Utilising the power of Artificial Intelligence to model the optimal supply chain and unlock benefits from your planning system.

Bob Rogers PhD and David Evans MSc October 2019

Configuring a supply chain planning system correctly is a complex task and a crucial one. No matter the capability of the planning software being used, failure to set the right parameters will result in the wrong plans being generated and inefficiencies propagated across the network as a matter of course.

Why is this? It happens because finding the right configuration – even in relatively simple supply chains – is extremely difficult to achieve. There are just too many combinations. In addition, real-world supply chains often involve global networks and a multi echelon of supply sites making the task exponentially more complicated. Even for the most effective and experienced planners this is an insurmountable challenge.

As a result, many supply chains are set up and planned in a suboptimal way resulting in a combination of issues such as:

  • Service Failures and Missed Sales
  • High cost to serve·
  • Overstocks on some items and under-stock on others
  • Factory Inefficiencies
  • Reactive rather than Proactive Plans
  • Write offs and Waste

For organisations that have spent many millions on next-generation planning systems this culminates in a failure to fully leverage the benefit of that investment as deployments merely accelerate the transmission of issues.

With many businesses facing up to the biggest economic challenge since the Second World War it is no longer acceptable for supply chains to be run in this way. As we come out of the Covid-19 crisis reducing costs, freeing up cash and improving margin will differentiate those that survive from those that don’t. Ensuring that the supply chain is set up correctly will become a key competency.

Why is configuring a planning system so complex?

Identifying the optimal supply chain network is a mathematical exercise that requires detailed evaluation of the cost versus service implications of every potential set up.

Configuration needs to determine factors such as:

  • Network design
  • Product segmentation strategy
  • What stock to hold and where to hold it
  • Factory sequence and rhythm

Each configuration is a trade off between the level of service given to the customer and the underlying cost needed to deliver that. This requires determining the multiplicity of potential configuration options and comparing these to identify the optimal cost versus service balance.

In practice this is often a task not well suited to humans. In extended networks the potential configuration options proliferate into limitless possibilities. Assessing and correctly comparing all the conceivable future realities based on variability, risk, probability and underlying cost is not feasible. Furthermore the exercise needs to be dynamic; continually recalibrating to take account of new data points and changes to patterns and trends in the data.

What is needed is the capability to configure the system correctly such that:

  • Profitability is maximised.
  • Plans are configured to respond to current and future risks.
  • Variability is built into plans enabling maximised efficiency.
  • Human error and bias are eliminated from the planning process such that pro-active rather than reactive behaviour becomes the norm.

Orchestrated Intelligence enables organisations to meet these goals.

Our proprietary Cognitive Self-Modelling Supply Chain considers the millions of options that are available in real time and identifies a costed and optimised solution that is the best fit for your organisations specific strategic needs.  It does not replace the planning system or the planner. It is designed to work with your existing system, retrieving data from it to find the best solution across multiple dimensions of cost and service

We do this using Artificial Intelligence (AI); a new generation of software algorithms that demonstrate self- learning and the capability to make decisions. AI’s power is its capability to process multiple data inputs simultaneously and to use this information to; compare outcomes, make informed choices and to build knowledge and self correct so that those choices improve over time. Already AI is demonstrating that where decisions are based on evaluating data then humans are no match for the capability.

Orchestrated Intelligence harnesses the power of AI to build scientific models of the current supply chain planning set up and simulations of all the potential configurations. It does this using a unique concept called SMSpace™ ; a multi dimensional virtual hologram used to build and compare potential future supply chains for a particular network.

In SMSpace the tool models all the configuration options and plays these out over time, assessing both the probability of the outcomes and the cost versus service profile of each one. Sales, costs, constraints and strategic direction are all considered as it identifies the best configuration to respond to current and future supply chain variability and risk. In doing so it enables improvement in supply chain management and unlocks benefits from better planning answering questions such as:

  • What is the optimal E2E network set up?
  • What replenishment models should be applied?
  • What is our Segmented service level strategy?
  • Where should stocks be held?
  • What Safety Stock Levels should we hold?
  • What is the optimal delivery frequency?
  • What is the best sequence of supply plans?

As well as modelling the supply chain, the tool monitors performance versus the optimal configuration enabling continuous fine-tuning such that as variables change the software re- calibrates monitoring everything, configuring in real time and flagging high priority issues. It also makes underlying costs visible and transparent.

Orchestrated Intelligence aligns the power of AI/ML with a science-based modelling capability. It unlocks the hidden benefits latent in your planning systems by automating the configuration of the network and maintaining the optimal state as new risks and opportunities emerge, freeing up cash and delivering quantifiable benefit. It introduces a new paradigm of cost management, responsiveness and service to the enterprise: it is the Cognitive Self-Modelling Supply Chain.

Deployment is agile and easy; the tool does not replace the existing systems infrastructure and payback on investment is fast. Once data sources are identified and the capability plugged in, we have found that you can typically expect benefits of the following magnitude.

10%

Service
Improvement

30%

Stock
Reduction

50%

Reduction in
Discards

25%

Total Supply Chain
Cost Reduction

20%

Efficiency
Savings

Orchestrated Intelligence aligns the power of AI/ML with a science-based modelling capability. It unlocks the hidden benefits latent in your planning systems by automating the configuration of the network and maintaining the optimal state as new risks and opportunities emerge, freeing up cash and delivering quantifiable benefit. It introduces a new paradigm of cost management, responsiveness and service to the enterprise: it is the Cognitive Self-Modelling Supply Chain.

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