Supply Chain Planning & Execution During Shortages

Constraint-Based Supply Planning & Execution

Resilience, Supply Chain Management, Supply Chain Planning

This post has already been read 3747 times!

How to achieve optimal supply chain performance in constrained, volatile, and uncertain conditions

Constraint-based supply planning is a capability of One Network’s NEO Platform that enables companies and their trading partners to collaborate, create and execute supply plans that respect material and capacity constraints across the network. This improves productivity by automating the process of applying actual lead times, and supplier capacity and material constraints, enabling improved planning and execution decisions.

"Executing exactly to a plan in today’s fast-moving world is difficult. An in-memory planning system working on a snapshot of yesterday’s data will often make poor recommendations." -Joe Bellini Click To Tweet

The system will evaluate all viable alternatives and prioritize prescriptive actions based on targeted business objectives. These actions can be taken automatically as part of an autonomous workflow, or they can be presented in a workbench where planners can explore various scenario options in a graphical format.

Smart Prescriptions. Planners can interact with these smart prescriptions, by running various scenarios, and evaluating the outcomes, and thus determine the best set of resolutions. The system AI framework (called “NEO”), will remember decision sequencing that generated superior results, and then offer those in the future. Once the planner decides on a scenario, the chosen set of actions can be executed from the workbench as part of the seamless planning/execution platform. Planners thus fulfill demand by evaluating all possible alternatives, such as using different sources, substitute components, or alternative work definitions, and can select the course of action generating the optimal outcome. Multi-party collaboration is a core capability in the decision-making process and all data related to decisions, both structured and unstructured, is attached to the transactions.

Smart prescriptions are contextual and dynamic. The constraints generated by demand and supply shifts are constantly changing. Smart prescriptions are sensitive to this changing context. Executing exactly to a plan in today’s world is difficult, daily execution decisions frequently diverge from the planned world. Thus, an in-memory planning system working on a snapshot of yesterday’s data will often make poor decision recommendations in some parts of the trading partner ecosystem. And those decisions can often impact customer service levels, revenue, and cost.

Planning married to execution. Combined planning and execution on a single platform, that can run continuously and incrementally, is required to solve for constrained supply in today’s chaotic world. Both ERP and advanced planning solutions must run constrained supply algorithms in batch mode given their architectural designs. Thus, advanced planning and ERP miss real-time contextual changes in the network that are material to decision-making and improved outcomes.

A constrained supply plan respects constraints by moving orders to earlier time buckets, offloading to an alternate resource/work definition/supply source, modifying transportation modes, or adjusting labor plans. If these measures are insufficient, then the constrained supply plan will constrain demand, running algorithms to optimize for downstream mix and volume. This way trading partners are not expecting to receive goods which are not coming thus reducing overall network chaos and variability.

The limitations of ERP solutions mean they frequently shift the burden to planners. ERP configurations set flags to allow demand to overrun constrained supply in the case that the constraints cannot be resolved due to their limited visibility across the supply chain network. Planners are then left to try to resolve across multiple application silos and trading partner echelons, which typically doesn’t work well.

Furthermore, in these solutions, lead times must be treated as hard constraints when running the plan.

On the NEO Platform, machine learning algorithms determine accurate lead times across trading partners echelons. Demand inside of manufacturing or procurement lead time can be addressed through various means, such as last-minute allocation, transportation mode adjustments, or buy versus make.

The Digital Supply Chain Network™ maintains a real-time single version of the truth across the network with robust demand, supply, and logistics services. This enables trading partners with many more degrees of freedom to solve for demand and supply variability.

In a typical ERP hub and spoke configuration flags are set that allow the planning to run without respecting lead times when demand shifts inside of lead time. And given the separation of planning and execution wouldn’t have known the actual lead time in the first place. So, understanding and resolving for constrained supply upstream in the supply network is a major challenge, if not an impossible task.

"On a network with a real-time single version of truth, and robust demand, supply, and logistics services, trading partners have more degrees of freedom to solve for demand and supply variability." -Joe Bellini Click To Tweet

Scenario structures include hard constraints, such as max quantity or days of supply, along with soft constraints, such as demand splits and sourcing or allocation percentages. NEO’s linear programming engines will expose the ability to prioritize objectives, ensure soft constraint optimization, and extend goal functions to include variables such as profitability and landed cost. Constraints will forward and back propagate across the network. Less sophisticated heuristics-based planning runs the risk of propagating actions which create more problems than they are solving.

One Network’s constrained supply capabilities include the typical configurations found in both advanced planning and ERP, but go much further to drive more value. The network is capable of redistributing progressively downstream any upstream material constraints. The constraints are often complicated by lead time, lot sizes, and multi-level sourcing of items. Considerations include simultaneous component usage, plant capacity, and the time to produce and ship.

Flexible modeling constructs support a range of supply chain models enable high value solution configurations such as; multi-tier demand propagation forecast collaboration, fully constrained capacity and material forecasts, multi-tier demand driven constrained supply planning, optimized product mix and allocation based on constrained supply, constrained production order forecasts, multi-echelon inventory optimization (MEIO), concurrent order/logistics planning and execution, and goal-based channel allocation.

Constrained Supply Planning Requires a Digital Supply Chain Network

Constrained Supply Planning is a critical capability today, and can only be effective if underpinned by a sophisticated digital supply network, one that marries planning and execution, and concurrently optimizes across demand, supply, and logistics.

This real-time concurrency across orders and logistics based on a single version of the truth, enables optimized execution, by combining planning and execution across the inbound supply process. As the network resolves for constrained supply issues, it is constantly re-planning based on actual conditions, to generate targeted outcomes based on system rules and policies. Only advanced CSP capabilities can achieve outcomes in both revenue and cost.

Joe Bellini
Latest posts by Joe Bellini (see all)