Build a security and access model for predictive demand modelling appropriate to the sensitivity of the data involved.
Predictive Demand Modelling and Supply Chain Optimisation
A structured, applied course in predictive demand modelling — designed to be used the week you return.
📍 Istanbul🗓️ 5 training days📚 4 modules🎓 Accredited certificate
5intensive training days
4scientific modules
8training sessions
32detailed points
Course Overview
Vendors sell predictive demand modelling as a product; it behaves in practice as a change programme. The distance between a working prototype of this part of digital and data-driven work and a system the business can depend on is where most budgets disappear. It is appropriate for those preparing to take on wider responsibility for the wider digital and data-driven work agenda. The course leaves participants able to diagnose weaknesses in predictive demand modelling before they become incidents. Progress on this part of digital and data-driven work is usually lost in the gap between approval and execution. This programme builds this area of digital and data-driven work from first principles, without padding and without omitting what matters. Sessions alternate between guided analysis of predictive demand modelling and supervised application. Practitioner evidence points the same way: the digital and data-driven work discipline improves fastest where responsibility for it is named and owned. Participants finish with a short, specific brief on the practice within digital and data-driven work ready to put in front of a decision maker.
Expected Learning Outcomes
Assign clear ownership for each element of predictive demand modelling across the functions involved.
Anticipate the objections that predictive demand modelling will attract internally and answer them in advance.
Identify the failure points in predictive demand modelling most likely to cause loss, and control them first.
Establish monitoring that detects model or service degradation in predictive demand modelling before users report it.
Define escalation and fallback for predictive demand modelling when the automated path fails.
Map the regulatory obligations that apply to predictive demand modelling in each market of operation.
Who Should Attend
Operations managers whose processes are changed by predictive demand modelling.
Team leaders and supervisors who put predictive demand modelling into practice day to day.
Public sector digital leads applying predictive demand modelling under procurement and privacy rules.
Information security officers reviewing the exposure created by predictive demand modelling.
Experienced practitioners formalising an approach to predictive demand modelling that has grown up informally.
Vendor and contract managers overseeing suppliers involved in predictive demand modelling.
Course Modules
Predictive demand modelling: measuring benefit and retiring what it replaces
2 sessions · 8 pointsSession 1The pilot that actually settles the argument about predictive demand modelling
- Establish who is informed, consulted and accountable in predictive demand modelling.
- Define the trigger that would require predictive demand modelling to be redesigned.
- Design the pilot for predictive demand modelling so that a negative result is still useful.
- Set escalation thresholds for predictive demand modelling that work out of hours.
Session 2The governance predictive demand modelling needs and the governance it does not
- Compare the cost of predictive demand modelling with the cost of its absence.
- Classify the data in predictive demand modelling and apply access controls that match the classification.
- Specify the fallback path when predictive demand modelling is unavailable.
- Identify the skills the team lacks to operate predictive demand modelling independently.
Predictive demand modelling: vendor selection and avoiding lock-in
2 sessions · 8 pointsSession 1Who owns predictive demand modelling once the project team disbands
- Establish what evidence demonstrates predictive demand modelling is under control.
- Identify every system predictive demand modelling must read from or write to.
- Agree who is on call for predictive demand modelling outside working hours.
- Decide which legacy process predictive demand modelling retires, and set the date.
Session 2Explaining predictive demand modelling to people whose jobs it changes
- Build the user briefing that explains what predictive demand modelling does and does not decide.
- Prepare the summary of predictive demand modelling that senior management will read.
- Record what was learned when predictive demand modelling did not go as planned.
- Plan how predictive demand modelling is versioned and how a bad release is rolled back.
Predictive demand modelling: monitoring, drift and operational ownership
2 sessions · 8 pointsSession 1Building lasting competence in predictive demand modelling
- List the data sources predictive demand modelling consumes and confirm each has a named owner.
- Assign responsibility for keeping documentation of predictive demand modelling current.
- Confirm that reporting on predictive demand modelling reaches the people who can act.
- Close out actions on predictive demand modelling rather than leaving them open indefinitely.
Session 2The cost of predictive demand modelling and how to present it
- Arrange the handover of predictive demand modelling so capability survives staff changes.
- Define the service level predictive demand modelling must meet and what happens when it is missed.
- Set the metrics that will show whether predictive demand modelling is drifting from its intended behaviour.
- Name a single owner for each element of predictive demand modelling.
Predictive demand modelling: security, privacy and regulatory obligation
2 sessions · 8 pointsSession 1Making predictive demand modelling work when resources are constrained
- Review whether predictive demand modelling is aligned with the objectives of the transformation programme.
- Verify that predictive demand modelling still performs when input volume doubles unexpectedly.
- Distinguish symptoms from causes when predictive demand modelling underperforms.
- Rank the weaknesses in predictive demand modelling by consequence rather than by ease of fixing.
Session 2Proving predictive demand modelling paid for itself
- Test predictive demand modelling against edge cases drawn from real historical records.
- Confirm that contractual obligations around predictive demand modelling are understood.
- Define the exit route from the supplier supporting predictive demand modelling.
- Assess the regulatory obligations predictive demand modelling triggers in each jurisdiction.
Choose the package that suits you
Silver Package
At least 3 people
USD1,250
- Workshop or Program Participation
- Airport Transfers
- Customized Badge
- Expert Mentorship (Private Sessions)
- Supervision & Secretarial Services
- Accredited Certificate of Participation
- Complete Training Kit
- Coffee Break
- Closing Ceremony
Gold Package
At least 3 people
USD1,850
- 5-night stay in a 5-star hotel
- Workshop or Program Participation
- Airport Transfers
- Customized Badge
- Expert Mentorship (Private Sessions)
- Supervision & Secretarial Services
- Accredited Certificate of Participation
- Complete Training Kit
- Coffee Break
- Closing Ceremony
Complete your registration
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