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Self-Steering Supply Chains within the Chemicals Industry

Redefining supply chain agility through decision intelligence

The challenge

Four key challenges can be identified within the supply chain of chemical companies, leading to a list of disruptions and value leakages

Raw Material Risk

Global black-swan events (e.g. COVID, Suez Canal, war in Ukraine, …) in combination with increasing demand of specific commodities have created large disruptions in the price and availability of raw materials within the chemicals industry

Unplanned Variability

Cyclical supply chain dynamics, price volatilities, a broad portfolio mix of commodities and many other factors create optimization puzzles for chemical supply chain planners. Marked by its capital-intensive production assets, the chemicals industry specifically is put under a lot of pressure

Low Service Level, High Costs

Throughput yields are volatile due to multiple external (e.g. pressure, temperature, humidity, …) and internal factors (e.g. assets ageing, catalyst life cycle, …) Precisely weighing in these components is difficult, leading to unforeseen deviations from the plan and firefighting activities

Resource Inefficiency

Due to a lack of system integration, coupled by (outdated) applications stretched beyond their core capabilities, Supply chain managers are often left in the dark trying to identifying hiccups through mails or phone calls, resulting in them being addressed much later after the facts. Resources have to be focused on low value activities to join all this together.

Decision Intelligence as an Answer

Decision Intelligence is the digitization, augmentation, and automation of decision making. It requires a platform that is connected outside and in, real time and always on, thinking, learning, and autonomous. It delivers the decision agility and scale required to cope with the rapidly increasing complexity of your business. A decision intelligence use case typically consists of 6 key components.


Event or “trigger” which initiates the decision-automated process, often expressed in a specific threshold being breached (e.g., projected stock-out, late delivery, capacity bottleneck, …)

Prioritization & Trade-off

Apply a series of prioritization logics which not only help determine which triggers should be addressed first, but also which corrective actions might be preferred over others by comparing their impacts and benefits


Possible action(s) which can be taken to address, mitigate or resolve the risks tied to the initial trigger(s)


Summarize the decision-automation outcome in a comprehensive recommendation, which provides a clear overview of the What, Why and How. Enable options to allow the end-users to accept of reject the provided recommendation

Feasibility Checks

Review the list of relevant constraints & other considerations when assessing the “feasibility” of each proposed action. Although we want to resolve the issue at hand, we don’t want to create another one in return.


Once the recommendation is accepted, the action can be taken automatically via the use of a write-back where the necessary information will be written back into the planning source systems

Raw material risk

Automating decisions that reduce the impact of raw material constraints, including:
  • Dynamic MRP parameter setting
  • PO rescheduling & consolidation
  • Supplier communication for allocation & rebalancing
  • Changing supplier selection based on a dynamic supplier risk assessment
  • Optimising raw material allocation for FG production

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Unplanned variability

Automating decisions around planning alongside APS to improve resilience against short-term demand variability and improve planning effectiveness. Including:
  • S&OE decisions
  • Inventory optimisation and rebalancing
  • Dynamic safety stock setting
  • Dynamic planning parameter setting

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Low service level, high cost

Automating the decisions around orders and transport to improve customer service level and customer satisfaction, whilst considering sustainability impact:
  • Order allocation, fulfilment and management decisions
  • Transport and logistics decisions, such as air vs freight, direct shipment
  • Dynamic ATP Prediction

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Resource Inefficiency

Automating the collection and analysis of internal and external data to focus resources on value-add activities
  • Rapid Response Control Tower (RRCT)
  • Automated Master Data Management

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