Supply Chain Analytics: Forecasting & Performance Improvement Training Course

Turns supply chain data into insight, from checking data quality and analysing demand trends to improving forecast accuracy and tracking performance metrics.

SFD · Supply Chain Planning, Forecasting & Demand ManagementAll Levels#2687ClassroomEnglish , Arabic

Duration

5 Days

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Free substitution at any time · 30+ days' notice: full refund minus 10%. Cancellation policy

Sectors:Procurement, Contracts & Supply ChainCategories:Supply Chain Planning, Forecasting & Demand Management

Course Overview

The Supply Chain Analytics: Forecasting & Performance Improvement Training Course equips supply chain professionals, procurement specialists, logistics managers, and operations leaders with the analytical knowledge and practical tools required to improve forecasting accuracy, optimize supply chain performance, and support data-driven decision-making. The programme focuses on transforming operational and commercial data into actionable insights that strengthen planning, reduce costs, improve service levels, and enhance supply chain resilience. Modern supply chains operate in environments characterized by demand fluctuations, supplier uncertainty, changing customer expectations, inventory pressures, and increasingly complex distribution networks. Effective supply chain analytics enables organizations to identify emerging trends, anticipate disruptions, balance supply with demand, and allocate resources more efficiently. This course explores how analytical methods can improve visibility across procurement, inventory management, warehousing, transportation, and distribution activities. Participants examine the principles of demand forecasting, statistical analysis, inventory optimization, supplier performance measurement, and supply chain planning. The programme covers historical data analysis, demand patterns, forecasting techniques, inventory classification, lead-time assessment, and the interpretation of key performance indicators. Particular emphasis is placed on selecting appropriate analytical approaches based on data quality, business requirements, operational constraints, and the consequences of forecasting errors. The course also addresses the practical application of performance dashboards, scenario analysis, root-cause investigation, and continuous improvement methodologies. Participants learn how to evaluate supply chain efficiency, identify performance gaps, monitor cost drivers, and develop improvement initiatives supported by measurable evidence. The programme introduces the role of spreadsheets, business intelligence tools, and advanced analytics in strengthening forecasting processes and operational decision-making. Through practical exercises, case studies, and integrated supply chain scenarios, participants develop the capability to interpret data, evaluate alternative planning decisions, and recommend improvements across the supply chain. The course is suitable for organizations seeking to improve demand visibility, optimize inventory investment, enhance supplier reliability, reduce logistics costs, and establish a performance-oriented supply chain management culture.

Learning Objectives

Explain the principles of supply chain analytics and its contribution to operational and strategic performance.
Identify relevant data sources and assess the quality, completeness, and reliability of supply chain information.
Analyse historical demand data to identify trends, seasonality, variability, and unusual demand patterns.
Apply appropriate quantitative and qualitative forecasting techniques to estimate future demand.
Evaluate forecasting accuracy using relevant error measurements and performance indicators.
Assess inventory performance and apply classification techniques to prioritize stock management activities.
Analyse supplier lead times, delivery reliability, and procurement performance using operational data.
Develop supply chain performance dashboards aligned with organizational objectives.
Identify the root causes of supply chain inefficiencies, service failures, and excessive operating costs.
Apply scenario analysis to evaluate supply, demand, inventory, and distribution alternatives.
Develop evidence-based improvement initiatives to enhance efficiency, service levels, and supply chain resilience.
Establish continuous monitoring and performance review practices to sustain measurable supply chain improvements.

Who Should Attend

This programme is designed for professionals involved in supply chain planning, procurement, inventory management, logistics, operations, and performance improvement. Supply chain directors and managers responsible for end-to-end supply chain performance. Demand planners and supply planners involved in forecasting, replenishment, and balancing supply with demand. Procurement managers and purchasing specialists evaluating supplier performance, sourcing decisions, and purchasing trends. Inventory control managers and warehouse professionals responsible for stock availability, inventory accuracy, and working capital. Logistics and distribution managers seeking to improve transportation efficiency, delivery performance, and distribution planning. Operations managers responsible for production planning, resource utilization, and operational efficiency. Supply chain analysts and business analysts working with operational data, performance reports, and analytical models. Sales and operations planning professionals coordinating demand forecasts, production plans, procurement, and inventory. Performance improvement and operational excellence specialists developing data-driven improvement initiatives. Finance professionals involved in inventory valuation, working capital management, and supply chain cost analysis. Project managers responsible for supply chain transformation and digital improvement initiatives. Government and public-sector professionals managing procurement, distribution networks, strategic inventory, and service delivery.

