Digital Manufacturing or Industry 4.0 is changing manufacturing through exponential levels of information and connectivity. Business models are growing, driven by the initiation of smart manufacturing and operations. Systems and networks are proficient of independently exchanging and reacting to information in order to manage industrial production processes. In the heat of becoming industry leaders, organizations are competing to figure out the right approach for adopting technologies that jointly outline digital manufacturing. Companies are conscious of the advantage that these technologies can deliver to an organization, but are often ingenious when it comes to the embracing and implementation of such technologies.
Digital manufacturing focuses on realising the assured benefits of the end-to-end digitization of all physical assets, as well as mixing into the digital ecosystem with value chain partners. The proven model encompasses assessing an organization’s willingness in categories such as technology, infrastructure, people, and culture and then choosing a key business case with which to start.
Manufacturing organizations, though aware of the benefits that digital manufacturing technologies can deliver, struggle to effectively adopt and implement digital technologies. According to that same BCG study, almost half of the organizations in the U.S. are not yet prepared for the arrival of new technologies for digital manufacturing. From a manufacturing perspective, less than 40% of U.S. manufacturers have either applied or planned to apply new technologies.
For asset-intensive organizations, the journey to digital manufacturing begins by selecting a key business case, conceptualizing it, running a pilot, analysing results and adapting the model’s goal to scale. The assessment of expected financial and non-financial benefits can help organizations to select the priority use case for implementation. Typical steps an organization should follow to embark their digital journey are outlined below (Figure 1):
For asset-intensive companies such as metals and paper mills, mines, and chemicals plants, predictive maintenance often represents the highest value area. Predictive maintenance addresses key business challenges on the factory floor—unplanned machine breakdown or a lack of asset visibility—and delivers the highest returns. The predictive maintenance case, upon successful implementation, creates substantial infrastructure and expertise for widening the digital footprint across the organization.
The major challenge faced on the factory floor is the breakdown of equipment that occurs due to machine degradation, component wear, and other factors invisible to operators. These machine breakdowns have direct implications on the organization’s finances, productivity, and reputation. Other challenges include difficulty in manual monitoring of equipment health, a lack of asset visibility, and a lack of standardization in efficient maintenance planning processes.
The predictive maintenance approach helps to monitor and assess equipment health based on the analysis of various parameters, including temperature, pressure, and vibration, RPM, and flow rate. Various data processing systems capture this data using sources like OPC, Historian, and SQL. They then perform data analysis, where the streamed equipment data is compared with pre-identified failure patterns, captured and stored using historical data. Machine learning processes enable the system to analyse and store machine failure patterns that iteratively learn from data, allowing systems to find hidden signs without any explicit conditions. A match in the streamed equipment data with pre-identified failure patterns triggers alarms and notifications indicating a deterioration of machine health and the potential for equipment failure.
Predictive Maintenance Design
The predictive maintenance approach varies across industries and largely depends on the types of industry-specific machines and equipment in use. It is essential to have a master list of all the equipment on the plant floor so as to ascertain which machines require continuous monitoring for seamless operations. The equipment list needs to be reviewed for completeness to ensure no critical equipment is ignored.
Once the equipment list has been created, the next step is to evaluate just how indispensable the equipment is with respect to overall operations and maintainability. There are various factors that influence the equipment criticality evaluation and a few critical factors are shown in the graphs below. All factors should be considered collectively when evaluating criticality.
The key benefits that Predictive Maintenance approach brings to the organization are categorized as strategic benefits and operational benefits as shown below:
Digital Supply Network
The real-time flow of in¬formation, and the ability to analyse it can allow for greater operational efficiencies and more nimble performance by using digital data to drive physical action in the form of maintenance and upkeep. This flow and analysis embody the characteristics of the DSN as detailed below.
The constant flow of data from connected physical assets and related systems enables “always-on” agility, in which unforeseen situations and changing conditions in machinery can be illuminated in real time, mitigating potential damage. A connected community of assets and systems can provide a greater scale and scope of data to enable more accurate predictive analysis, enabling organizations to intelligently optimize decision making and use of machinery. Further, aggregating the data of sensors on connected machinery throughout the production process can allow for end-to-end transparency while choosing the right analytics and algorithms to make sense of those data can enable holistic decision making about approaches to maintaining assets, to optimize their performance based on their role within the network as a whole.