Advanced Supply Chain Network Design Training Course

Course overview

Supply chain networks built around historic plant locations, supplier choices and transport lanes often become expensive or fragile when demand shifts, lead times lengthen, tariffs change or service expectations rise. Senior planners and supply chain managers need to answer difficult investment questions with evidence: where should inventory sit, which facilities should serve which markets, when does a new distribution centre pay back, and what service or risk trade-offs follow? This course equips participants to build and defend those answers using structured network-design analysis rather than spreadsheet assumptions alone.

Participants learn to frame network decisions, prepare model-ready data, segment demand and service requirements, and translate operational constraints into optimisation scenarios. The course covers facility-location modelling, capacity allocation, transport cost modelling, inventory positioning, sourcing alternatives, total landed cost, carbon considerations and disruption scenarios. Participants work with network-design logic including centre-of-gravity screening, mixed-integer optimisation concepts, scenario comparison, sensitivity analysis and executive-ready business cases. They learn when a model is robust enough to support a decision and when data gaps, assumptions or organisational constraints require further validation.

Delivery combines instructor-led modelling demonstrations, worked cases, team-based scenario reviews and hands-on exercises using Excel Solver, Python PuLP and AnyLogistix concepts. Each participant develops a network-design decision pack based on a realistic multi-site supply chain: a data specification, baseline network, alternative scenarios, quantified trade-offs, recommendation and implementation risks. The final day includes a review panel in which participants present their recommendation as they would to a supply chain investment committee.

The course is suited to experienced supply chain professionals moving from operational planning into strategic network, footprint, sourcing or logistics design responsibilities. It is equally valuable for managers sponsoring major warehouse, manufacturing, outsourcing or regional distribution decisions who need to challenge modelling assumptions and approve investment cases confidently.

Course objectives

By the end of this course, participants will be able to:

• Define a network-design problem statement with scope, decision variables, constraints and measurable service objectives
• Prepare demand, facility, lane, capacity and cost data for a network optimisation model
• Build facility-location and customer-allocation scenarios using centre-of-gravity and optimisation methods
• Model total landed cost across sourcing, production, warehousing, inventory and transport decisions
• Evaluate inventory positioning and service trade-offs across centralised, regional and local fulfilment networks
• Run sensitivity analyses for demand growth, capacity limits, fuel costs, lead times and disruption events
• Interpret optimisation outputs to identify binding constraints, infeasible assumptions and decision risks
• Produce an executive network-design recommendation with financial rationale, implementation roadmap and risk controls

Benefits of attending

Target competencies

Who should attend

• Supply Chain Network Design Managers — who evaluate facility footprints, flows and capacity investments
• Supply Chain Strategy Managers — who translate growth, sourcing and service plans into network decisions
• Logistics and Distribution Managers — who must improve transport, warehouse and fulfilment performance across regions
• Demand and Supply Planning Managers — who provide demand, inventory and capacity inputs for strategic modelling
• Procurement and Sourcing Managers — who assess supplier-location, landed-cost and supply-risk trade-offs
• Operations Excellence Analysts — who build evidence-based business cases for supply chain transformation initiatives

Requirements and prerequisites

Participants should have practical experience in supply chain planning, logistics, procurement, manufacturing operations or distribution management. They should understand demand forecasting, lead times, capacity, inventory, transport costs and basic financial measures such as fixed versus variable cost. Confidence working with spreadsheets, including formulas, filters, pivot tables and charts, is expected. Familiarity with linear programming, Python or specialist network-design software is useful but not required; these methods are introduced through guided exercises. Participants do not need to be data scientists or software developers, but should be comfortable interpreting quantitative scenarios and challenging assumptions.

Training methodology

The programme uses a progressive five-day network-design case built around a manufacturer serving multiple markets through plants, suppliers, distribution centres and transport lanes. Instructor-led sessions introduce the decision logic and modelling methods before participants apply them in Excel Solver, guided Python PuLP examples and AnyLogistix-style scenario exercises. Teams clean data, define constraints, compare network options and challenge each other’s assumptions. Daily debriefs focus on interpreting results for operational leaders. The course closes with an application-planning workshop and a presentation of each team’s recommendation to a simulated investment committee.

Course outline

Day 1: Framing the network decision

• Supply chain network design decisions and strategic planning horizons
• Network scope definition: nodes, flows, products, markets and time periods
• Service policy translation into lead-time and fulfilment constraints
• Fixed, variable and step-cost structures in network decisions
• Data requirements for demand, lanes, facilities, suppliers and capacities
• Baseline network mapping using source-to-market flow diagrams
• Centre-of-gravity analysis for initial location screening

Workshop: Participants map the baseline network for a multi-country business and produce a model-input data specification with documented assumptions.

Day 2: Modelling facilities, capacity and flows

• Facility-location problem formulation and binary decision variables
• Customer-to-facility allocation logic and service-radius constraints
• Capacity constraints for plants, warehouses and cross-dock operations
• Transport lane cost construction using rate, distance, mode and utilisation data
• Excel Solver configuration for constrained network scenarios
• Introduction to mixed-integer linear programming and feasible solutions
• Reading objective values, allocations and binding constraints

Workshop: Participants configure and run an Excel Solver facility-location model, then produce a recommended customer-allocation plan.

Day 3: Cost-to-serve and inventory positioning

• Total landed cost decomposition across sourcing, production and distribution
• Cost-to-serve analysis by customer segment, channel and geography
• Centralised versus decentralised inventory network trade-offs
• Safety-stock pooling and lead-time effects on inventory requirements
• Multi-echelon inventory positioning concepts
• Supplier-location and make-buy alternatives in network models
• Carbon emissions and sustainability constraints in transport design

Workshop: Participants compare centralised and regional distribution scenarios and produce a cost, service, inventory and emissions trade-off table.

Day 4: Risk, resilience and scenario analysis

• Demand uncertainty scenarios and volume sensitivity ranges
• Capacity loss and facility disruption stress testing
• Supplier failure, geopolitical exposure and sourcing diversification options
• Fuel, tariff and exchange-rate sensitivity in landed-cost models
• Resilience metrics: recovery time, alternate capacity and single-point exposure
• Scenario management using AnyLogistix-style network experiments
• Python PuLP model structure for repeatable optimisation runs

Workshop: Participants stress-test their preferred network against disruption and demand-growth scenarios and produce a resilience action register.

Day 5: Decision governance and implementation

• Validating model assumptions with operations, finance, procurement and commercial stakeholders
• Interpreting counterintuitive optimisation results and model limitations
• Financial appraisal of network changes using payback, NPV and transition cost
• Phased implementation planning for facility, lane and inventory changes
• Change-management risks in customer migration and warehouse transitions
• Executive dashboard design for cost, service, capacity and risk outcomes
• Network design governance and post-implementation performance review

Workshop: Participants present a complete network-design decision pack to a simulated investment committee and produce a 90-day validation and implementation plan.

Tools & standards covered

Microsoft Excel Solver, Python PuLP, AnyLogistix, ASCM SCOR Digital Standard

A typical training day

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

What the fee includes

How you can take this course

Certification

Participants who complete the full five days receive the Skillset Development Certificate of Completion, stating the course title, course code, dates and delivery format — suitable for professional-development records and employer reimbursement.

Frequently asked questions