Demand-Supply Balance
Achieving an optimal demand-supply balance is a critical capability in managing today’s dynamic and complex supply chains. Traditional planning tools often struggle to provide actionable insights due to their inability to process real-time data and adapt to changes in demand and supply conditions. This results in challenges such as backorders, excess inventory, and inefficient push-in or pull-out decisions.
Decision Intelligence transforms the demand-supply balancing process by dynamically generating updated forecasts using advanced machine learning techniques. These forecasts integrate real-time demand signals, historical patterns, and market trends to provide an accurate and actionable view of future requirements. Additionally, DI helps identify critical decision points arising from mismatches, such as how to handle backorders, mitigate excess inventory, or adjust production schedules.
On the supply side, Decision Intelligence leverages advanced data crawlers to achieve full visibility into key factors like lead times, plant utilization, production capacity, and material availability. By continuously analyzing these variables, it generates intelligent recommendations, such as:
- Creating or modifying production orders.
- Determining which plant or distribution center (DC) should handle specific orders.
- Selecting the best-fit supplier or identifying alternative suppliers in case of constraints.
- Optimizing shipment schedules and resource allocations.
By automating and enhancing these decisions, organizations can reduce costs, improve service levels, and maintain a more agile and resilient supply chain. Decision Intelligence ensures that every decision along the demand-supply chain is data-driven, precise, and aligned with overall business objectives.
Achieve equilibrium between supply and demand in real-time:
- Use intelligent agents to continuously monitor demand signals and align them with supply constraints.
- Automate adjustments to production, procurement, and inventory plans to optimize resource allocation.
- Minimize inefficiencies and improve profitability by achieving dynamic balance across the value chain.
Key Automated Decisions:
- Reallocate supply resources to match dynamic demand patterns.
- Balance production schedules with inventory and customer needs.
Tangible Outcomes:
- Reduction in excess production by 15%.
- Minimized supply-demand imbalances leading to cost savings.
- Greater operational agility to respond to market changes.
- Improved customer satisfaction through timely deliveries.