Learning Outcomes

Explain the role of analytics in improving supply chain planning, operational visibility, and management decisions.
Assess supply chain data quality and prepare datasets for reliable analysis.
Interpret historical demand patterns and identify trends, seasonality, variability, and potential anomalies.
Select forecasting methods appropriate to different demand characteristics and planning requirements.
Calculate and interpret common forecasting error measures to evaluate model performance.
Analyse inventory levels, stock movements, lead times, and replenishment requirements.
Apply inventory classification and prioritization techniques to improve stock management.
Evaluate supplier performance using delivery, quality, lead-time, and reliability indicators.
Design performance dashboards that communicate supply chain results and emerging risks.
Identify the operational and commercial factors contributing to supply chain inefficiencies.
Evaluate alternative supply chain scenarios and recommend improvements based on quantitative evidence.
Develop measurable action plans for forecasting improvement, cost optimization, and supply chain performance enhancement.

Course Outline

Day 1

Fundamentals of Supply Chain Analytics and Data Management

The strategic role of analytics in modern supply chain management.
Understanding end-to-end supply chain processes, including procurement, planning, inventory, warehousing, and distribution.
Identifying analytical requirements across operational and strategic supply chain functions.
Understanding structured and unstructured supply chain data sources.
Assessing data accuracy, completeness, consistency, and timeliness.
Preparing operational datasets through data cleaning, validation, and standardization.
Using descriptive statistics to summarize supply chain performance and operational variability.
Identifying trends, outliers, recurring patterns, and data inconsistencies.
Establishing baseline performance measures for cost, service, inventory, and delivery.
Understanding the limitations of historical data and the risks of misleading analytical conclusions.
Practical Exercise: Review a sample supply chain dataset, identify data quality issues, calculate basic descriptive measures, and summarize the principal operational findings.

Day 2

Demand Forecasting and Forecast Accuracy Improvement

Understanding the relationship between demand forecasting, supply planning, procurement, and inventory availability.
Identifying demand patterns, including stable demand, trends, seasonality, and intermittent demand.
Selecting historical data periods and preparing suitable forecasting inputs.
Applying qualitative forecasting methods based on expert judgment and market intelligence.
Understanding quantitative forecasting techniques, including moving averages and exponential smoothing.
Exploring trend analysis and seasonal forecasting approaches.
Evaluating the suitability of forecasting methods for different products and demand patterns.
Measuring forecast performance using mean absolute error, mean absolute percentage error, and other appropriate error measures.
Understanding forecast bias and the operational consequences of systematic overforecasting or underforecasting.
Incorporating promotions, market changes, product lifecycle considerations, and exceptional events into forecasting decisions.
Establishing a structured process for reviewing, updating, and improving forecasts.
Practical Exercise: Develop forecasts for selected products using historical demand data, compare alternative methods, calculate forecasting errors, and recommend the most appropriate approach for each demand pattern.

Day 3

Inventory Analytics, Replenishment, and Working Capital Optimization

Understanding the relationship between inventory availability, customer service, operating costs, and working capital.
Analysing stock movements, consumption patterns, inventory turnover, and days of inventory on hand.
Applying ABC classification to prioritize inventory according to value or organizational criteria.
Exploring demand variability and inventory segmentation for differentiated stock policies.
Understanding safety stock and its role in protecting against demand and lead-time uncertainty.
Calculating reorder points and evaluating replenishment parameters.
Assessing supplier lead-time variability and its effect on inventory requirements.
Identifying excess inventory, slow-moving stock, obsolete items, and stockout risks.
Evaluating service levels, fill rates, and the trade-offs between inventory investment and availability.
Using inventory analytics to support purchasing priorities and replenishment decisions.
Developing measures to reduce unnecessary inventory while protecting operational continuity.
Practical Exercise: Analyse an inventory dataset, classify stock items, calculate selected replenishment indicators, and develop recommendations to reduce excess inventory and stockout exposure.

