Digital Twins 63d80cad04f03

Where Do Digital Twins Add the Most Value?

Jan. 30, 2023
There’s a learning curve with simulation tools.

Advanced technologies have proliferated in manufacturing, and today’s factories are already nearly unrecognizable compared to a decade ago. As manufacturers continue their digital journey, they are increasingly leveraging operations and supply chain simulations to inform their current and future planning.

Gone are the days that connected machines and faster, more powerful data networks, digital twins and simulations were nice-to-have tools. November 2022 research from the Manufacturing Leadership Council found that 58% of manufacturers either already have extensively digital plant floor networks or will within two years’ time.

Manufacturers that want to have an edge in this increasingly digital future can develop a digital factory road map to help leadership teams better understand current operations, identify areas for improvement and build the necessary resiliency and agility to respond to disruptive change.

Effectively leveraging advanced technologies such as digital twins, artificial intelligence (AI) and Internet of Things (IoT) devices is not a capability to develop and implement overnight, but rather a journey that may begin with simulations to better understand operations and supply chains.

Headwinds to Navigate

Supply chains are increasingly complex, and top-performing companies are embracing data and digital tools to win market dominance. The global digital manufacturing market is expected to reach nearly $1.37 trillion in 2030, up from $276.5 billion in 2020, according to Allied Market Research.

Although the digitization of middle-market manufacturers may look different than that of their upmarket competitors, these companies increasingly need the ability to systematically approach operations to combat rising costs and cost volatility, improve their access to and the integrity of information, deal with growing pains and handle capacity constraints.

While specific issues in these areas may vary from manufacturer to manufacturer, these headwinds are common across the industry. Here’s a look at some of the specific challenges in each area:

Rising Costs and Cost Volatility

  • Supply chain disruptions and rising freight and material costs are forcing companies to pay more. Efforts to reduce disruption to customers may also lead to unplanned expenses.
  • Businesses continue to use pay increases, overtime and temporary labor to overcome shortages across the workforce and maintain production levels.
  • Despite improvements in supply chain dynamics, “normal” conditions are not expected to return in the short-term, and manufacturers must prepare for future disruptions and bottlenecks.

Improving Access to and Integrity of Information

  • With the growing volume of data available to companies, understanding operational capacity can be exponentially more difficult if manufacturers do not have teams and technologies in place to analyze that information.
  • Similarly, organizations may find it challenging to determine how best to harness data to guide decision-making across various teams.
  • Companies need to ensure that they have proper cybersecurity and other data security protocols in place to protect their information as operations become ever more data centric.

Growing Pains

  • Resource and technology constraints can limit access to timely, accurate insights.
  • Stagnate costs data, historical processes and system constraints may impact effective decision-making.
  • Rapid growth and product portfolio complexity may limit profitability insights.

Capacity Constraints

  • Customer demand being at an all-time high combined with rapid price increases will continue to present a puzzle.
  • Demand variability can require companies to pivot quickly and adapt their production and distribution as customer needs ebb and flow.
  • Distribution and manufacturing capacity constraints may have labor implications that necessitate changes to workforce levels or strategies.

Enter the Simulation

Simulations powered by digital twins—essentially virtual replicas of existing products, machines, networks and/or plants—are just one tool enabling manufacturers to model and analyze various scenarios, helping them navigate the challenges outlined above. They can help organizations improve efficiency and reduce costs by understanding the likely outcomes of scenarios before implementing changes across the business.

Manufacturers can leverage simulations as part of their digitization roadmap to begin to transform strategic decision-making. The use of such models is flexible and easily adapted to new strategic opportunities and can help manufacturers address a multitude of production issues. such as:

Improving overall production output: Running scenarios with various inputs (in terms of price, materials, supply levels and demand levels) can give a clearer understanding of what output levels might be.

Understanding process change impacts: Updating the production process might have unforeseen consequences in terms of production fulfillment, waste and labor. Simulations can help teams understand what these impacts will be before the organization broadly implements new process changes.

Aligning capacity to demand: Assessing current and future capacity can help identify process bottlenecks and improve attainment to meet customer demand.

Achieving growth targets: Prioritizing capital investments can help avoid unnecessary costs and improve the organization’s ability to meet growth targets.

Reducing costs: Identifying overhead and shared service costs can improve margins and profitability.

A Digital Twins Road Map

The use of digital twins may be an important goal for manufacturers that don’t currently have such simulation models in place, but it can be daunting for teams to figure out where they should begin to implement this technology.

These steps can help companies shape a digital twins road map:

1. Assess foundational IT and operational systems: Manufacturers should review their current IT and operating systems to determine necessary technology upgrades. This is the crucial groundwork that will enable more advanced simulations.

2. Ensure proper data protocols are in place: For simulations to be as accurate as possible, organizations need to ensure other technologies are harnessing and analyzing organizational data effectively. The quality of the inputs will have a significant impact on the quality of the outputs.

3. Understand labor needs: Companies will need to assess the digital skill set of their workforce to ensure employees have the technical skills needed to interface with these advanced technologies (“new-collar” workers). It will also be important to train/upskill existing employees on new technologies the organization adopts.

Digital twin simulations can release trapped optimization potential and improve transparency, ultimately benefiting an organization’s strategic planning, decision-making and overall operational excellence.

Katie Landy is industrials senior analyst with  RSM US LLP.

Sponsored Recommendations

Voice your opinion!

To join the conversation, and become an exclusive member of IndustryWeek, create an account today!