June 20, 2024

 

Transforming your maintenance program requires adopting essential best practices that drive efficiency and reliability. Start by implementing a proactive maintenance strategy, utilizing predictive analytics to foresee potential issues before they escalate.

Regularly train your maintenance team to ensure they are equipped with the latest knowledge and skills. Leverage technology such as IoT sensors and maintenance management software to monitor asset health in real-time. Establish a robust data management system to track performance metrics and inform decision-making. By focusing on these best practices, you can significantly reduce downtime, lower maintenance costs, and extend the life of your assets.

Assess Current Strategy: Begin by understanding where your maintenance practices currently stand. Analyze metrics like planned versus reactive maintenance to establish a performance baseline. Evaluate your ability to capture and analyze asset data effectively, as this forms the foundation for informed decision-making and process improvement.

Identify Strategic Assets: Not all assets require predictive maintenance. Focus on identifying critical assets that directly impact revenue and production throughput. For instance, high-throughput production lines may require different maintenance priorities compared to assets with lower production demands.

Determine Failure Indicators: Predicting failures involves identifying specific indicators for each asset type and environment. Historical performance data and failure studies help pinpoint trends and potential issues early on. Be cautious of false positives by validating indicators against operational context.

Implement Real-Time Actions: Timely action based on real-time data is crucial for effective predictive maintenance. Automate data analysis processes to swiftly identify anomalies and issues. This proactive approach minimizes downtime and optimizes maintenance efforts, freeing up resources for more strategic tasks.

Measure and Refine: Continuously monitor and refine your maintenance program. Measure the impact of enhancements using key metrics such as Overall Equipment Effectiveness (OEE), Mean Time Between Failures (MTBF), and energy efficiency. Adapt and scale your strategies based on performance feedback to drive continuous improvement across your operations.

Optimize your asset management practices to achieve superior efficiency, reliability, and cost-effectiveness! By embracing proactive maintenance strategies and leveraging advanced technologies such as IoT sensors and predictive analytics, you can anticipate issues before they occur and minimize downtime. These efforts ultimately drive greater value for your organization.

June 20, 2024

In Asset Performance Management (APM), Maintenance Maturity defines the stages an organization progresses through in managing and maintaining assets. Starting from reactive maintenance, where issues are fixed after failures occur, companies can advance to predictive and prescriptive maintenance, using data-driven insights to prevent problems before they happen. By understanding and improving their Maintenance Maturity, organizations can boost efficiency, minimize downtime, and extend asset lifespans. Here to explore the stages of Maintenance Maturity and how to achieve an optimized maintenance strategy:

Reactive Maintenance: This stage involves addressing equipment issues only when they occur, often resulting in unplanned downtime and higher repair costs. While common and sometimes necessary, relying solely on reactive maintenance can lead to inefficiencies and potential safety risks.

Preventive Maintenance: In this stage, maintenance activities are scheduled based on predetermined intervals or criteria to prevent failures before they happen. This approach reduces the likelihood of unexpected breakdowns compared to reactive maintenance. However, it can be limited in effectiveness if maintenance tasks are not tailored to specific equipment conditions or operational needs.

Condition-based Maintenance: Here, maintenance decisions are guided by the actual condition of equipment, determined through regular monitoring and analysis. By assessing factors like equipment performance, wear and tear, and environmental conditions, organizations can optimize maintenance schedules for better return on investment (ROI). This approach helps in predicting maintenance needs more accurately than preventive maintenance alone.

Predictive Maintenance: This stage utilizes advanced data analytics and monitoring techniques to predict when equipment failure is likely to occur. By analyzing historical data, sensor readings, and other relevant factors in real-time, organizations can anticipate maintenance needs precisely. Predictive maintenance minimizes downtime, reduces maintenance costs, and optimizes asset lifespan.

Prescriptive Maintenance: The highest level of maintenance maturity, prescriptive maintenance not only predicts failures but also prescribes the most cost-effective maintenance actions. It integrates advanced analytics, machine learning, and AI to recommend optimal maintenance strategies based on asset performance, business priorities, and economic considerations. This approach maximizes efficiency and minimizes costs by aligning maintenance practices with strategic goals.

Assessing your organization’s maintenance strategy against these stages can help identify opportunities for improvement and efficiency gains.

Apr 20, 2023

Smart manufacturing involves creating a more connected shop floor. The plant can bring together its manufacturing systems with its maintenance schedules and business planning systems. This creates significant advantages including the ability to collect and analyze real-time data and improved, quicker decision-making at various points within the production line. Unfortunately, not every shop floor is fully connected today, even in Malaysia.

These past few years have put the manufacturing industry through the wringer and added to the list of challenges it faces. There have been massive supply chain disruptions that have affected the manufacturing process. At the same time, there have been huge spikes in consumer demand for a range of products. Manufacturers understandably want to get ahead of this and ensure they can meet such demand.

