Supply Chain Data Analytics Training Course

Duration 10 days 19 modules · 65 contact hours
Level Intermediate Certificate on completion
Who attends Supply Chain Analysts, Demand Planners and others
Fee from USD 2,200 KSh 190,000 for Kenyan organisations
Where 52 cities + online See dates by city
Next start 9 Nov 2026 Course code SD-DA-071

Before you join: Participants should understand basic supply chain processes such as purchasing, inventory control, order fulfilment, production planning or transport operations.

Dates & prices

Two start dates a month in every city, and live online on weekdays or weekends. Fees are per participant; classroom fees include tuition, materials, lunch and refreshments, and the certificate.

Suggest a date or city

Showing the next six months. Later dates, other cities and private deliveries for your team are available on request.

Course overview

Supply chain teams hold large volumes of order, inventory, transport, supplier and production data, yet many decisions still rely on static reports, disconnected spreadsheets and late exception reporting. This course addresses the gap between operational data and timely action. Participants learn to frame business questions, assess data quality, calculate meaningful performance measures and present findings that support planning, procurement, logistics and customer-service decisions.

The course covers the full analytical workflow for supply chain work: defining metrics, extracting and structuring data, cleaning records, building Excel and Power BI models, querying data with SQL, analysing demand and inventory patterns, measuring supplier and transport performance, and identifying root causes of service or cost problems. Participants apply methods such as ABC-XYZ classification, Pareto analysis, safety-stock analysis, forecast error measurement, control charts and SCOR performance attributes. They also learn how to distinguish correlation from causation and communicate uncertainty responsibly.

Instruction combines short technical briefings with worked cases drawn from distribution, manufacturing, procurement and logistics. Participants build analytical outputs from the first week, using realistic order, stock, supplier and freight data. They complete guided individual and group work, review decisions with peers and prepare an application plan for their own role. The final capstone requires each participant to build and present a complete supply chain analysis, including data preparation, metrics, findings, recommendations and a dashboard or decision brief.

The course suits professionals who already work with supply chain data and need a structured method for turning it into decisions. It is especially relevant where teams need clearer service, inventory, supplier, transport or forecast-performance reporting.

What you will be able to do

  • Define supply chain analytical questions, measures and decision requirements using an analytical charter
  • Prepare order, inventory, supplier and transport data using structured data-quality checks
  • Build repeatable Excel and Power Query data models for supply chain reporting
  • Write SQL queries to extract, join, filter and aggregate operational supply chain data
  • Calculate demand, inventory, service, supplier and transport performance metrics
  • Apply ABC-XYZ, Pareto, forecast error and safety-stock analysis to operational decisions
  • Create Power BI dashboards and decision briefs that explain exceptions and recommended actions
  • Present a complete supply chain data analysis with evidence, assumptions and implementation priorities

Benefits of attending

For you

  • Produce credible supply chain analyses rather than manually assembled status reports
  • Identify inventory, service and cost exceptions before they become management escalations
  • Use SQL and Power BI confidently with operational supply chain data
  • Explain analytical findings clearly to planners, managers and non-technical stakeholders
  • Build a portfolio-quality supply chain analysis for immediate workplace use

For your organisation

  • More consistent definitions for service, inventory, supplier and transport performance
  • Faster identification of stockout, excess inventory and late-delivery risks
  • Reduced manual reporting effort through repeatable data preparation and dashboards
  • Better evidence for replenishment, supplier review and logistics improvement decisions
  • Clearer traceability from operational data to management actions and outcomes

Course modules

19 modules over 10 days

Participants can define an operational analytical problem, its users, measures and decision cadence.

  • Supply chain decision questions translated into measurable analytical requirements
  • SCOR performance attributes linked to cost, reliability, responsiveness and assets
  • Grain, dimensions and measures defined for order-to-delivery data
  • Decision cadence mapped across daily execution, weekly planning and monthly review
  • Analytical charter records users, decisions, data sources, metrics and refresh frequency
  • Data ethics addresses supplier confidentiality, customer data and access controls

Participants can map common supply chain data sources and specify the records needed for analysis.

