About the Course
Organizations invest in supply chain analytics because they need results they can prove across service, cost, and resilience. In practical terms, that means you must demonstrate data quality control, KPI design, dashboard interpretation, demand analysis, and exception management in ways that support planning and executive review. This course uses recognized analytics discipline and supply chain metrics so you can work with inventory turns, fill rate, forecast accuracy, on-time-in-full performance, and root-cause analysis instead of relying on generic reporting. It also reflects how teams now use cloud dashboards and automated data capture to keep pace with faster planning cycles.
The course turns scattered knowledge into a structured supply chain analytics workflow. You will practice building KPI trees, cleaning operational data, mapping process bottlenecks, designing a control dashboard in Power BI or Tableau, and shaping a forecast using Excel and introductory Python concepts. You will also be introduced to how SQL supports querying operational data and how statistical process views support practical diagnosis, while hands-on work focuses on deliverables you can use after class. What you will learn: you will learn to assess supply chain data readiness, design analytics outputs for planning and control, and present findings in a way that supports action. In this course, you will practice building dashboards, KPI scorecards, and root-cause analyses hands-on, while predictive modeling and broader analytics automation are introduced at a foundation-to-intermediate level.
The course is designed for professionals working under real constraints such as incomplete data, multiple systems, limited analytics capacity, and pressure to justify inventory, transport, and service decisions quickly. It is structured to help you make progress even when data maturity is uneven and decision timelines are short.
Target Audience
This course is designed for professionals who need to improve supply chain decisions with trusted data, practical metrics, and usable dashboards.
- Supply Chain Analysts tracking service, inventory, and exception patterns
- Demand Planners building forecast views from historical order data
- Inventory Control Specialists monitoring stock accuracy and replenishment signals
- Logistics Coordinators analyzing lane performance, dwell time, and delays
- Procurement Analysts reviewing supplier performance and purchase order trends
- Operations Managers overseeing KPI reviews across planning and execution
- Supply Chain Planning Managers aligning demand, supply, and inventory data
- Transportation Analysts measuring route performance and carrier reliability
- Business Intelligence Analysts supporting supply chain reporting and dashboards
- Finance Partners evaluating supply chain cost drivers and working capital impact
Course Objectives
This course equips you to design, execute, and measure supply chain analytics initiatives that improve forecast accuracy, strengthen operational control, and support faster decisions.
- Assess supply chain data readiness using an operational data quality checklist and ERP, WMS, and TMS inputs.
- Apply descriptive and diagnostic analytics to inventory, transport, and order-fulfilment performance trends.
- Build a supply chain KPI scorecard using OTIF, forecast accuracy, inventory turns, and fill rate.
- Construct a Power BI or Tableau dashboard for daily planning and exception review.
- Evaluate data consistency and process variation against a practical root-cause analysis workflow.
- Navigate cross-functional reporting requirements for procurement, logistics, planning, and operations stakeholders.
- Implement a demand-tracking workflow using Excel trend analysis and introductory SQL querying.
- Synthesize dashboard findings into a concise supply chain improvement report and action plan.
Requirements & Prerequisites
Prerequisites required: working familiarity with supply chain or logistics operations, basic Excel use, and comfort reading operational reports. No coding is required for completion, although introductory SQL and Python concepts are included at an operational awareness level. Participants should bring a laptop with spreadsheet software and access to sample operational data where available; datasets and lab materials are provided for the exercises. This course is suited to foundation-to-intermediate learners who want practical analytics capability, not advanced data engineering.
Local Application and Business Return in your market
How participants can apply the training in local operating conditions, and the return their organisation can plan for.
How participants apply this
Expected ROI
Training Methodology
This is a practical, outcome-driven course designed to turn supply chain analytics aspiration into measurable action and credible reporting.
Methodology includes:
- Hands-on calculation of OTIF, forecast accuracy, and inventory turns from sample supply chain datasets.
- Scenario simulation on a late-shipment disruption and replenishment constraint.
- Assessment using a supply chain data quality checklist and KPI audit matrix.
- Stakeholder mapping across planning, procurement, warehouse, transport, and finance reporting lines.
- Case study analysis from retail, manufacturing, e-commerce, and third-party logistics operations.
- Group workshop to build a daily supply chain dashboard under time and data constraints.
- Reflection exercise comparing current reporting practices against benchmark KPI and analytics discipline.
Upcoming Sessions
Next available dates worldwide
Certification
Recognized credentials that advance your career
Participants who complete the Data Analytics for Supply Chain Management Training Program earn a Trainingcred Certificate of Achievement, demonstrating professional competence and alignment with global standards in learning and development.
NITA Accredited
Accredited by the National Industrial Training Authority, ensuring programs meet nationally recognized standards of quality and relevance.
CPD Certified
Recognized by the CPD Certification Service, ensuring every program meets internationally benchmarked standards of professional excellence.
Why this course earns its place on your CV
Accredited training, practitioner trainers, and peers on the same career track — the three things real expertise is built on.
Skills Relevance
- Master cutting-edge tools to optimize supply chain efficiency and predictability.
- Transform data into actionable insights for strategic supply chain decisions.
- Leverage analytics to reduce costs and improve delivery times.
Expert Delivery
- Learn from industry leaders with years of experience in supply chain analytics.
- Gain exclusive insights from real-world case studies by top logistics companies.
- Interactive sessions ensure you apply concepts in real-time scenarios.
Career Advancement
- Enhance your resume with skills sought after by Fortune 500 supply chain managers.
- Position yourself as a key player in driving organizational change through data.
- Unlock new career opportunities in logistics, procurement, and supply chain management.
Tools and platforms relevant to this field
Examples local teams may encounter, and that may be featured in training where they support the confirmed course scope.
These are field-relevant examples, not a promise that every tool will be covered. Exact coverage depends on the confirmed course scope, participant needs, and delivery format.
-
Power BI MicrosoftUsed to build KPI dashboards and exception views for inventory, service, demand, and supplier performance.
-
Tableau SalesforceUsed to visualize trends, compare sites or categories, and present supply chain performance to managers.
-
SAP S/4HANA SAPUsed as an enterprise system of record for supply chain, procurement, and planning data that analytics teams extract and model.
-
Oracle Fusion Cloud SCM OracleUsed to support planning, procurement, and logistics processes whose data can be analyzed for performance improvement.
-
Blue Yonder Blue YonderUsed in demand planning and fulfillment environments where teams need forecasting and operational visibility.























