Across the industrial sector, strategic alliances are being made to simplify everyday business operations, utilize data, and improve revenue generation. These strategic alliances have witnessed enterprises within more traditional industries partner with digital transformation service providers to solve problems. One example is the Oil and Gas industry turning to digital transformation to deal with struggles involving fluctuating oil prices and the need to optimize exploration procedures.
Google’s collaboration with Total to develop AI solutions that improve its analytical abilities and Equinor’s partnership with Microsoft to leverage the cloud highlight the increasing reliance on digital transformation solutions. Other industries such as the manufacturing industry, healthcare and education are not left out. Simio’s partnership with Pennsylvania State University to research the impact of virtual learning environments to STEM education is another example that shows that the digital transformation of traditional processes is not a passing fad.
According to BDO’s Middle-Market Digital Survey, a majority of C-level executives are turning to digitization to improve operational efficiency in an increasingly resource-constrained world. These executives have developed or plan to develop digital transformation strategies with the capacity to solve the unique challenges their enterprises face. Thus, digital transformation technologies used may range from leveraging the industrial cloud to store data to relying on the digital twin to solve complex scheduling and resource management problems.
3 Ways Digital Transformation can be applied to Improve Operational Efficiency
The diverse digital transformation solutions and tools available to end-users mean enterprises must make unique cases for how they intend to apply these solutions to improve operational efficiency…but a few important categorizations can be applied to develop functional strategies.
Risk-Based Scheduling – The financial and human effects of downtime have been thoroughly researched over time. In the industrial sector, enterprises lose thousands of dollars to unexpected machine failures or delays which derail the industrial operations and actionable schedules. Digital transformation technologies now provide industrial enterprises with the means to predict and develop accurate responses to unexpected risk factors in real-time through data analytics.
A Digital twin or a simulation modeling software provides the features for analyzing risks to production schedules and providing immediate solutions to complex operational challenges that may lead to downtime. With accurate risk-based schedules, the effects of a failed asset or inventory shortages can be analyzed to determine the best way to go about production without missing specified deadlines or wasting resources.
Optimizing Industrial Automation – The adoption of Industry 4.0 business models has put complete industrial automation at the forefront of what stakeholders intend to achieve. Digital transformation technologies provide the means to capture the data needed to automate every aspect of the industrial process. This includes the application of digital technologies such as edge computing captures the data and supports the analytical process needed to automate decentralized processes. One example is the use of robots to sort through throughput and make decisions regarding which goes further or which is returned due to defects.
The data captured from robots and other edge devices can be incorporated into larger digital transformation platforms such as a digital twin or an IoT platform to analyze facility-wide operations. Thus, decentralized automated assets can function on their own while relying on larger digital platforms to support centralized automation strategies.
Validation and Testing – Multiple digital transformation solutions exist that support the evaluation of operational strategies before implementation. The ability to evaluate risk and new ideas eliminate resource wastage and ensure enterprises do not fly blind when making complex decisions. The validation powers the digital transformation offers can be applied to both new and old industrial process.
One example of the new is evaluating the implementation of a large scale IIoT strategy that intends to monitor machine utilization. It is no secret that many DIY digital transformation projects fail during the implementation phase due to inadequate planning. With digital transformation tools such as simulation modeling software, the effects an IIoT implementation will have on operations can be evaluated. Digital transformation technologies can also be applied to evaluate the effects of increased customer demand to resource use, capacity planning, and scheduling.
Conclusion
The impact of a digitized factory floor is being recognized across all industrial sectors which is why approximately 96% of executives intend to increase budgets for integrating digital transformation strategies. But, how does the adoption of digital transformation look like in the physical world? You can find out by requesting a demo of Simio Digital Twin and its application within your industrial sector.