Manufacturing Execution System (MES)
Assembly Line
Smart manufacturing for personalised medicine
An experimental personalised cancer vaccine from Moderna has shown effectiveness against melanoma when combined with Merck & Co Keytruda immunotherapy, cutting the risk of recurrence or death by 44 per cent compared to immunotherapy alone. The vaccine uses mRNA sequences tailored to the unique mutational signature of each patient’s tumours to stimulate the production of neoantigens and elicit an adaptive immune response. Though this type of treatment shows significant promise, manufacturing and scaling up production of personalised medicine like this will be a challenging process.
The unique demands that personalised medicine will bring will require smarter, more advanced methods. Automation and smart software platforms such as manufacturing execution systems (MES) are becoming more commonplace in pharmaceutical manufacturing and will undoubtedly be essential in this new generation of therapeutics.
Examining Composability: What Should it Look Like in Real Life?
The dynamic nature of manufacturing operations necessitates a dynamic set of interactions between the four key personas in composable business. As we mentioned in the beginning of this post, in order to successfully implement the “anything is composable” mentality, developers need to be everywhere. A framework for success like the one described here can make that a reality for manufacturers of all types.
Rauland Collaborates With Panasonic Connect North America to Increase Factory Productivity with Automation Solutions
Challenge: Rauland, a subsidiary of AMETEK, is an American manufacturing company based in Mount Prospect, Illinois that produces workflow and life-safety solutions for hospitals and schools worldwide. Constantly growing as an organization, they recently set internal mandates to increase manufacturing capacity by 30%, while initiating a more data-driven approach across their business. To do this, the Rauland factory team of 350 workers had to directly address day-to-day challenges such as labor and inventory shortages, increasing changeover times and unknown scrap rates while working with 4,000 unique parts across four SMT lines. The Rauland team also wanted to improve their overall manufacturing productivity, efficiency and controls.
Solution: To achieve their goals, Rauland looked to Panasonic Connect North America to provide the company with automated manufacturing technology to turn its factory – and its workforce – into a data-driven operation. With Panasonic PanaCIM® Manufacturing Execution System (MES) software, Rauland was able to achieve exactly what it set out to do.
AQL in Medical Device Manufacturing and the role of MES in its implementation
AQL Defined: ISO 2859-1 establishes AQL (Acceptance Quality Limit), as a sampling-based method for determining quality through inspection of attributes. In simpler terms, AQL is the maximum number of defects allowed in a given lot above which the entire lot would be rejected, or the lowest level of acceptable quality.
An MES application, configured for the medical device industry, allows for the creation of sampling plans, based on risk and for those plans to be enforced either on a time or counter basis. The application supports AQL and allows the definition of measurement tools, along with their calibration status and switches severity automatically which is an advantage for high mix production facilities. It measures both variable and attribute data points based either on time or counter and can be integrated with material logistics to give users a complete view of the quality as materials move across the production line and are converted into the end product.
This month cover story of @TMDmag features our case study from @ultradent.
— Critical Manufacturing (@CriticalMfg) March 14, 2022
Read the story to learn how this global dental manufacturing company is taking big steps towards the future of manufacturing.https://t.co/u5IUiXQJMH
Make the Most of MES
MES software is by no means a new tool for manufacturing, having been around since the mid-1980s. However, for various reasons, manufacturers of all sizes in all industries often don’t take full advantage of the software’s capabilities. New benefits like scalability, affordability, low-code programming and traceability, complement traditional ones like error-proofing and optimizing workflow.
Miklosey says some end-users increasingly prefer using MES systems that prioritize baseline and scalable business intelligence (BI) analysis. These systems let companies access the software’s terabytes of native data with no need for third-party integration. Insights from this data help manufacturers be more responsive to their customers’ needs. FactoryLogix, for example, is readily accessible by the manufacturer’s BI platform of choice.
