Supply chain planning is evolving fast to respond to changing market dynamics, and so are the ways in which decisions are made. We’ve talked recently about Decision-centric planning (DCP), an emerging approach that promises to transform how supply chain decisions are made. A great deal of the change that must take place to evolve from today’s traditional, distinct, cyclic planning processes such as Sales and Operations Execution (S&OE), Sales and Operations Planning (S&OP), and Integrated Business Planning (IBP) to DCP is organizational rather than technical. But understanding how the enabling technologies of DCP really work is a key step in developing trust in this emerging and highly impactful way of managing supply chains.

There are a lot of facets and enablers of DCP, actually—more than one blog can cover—but we’d like to talk about one essential component here: the “digital brains” of AI in supply chain planning software, also known as “algorithmic agents” - or as we call them in our market-leading Atlas Planning Platform: the Expert Systems.

Finding Value in Your Supply Chain Data

We talked about DCP in depth in our recent white paper: Decision-Centric Planning: A Radical Reimagining of Supply Chain Decision Making. As a quick recap, DCP shifts the focus of supply chain planning decision-making from the process to the decision itself. Its four main components are Continuous Monitoring – tracking if an event occurs; Event Impact Assessment – measuring the impact of the “radius blast” of that event; Impact-Driven Decisions – responding to the decisions if needed; and Decision-Driven Composable Process, initiating the right process and engaging the right people. DCP shifts the traditional regimented sequence of decisions with one that gathers just the right resources together to support a mix of ad hoc and cyclical decisions.

The raw material behind each of those four components is data, and these days supply chains are getting a lot of it, all the time, from a broader-than-ever array of sources: ERP systems, POS, IoT signals, and on and on. That can be overwhelming for a human. Yet, it’s no problem for algorithms specifically designed to discover, receive, analyze, recognize patterns in the supply chain model, learn from and optimize that model over time, the way a human brain could – if it could scale to that capacity.

That’s what, for example, the Expert Systems in Atlas do within the supply chain planning software application; continuously learning new adaptive solutions and procedures from data it processes, as it identifies and mines patterns across the supply chain.

What Atlas’ Expert Systems Do for Supply Chain Planning

Real-Time Anomaly Detection:

Atlas’ Expert Systems continually monitor and make sense of all of the data and events, identifying unusual patterns or deviations from expected behavior, and guiding the planner in making decisions. These algorithmic agents find relationships and causality between events and initialize and modify their own parameters based on new insights. They’re a big part of the automation of routine supply chain planning activities to free up human intelligence for higher-order tasks.

Continuous Learning and Adaptation:

The Expert Systems continuously receive real-time data from across the supply chain as well as behavior and insights from different decision makers – planners, suppliers, collaborators, and more. They continuously interact with Atlas’ powerful business rules engine so there is a virtuous cycle that over time learns how different decision makers across skills and roles would respond.

Enhanced Decision Automation:

By incorporating relevant insights, the Expert Systems help automate complex decisions, optimize decision logic, tweak the business rules, and build those changes into machine learning algorithms. That process enables our customers to deploy decision automation, evolving toward a more autonomous planning environment. These are the brains behind our powerful AI capabilities, like the secret sauce that keeps everything flowing.

Better Decision Making:

As you explore ways to begin your journey to become more decision centric, John Galt Solutions’ Expert Systems are ideally suited to support this new mindset and help increase the quality of your business decisions. When an event – such as a change in demand or supply – is detected in the plan, Atlas fine-tunes across different elements such as urgency, relevancy, and timing to determine:

  • Who is impacted?
  • How long is the impact (e.g. duration)?
  • Is the impact across multiple customers/facilities?
  • What processes need to be initiated?

The Expert Systems are there to provide its learning-and-advising knowledge to ensure you have everything you need to drive better decisions.

We at John Galt Solutions have always kept a steadfast focus on helping innovators like you evolve from where you are to where you want – and need - to be, and DCP is a key step forward. We’ll be with you every step of the way, bringing the power of our Expert Systems to help you make the most of AI and machine learning in supply chain planning, and progress on your own DCP journey.