  • ERP transaction tables distinguished from master data and planning parameters
  • Order lifecycle events aligned from customer request through proof of delivery
  • Inventory movements classified by receipt, issue, transfer, adjustment and return
  • Supplier records connected through purchase orders, receipts, invoices and quality events
  • Transport data mapped across shipment, consignment, route, carrier and freight invoice
  • Data dictionary documents fields, owners, business definitions and update timing

Participants can assess operational data quality and document issues that affect decisions.

  • Completeness tests identify missing dates, quantities, locations and supplier identifiers
  • Validity rules compare values against permitted codes, ranges and formats
  • Duplicate detection separates repeated transactions from legitimate split deliveries
  • Timeliness checks measure delays between operational events and system posting
  • Reconciliation compares order, shipment, receipt and invoice quantities
  • Issue log assigns data defects, owners, business impact and correction priorities

Participants can build structured Excel models that calculate repeatable supply chain measures.

  • Excel Tables create controlled ranges for refreshable operational analysis
  • XLOOKUP and INDEX-MATCH connect transactions with product and supplier attributes
  • SUMIFS calculates volume, value and service measures across defined segments
  • PivotTables summarise orders, stock, spend and freight by operational dimensions
  • Date tables support monthly, weekly and fiscal-period trend analysis
  • Formula auditing traces errors through linked supply chain calculations

Participants can prepare repeatable supply chain datasets using Power Query transformations.

  • Power Query imports CSV, Excel and folder-based operational extracts
  • Column profiling exposes nulls, errors, distinct values and unexpected distributions
  • Merge queries joins orders with customers, products, suppliers and locations
  • Append queries combines monthly extracts into a controlled transaction history
  • Conditional columns classify on-time delivery, stock status and exception categories
  • Refresh steps document repeatable transformation logic and source dependencies

Participants can write SQL queries that extract and aggregate supply chain records for analysis.

  • SELECT statements retrieve required transaction fields from operational tables
  • WHERE clauses filter records by date, location, product and order status
  • INNER JOIN and LEFT JOIN connect orders, inventory and supplier data
  • GROUP BY aggregates quantities, values and counts across business dimensions
  • CASE expressions create service, delay and inventory exception classifications
  • Common table expressions organise multi-step supply chain data queries

Participants can measure order fulfilment performance and identify service failure patterns.

  • OTIF calculation distinguishes on-time, in-full and complete-order performance
  • Fill rate measures immediate demand satisfaction by line, order and customer
  • Order cycle time tracks elapsed time from request to confirmed delivery
  • Backorder ageing prioritises unresolved demand by customer commitment date
  • Pareto analysis isolates products, customers and locations driving service failures
  • Exception segmentation separates demand, stock, warehouse and transport causes

Participants can assess demand behaviour and evaluate forecast performance using established measures.

  • Time-series plots reveal trend, seasonality, intermittency and demand outliers
  • Mean absolute error measures average forecast deviation in demand units
  • MAPE and weighted MAPE compare forecast accuracy across product volumes
  • Forecast bias identifies systematic over-forecasting and under-forecasting patterns
  • Tracking signal combines bias and error for forecast-control decisions
  • Demand segmentation separates stable, seasonal, erratic and intermittent items

Participants can analyse inventory positions and recommend evidence-based stock policy actions.

  • Inventory position combines on-hand, on-order, allocated and backordered quantities
  • ABC classification ranks items by annual consumption value and management priority
  • XYZ classification groups items by demand variability and predictability
  • Safety stock estimates protection against demand and lead-time uncertainty
  • Reorder point combines expected lead-time demand with safety stock
  • Days of supply compares available stock against expected future consumption

Participants can evaluate supplier delivery, quality and spend performance for supplier reviews.

  • Supplier OTIF measures promised-date and received-quantity performance consistently
  • Purchase price variance separates price changes from volume and mix effects
  • Lead-time analysis compares planned, confirmed and actual supplier delivery dates
  • Supplier defect rate tracks accepted, rejected and returned receipt quantities
  • Spend segmentation groups suppliers by category, value, risk and dependency
  • Scorecards combine service, quality, cost and responsiveness measures

Participants can analyse freight cost, carrier performance and distribution service exceptions.

