Bakeries
Plan production with evidence, not guesswork alone.
A seven-person bakery adjusts production daily. Sales, unsold products and special orders are not consolidated in one view.
These cases show how we would apply our method to different professional situations. Business profiles, countries, timelines and gains are illustrative assumptions. They do not describe completed client engagements.
Illustrative audit · 4 pages · no registration
- Simulated schedule
- 6 weeks
- Support planned in the scenario
- 7 consulting days
- Simulated net time saving
- 6.7 h / month
Assumed internal commitment : 3 person-days. Consulting days are spread across the schedule and do not mean full-time attendance. Internal days are additional. Scope and a quote must be agreed for a real engagement.
01 / Proposed audit scope
Bakery
Planning for selected product families, without autonomous control of production.
What we would examine
Scenario dataset covering 12 weeks: sales, unsold items, special orders and shortages by product family.
02 / The process, department by department
| Department | Simulated starting point | Proposed process | Human approval |
|---|---|---|---|
| Operations & production | Decide quantities from scattered notes | Editable plan by product family | Head baker |
| Purchasing & inventory | Manually assemble ingredient needs | Preparation list checked against actual inventory | Purchasing lead |
| Management & planning | Track waste without linking it to decisions | Adjustment log and weekly indicators | Manager |
The approach to compare
Compare weekday averages with demand forecasting. Separate bread and pastries; show a range instead of presenting one figure as certain.
What stays with the professional
The baker adjusts and approves quantities against production constraints. Recipes, allergens and food safety remain under professional control.
03 / From audit to an independent team
6 weeks.
7 consulting days.
A simulated engagement timeline, with a decision and deliverable at every stage. Rollout depends on test results.
Week 1 · 1.5 consulting days
Observe & measure
Scenario dataset covering 12 weeks: sales, unsold items, special orders and shortages by product family.
Proposed deliverable Process map, baseline measurement and issue register.
Week 2 · 1 consulting days
Scope & decide
Compare simplification, existing features and AI. Define scope, access, full costs and stop criteria before building.
Proposed deliverable Audit report, priority matrix and test protocol.
Week 3 · 2 consulting days
Prototype
Compare weekday averages with demand forecasting. Separate bread and pastries; show a range instead of presenting one figure as certain.
Proposed deliverable Limited prototype, test set and approval workflow.
Weeks 4–5 · 1.5 consulting days
Test & decide
Assess unsold items and shortages together on comparable days. Do not accept an improvement that worsens availability.
Proposed deliverable Comparative assessment: proceed, adjust or stop.
Week 6 · 1 consulting days
Train & hand over
Train users on routine and exceptional cases. Appoint an owner and document the manual fallback. If testing fails, hand over findings and a correction plan.
Proposed deliverable Usage guide, team workshop and 30-day follow-up plan.
04 / What the scenario aims to achieve
Simulated net time saving
6.7 h / monthReduce planning preparation time and better document adjustments. Lower waste remains a separate hypothesis.
- Assumed monthly volume
- 26 daily plans
- Baseline time per unit
- 40 min
- Target time, including human review
- 20 min
- Monthly monitoring and maintenance
- 2 h
Transparent calculation
26 × (40 − 20) ÷ 60 − 2 = 6.7 h / month
Volume × (baseline time − target time) ÷ 60 − monthly monitoring.
If only half the volume benefits from the process, with the same monthly monitoring: 2.3 h / month
This calculation is a simulation, not a client measurement. Target time includes corrections and review. Time released is not a demonstrated cash saving; it depends on actual volume and adoption.
Costs to compare with the benefit
Cost advice, data preparation, integration, licences, training and maintenance before committing. Price, return on investment and revenue effects must be established for each engagement.
How we would verify the result
Compare both methods on a set separate from preparation examples. Measure total time, corrections, critical errors and actual use. Keep difficult cases in the assessment; suspend if quality or confidentiality deteriorates.
Professional acceptance criterion : Assess unsold items and shortages together on comparable days. Do not accept an improvement that worsens availability.
After the pilot
During the 30 days after an approved pilot: review metrics weekly, examine exceptions and decide whether to maintain, correct or expand the scope. This follow-up period is outside the scenario schedule and consulting days.
Before a pilot, define permitted data, access and any supplier reuse. Country-specific and professional rules must be checked for the real engagement.
Background reference: CNIL, using a generative AI system.Does this sound like your organisation?
Start with your reality: a time-consuming process, your current tools and the people involved. We can assess whether a similar approach makes sense for you.