Current challenges affecting manufacturing in Malaysia

Being able to forecast demand for products calls for good reporting tools.

Every plant is naturally keen to improve efficiency and keep costs low which they can do by modernizing processes, introducing automation, and systematizing workflows. All of these can assist in minimizing the time spent on labor-intensive tasks, reduce waste as well as optimize equipment usage.

And much like many other industry sectors, manufacturing faces its fair share of challenges:

• Labor shortages and quality of talent – Finding good quality candidates interested in this type of work is possible when the workplace is supported by technological capabilities to support human-led work
• Infrastructure – Plants need to lean on reliable, high-performance digital connectivity
• New technology adoption – These include the rising proliferation of AI (artificial intelligence) and data analytics tools. However, there are many barriers to this including a lack of awareness of options, the prohibitive upfront investment costs as well as resistance to change
• Regulatory framework – Legislative or regulatory changes can be complex. In turn, this increases operational costs and administrative burden, slowing things down
• Global economic factors – These include shifts in supply and demand, availability of natural resources, and input cost inflation

The increased rate of change, the pervasive use of technology, and the rise in connected devices, while bringing significant advantage, come with their own unique set of challenges. For example, greater reliance on connected devices calls for robust cybersecurity.

When the challenges above also include supply chain uncertainties, and the pressure to fulfill larger orders and to deliver them quickly, then there may also be product inconsistencies.

Plants do not just want to increase production capacity and labor performance – they also want to ensure that the goods are delivered according to the specifications set, consistently each time.

To do this, plants need to ensure they tick the boxes across several parameters:

• safety assurance at all times
• regulatory requirements and compliance
• robust cost management
• inventory transparency
• business continuity
• disaster recovery management
• innovation initiatives to support product development efforts.

What is one way to be assured that all of these boxes can be ticked off?

 

Introducing Edge Computing

Achieving all of these objectives is possible with the right technology. This is about having a fully-integrated and collaborative industry 4.0 manufacturing system that responds in real-time but also reports back relevant timely insights.

But what exactly is edge computing? It involves bringing data computation and storage close to the devices where the data is gathered. In other circumstances, such data would typically be moved to the cloud or a central data center.

By bringing data computation and storage closer to the device, there is reduced latency, better responsiveness, smoother operations, and improved performance of an application or equipment. Edge devices include IoT sensors, Edge gateways, smart camera and more. It may also involve highly automated industrial computing systems and servers.

In an Edge-to-Enterprise architecture, workers, operations, and data would be connected through HMI (human-machine interface) and MES (Manufacturing Execution System).

In an Edge system, IoT devices (such as smart sensors) and industrial gateways (devices to collect data from industrial machines and IoT devices) collect all manner of data (temperature, pressure, vibration, machine performance). Data is transmitted, processed, and filtered before being sent to a cloud-based enterprise system. It is the local processing that minimizes latency and improves overall efficiency.

In an Enterprise system, data collected from edge devices is sent to a cloud-based enterprise system which will analyze the data, developing insights with the use of advanced analytics and machine learning. These insights:

• optimize production processes
• reduce downtime
• improve machine performance
• assist with predictive maintenance and supply chain optimization.

Ultimately, there are many people across multiple layers of decision-making within a plant environment. There may be different roles, functions, and business outcomes across the organization and its various geographic locations. However, for these different business units to come together towards a common business goal, solutions and systems that can increase connection and present a single source of truth create a distinct advantage: empowered workers, real-time insights, and informed decision-making.

 

The advantage: a single source of truth

Edge computing helps in digital manufacturing and to remove some of the complexity associated with key processes and manage information overload. With processing occurring much closer to where data is gathered (as opposed to sending it to a centralized cloud or data server), business visibility is improved quickly which can lead to a reduction in energy consumption and fewer errors.

Other benefits include real-time data processing and analysis and improved data privacy and security. The latter is especially important given the rise in the frequency and sophistication of cyber-attacks and scams.

New edge devices can also be easily added or removed without disrupting the entire ecosystem. This allows for a modular approach, wherein enterprise businesses can scale their edge infrastructure according to their needs. Further, with the reduction in data transmission, there is overall bandwidth improvement and network efficiency. Engineering or maintenance projects may be accelerated, improvements and efficiencies may result in terms of system standardization and there may be better operator awareness and agility.

In conclusion, a platform-first approach leads to a more connected operational environment. This improves asset integrity, creates better environmental and regulatory compliance, and better coordination during plant turnarounds and upgrades.

Are you asking any of these questions: How do I manage my fleet of Edge devices remotely? How can I reduce latency and distribute the processing load on my network? How can I experiment with IIoT cost-effectively? How can I utilize the cloud to access applications and information in multiple locations? 

