The end of linearity: How simulation digital twins are rewriting supply chain strategy
Traditional supply chain planning and optimisation is confined to linear assumptions and largely rigid frameworks. But that linearity won’t wash in the new age of systemic shocks and continuous disruption which span the functional siloes addressed by individual systems. Adapting to these evolving situations requires access to simulation-driven environments that enable leaders to make quick and effective strategic decisions. Simulation Digital Twins are the solution. They act as decision ecosystems that support continuous redesign and utilise AI to add relevant insight to users.
The issue with static policy
Spreadsheets, ERP modules and legacy APS systems are isolated tools. They stay reliant on static or ‘optimised’ assumptions and struggle to adapt to real-world change. What that means for business leaders is delayed decision-making, misaligned targets and outcomes, and a growing chasm between strategic ambition and operational execution across the supply chain.
Pre-defined policies are keeping business operations and supply chains rooted to the spot. By executing operations within established guardrails, the business struggles to adapt and respond to real opportunity for growth and efficiency. It’s a significant and completely unnecessary friction.
But what if it was possible to challenge those constraints, rather than just accepting them? Take SKU range for example. A decision to carry a wide range of sizes / flavours / colours might appear beneficial for customer choice but what are the knock-on supply chain implications? The combination of siloed decision making, rigid policies and rapidly changing macro factors are holding leaders back from realising potential growth, and avoiding unnecessary costs.
After all, a distribution centre isn’t a diagram on a piece of paper. It’s a thriving, living environment with its very own resources, flows, trade-offs and constraints. The solution lies in modelling the flows in the site with adaptive, simulation-driven decision support, with an appreciation of the impact of decisions made inside the operation which land elsewhere and vice versa.
Modelling with accuracy
Adaptive simulation technology accurately replicates all the flows and relationships that exist in the real supply chain. This encompasses strategies and policies which govern transport, inventory and fulfilment, offering joined-up decision making highlighting the interrelationships between previously siloed functions. Through intensive experimentation teams can explore strategies across multiple scenarios, assess trade-offs and fully test and evaluate any decisions before implementation.
One of those scenarios could be two functions competing for the same space in a facility. An illustrative example is manufacturing operations needing to expand due to consumer demand, eating up precious square footage previously assigned to the finished goods function. Kallikor’s digital twin technology can shed light on this potential conflict, enabling businesses to resolve optimally between those two operations.
A change in one area can be analysed for how it impacts another. For example, a retailer’s commercial function negotiates a new buying multiple from a supplier, improving their own KPI for gross margin. The warehouse and store operational impact in terms of space and handling costs can be assessed and balanced against the upside potential resulting in a comprehensive decision for the overall good of the business.
In terms of established guardrails, execution systems might be working at full capacity within defined limits, but the actual cause of inefficiency might sit in the definition of the operating policy in another area. A good example of this is perhaps store delivery cycles vs warehouse operations. In reality, the goal to deliver every item to each store as frequently as possible to reduce store inventory may be loading the warehouse and store with unnecessary levels of handling costs. The issue has been historically that these decisions were made in isolation without insight and visibility of the broader impact. .
Structural experimentation is different as it establishes a single source of truth. Discrete event simulation recreates and records the specific actions and outcomes that we see in the real-world. With this data, leaders can test network configurations, evaluate any design and policy changes in rapid time and align departments including finance, commercial, operations and strategy around the same evidence-led information.
Matching business ambition
The final disadvantage with static tools is the lack of true foresight. Simulation can model the impact of a range of hypothetical future scenarios – far beyond the scope of forecasting engines or execution systems. This range based panning enables the business to be ready to handle a much wider variety of potential disruptions than focusing on forecast accuracy and execution constrained responses.
Global disruption is more unpredictable by the day, and in this context rigid, retrospective tools are no longer fit-for-purpose. Simulation-based digital twins are living environments that mirror the real-world complexity of operations, exposing hidden constraints and empowering companies to experiment structurally and be ready for a wide variety of scenarios.
Instead of being confined to static and outdated guardrails, proactive simulation enables leaders to objectively question established policies, sign-off on strategies before they are implemented and align decisions across all departments. Businesses can plan for growth and resilience, having considered a realistic range of the challenges they may face ahead.