Day 4

Supply Chain Performance Measurement, Supplier Analytics, and Dashboards

Establishing supply chain key performance indicators aligned with business objectives.
Measuring supplier delivery reliability, lead-time performance, quality, and responsiveness.
Analysing procurement cycle times, purchase order performance, and purchasing expenditure.
Evaluating warehouse productivity, order accuracy, inventory accuracy, and fulfilment performance.
Measuring transportation costs, delivery lead times, and on-time delivery performance.
Understanding the relationships between service levels, operating costs, and customer satisfaction.
Developing dashboards for monitoring supply chain performance and operational exceptions.
Using spreadsheet-based analysis and business intelligence tools to present meaningful insights.
Applying root-cause analysis to recurring delivery failures, inventory problems, and cost variances.
Establishing performance thresholds, escalation criteria, and management review routines.
Translating analytical findings into clear reports and recommendations for decision-makers.
Practical Exercise: Design a supply chain performance dashboard using sample procurement, inventory, supplier, and logistics data, then identify the principal performance gaps and corrective actions.

Day 5

Advanced Scenario Analysis, Performance Improvement, and Implementation

Integrating forecasting, procurement, inventory, and logistics analytics into a coordinated planning process.
Understanding scenario analysis and sensitivity assessment for supply chain decisions.
Evaluating the operational effects of demand surges, supplier delays, cost increases, and distribution disruptions.
Comparing alternative sourcing, replenishment, and distribution strategies.
Assessing the relationship between forecasting accuracy, inventory investment, service levels, and total supply chain cost.
Prioritizing improvement initiatives according to expected impact, feasibility, cost, and implementation risk.
Applying structured problem-solving and root-cause analysis to persistent supply chain challenges.
Developing improvement plans with clear targets, responsibilities, milestones, and performance indicators.
Establishing continuous monitoring processes to measure improvement results and identify emerging issues.
Understanding the role of automation, predictive analytics, and artificial intelligence in supply chain decision support.
Strengthening collaboration between procurement, finance, operations, sales, and logistics teams.
Presenting analytical recommendations and building a business case for supply chain improvement initiatives.
Final Workshop: Participants receive an integrated supply chain case containing historical demand, inventory records, supplier lead times, procurement transactions, delivery performance, and operating cost information. Working individually or in teams, they assess data quality, develop and evaluate demand forecasts, identify inventory and supplier performance issues, and analyse operational scenarios. Each team prepares a supply chain improvement plan with measurable objectives, recommended interventions, implementation priorities, and performance indicators. The workshop concludes with a management presentation explaining the expected operational and financial benefits, the assumptions behind the recommendations, and the approach for monitoring results.

Upcoming Dates

Selected date

Geneva · 1 – 5 Mar 2027 · Classroom

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Sharm El-SheikhScheduled	14 – 18 Dec 2026	€4,300	€3,870until 23 Nov	Select Download Brochure
SingaporeScheduled	21 – 25 Dec 2026	€6,700	€6,030until 30 Nov	Select Download Brochure
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AmsterdamScheduled	11 – 15 Jan 2027	€5,400	€4,860until 21 Dec	Select Download Brochure
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CairoScheduled	18 – 22 Jan 2027	€3,300	€2,970until 28 Dec	Select Download Brochure
CasablancaScheduled	25 – 29 Jan 2027	€4,300	€3,870until 4 Jan	Select Download Brochure
DammamScheduled	1 – 5 Feb 2027	€4,200	€3,780until 11 Jan	Select Download Brochure
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DubaiScheduled	14 – 18 Feb 2027	€4,400	€3,960until 25 Jan	Select Download Brochure
FrankfurtScheduled	22 – 26 Feb 2027	€5,800	€5,220until 1 Feb	Select Download Brochure
GenevaScheduled	1 – 5 Mar 2027	€6,300	€5,670until 8 Feb	✓ Selected Download Brochure
IstanbulScheduled	1 – 5 Mar 2027	€4,700	€4,230until 8 Feb	Select Download Brochure
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Available in: Istanbul · Dubai · London · Riyadh · Doha · Amsterdam

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