If any of these questions apply to you, let’s talk, please get in contact with our Digital Transformation experts.

Apr 18, 2023

Take a look at your everyday life and you’re sure to be experiencing or relying on the fruits of the manufacturing sector. From your hairdryer and toaster down to your more sophisticated smart fridge or home security camera, manufacturing is and continues to remain a critical feature of the modern economy.

Essentially a place where products are manufactured or produced, the factory is the hub of such activity. The factory is an industrial facility, often part of a massive value chain creating a unique competitive advantage for companies and immense value for the customers they serve.

Rapid and increased change affect the manufacturing sector

However, manufacturing challenges have only heightened as a result of the global pandemic. The industry has faced significant difficulty, thanks to highly dynamic shifts in demand and disruption.

From a supply chain perspective, the global pandemic has created massive raw material and parts shortages. Logistics and delivery have suffered, resulting in businesses having to take a completely new approach to supporting customers.

From a workforce perspective, there have been sustained shocks in the form of shortages of skilled talent, disruption to how work is performed, a rise in new work procedures and increased safety and health protocols which have knock-on impacts on productivity, performance and service levels.

 

What is the state of the factory floor today?

In many factories, there are often manual processes and some degree of semi-automation. There may be siloed systems and a lack of integration which results in  duplication of effort, increased labour and the possibility of increased error impacting data quality. As a result, business leaders have to contend with a lack of visibility and real-time insights which impact their ability to make informed decisions.

 

From factory to smart factory

The factory of the 21st century and beyond will be a move from isolated cells to more fully integrated data and data flows, smoother communication, increased automation and a deeper reliance on technology to assist human-led effort. This may involve the application of machine learning and artificial intelligence.

What would a business gain from this smarter factory?

First, integrated, smoother communication along the entire value chain would reduce work-in-progress inventory. While increased automation may displace some skilled labour, such labour could be better placed to support more strategic tasks associated with managing the factory of the future.

Which aspects of factory management would be involved? In a typical manufacturing environment, raw materials are received and prepared in order for parts to be produced and then assembled into final products before it is packaged and shipped. There are numerous stations, each serving different purposes and involving production, scheduling, quality control, maintenance and some aspect of WIP (work in progress).

When the manufacturing operation and processes are integrated, this would entail the collation and interpretation of data through a single digital dashboard with various user interfaces with business requirements driving the business processes, physical equipment and level of automation created.

 

An Industry 4.0 approach

The smart factory is a digitally connected enterprise. This is known as taking an Edge-to-Enterprise approach where workers, operations and data are connected using HMI (human-machine interface) and MES (Manufacturing Execution System).

This is what Industry 4.0 is about – creating a more digitally connected enterprise, or in this case, a more connected smart factory.

It involves advanced and predictive analytics, the facilitation of remote collaboration and remote monitoring, backed by a robust network infrastructure and cyber security framework.

There are three aspects to delivering ROI here.

First, people drive the change forward through ownership of the business issues and their ability to lead the way forward. They put the systems in place that help them create more connections (where there were previously none), create the tools to collect, visualise and analyse data based on their own business requirements, and finally, to take the requisite action needed.

Second, processes provide insight on yield, utilisation and efficiency levels. These processes shine a light on the business’s current state of operations by collecting relevant and timely data based on parameters they set. By gaining clearer visibility through dynamic real-time analysis, the factory is able to make more informed decisions.

Finally, assets are better managed through real-time visualisation of their reliability and availability as well as quicker response times to mitigate damage or loss.

The transformation of the factory floor is a digital one, rooted in foundational technologies associated with Industry 4.0. These are in the areas of connectivity, data analytics, HMI and asset performance.

• Connectivity, data and computational power
  The modern factory relies on connectivity, data and computational power. Through connected sensors, digital dashboards, cloud technology and robust cybersecurity, the business is able to keep their finger on the pulse
• Data analytics and intelligence
  Data without context may provide an inaccurate or incomplete view. Advanced analytics combined with the power of artificial intelligence and machine learning, allow the factory to obtain relevant insights in a timely manner
• Human-machine interaction (HMI)
  Essentially a user interface that connects a person to a machine or device, usually in an industrial setting, this provides the factory with real-time data visualisation. This may entail virtual reality, robotics, AGVs (automated guided vehicles), RPA (robotic process automation), predictive maintenance as well as simulations
• Asset reliability and performance
  Systems that connect various assets, collecting operations data and non-conformity events in real-time bring this together to provide significantly more visibility and enable dynamic analysis which, in turn, provides enhanced decision support.

 

Real World Application

Let’s take a closer look at one of our clients, a home appliances manufacturer, to understand how this works in practical terms.

This manufacturer was experiencing a lack of real-time information on numerous unscheduled downtime occurrences. This was a result of insufficient raw materials, inadequate production speeds and a few quality-related issues.

