Training on Data Analytics & Predictive Insights for Supply Chain
Build predictive analytics skills to improve demand forecasting and supply chain performance.
Next intake
20 Jul 2026 · Dakar
Duration
10 days
Live instruction
Delivery
Physical + Virtual
Cohort based
Level
Advanced
Working professionals
Certification
NITA reimbursable
For Kenyan cohorts
Language
English
All materials
About this programme
This course builds advanced capabilities in using data analytics and predictive insights to improve supply chain planning, performance, and resilience. Participants gain structured exposure to descriptive, diagnostic, predictive, and prescriptive analytics applied across procurement, logistics, inventory, and distribution. The course emphasizes data-driven decision-making, forecasting accuracy, risk anticipation, and performance optimization in complex supply chain environments.
Duration
10 Days
Who Should Attend:
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Supply chain and logistics professionals
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Procurement and demand planning managers
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Operations, inventory, and distribution analysts
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Data, business intelligence, and performance teams
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Finance and risk professionals supporting supply chain decisions
What you'll walk away with
By the end of the course, participants will be able to:
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Apply supply chain data analytics frameworks and metrics
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Use predictive models to forecast demand and supply risks
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Translate analytics insights into operational decisions
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Improve inventory, logistics, and procurement performance
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Support resilient and responsive supply chain strategies
What we cover, module by module
Module 1: Supply Chain Analytics Foundations
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Role of analytics in modern supply chains
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Data types, sources, and quality challenges
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Key supply chain KPIs and dashboards
Case Study: Poor data visibility causing planning failures
Practical: Mapping analytics use cases across the supply chain
Module 2: Descriptive & Diagnostic Analytics
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Performance measurement and root-cause analysis
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Trend, variance, and exception analysis
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Visualization for decision support
Case Study: Identifying bottlenecks using analytics
Practical: Building a performance analysis framework
Module 3: Demand Analytics & Forecasting Models
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Demand patterns and forecasting techniques
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Time-series analysis and causal models
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Forecast accuracy measurement
Case Study: Forecast errors leading to stockouts
Practical: Designing a demand forecasting approach
Module 4: Predictive Analytics for Supply Risk
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Supplier risk, disruption, and lead-time variability
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Scenario modeling and stress testing
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Early warning indicators
Case Study: Predicting supply disruptions
Practical: Developing a supply risk prediction model
Module 5: Inventory & Network Analytics
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Inventory optimization and service levels
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Network design and flow analytics
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Cost-to-serve analysis
Case Study: Excess inventory despite high demand
Practical: Optimizing inventory strategies using analytics
Module 6: Logistics & Transportation Analytics
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Route optimization and capacity planning
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Freight cost and carrier performance analytics
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Last-mile performance insights
Case Study: Transportation inefficiencies identified through data
Practical: Analyzing logistics performance indicators
Module 7: Predictive & Prescriptive Analytics Applications
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Predictive vs prescriptive decision models
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Simulation and optimization techniques
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Analytics-driven decision frameworks
Case Study: Prescriptive analytics improving service levels
Practical: Translating predictive insights into decisions
Module 8: Digital Tools, AI & Automation in Supply Chain Analytics
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Analytics platforms and data integration
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AI and machine learning applications
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Automation of analytics workflows
Case Study: AI-enabled supply chain forecasting
Practical: Evaluating analytics and AI tools
Module 9: Governance, Ethics & Analytics Risk Management
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Data governance and quality assurance
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Ethical use of data and AI
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Cyber and model risks
Case Study: Analytics misuse affecting decisions
Practical: Designing an analytics governance framework
Module 10: Strategy, Integration & Analytics Roadmap
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Embedding analytics into supply chain strategy
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Change management and capability building
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Developing a supply chain analytics roadmap
Case Study: Analytics-driven supply chain transformation
Practical: Creating an organizational analytics action plan
Where the change lands
Personal Impact:
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Enhanced analytical and data interpretation skills
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Stronger forecasting and predictive thinking
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Improved decision-making confidence
Organizational Impact:
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Improved forecast accuracy and service levels
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Reduced costs and operational risks
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Stronger data-driven supply chain performance
Dates and locations
Upcoming intakes
Every intake is limited to a small cohort. Booking closes when a date fills or three weeks before the start, whichever comes first.
| City | Starts | Ends | Delivery | Book |
|---|---|---|---|---|
DakarNext | 20 Jul 2026 | 31 Jul 2026 | In-Person | Book |
Johannesburg | 20 Jul 2026 | 31 Jul 2026 | In-Person | Book |
Dar es Salaam | 20 Jul 2026 | 31 Jul 2026 | In-Person | Book |
Nakuru | 20 Jul 2026 | 31 Jul 2026 | In-Person | Book |
Pretoria | 27 Jul 2026 | 07 Aug 2026 | In-Person | Book |
Kisumu | 27 Jul 2026 | 07 Aug 2026 | In-Person | Book |
- DakarNext
20 Jul → 31 Jul·In-Person
Book this intake - Johannesburg
20 Jul → 31 Jul·In-Person
Book this intake - Dar es Salaam
20 Jul → 31 Jul·In-Person
Book this intake - Nakuru
20 Jul → 31 Jul·In-Person
Book this intake - Pretoria
27 Jul → 07 Aug·In-Person
Book this intake - Kisumu
27 Jul → 07 Aug·In-Person
Book this intake
Common questions.
Still not sure? Send us a note and a facilitator will get back to you within a business day.
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Course finder
Find the right course for you
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For corporate teams
Training 10+ professionals?
We deliver Training on Data Analytics & Predictive Insights for Supply Chain in-house at your offices, at a venue we arrange, or fully virtual. Customise the curriculum against your KPIs, and get a bespoke price for the cohort size you need.