Introduction:

Chemical supply chain planning and demand forecasting represent institutional systems governing how production volumes, inventory positions, and material flows are structured across complex petrochemical operations. Their role lies in balancing market demand, production capacity, storage constraints, and distribution continuity within high capital and safety critical environments. This training program presents forecasting framework models, planning architecture structures, inventory coordination systems, and decision support governance applied in integrated refining and petrochemical complexes. It provides an institutional perspective on how structured planning and forecasting systems support operational stability, customer fulfillment reliability, and long-term commercial performance.

Program Objectives:

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

Identify chemical supply chain planning system architectures in petrochemical environments.
Classify demand pattern models and forecasting methodology structures.
Evaluate production, inventory, and distribution coordination frameworks.
Assess uncertainty management and risk buffering structures within chemical supply networks.
Analyze performance measurement and decision governance models for supply chain planning systems.

Target Audience:

• Supply chain and materials planning professionals.

• Production planning and scheduling specialists.

• Warehouse and logistics management staff.

• Commercial and sales planning analysts.

• Operations coordination and performance management personnel.

Program Outline:

Unit 1:

Chemical Supply Chain Planning System Architecture:

• Institutional positioning of supply chain planning in petrochemical operations.

• Structural interfaces between refining units, petrochemical plants, warehouses, and distribution networks.

• Planning horizon classification models, short-term, mid-term, and long-term.

• Capacity constraint mapping and resource allocation structures.

• Governance frameworks for integrated planning decision authority.

Unit 2:

Demand Forecasting Framework Models for Chemical Products:

• Demand behavior typology across commodity and specialty chemical markets.

• Time series and causal forecasting model classifications.

• Market segmentation and customer portfolio demand structures.

• Seasonality and cyclicality influence architecture in petrochemical demand.

• Forecast accuracy measurement and bias monitoring frameworks.

Unit 3:

Inventory and Production Coordination Structures:

• Safety stock classification and buffering logic models.

• Finished goods, intermediates, and raw material inventory segmentation frameworks.

• Production rate alignment structures with forecasted demand profiles.

• Storage capacity constraint integration within planning systems.

• Material availability synchronization across supply nodes.

Unit 4:

Uncertainty, Risk, and Volatility Management Frameworks:

• Demand variability classification structures.

• Feedstock availability and logistics disruption risk models.

• Scenario planning and sensitivity analysis architectures.

• Contingency planning and alternative sourcing frameworks.

• Market price volatility interaction with supply planning systems.

Unit 5:

Planning Performance Governance and Digital Integration:

• Supply chain planning performance indicator architectures.

• Sales and operations planning (S&OP) structural models.

• Forecast–plan–execute feedback loop frameworks.

• Digital planning platform and analytics system integration structures.

• Long term planning maturity and organizational capability development models.

Chemical Supply Chain Planning and Demand Forecasting

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