Digital Twins: Enabling Next-Gen Manufacturing

Anna Ponza

As the manufacturing world confronts deep transformation and the need to capitalize on the Industry 4.0 bonanza, digitization of equipment, processes, and products are gaining more relevance. Although it’s been around for decades, the concept of a digital replica of a physical asset is poised to enter a new era.

The decreasing cost of sensors and the increasing progress of computational capabilities have made the Internet of Things (IoT) ubiquitous, widely accessible, and scalable. However, it’s not enough to collect seamless floods of data from the field, it’s also about being able to analyze them and act upon outcomes by means of distributed nets of actuators. Digital and physical layers collapse into a sort of superior meta-entity, thus overcoming constraints related to distance, time, and accessibility.

Not only is there considerable potential business value, there’s also an opportunity to weave unprecedented human-machine and machine-machine interaction models, feed revenues with innovative business models, and predict and prevent operations that could turn out to be dangerous for people and the environment.

The broad picture

Since its inception, definition and use of digital twins have undergone a significant evolution: once confined to the product lifecycle management (PLM) space and to a single entity, it now has the potential for further dimensions and perspectives.

In particular, we envision two complementary realms:

1. Digital twin of an asset

When a piece of equipment is digitally cloned and connected, it becomes a node of the intelligent factory, the supply chain and, more broadly, the entire ecosystem.

  • It can easily receive and transfer information from/to its peers to participate in the orchestrated production process.
  • It can be accessed by business partners as diverse as manufacturers, operators, service providers, and even regulators.

2. Digital twin of a product

Equipping a product with its digital companion might create a variety of advantages:

  • Behavioral outlook at any given point of the lifecycle, from development to disposal
  • Models of deviation from the as-designed/as-built
  • Prompt process adjustments

Nonetheless, judgment calls are always recommended before embarking on a digital twin initiative; while it’s easy to imagine that fairly complex products can easily pay off in cost and effort burden, simple consumer goods are unlikely to, at least in the short term. 

Fig.1 : Digital Twins broad picture : products, assets, stakeholders

The call to action: Priorities

Although many leading manufacturing industries are investing in digital transformation, the path might be paved with obstacles. A handful of strategic priorities must be addressed to try to avoid the perils of such a journey:

  • Reframe the value generation paradigm and clearly identify the benefits to be achieved.
  • Translate OT/IT convergence into a technology adoption/refresh roadmap.
  • Define the organizational structure that best fits the new set of capabilities; engages the internal workforce and the partners; and sets roles, expectations, and collaboration models.
  • Grow incrementally by starting with a small, self-consistent pilot scenario and, once it proves successful, scaling it up and out.

Fig.2 : Digital Twins enabling capabilities

The business benefits

Organizations willing to be front-runners rather than laggards need to cascade these priorities down business models, processes, and working models and reshape them accordingly.

1. Offshore revamp

Digital twins unarguably offer the chance to mitigate troubles with offshore processes’ quality and overcome workforce skills gaps. Moreover, new models could be devised where manufacturing micro-locations branch out to optimize the supply chain, achieve high levels of customization, supply spare parts quickly and at their final destination, and operate in formerly inaccessible areas (e.g., harsh or dangerous environments).

2. Value services

Traditional methods of purchasing of products and assets are being pushed aside to the advantage of value-centered pricing and paying models. Actual operating performance can be measured and monitored over time, thus enabling pay-per-use and pay-per-value formats that don’t account for downtimes or outages. Accurate customer service, efficient handling of warranties and recalls, and proactive maintenance packages spawn additional streams of business opportunities.

3. Shared intelligence

Technological advances enable equipment and products to understand and interact with their surrounding environments. Data captured or generated is exchanged, typically via the cloud, shared among multiple entities, and made available to feed additional applications like AI engines. As a consequence, once futuristic scenarios – like near-real-time rebalancing of workloads across multiple shopfloors or adaptive behavior of assets – are now within reach. Also, selling data to third parties outside conventionally established networks (e.g., weather/traffic forecast service, insurance, etc.) could generate additional revenue and alliances.

4. Standardizing at scale

The rush to capture IoT business opportunities early has led to the proliferation of standards for devices, data management, and communication. However, in order to scale out digital twins technology, standardization is critical to simplify interoperability, increase synergies, and improve cybersecurity.

5. Collaborative design and engineering

The democratization of authority implies that the final consumer has the right to decide and determine an increasing number of product features or even dictate improvements. In order to react promptly to fast-changing demand and make this approach sustainable, formerly siloed hardware and software domains need to be thoroughly paired and combined by design. Furthermore, as in-the-field data are collected and analyzed seamlessly, retrofit processes can be set up to leverage insights and adjust design and engineering parameters. Usability, performance, and materials strength can vary a lot depending on specific operating environments and conditions, so problems can be predicted and mitigated precisely where they’re likely to happen.

6. The evolution of maintenance

Time-based maintenance has been almost abandoned in favor of preventive, rule-based methodologies. In this regard, most common industry standards appear to be limited because of the small amount of data that can be processed and the inability to analyze anomalies other than limits-breaking. Despite persisting legal obligations around preventive approaches, the combined advent of cheap sensors and artificial intelligence is bringing maintenance one step further: real-time field data can be used to feed machine learning algorithms capable of recognizing unusual trends and predicting failures well before they actually occur. This is expected to deliver significant benefits in ensuring high resource availability and optimizing service efforts, particularly with prescriptive maintenance, which is arguably the next evolutionary stage. Not only will the system suggest parts for replacement or repair; it’ll also issue a work order then set up and oversee the related workflow.

Fig.3 : Digital Twins end-to-end scenarios

Where to start

First and foremost, it’s paramount to assess an organization’s level of maturity through dedicated evaluation frameworks. A design thinking approach can identify compelling issues to be solved or enhancements to be implemented, along with related drivers and enablers. Once the most valuable scenario is identified, start small, prove the solution’s effectiveness, and then scale up to capitalize on trust gained among stakeholders.

Also, organizations tend to be reluctant when it comes to changing business models and moving towards an open, collaboration-oriented working mindset. As such, new governance models will be required to manage the emerging cohort of digital employees. As more mundane, repetitive tasks are handed over to automated tools, conventional performance KPIs (e.g., throughput) are no longer meaningful. The ability to act on non-routine situations, the collaborative mindset, and the risk-taking attitude based on personal judgment calls will likely top the new evaluation frameworks. Culture, awareness, and engagement need to be relentlessly fostered in order to succeed.

Learn how “Asset Operations Companies Turn Digital Twinning Into Digital Winning.”


Anna Ponza

About Anna Ponza

Anna Ponza is a Business Transformation Principal, and having been with SAP for two years now, helps customers who want to thrive in the digital era and improve their own processes, business models, technology foundations. Value propositions, innovation roadmaps, management of complex projects and thought leadership are the most recurrent services that Anna delivers. Before joining SAP, she spent 17 years of her professional career with several large organizations, covering different innovation-oriented roles, spanning from Research and Development Engineer to IT Senior Manager.