Decision Support for Supply Chain Management

Complex supply chains are difficult to manage:

Working Capital

Limited Financial Flexibility

Financial Risk


Unused Knowledge Potential

Information Risk


Low Confidence in Decisions

Strategic Risk

Risk Management

Risk of Exposure by Missing KPIs

Business Risk

Hadean Process Live Simulation provides you with the best predictions and decision support

Benefits And Outcomes:

  • Optimise Working Capital – Identify key sensitivities impacting working capital requirements. Minimise tied-up cash, reduce the cost of capital and prioritise high-value investment.
  • Better Decisions – Live simulations presents every what-if scenario, generating rapid data-supported responses to changes and thus reducing time-sensitive costs.
  • High Quality Predictions – Data driven predictions taken from user-driven and machine learning live simulations. This enables improved planning for optimal resource allocation and efficiency.
  • Risk Management – Understand and estimate the risks and KPIs of your process changes. Reduce risk and drive KPIs by focusing on and investing in the most impactful areas.

Customer Success

Multinational Pharmaceutical Company

Hadean are providing a unified platform for high quality, real time insights that allows this company to rapidly respond to unpredictable scenarios across their global supply chain: parameterised simulations that model upstream/downstream effects (via interactive dashboards)

  • Intuitive visualisation of end-to-end supply chain with critical info (e.g delays, stock tolerances, $’s worth of delays)
  • Rapidly configured parameterised simulations that model upstream/downstream effects (via interactive dashboards)
supply chain

Download the decision support solution brief to discover the vast improvements in efficiency with Hadean

For supply chain managers, there are key questions that require decision support:

  • How do you protect working capital within your processes?
  • How do you manage risk vs benefit?
  • How do you predict possible outcomes of your decisions before making them?
  • What unintended consequences might your decisions lead to?
  • How can you make decisions with confidence that it will not risk your KPIs?
  • How do you utilise machine learning to improve your decision making?