From static plans to adaptive decision-making. By Will Lovatt
Traditional supply chain planning is often built on linear assumptions and rigid frameworks, particularly in manufacturing. But in an age of systemic shocks and continuous disruption, static planning is no longer enough. Leading organizations now need to shift to adaptive, simulation-driven environments to drive strategic decisions at the C-suite level. Enter digital twins: decision ecosystems that evolve in real-time, integrate AI for relevant insight, and support continuous redesign.
Rigidity with static tools
Traditional planning tools are no longer fit-for-purpose. Spreadsheets, ERP modules and legacy APS systems are often slow, siloed and overly reliant on static assumptions. They fail to reflect real-world complexity and are unable to adapt quickly to change. The result is decision latency, misaligned incentives and a growing gap between strategic ambition and operational execution across the supply chain.
Organizations are also running their supply chains based on pre-defined policies, and they are executing operations within those guardrails. While the business moves on, policy remains static, falling out of sync with the real needs of the business, and potentially causing significant and unnecessary operational friction.
Businesses should be asking themselves whether they can recognize and challenge these constraints. For example, a stocking policy may require inventory to be held in bulk in a specific location, but as demand patterns change it may be the case that this is no longer the right decision.
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 than months, explore the impact of changes and align finance, operations and strategy around the same set of evidence-led data.
Keeping pace with business ambition
The final drawback of static tools is that they offer no foresight. Spreadsheets can’t predict the impact of a hypothetical future scenario, but simulation can. For example, a shortage in transport capacity, perhaps due to macroeconomic factors beyond an organization’s control, might lead to poor customer service. This can be accounted for in the simulation, thereby alerting stakeholders to the issue, allowing revised approaches to be tested in order to protect customer service.
As the unpredictability of global disruption accelerates, manufacturing supply chains can no longer afford to rely on rigid, retrospective tools. Simulation-based digital twins are living models that mirror the real-world complexity of operations, expose hidden constraints, quantify trade-offs and enable structural experimentation.
Instead of optimizing within static guardrails, leaders can use this visibility to question policies, validate strategies before implementation and align decisions across teams. The result is a supply chain capable of keeping pace with business ambition. It’s built to adapt, not just react.
Will Lovatt
Will Lovatt is Chief Revenue Officer at Kallikor. Kallikor enables enterprise supply chains to move from static planning to dynamic, simulation-led design. Its platform empowers teams to model complexity, test ideas safely, and unlock high-impact improvements faster and with greater confidence, leading to better decisions, reduced costs, and supply chains that adapt in real-time to shifting business needs.