objective evidence of discrepancies before taking any action. Lead times, compliant receipt rate, urgent orders; missing data and denominators.

  3. Examine the causes

    Objective: Avoid treating only the symptoms. Pareto, questioning causes, distinguishing correlation from causation.

  4. Make trade-offs across the action portfolio

    Objective: Select sustainable actions. Weighted criteria, capacity constraints, dependencies and risks of shifting the problem elsewhere.

  5. Prepare the roadmap

    Objective: Make actions executable and measurable. Responsibilities, milestones, resources, outcome indicators and safeguards.

  6. Simulate the pilot and its review

    Objective: Test the rules before rollout. Test scope, stopping criteria, adverse effects and standardisation.

What you take away

  • Process diagnosis with a baseline and an evidence register.
  • An evidence-based action prioritisation matrix.
  • An operational roadmap with responsibilities and dependencies.
  • A pilot protocol and review dashboard.

Method and assessment

  • Short teaching inputs followed by immediate practice using the continuing case study.
  • Individual work followed by work in pairs, with cross-review of outputs.
  • Guided group discussion: what works, what needs correcting and what remains a human decision.
  • Preparation: Identify a recurring process failure and the decisions that an improvement review should enable. Arrange access to the tools authorised by the organisation; use the fictitious case file provided or data anonymised in advance.

Observable criteria: Each priority finding refers to an identifiable data point or observation. ; Baseline calculations are checked independently of AI responses. ; Each selected action addresses a documented cause and has an owner and a deadline. ; Each indicator specifies its formula, source, frequency, baseline and justified target. ; The simulation leads to a reasoned decision: continue, adjust or stop..

Frequently asked questions

Do you need to be proficient in a quality management method?

No, the guidance needed for diagnosis, prioritisation and the improvement cycle is explained.

Does AI choose the actions?

No. It proposes and critiques; participants make the trade-offs based on evidence and operational constraints.

Can you use supplier data?

Only anonymised data, and only in authorised tools, in accordance with the organisation’s confidentiality rules.

Are the gains demonstrated during the day?

No. The simulation checks the framework; the actual effects will need to be measured during the pilot.

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