  • Freight cost per shipment, kilogram and kilometre exposes cost variation
  • Carrier OTIF compares promised delivery dates with confirmed delivery events
  • Route analysis identifies recurring delays, low utilisation and cost outliers
  • Shipment consolidation rates measure opportunities for load and route improvement
  • Accessorial charges separate base freight from avoidable service fees
  • Control charts distinguish normal delivery variation from special-cause delays

Participants can investigate supply chain exceptions and distinguish symptoms from actionable causes.

  • Five Whys traces recurring service failures to process and data causes
  • Fishbone diagrams structure potential causes across people, process and systems
  • Process maps locate handoffs, queues, rework loops and approval delays
  • Stratification tests whether failures concentrate by product, supplier or location
  • Correlation analysis identifies associations without claiming unsupported causation
  • Corrective-action registers record owners, due dates, evidence and expected effects

Participants can use statistical process control to monitor variation in supply chain performance.

  • Run charts show shifts, trends and cycles in operational performance
  • Individuals charts monitor continuous measures such as delivery lead time
  • p-charts monitor proportions including late orders and damaged deliveries
  • Control limits distinguish common-cause variation from special-cause signals
  • Sampling considerations address data volume, frequency and process stability
  • Escalation rules define investigation triggers and response responsibilities

Participants can create a Power BI model that supports consistent supply chain reporting.

  • Star schemas separate fact tables from product, customer and date dimensions
  • Relationship direction prevents ambiguous filtering across operational data tables
  • DAX measures calculate service, inventory, demand and supplier performance
  • Calendar tables support comparable month, quarter and year trend measures
  • Drill-through pages connect summary exceptions with transaction-level evidence
  • Row-level security concepts protect supplier, customer and location information

Participants can design decision-focused dashboards that make supply chain exceptions visible.

  • KPI cards display targets, actuals, variance and decision status
  • Exception-first layouts place urgent service, stock and cost signals prominently
  • Line charts show demand, forecast and service trends over time
  • Scatter plots reveal relationships between variability, value and service performance
  • Heat maps identify location, supplier and product concentration patterns
  • Annotation explains thresholds, assumptions, actions and data-refresh timing

Participants can test supply chain scenarios and communicate the implications of assumptions.

  • What-if parameters test service targets, lead times and demand changes
  • Sensitivity analysis ranks assumptions by their effect on inventory requirements
  • Scenario tables compare base, constrained and recovery operating conditions
  • Capacity calculations estimate workload against available labour, storage and transport
  • Risk matrices prioritise supply disruptions by likelihood and operational impact
  • Recommendation logic links analytical findings with owners, timing and expected benefits

Participants can establish practical controls for trusted supply chain reporting and analysis.

  • Metric definitions establish one calculation method for each reported measure
  • Data ownership assigns accountability for source fields and business rules
  • Refresh calendars align reporting availability with operational decision cycles
  • Version control records report changes, approved logic and release dates
  • Access matrices define permitted users for commercial and customer data
  • Analytical backlog prioritises reporting requests by value, urgency and effort

Participants can present analytical findings as clear recommendations for operational and management audiences.

  • Situation-complication-question-answer structure frames an evidence-based management story
  • Executive summaries state performance issue, cause, recommendation and decision required
  • Confidence statements explain data limitations, assumptions and material uncertainty
  • Waterfall charts separate drivers of cost, service or inventory change
  • Recommendation slides assign actions, owners, dates and success measures
  • Challenge preparation anticipates questions on definitions, evidence and trade-offs

Participants can build, review and present a complete supply chain analysis with a defensible recommendation.

