The digital transformation of a manufacturing plant is a complex process in itself and making sure it achieves the enterprise goals can be even more challenging. This is also the difference between adopting and using new technologies and a successful digital transformation.
As an IT leader in the manufacturing industry, you deal with all aspects of digital transformation, from the Industrial Internet of Things (IIoT) to data security. On top of that, you must keep plant floor issues in mind while planning any changes.
Until your plant has transformed into an efficient system that harmoniously utilizes the technologies you’ve introduced, the digital transformation isn’t successful. In the worst-case scenario, you end up with an inefficient plant after investing in digital technologies.
Production vs. maintenance
There has to be a better way of effectively achieving business objectives than choosing one over another. The traditional structure of a facility makes it virtually impossible to optimize production and maintenance activities.
A segregated facility without efficient modes of communication between its internal elements offers little room for analysis. Instead, the only feasible option is to halt production to give way for maintenance – which might not even be required.
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IT leaders are in the best position to offer ways for solving an age-old dilemma. For starters, a digital system alone can significantly improve scheduling processes to maximize the time for value-adding work. Teams at the forefront of maintenance can relate to how unplanned priorities can easily throw them off and put valuable wrench time at risk.
Moreover, the downtime between work order execution, such as obtaining and preparing parts, provides opportunities to make better use of waiting time. Quantifying the sources of non-productive tasks can present ways to minimize them or, at least, present options for more efficient multi-tasking.
In addition to the digitization of work schedules, a more digitally mature operation enables a facility to challenge the need for maintenance in the first place. Preventive maintenance activities conventionally require arbitrary criteria for frequencies and routines. These habits arise from a lack of better choices rather than purposeful measures.
Enabling the technological framework that allows communication between equipment, and facilitates the transfer of information between devices, allows for a more targeted strategy for servicing needs.
Introducing operational technologies bridges the gap where traditional methods and processes fall short. IT leaders need to consider the forms of information that will broaden the insights in aligning maintenance and production practices, starting with the design phase and throughout execution.
Proctor and Gamble (P&G) decided to leverage AI, machine learning, and edge computing to transform their digital manufacturing platform and improve the production process of baby care and paper products:
Diaper manufacturing: To ensure a high-quality diaper, the production process includes assembling many layers at high speed and great precision – now, machine telemetry and analytics will constantly monitor production lines and prevent potential issues in material flow, reduce downtime, and lower maintenance costs.
Predicting paper towel length: Data from the sensors on the production line, advanced algorithms, machine learning, and predictive analytics will help improve manufacturing efficiency and deliver products of higher quality.
With their new solution, P&G will be able to digitize and integrate data from their manufacturing sites and enable real-time visibility – employees will analyze production data and leverage AI to improve their production process. This kind of insight will enable good information flow, notify employees about changes in the production process, and ensure that production and maintenance departments’ tasks are aligned with minimal disruption.
Digital technologies for maintenance
With the many available technologies for improvement, companies often face the challenge of prioritizing initiatives that bring the most value. More than an issue of insufficient funds, a survey reports that more than 38% of respondents see the lack of a clear path as the top barrier to successful implementation. In the workplace, this concern can present as a lack of morale or motivation from the teams to sustain digital transformation endeavors.
It is easy to get lost in the promise of innovation and the excitement of upcoming technologies. Machine learning (ML) and artificial intelligence (AI), for example, redefine how to identify and execute maintenance activities.
[ Related read Edge computing: 5 use cases for manufacturing ]
Installing sensors in the machines within a facility can obtain information about the condition of an asset. These data points feed into centralized software that allows advanced analysis and predictive analytics. Automated decision-making capabilities become a reality, which enables facilities to move away from subjective alternatives.
On top of acquiring new tools, a holistic approach to taking on groundbreaking projects considers cross-functional impacts. The goals of both technology and operations teams need to align in terms of scope, objectives, and skills gap assessments.
With the changes in work demand, potential upskilling opportunities and workforce engagement can significantly improve the buy-in from internal departments.
An abundance of improvement ideas can benefit from agile planning that allows adaptability and iterative feedback loops. In a scenario with a limited budget and a long list of proposals, for example, coming up with a minimum viable product (MVP) can act as proof of concept to establish priorities. MVPs that continue to deliver against set goals can secure additional funding until a final solution emerges.
Aligning maintenance and production
The immediate effects of digital transformation stem from two main branches – digitalized work management processes and predictive analytics.
Digitalizing work management processes enables teams to tap into the features of an interconnected system. For instance, updating the progress of work orders relies on some form of manual input from the accountable technical group. With the rise of digital systems, features of modern maintenance software allow for mobile capabilities that are as familiar as using a personal smartphone or tablet. In the background, software capabilities allow for streamlined scheduling and efficient work allocation according to team availability.
In terms of maintenance demands, advanced analytics enables an optimized approach for identifying maintenance requirements. Instead of relying on a fixed, schedule-based routine, predictive maintenance strategies provide more deliberate criteria for determining the need for servicing. Resources that go into the upkeep of assets can focus on value-adding work to ensure reliability while minimizing waste.
By optimizing the maintenance aspect of the business, facilities can plan for longer durations of uninterrupted production. While shutdowns are necessary for most operations, more efficient planning and scheduling can maximize its advantages. Weighing the costs and benefits of both production and maintenance through a holistic lens enables companies to achieve overall efficiency.
IT leaders in the manufacturing industry bridge the gap between plant floor needs and the requirements of digital transformation. With all the capabilities that emerging technologies offer, the journey toward digitalization is a business initiative that aims to improve overall performance. The ultimate measure of a successful implementation depends on achieving reduced downtime, increased production, and constructive interaction between stakeholders.
[ Get answers to key digital transformation questions and lessons from top CIOs: Download our digital transformation cheat sheet.
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