Description

This curriculum spans the breadth of a multi-workshop operational transformation program, addressing the technical, organizational, and governance challenges involved in aligning data-driven supply chain strategies with enterprise systems, cross-functional processes, and regulatory frameworks.

Module 1: Defining Strategic Objectives with Data-Driven Constraints

Align demand forecasting models with executive-level revenue targets while accounting for historical forecast bias.
Negotiate data granularity requirements between supply chain planners and IT teams when integrating ERP and CRM systems.
Decide whether to prioritize cost reduction or service level improvements when both cannot be simultaneously optimized.
Establish KPI ownership across procurement, logistics, and sales to prevent conflicting performance incentives.
Assess the feasibility of real-time decision-making based on current data latency across warehouse management systems.
Document assumptions in strategic models when external factors (e.g., tariffs, labor strikes) lack reliable historical data.
Balance long-term network design decisions with short-term operational flexibility in volatile markets.

Module 2: Data Integration Across Heterogeneous Supply Chain Systems

Map master data entities (e.g., SKUs, locations) across legacy systems with inconsistent naming conventions and hierarchies.
Design ETL pipelines that reconcile transactional data frequency mismatches between SAP and third-party logistics providers.
Implement data validation rules to detect and handle duplicate shipment records from overlapping TMS and WMS feeds.
Choose between API-based integration and batch file transfers based on system uptime and data volume constraints.
Resolve unit-of-measure discrepancies (e.g., pallets vs. cases) during data consolidation for inventory reporting.
Establish data lineage tracking to audit root causes of inventory reconciliation errors.
Negotiate data access rights with suppliers who use proprietary platforms with limited export capabilities.

Module 3: Demand Sensing and Forecasting at Scale

Select between exponential smoothing and machine learning models based on data availability and forecast horizon.
Incorporate point-of-sale data from retail partners while managing data freshness and coverage gaps.
Adjust baseline forecasts for planned promotions using lift factors derived from A/B test results.
Handle intermittent demand for slow-moving SKUs using Croston’s method or classification-based approaches.
Quantify the impact of weather events on regional demand and integrate meteorological data feeds.
Implement forecast override controls to prevent unexplained manual adjustments from distorting model training.
Validate model performance using out-of-sample testing across multiple geographies and product categories.

Module 4: Inventory Optimization Under Uncertainty

Set safety stock levels using service-level targets while accounting for lead time variability from suppliers.
Allocate constrained inventory across distribution centers using expected profit margin rather than equal share.
Model the trade-off between carrying costs and stockout penalties in multi-echelon networks.
Update reorder points dynamically when supplier reliability degrades due to geopolitical disruptions.
Integrate shelf-life constraints into inventory policies for perishable goods in cold chain logistics.
Implement ABC-XYZ classification to prioritize optimization efforts on high-value, high-volatility items.
Coordinate inventory policies with procurement contracts that include volume commitment penalties.

Module 5: Network Design and Facility Location Modeling

Evaluate trade-offs between centralized vs. regional distribution centers using total landed cost simulations.
Incorporate carbon emission costs into network models when operating under regulatory compliance mandates.
Assess the impact of potential customs delays when locating facilities near international borders.
Model capacity constraints in existing facilities when simulating demand growth scenarios.
Quantify the cost of service level degradation when consolidating underutilized warehouses.
Integrate labor availability and wage rates into site selection scoring models.
Validate network recommendations against real estate lease termination costs and exit penalties.

Module 6: Supplier Risk Assessment and Resilience Planning

Develop supplier risk scores using financial health indicators, geopolitical ratings, and delivery performance.
Implement dual-sourcing strategies for critical components while managing increased procurement complexity.
Simulate disruption scenarios (e.g., port closures) to identify single points of failure in the supply base.
Integrate supplier audit findings into risk models with weighted scoring for compliance violations.
Balance nearshoring benefits against higher unit costs when redesigning sourcing strategies.
Establish early warning triggers based on supplier news monitoring and shipment tracking anomalies.
Coordinate risk mitigation plans with suppliers who lack digital integration capabilities.

Module 7: Real-Time Decision Support for Logistics Operations

Design dynamic routing algorithms that adjust for real-time traffic and delivery window constraints.
Implement load consolidation rules that optimize trailer utilization without delaying time-sensitive shipments.
Integrate carrier performance data into tendering decisions to reduce late deliveries.
Automate freight audit exceptions by matching carrier invoices against contracted rates and actual shipment data.
Deploy edge computing solutions for real-time tracking in remote warehouses with limited connectivity.
Balance fuel cost savings from slower speeds against customer service level agreements.
Validate GPS-based dwell time data against dock scheduling systems to identify bottlenecks.

Module 8: Governance, Change Management, and Continuous Improvement

Establish data stewardship roles to maintain product and location master data accuracy across business units.
Design model retraining schedules based on data drift detection in demand and supply patterns.
Implement version control for optimization models to support auditability and rollback capabilities.
Conduct operational readiness reviews before deploying new planning algorithms in production systems.
Develop escalation protocols for when automated recommendations conflict with planner expertise.
Measure adoption rates of new tools and adjust training programs based on user role and region.
Facilitate cross-functional alignment sessions to resolve conflicts between finance and operations on inventory targets.

Module 9: Ethical and Regulatory Considerations in Data Usage

Assess data anonymization requirements when sharing logistics data with third-party analytics vendors.
Ensure compliance with GDPR when storing customer delivery addresses in planning databases.
Document algorithmic decision logic to support explainability requirements under internal audit.
Address bias in historical data that may skew forecasts for underrepresented markets.
Implement access controls to restrict visibility of sensitive supplier pricing data.
Validate that optimization outcomes do not inadvertently violate antitrust regulations on pricing coordination.
Report carbon footprint calculations using standardized methodologies for ESG disclosures.