  • Analytical charter defines the business decision, users, scope and success measures
  • Source data is profiled, cleaned, joined and documented for analysis
  • Performance metrics quantify service, inventory, supplier or transport issues
  • Root-cause evidence distinguishes recurring patterns from isolated operational events
  • Power BI dashboard communicates exceptions, trends, drivers and priorities
  • Recommendation plan specifies actions, owners, timing, risks and monitoring measures
  • Peer review tests calculation accuracy, decision logic and presentation clarity

Exercises and examples are adapted to each cohort's sectors and roles.

What you take back to work

  1. Supply chain analytical charter template
  2. Data-quality assessment checklist
  3. Excel and Power Query reporting model
  4. SQL supply chain query library
  5. Power BI supply chain dashboard model
  6. Capstone decision analysis and recommendation plan

Who should attend

  • Supply Chain Analysts — need repeatable methods for converting operational data into decisions
  • Demand Planners — need to measure forecast accuracy, bias and demand variability
  • Inventory Planners — need evidence for stock policies, replenishment priorities and exceptions
  • Procurement Analysts — need clearer supplier performance, spend and delivery analysis
  • Logistics Managers — need to identify freight cost, carrier service and delivery problems
  • Operations Managers — need actionable reporting for service, cost and capacity decisions

Skills and tools covered

Supply chain metricsData quality assessmentSQL queryingInventory analysisPerformance dashboardingAnalytical storytelling

Tools and standards

Microsoft Excel, Power Query, Microsoft Power BI, SCOR

How the course runs

Training methodology

The course uses short instructor briefings followed by case-based analysis using order, inventory, supplier, freight and forecast data. Participants work individually and in groups to define metrics, prepare datasets, build models and interpret exceptions. Guided simulations show how decisions change when demand, lead time, service targets or capacity assumptions shift. Trainers provide feedback on calculations, visual design and recommendations. Each participant develops an application plan linked to a current supply chain reporting or decision problem.

Requirements and prerequisites

Participants should understand basic supply chain processes such as purchasing, inventory control, order fulfilment, production planning or transport operations. They should be comfortable working with spreadsheets, including entering formulas, sorting, filtering and using tables. Experience with operational reports or ERP extracts is useful. No prior SQL, Power BI, Python, statistics qualification or data science experience is required. The course explains analytical concepts before applying them, but it moves quickly into practical work with supply chain datasets. Participants should be prepared to interpret figures and discuss operational decisions.

A typical training day

08:30 – 10:30First session
10:30 – 10:45Refreshment break
10:45 – 12:30Second session
12:30 – 13:30Lunch and networking
13:30 – 15:00Third session
15:00 – 15:15Refreshment break
15:15 – 16:30Workshop and daily review

Live online deliveries follow the same structure in East Africa Time, with shorter screen blocks and longer breaks.

What the fee includes

  • Instruction by a practitioner facilitator
  • Full course workbook and materials
  • Exercise files, templates and case studies
  • Refreshments and lunch on classroom deliveries
  • Certificate of completion
  • Post-course application plan

Frequently asked questions

You should understand basic supply chain processes and be comfortable with everyday spreadsheet work. Prior SQL, Power BI, statistics or data science experience is not required.

Bring a laptop capable of running Excel and accessing the course data files. If permitted by your employer, bring examples of current reports or non-sensitive supply chain problems for your application plan.

It is designed for analysts, planners, procurement staff, logistics professionals and operations managers who work with supply chain data. It is most useful for people moving from manual reporting towards repeatable analysis and decision support.

The methods, datasets, metrics and cases are specific to supply chain decisions. Participants work with OTIF, forecast error, inventory policy, supplier performance, freight cost and operational exceptions rather than generic business examples.

Participants can use the analytical charter, data-quality checks, metric definitions and dashboard model on an active reporting problem. The capstone structure also provides a method for preparing evidence-based recommendations for management reviews.

The first week establishes data structures, quality controls, Excel, Power Query, SQL and core supply chain measures. The second week develops inventory, supplier, transport, statistical, dashboard and decision-support work before the final capstone presentation.

Questions about this course?

Ask about dates, fees, group bookings or running it for your team. Our training team replies by email.

Ask on WhatsApp info@skillsetdevelopment.com

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