As a result, production staff found themselves reacting to these unscheduled downtime periods poorly. The production team was unable to operate at maximum efficiency and capacity to fulfil the scheduled demand.

Having reviewed the numerous issues and discussed the business requirements, one of the key solutions we proposed rested on the implementation of an algorithm that allowed downtime codes to be propagated from the machine centres.

This solution was implemented across 17 lines in the plant in the first phase of the implementation of our plan. Our solution featured a digital system platform powered by MES (manufacturing execution system) performance, Edge computing and CMMS (computerised maintenance management system):

• A new operational dashboard provided real-time insight on production OEE (overall equipment effectiveness) and ANDON (a system that quickly alerts operators about an issue or problem as it arises). By eliminating movement and gathering loss and allowing for remote monitoring, reporting and productivity increased
• A CMMS implementation digitalised the maintenance process workflow which led to reduced response time, improvements to predictive maintenance and better spare part inventory management
• A Work Order Management system was implemented which greatly enhanced planning and addressed ad hoc rescheduling of work orders
• A new work instruction system was installed which helped to ease the training of new operators
• The system was integrated with the telegram API which allowed critical process notifications to be provided via telegram.

As a result of these changes, the team was able to quickly identify areas for improvement, whether in unplanned downtime, equipment effectiveness or labour inefficiencies. Mechanics began to focus on the overall line performance instead of just individual machines or operators. Additionally, training conducted became more tailored to address these potential challenges based on data received, and not simply, based on opinions.

Industry 4.0 provides tremendous assistance to manufacturers.

From improved product quality and defect tracking, real-time supply chain management, predictive maintenance, faster product prototyping through to output and demand forecasting, manufacturers stand to reap significant gains when Industry 4.0 strategies are implemented. Manufacturers may not only realise business goals but also reduce costs and increase profitability through improvements and optimisation right across their value chain.

 

If you would like to enjoy the latest info, please get in contact with our Digital Transformation experts.

Jun 6, 2022

What is MES?

Manufacturing Execution System (MES) helps manufacturing companies to monitor, track, document, and control the process of goods manufacturing from raw materials to a finished products. MES helps to improve the product quality, increase production uptimes, and reduce inventory and costs.

The goal of MES is to achieve product traceability improvement and gather real-time and accurate production data. MES controls all the activities happening on the shop floor, which begin with the customer’s order, MRP system, master schedule, and then building up the products in the most effective, low cost, and high-quality way. It is used to help manufacturers to gain insight into their manufacturing operations to analyze what can be done to improve performance as well as to cut costs and maximize production efficiency.

Real-time data

• Manufacturer is able to make informed business decision with the available of dashboard, reports, and KPIs in real-time.
• Visualization of the machine status with the overview of machine status & OEE
• Real-time data on keeping track of production floor info such as machine breakdown, labor usage, raw material etc

 Detailed product genealogy

• Illustrates the detailed product genealogy with end-to-end traceability and quality checks with automatic electronic record keeping.

Management of resources and quality control

• Keep track of your production floor employee’s productivity and quality of work remotely to quickly make adjustments as needed.

 

The Differences between MES & ERP?

MES & ERP Systems serve different roles in a manufacturing plant. MES drive manufacturing operation by managing and reporting on shop floor manufacturing process and operations activities in real-time. It handles complex production processes with full integration into factory automation and plant floor hardware. It manages the actual shop-floor MES works within timeframes of days, shifts, hours, and minutes.

ERP systems mostly focused on basic plant schedules including production, material use, delivery, and shipping, It is a mix of inventory management, procurement, supply chain, finance & accounting, human resources, and customer relationship management. It focuses on working within time frames of months, weeks, and days. Both MES & ERP Systems integration will provide clear operational insights into your shop floor as compared to these systems working separately.

This is where the MES system plays an important role by serving as a functional layer between the ERP and the process control systems on the shop floor, providing manufacturers with real-time workflow visibility, flexibility, and insights into how best to improve the enterprise-wide manufacturing process.

 

How do MES & ERP work together?

MES & ERP complement each other very much in the manufacturing production floor. By integrating both of them, manufacturers are able to get a complete, holistic view of real-time production. For example, if you have an ERP system,  you can determine which products to manufacture; If you have MES, you can determine how to manufacture your products with efficiently and profitably as possible.

MES feeds ERP with timely information, such as parts and serial number usage, information about production levels, and scrap material. ERP encourages accurate demand forecasting helping the manufacturer to reduce inventory by avoiding overproduction. This will helps the production manager to keep track of what is going on within the shop floor level to see if changes need to be made to remove bottlenecks or improve production performance or processes.

If you would like to enjoy the latest info, please get in contact with our MES team of experts.