Global supply chains have never been more visible or critical. Determining which products to procure when, in what quantities, and from which sources without running into excess or low stock has been the goal of supply planners for most of human history.

With modern industrial processes spanning geographies and verticals, it is humanly impossible to effectively optimize across the vast number of variables at play in supply, production and distribution planning for large enterprises.

The emergent complexity and need for fast decisions has pushed the traditional supply chain planning tools, equations and mathematical models to breaking point. We now live in a world where increasingly larger losses are tolerated as “inevitable”.

Further complicating the challenge is the clear disconnect between Demand Planning & Inventory Planning processes; consisting of rule-based optimization of excessive research equations and numerous parameters (min/max inventory levels, safety stocks, lead time etc.)

Very few companies manage to achieve or maintain this delicate continuous equilibrium. And today’s increasingly complex supply chains buck all traditional forecast-driven attempts to manage them. Supply chain leaders have quietly admitted that something is deeply wrong with the current system, and the available software tools that help visualize and assist in planning solve only a small fraction of the real problem.

Supply Chains without Forecasts: Planning with Deep Learning

Reimagining the Future of Supply Chains with Autonomous Requirements Planning

Global supply chains have never been more visible or critical. Determining which products to procure when, in what quantities, and from which sources without running into excess or low stock has been the goal of supply planners for most of human history.

With modern industrial processes spanning geographies and verticals, it is humanly impossible to effectively optimize across the vast number of variables at play in supply, production and distribution planning for large enterprises.

The emergent complexity and need for fast decisions has pushed the traditional supply chain planning tools, equations and mathematical models to breaking point. We now live in a world where increasingly larger losses are tolerated as “inevitable”.

Further complicating the challenge is the clear disconnect between Demand Planning & Inventory Planning processes; consisting of rule-based optimization of excessive research equations and numerous parameters (min/max inventory levels, safety stocks, lead time etc.)

Very few companies manage to achieve or maintain this delicate continuous equilibrium. And today’s increasingly complex supply chains buck all traditional forecast-driven attempts to manage them. Supply chain leaders have quietly admitted that something is deeply wrong with the current system, and the available software tools that help visualize and assist in planning solve only a small fraction of the real problem.

Autonomous Requirements Planning (ARP) - The Future of Supply Chain

With Autonomous Requirements Planning (ARP) Seeloz introduces a fundamental paradigm shift to the underlying structure of supply chain planning with artificial intelligence, making forecasts obsolete!

Under the hood: Seeloz Machine Learning Advantage

We’ve used the same techniques computers used to beat the world’s leading chess grandmaster and applied them to winning the supply chain game! This differs from status quo AI supply chain planning systems that are simply creating enhanced regression models to refine traditional forecasts.

On the other hand, at any given time our AI engine is churning out millions of possible scenarios of how the supply chain game might develop. It then picks out moves that maximize future success in the vast majority of these anticipated futures. These moves are then translated into decisions that are seamlessly pushed to the transactional ERP’s for human approval/update & immediate execution. This process completely outperforms traditional AI planning tools in real world environments.

ARP-Driven Supply Chain Redefinition

By introducing SCAS (Supply Chain Automation Suite) the world’s first ARP, Seeloz is redefining supply chain planning across four primary supply chain types:

  1. SCAS Production: Autonomously driving complex production and manufacturing supply chains.
  2. SCAS Distribution: Autonomously driving procurements & cross-warehouse movements of goods across outbound distribution supply chains.
  3. SCAS MRO (Maintenance, Repair & Overhaul): Autonomously driving procurement & inventory management processes to streamline maintenance and repair supply chains.
  4. SCAS OSPAS (Oil Supply Planning & Scheduling): Autonomously determining quantities to extract, refine & distribute while continuously balancing supply & demand to minimize  demurrage, hauling, transmix batches.

So What?

ARP holds the promise of becoming a reliable auto-pilot to facilitate the captains of modern supply chains. It serves as the essential, reliable and robust artificial intelligence that reaches and maintains the delicate balance while cruising at the high velocity of modern commerce.

Seeloz has a critical role to play in the world’s future. Our vision is to make the world’s supply lines more robust, adaptable and efficient and do our part in helping save the world!