Data Contextualization: Frontline Operations Platforms vs. Traditional MES

With the rise of no-code IIoT-native platforms and digital transformation, comes a promise of gathering and collecting data and real-time visibility for actionable improvements. But can’t an MES system do that?

Just how does adding context to your data make it more valuable, and why do MES systems limit the potential in your information? Frontline Operations Platforms with no-code, IIoT and native analytics can bring your data to life and grant you flexibility, in ways that IT forward MES systems haven’t been able to achieve.

Some consider Manufacturing Execution Systems (MES) to be tried and true. They’ve been developed over the last few decades to serve the needs of operations, and have been a primary way for organizations to track production, capture and log data, and start understanding how their operations work.

Records are vital to MES. Storing information in complex data structures from every corner of an operation is very useful for continuous improvement, and critical for regulated industries. MES systems have been built with this in mind. You can call upon this information later for material traceability, genealogy.

Some of the problems with data stored in an MES are fundamental to the way these systems exist today: MES are monolithic and require extensive support and expertise to implement. IT support is a necessity, and routine updates often require the same hands-on expertise from vendors, which come with a long time-to-value, and a high total cost of ownership. This leads to issues like:

• Siloed data
• Collection methods “As-Built”
• Delayed data analysis
• Data with no context

Depending on how they’re implemented, MES systems can further build up “Data Silos.” Dated infrastructure leads to systems that can’t always communicate with each other, and data being stored by departments or functions. To pull information for a holistic view across the shop floor, a team may have to gather information across multiple systems, computers, databases, and records. Inaccessible databases that require IT support put your data in a form of cold storage, making it difficult to access it on the fly.

Rigidity in MES systems often limits the means of data collection. Data must be correctly entered into your system, as the MES is configured. If your process differs from the MES’ then it can be quite costly to have the vendor change or adjust the software. Changing the color of a button can be a process that takes months of IT support, and requires tremendous resources. This makes it tricky to stay agile, and continuously improve. Essentially, MES locks down your processes and requires uniformity in your information.

When it comes time to analyze data, the flaws of an MES come to light. You may have select analytics capabilities built into the system, and anything more will require you to export your data. Handling and manipulating thousands upon thousands of records in Microsoft Excel to create graphs and charts can be tedious, and can require data scientists to make meaningful insights.

In addition, without real-time analytics, after the work it takes to export data and create dashboards, the data will be already out of sync with what is currently happening in your operation. Keeping dashboards up to date doesn’t add any value to your teams. With every change to your process, new data collection through this dated process will extend the time it takes to actually solve a problem and implement a change.

Context with data is important. Tracking machine downtime is a good first step at improving your operations, but the value comes from being able to detect the cause of downtime, and what factors are influencing it. MES systems can’t always provide that data, because they simply don’t have the means to capture it. Legacy systems are designed to intake and store records, like machine state changes, but usually do not have a way to store operators’ experiences and inputs through notes and images. To improve, contextualization is crucial. Organizations need data to transform information into tangible and actionable items.

Oliver Néron spent 25 years observing these issues in the life science industry: “[In MES], If the data cannot be extracted or contextualized, even terabytes of data can go useless. 21 CFR Part 11 might have led to closed systems in the past where data was typically locked down, but the new open systems offered can amplify the value of data with analytics and AI.”

How can a system overcome these challenges while being flexible and providing the context your organization needs? Through integrating No-Code Applications that support IIoT.

The Tulip platform is designed to overcome some of the challenges that come with handling data in a monolithic MES system. Being able to add context to data across operations and access real-time analytics in the cloud makes it easy to utilize and leverage your data to its maximum potential.

The cloud-based nature of Frontline Operations Platforms provides an incredible advantage when it comes to storing, accessing, and manipulating data. Tulip makes it easy to pull data from sources outside of apps like:

• ERP systems like SAP and Netsuite, SQL databases, and HTTP APIs
• Networked and Legacy Machines
• IIoT devices and sensors
• Computer vision detection
• Human data entries

It is easy to add a new data source into your apps, and connectors in the Tulip Library make it easy to be up and running in a matter of minutes. Machines can be connected and updated quickly as well, without the need for extensive custom code.

Being able to view and create analytics and dashboards makes it easy to see the real-time status of your operation, and watch improvements closely.

Centralizing data across apps and tables in a shared structure allows you to call upon important information upstream or down, and quickly see patterns and trends. The no-code advantage shines through built-in analytics, eliminating the need for complicated, additional business intelligence software.

Operators on the frontline work closely with machines every day and often see valuable first-hand knowledge. Integrating IIoT into a no-code app platform allows you to record information from the operator’s actions to better understand what is happening in your operations. Smart tools and sensors can provide context to machine downtime and inefficiency in operations.

Humans are the center of your operation. While MESs are designed for systems and machines, solutions based on no-code platforms can be designed for humans as well. Tulip allows you to build visual work instructions for operators to follow and incorporate standardized data collection via prompts, such as downtime reasons and machine status. Use this human data to augment data collected via machines.

Streamline in-line quality data collection, leaving room for quality checks in the operator’s existing workflow. With guided workflow apps, it is easy for operators to add notes and images, which can help an engineer quickly identify the root cause of a bottleneck.

Context can help engineers identify patterns, trends and correlations. Instead of tracking machine-focused metrics, it becomes easy to monitor your whole process, creating a truly connected facility.

While MES systems are extremely valuable to the success of an operation, Frontline Operations Platforms provide more value through data contextualization. Being able to unlock the true potential of your data speeds up innovations in your process, and lets you unlock complimentary use cases across every part of your shop floor in ways that are infeasible to scale to with an MES.