August 17, 2021
Manufacturing process analysis (MPA) allows for a thorough analysis of the performance of production processes. The insight provided by MPA offers a way for manufacturers to align business goals with operational processes.
Manufacturers have used manufacturing process analysis for close to two decades. Yet, the emergence of smart manufacturing has increased the value of MPA.
Smart manufacturing often involves the digitisation of all processes and activities. This increases the availability of data. Unfortunately, understanding what to do with that data is not always easy.
Taking the time to develop the right data analysis pipelines and framework helps you make more sense of the data. Here is a closer look at the use of manufacturing process analysis for modern manufacturers.
What Is Manufacturing Process Analysis?
Manufacturing process analysis (MPA) is the performance analysis of a manufacturing process. It is a method of analysing processes to ensure that they meet the organisation’s standards and align with their objectives.
MPA can cover any stage of the product life cycle, including:
1. Product design
2. Assembly
3. Quality control
4. Scheduling
5. Maintenance
MPA is often used in conjunction with artificial intelligence (AI) and machine learning (ML) systems to improve smart manufacturing processes.
Smart manufacturing is where most of the manufacturing sector is headed. According to recent studies, the machine learning market is expected to exceed a compound annual growth rate (CAGR) of 38.6% until at least 2028.
Affordable industrial Internet of Things (IIoT) sensors and wireless connectivity have made smart manufacturing more accessible to companies of all sizes. The challenge is collecting and analysing the data obtained from the IIoT sensors, which is where MPA can help.
Manufacturing process analysis combined with AI and ML offers more insight with less effort. Effective big data analytics allows for more agile business operations and faster improvements. You can quickly assess the efficiency of any process, detect bottlenecks, and use ML applications to find solutions.
ML systems can build and test models to improve everything from production capacity and product quality to sales and customer satisfaction.
How to Develop an MPA Strategy
Analysing manufacturing processes using AI and ML systems requires a strategy. The typical workflow for manufacturing process analysis includes the following stages:
1. Value identification
2. Data acquisition
3. Data processing
4. Data analysis
5. Machine learning
6. Model deployment
Businesses must first assess the value of a process and the potential value of any improvements. For example, a manufacturer may want to reduce the amount of waste generated by a specific process. The reduction in waste, cost savings, and time savings should be tracked using clearly defined metrics.
MPA requires the collection of data from all components of a process. This may include data from sensors connected to manufacturing equipment, along with data related to total output, labour, and costs.
The data is then processed. It must be cleansed to ensure the data set contains no duplicate or incomplete data sets. After acquiring and cleansing the data, different analytical and machine learning models can be used to generate insights.
Using MPA to Improve Product Quality
Using MPA as part of the decision-making process can lead to many advantages, including improving product quality. A detailed analysis of a manufacturing process helps detect issues that may impact the quality of a product.
The insight obtained through the analysis can find the root cause of a quality issue, allowing engineers and analysts to implement a fix. Manufacturers can address quality issues faster when using the right tools.
Increasing Overall Equipment Effectiveness
The efficiency and cost of a process often depend on the efficiency of the equipment used. MPA helps increase overall equipment effectiveness (OEE), which leads to greater output quality and return on investment (ROI).
More efficient machines require have less downtime and require less maintenance. They are also less likely to produce defects. These benefits allow companies to get more value out of a manufacturing process.
Data visualisation is especially useful for increasing OEE. Visualising the data in real time allows engineers and manufacturing managers to monitor the performance of machines and track the causes of faults.
Give Engineers Access to Self-Service Analytics
Manufacturing process analysis gives engineers access to self-service analytics. The right approach ensures that the data is visualised in a way that makes sense to those without an analytical background. Users can review the data from dashboards and automated reports.
Engineers, managers, and executives can review data to improve their decision-making processes. They do not need to wait for an analyst to compile a report and summarise the results of the MPA.
These benefits result in reduced time-to-market and greater flexibility for addressing any problems that arise with a manufacturing process.
Challenges of Implementing Manufacturing Process Analysis
Implementing an MPA strategy includes three key challenges:
1. A high volume of data
2. Wide variety of data
3. Access to real-time data
Analysing a manufacturing process may require access to a high volume of data. Manufacturers may collect data from sensors, vendors, suppliers, and various third parties.
Data collection also comes from a wide variety of sources. The data may be stored in different databases, cloud networks, and physical locations.
Collecting and analysing a large amount of varied data also limits the ability to process it quickly. Manufacturers cannot obtain real-time analytics with standard data collection methods. Using a dedicated solution for data gathering, processing and analysis is key for getting valuable and actionable insights. Choosing the right partner is also essential for a successful MPA strategy. Work with an experienced data science company to implement the systems needed to gather data, analyse it, and design better processes.
