Internal supply chain investment

According to a Material Handling Institute survey, 50% of companies plan to invest $1 million+ in internal supply chain technologies over the next 2 years. But how can you identify the right type and level of investment, and ensure you allocate that spend to meet customer demand at minimum cost?

With predictive digital twins, you can answer both of these questions and make evidence-based decisions encompassing all elements of the internal supply chain – from process and product flow to automation, material handling, and inventory and storage requirements. Let’s look at the benefits of predictive digital twins in three such scenarios.

What’s the impact of a major process change on WIP, traffic flows and routing?

If you introduce a new product or line into a facility, what’s the impact on internal logistics? Will new congestion points be introduced? What are the health and safety implications? Can we smoothly transport raw materials, components or finished products to or from assembly areas?

By creating a predictive digital twin, you can model new processes and flows, and identify pinch points before they emerge in reality – all in a risk-free, digital environment.

For example, Britvic Soft Drinks used our WITNESS predictive simulation software to understand how a new high-speed bottling line would affect internal site logistics.

First, the team looked at the potential implications outside of the site. The predictive digital twin of the future facility simulated how vehicles would enter the site, flow through parking bays to loading bays, how loading and unloading would work, and how the vehicles would then leave the site.

Having made a number of key investment decisions using this simulation, they then modelled the internal logistics movements, including forklift flows bringing raw materials to the line, taking finished products to warehousing and transporting full pallets to loading bays for loading onto vehicles.

Until reviewing the simulation, Britvic hadn’t realised how much congestion would occur to and from the loading bays, creating both delays and safety issues. Using the digital twin, the team identified a safer, more efficient solution that included one-way traffic flows to segregate vehicles while maintaining the required logistics efficiency.

How do we optimise material handling to respond to the changing market?

We’re currently seeing several trends that have a significant (but often overlooked) impact on material handling.

One is the move away from single-use plastics towards more sustainable materials. Members of the UK Plastics Pact achieved a 30% reduction in 'problematic plastics' since 2018, leading to major changes in production processes.

Another trend is the rise in consumer bulk-buying during the pandemic. After all, the internal logistics associated with producing a 24-unit pack are very different to that required for a 4-unit pack.

In responding to such changes in customer behaviour, you don’t want to buy 30 forklifts if you only need 20. And would you be better shifting to AGVs?

Predictive digital twins can help you create a water-tight business case for your proposed MHE investments – so you can both design and rightsize your fleet to handle materials and products at the right pace and at the lowest cost.

If you don’t model future scenarios to understand the potential impact, you might well find yourself investing in the wrong equipment or processes, not to mention incurring extra pain and rectification costs due to unexpected bottlenecks and delays.

What’s the best way to integrate an automated storage and retrieval system into our processes?

Demand for automated storage and retrieval systems (ASRS) is accelerating. The global ASRS market is projected to grow by 8% by 2025, driven by pressure from just-in-time supply chains and technical skills shortages.

Using predictive digital twins can help you make more informed decisions about both the ASRS investment itself and how best to incorporate it into your facility and business process.

For example, how big should the ASRS be? And what performance level do you actually need to meet requirements without causing bottlenecks? If you could validate that your processes can cope with 30-second retrieval instead of 20-second retrieval, you could save significantly on the capex of the project.

Importantly, predictive digital twins will also help you understand how something like an ASRS will affect upstream and downstream processes.

We recently helped a major automotive supplier build a robust business case for ASRS investment. In addition to modelling the specific capabilities of the ASRS, the project team could see the broader impacts of the investment upon overall process capability.

Analysis of the predictive digital twin enabled the team to understand the trade-off between the capability of the ASRS and the resulting wider process control logic that they could implement – resulting in a significantly lower cost to serve their end customer.

They could then home in on the ASRS performance needed to enable that buffer level.

Plan or invest based on evidence, not instinct

Given the complex interplay of dynamic processes within most company’s internal logistics, it can be hard, if not impossible, to fully understand the many knock-on effects of people, process or technology changes.

And you don’t want to be caught out post-implementation, be it through unexpectedly poor end-to-end KPI impact, hidden or new bottlenecks, damaging product delays, or costly non-value adding logistical issues.

Predictive digital twins help you pre-empt problems and pitfalls, giving you an end-to-end view of the dynamic interactions within your internal supply chain. That way, you can make informed planning and investment decisions, fully confident in a sound de-risked business case.

Would you like to learn how predictive digital twins can help you overcome key business challenges to help build a more resilient and adaptable future? Watch our webinar recording 'Strategically Planning for the Future: Developing Resilient, Agile Processes Using Predictive Digital Twins'.