October 8, 2018 | Written by: Ashok Kumar
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Manufacturing is changing due to advancements in the Internet of Things (IoT) and Artificial Intelligence (AI). With consumer demand for customization driving new requirements for speed and agility, many manufacturers are struggling to keep up. On top of this, more competition and increased demand is putting higher pressure on manufacturers to increase overall plant performance. A key factor preventing manufacturers from utilizing this wealth of data is the use of outdated manufacturing execution systems (MES). If manufacturers want to capture the potential of Industry 4.0, they must evolve their MES to gain proactive insights from the data that they collect in the plant today.
The problem with traditional MES systems
An MES can manage and monitor the work in progress on the plant floor. They form the transactional center of the traditional manufacturing plant and we typically use these systems to control production.
The problem is that most MES systems were designed long ago – in nearly all cases pre-IoT. These systems were never designed to integrate data from the multitude of sensors on the plant floor. Nor were they designed with data aggregation principles in mind. They were also never designed to aggregate data in real-time or store that data efficiently for long periods. As a result, MES systems constrain the ability to see a plants operation holistically and take action. Data is siloed and often simply not captured at all. We lose invaluable insights daily and hourly.
So what? These MES constraints mean that much of the Industry 4.0 promise is out of reach. Many manufacturers are unable to deploy and scale AI/analytics. They simply do not have the data about their operations, the ability to predict disruptions, act in real-time, and optimize in the future.
How are IoT and AI displacing traditional MES
Smart manufacturers recognize there is an alternative to traditional stove-piped MES systems. They are deploying centralized operational control towers to maximize performance potential by pinpointing and predicting production loss and prescribing the best action. They understand that value comes from looking across plant operations and examining data holistically, in real-time, from multiple operations rather than a single piece of equipment or process on the line.
Clients are also looking for industrial process workers to not be tied to an operator station. This allows them to be more productive managing a production line or a set of machines. More so, shop floor applications, top floor application synergy, and advances in OT/IT (operation technology/ information technology) connection is proving the point each day that traditional MES is dead.
Transformation leaders must think differently
A modern approach to complement or displace MES drives benefits across product, process, and equipment and focuses on an integrated KPI dashboard and analytics solution. It utilizes the vertical and horizontal integration points that derive data within an organization and drives AI-powered business insights with that data. The journey starts with classifying key data from equipment, processes, and quality inspections that drive over 80% of the recurring pain points in a plant. Then analysis of the data drives key insights that predict issues in advance. This can then scale into a true form of automation using AI and drive substantial benefits in any plant.
Few insiders would challenge the value of displacing or modernizing MES systems. Yet many projects run into resistance due to the complexity of brownfield deployments and from leaders unable to challenge the status quo. True transformation leaders must think differently. They must overcome these objections by developing pragmatic business cases and phased deployment plans. Only by modernizing MES can leaders capture the benefits of Industry 4.0 and be the disruptor, not the disrupted.
Displacing MES in the consumer packaged goods industry
We have recently built from scratch and implemented such a change in a fast-moving, bottling plant. We were successful in displacing the traditional MES environment and eliminated the historian at the ground level. In this project, we are leveraging 5000 data parameters, from 140 devices in two production lines. We are filtering 18 OEM machine protocols and leveraging it in our cloud platform. The theme also supports leveraging Maximo in operations optimization as we created specific modules to drive the plant operations effectively. The goal was to provide the transactions, reporting and necessary visualization leveraging the standards we have already built with Maximo. We are using the following modules to extendMaximo capabilities into manufacturing operations:
- Production Health Insights (PHI)
- Performance Health Insights (PFHI)
- Quality Health Insights (QHI)
- Energy Health Insights (EHI)
- Asset Health Insights (AHI)
- Supply Chain Health Insights (SCHI)
At the back end, as we pull the data into our cloud, clients can easily leverage the root-cause analysis (RCA) and other key data points to aid in prediction of issues and optimization of operations using AI.
Manufacturers are under pressure to transform – but MES constraints are real. They need to think differently. They need to think pragmatically. This module-based approach can help clients engage in either a greenfield (developed from scratch) or brownfield (working off an existing program) environment. It also helps in deploying solutions in a organic way and avoids a big bang disruptive approach.