Meeting the IPC’s Connected Factory Exchange (CFX) standard in MES is another challenge being addressed by software suppliers. Aegis greatly contributed to the development of the CFX, which emphasizes plug-and-play connections to simplify machine-to-machine communication, analogous to USB computer devices.
How to calculate digital transformation ROI
To simplify and prioritize the digital vision, first consider how digital transformation for manufacturing integrates three key business components:
- Supervisory control and data acquisition (SCADA), programmable logic controller (PLC) and control (machine automation)
- Manufacturing execution system (MES), which includes: (part traceability, machine monitoring and machine management, i.e., recipes and so on)
- Enterprise resource planning (ERP), which includes: (AP/AR, raw materials, purchase orders, inventory, scheduling and tracking).
Achieving large profitability and competitive gains requires seamless integration of three business components. However, it is important to begin at the machine automation level, then incorporate the MES and finally the ERP. The reason for following this path is based on data requirements but also because it is the easiest path for development.
Big Data Analytics in Electronics Manufacturing: is MES the key to unlocking its true potential?
In a modern SMT fab, every time a stencil is loaded or a squeegee makes a pass, data is generated. Every time a nozzle picks and places a component, data is generated. Every time a camera records a component or board inspection image, data is generated. The abundance of data in the electronics industry is a result of the long-existing and widespread process automation and proliferation of sensors, gauges, meters and cameras, which capture process metrics, equipment data and quality data.
In SMT and electronics the main challenge isn’t the availability of data, rather the ability to look at the data generated from the process as a whole, making sense of data pertaining to each shop floor transaction, then being able to use this data to generate information from a single point of truth instead of disparate unconnected point solutions and use the generated insight to make decisions which ultimately improve process KPIs, OEE, productivity, yield, compliance and quality.
MES & Machine Learning
As the manufacturing sector continues to embrace digitalization, fully integrated manufacturing execution systems will become more and more useful for managing facilities. However, it is expensive for a plant to fully revamp their IT infrastructure. Manufacturers with partially integrated or non-existent MES won’t upgrade unless there are benefits that outweigh the costs, and returns that can be realized.
Incorporating a MES and subsequent machine learning platform into a facility’s or organization’s infrastructure reduces the cost of manual data processing. Tasks that have traditionally taken hours of manual labor, such as aggregating line data to identify trends, can be automated and completed in minutes or less. In this case, machine learning isn’t competing with statistical process control (SPC) or other traditional quality methods; it’s augmenting them so that engineers spend less time to get better insights into their operations.
What is an Additive Manufacturing Execution System?
Additive manufacturing (AM) is the industrial production name for 3D printing. Using computer aided design (CAD) or 3D object scanners, additive manufacturing allows for the creation of three-dimensional objects by depositing materials, usually in layers. As production continues to grow and additive manufacturing industrializes, manufacturers require effective strategies to help them manage their additive manufacturing workflows.
Vaccine production: Marburg has the right stuff
BioNTech manufactures BNT162b2 in collaboration with US pharmaceutical specialist Pfizer. The company has started manufacturing at the production site in Marburg, in the German state of Hesse. The plant there comes with an ultramodern production facility for recombinant proteins. The relevant expertise is also available, since BioNTech also acquired a highly qualified employee base along with the production facility, all of whom are experienced in developing new technologies.
The facility in Marburg had been producing influenza vaccines based on flu cell culture, then changed over to recombinant proteins for cancer treatments and now manufactures mRNA vaccine.
All the improvements at the Marburg plant are Industry 4.0-compatible. One of the challenges with the conversion was the fact that it involved switching from rigid to mobile production with many single-use components. At the same time, working with mRNA meant a higher clean room class than was previously required in the facility. Paper is now an avoidable “contamination factor” that doesn’t arise with digital production. That was the basis for opting for the Opcenter Execution Pharma solution from Siemens as the new MES. This solution enables complete paperless manufacturing and fully electronic batch recording.