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This combination of static insight, rigid policies and rapidly changing macro factors are preventing strategic level decisions from being made by business leaders. After all, a manufacturing distribution center isn’ t a blueprint on a piece of paper. It’ s a living environment with its very own resources, flows, trade-offs and constraints. The solution is a shift towards modelling the live environment of the site, which is enabled by adaptive, simulation-driven decision support.
Modelling with precision
Adaptive simulations model all flows and relationships across the supply chain, such as transport, inventory and fulfilment, with this joined-up visibility particularly important in the manufacturing space.
Before adaptive simulation technology, there was no feasible way of identifying that two functions in the warehouse were about to compete for the same space. For example, where manufacturing operations need to expand due to consumer demand, the finished goods warehouse management function often must cede precious square footage to meet the core manufacturing needs of the business. This creates a growing conflict between the needs of the manufacturing function in a constrained site compared to the finished goods department for the outbound supply chain.
A simulation digital twin can shed light on this potential conflict, enabling businesses to take steps to balance the strategic outlook of those two operations. Modular capabilities mean that the simulation of a change in one area can be assessed for how it impacts another. For example, staffing levels in finished goods can be adjusted to assess its impact on the flow of outbound product, before any change is made in the real environment. Businesses can gauge needed resourcing levels to maintain throughput or meet higher levels of demand, while redeploying staff to where they are most needed for greater efficiencies.
In the context of established guardrails, execution systems may be working extremely hard within defined limits whereas the root cause of the inefficiency lies in the definition of the operating policy. For example, teams might misidentify picking strategies as a source of inefficiency, but the real issue is in fact the warehouse layout. Decision-makers are therefore trapped within systems that don’ t present the right levers to pull to allow tangible change to happen.
This is where structural experimentation is different as it creates a single source of truth. Discrete event simulation recreates and records the specific actions and outcomes present in the real-world equivalent. With this information, leaders can test and compare network configurations, evaluate warehouse design changes in hours rather
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