Embedding Predictive Digital Twins In Operational Planning

Predictive simulation and digital twins are increasingly used to help maximise return on investment from capital projects. But another important (and often underutilised) application relates to operational decision making.

As cyber-physical systems become increasingly complex, so too do operations. Add mounting pressure from inflation and supply chain disruption, and planning becomes even more difficult.

Predictive digital twins are dynamic digital representations that help you optimise asset and process performance, so operations can help drive better business results. Here are 4 operational use cases where they streamline planning, deliver cost savings and help the organisation deliver more value.

Use case 1: Batch sizing and sequencing

Across manufacturing lines, what’s the optimum batch size and sequence for different products? If you have 5 products, should the production sequence be A, B, E, D, C based on changeover times? Or should C be the first, so it’s out of the way? And what’s the impact on profitability for different combinations?

These are complex questions when you factor in different product content, supply chain considerations and set-up and changeover times. Predictive digital twins can provide answers based on key KPIs. For example:

• KPI is throughput: The digital twin can determine which sequence will deliver the most units based on product mix, demand schedules and changeover times
• KPI is profit: The digital twin can calculate the optimal product mix based on the demand schedule and the relative profitability of different customers
• KPI is customer satisfaction: The digital twin can determine the ideal size and sequence to deliver on priority orders ­– while sequencing subsequent production to deliver on secondary KPIs

Predictive simulation can also help you make more informed decisions based on what’s happening in real-time. What should you do if there’s an issue with a line and the maintenance engineer is sick? Switch product? Go on and do rework? The modelling gives you a risk-free way to understand the impact of different choices, so you can proceed with the scenario that delivers the best results.

Use case 2: Staffing and shift scheduling

Labour requirements are key to operational planning – in terms of achieving performance and optimising costs. So what’s the best way to deploy staff and structure schedules to deliver the right outcomes as demand shifts over time?

Predictive simulation and digital twin technology make it easier to answer these questions. Barclays Merchant Services discovered this when looking to optimise call centre operations. Their centre handles more than 20 million calls annually, and they needed to know when and how to deploy employees so as to meet service level requirements and manage costs.

By modelling call centre operations in our WITNESS Horizon predictive simulation software, they optimised the number of employees required to handle calls, as well as the number and length of shifts. This enabled them to reduce overtime costs by between £80,000 and £100,000 a year – while still achieving their customer service targets.

Use case 3: Succession planning

Predictive simulation doesn’t just help make evidence-based decisions on short-term staffing. It also facilitate the medium- and long-term operational planning that’s key to delivering lengthy work programmes.

We’re increasingly seeing this in the defence, energy and nuclear sectors, where programmes can take decades and need effective ongoing succession plans built in. Different skills are needed in different phases, and if you don’t have enough qualified people at the right time, the programme faces costly risks and delays. In some cases, people who will see out the programme won’t even be born when it starts!

Predictive simulation helps you understand in detail what capabilities you need and when, so you can implement succession plans that ensure operational continuity.

Use case 4: Capacity and supply chain planning

Predicative simulation and digital twins also help you make evidence-based supply chain decisions that reduce risk, optimise resource allocation and deliver greater operational control.

Mars Chocolate North America is a great example. It has 29 product lines running across 6 US sites, each with different product mixes and chocolate consumption requirements. Mars’ quality standards limit bulk storage capabilities, making accurate planning essential.

With so many dynamic processes at play, would it be better to make all chocolate types where they’re required or manufacture in fewer, larger facilities and ship regionally? Using a predictive simulation model in WITNESS Horizon, Mars analysed operational performance based on different scenarios across the lines and sites. This helped define the optimal way to work within its constraints while identifying previously unknown risks and highlighting opportunities for cost savings and performance improvements.

What complex operational challenges are you facing?
Contact us today to discuss, or learn more about how predictive simulation and digital twins can help you by registering for our upcoming free webinar:
Operational Planning Using Predictive Simulation