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    Predictive Asset Management

August 17, 2021

Digital twin & its practical implementation in Industries

Digital twin

Digital twin refers to the exact virtual prototype of an object, process, or system covering its entire life cycle. This prototype is based on real-time data and uses simulation, machine learning, and reasoning to help decision-making.  

Digital twins form a bridge between the physical and digital worlds. 

The concept of the digital twin was first used by NASA. With the development of the Internet of Things (IoT), the usage of digital twins is expanding. With the onset of Industry 4.0, the digital twin is becoming inevitable to be used by businesses of all scales to optimize and increase profits of their operations. By allowing the creation of digital copies of real-time objects digital twins have opened new avenues of production, maintenance, design, research, and innovation.

Digital twin allows designing, and real-life simulation of an entity even before it is created. This enables the production of only those products that match the standards defined. For instance, with digital twins, 10 different car models can be digitally created, and with real-life simulation can be digitally checked for possible shortcomings, and based on this a near-perfect product can be created. 

Similarly, with digital twins, it is easier to test products in different scenarios without physically requiring the test scenarios. It helps in the optimization and customization of products. In the automobile industry, a digital twin of a car can be tested to handle different collision scenarios, and accordingly, the safety procedure of the car can be updated.

Digital twins in combination with the Internet of things allows for real-time monitoring of objects. With the usage of sensors, the digital twin is continuously monitored remotely. This enables early diagnosis of any future breakdown of the object or system.

Digital twin equipped with real-time data is a tool without rivals in making predictions about possible breakdowns, future wear, and tear. This enables businesses to undertake more efficient preventive maintenance. This reduces the downtime of any unit. With a digital twin, it is also possible to identify the part most likely to malfunction and possible routes to fix the problem.

Teleoperations is one of the key advantage areas of the digital twin. Through teleoperation, it is possible to control the physical object,  system, or process remotely. It allows controlling physical objects virtually, virtual objects physically and virtual objects virtually. The combination of these possibilities helps in creating a teleoperation ecosystem.  It is of indispensable value in controlling operations in hazardous industries and the environment. 

As businesses adopt digital operations and industry 4.0, the firms employing this technology are already gaining an edge in the market. Digital twins are enabling them to better predict, design, maintain and operate their products. The availability of increasingly accurate models and predictions gives them an edge over those that have not adopted the norms yet. 

INDUSTRIES EMPLOYING DIGITAL TWIN

POWER GENERATION- Power generation industries employ humongous machinery and operation designs. Digital twin not only helps in better design and modification but also in time maintenance. This sector cannot face shut down as it provides power to all other sectors. Digital twins help in checking any possible fault well in time. 

LARGE PHYSICAL STRUCTURES AND THEIR SYSTEMS-  Digital twins are imperative in designing large physical structures such as bridges and offshore drilling operations. At the same time, the digital twin is used in the maintenance and remote operations of systems within these structures. For instance,  in the case of oil drilling, it helps to predict the depth and exact location of drilling operations most viable for the equipment.

MANUFACTURING OPERATIONS- The manufacturing operations are increasingly using digital twin at all levels. Ffom product design, customization, process, maintenance to delivery of the product.

HEALTHCARE SERVICES- Just like with physical objects, a digital twin of a human can be created. In the healthcare system, the digital twin is used to make a prognosis of the patient, try the possible treatments, check their consequences. It is being used to develop better medical facilities. 

AUTOMOBILE INDUSTRY– The automobile industry operates in complex interwoven systems. The digital twin helps in the design, simulation, and prediction of the automobile as well as that of the environment surrounding it.

These industries are the most prominent employers of digital twins but by no means the only users of digital twins. 

DIGITAL TWINS AND BUSINESS FUTURE

As per the The “Digital Twins Market by Technology, Solution, Application, and Industry Vertical 2020-2025” report

  • Up to 89% of all IoT Platforms will contain some form of Digital Twinning capability by 2025 
  • Digital twinning will become standard feature/functionality for IoT Application Enablement by 2027

How can Makoro™ assist?

Makoro™ employs a cloud-agnostic approach that fully leverages the customer’s infrastructure and installs on the edge, in public/private/hybrid cloud, and in the customer’s data centers to expedite deployment. Makoro™ is also available as a fully managed application suite on Makoro™ Cloud.

Makoro™ Predictive Asset Performance Management solution collects and correlates data from the manufacturer’s internal applications, inventory management systems, and maintenance management systems in real-time and generates recommendations for optimizing asset and workforce utilization while reducing overall maintenance costs. The digital performance twin in Makoro™ Mind constructs an asset health and performance model from data from linked devices and maintains it through periodic re-training. As a consequence of real-time IoT data from the asset, engineers have a full picture of how the asset is operating. Makoro™’s Performance Twin enables the identification of possible asset issues, remote troubleshooting, and integration with Makoro™’s Recommendation System to offer contextual suggestions for proactive asset performance improvement.

And, because Makoro™ enables secure access to your plant’s predictions, insights, and recommendations from any device, it plays a critical role in your remote operations strategy, enabling your operations executives to make more informed, timely, and consistent decisions about their plant’s operations remotely.

Request a demonstration to discover what Makoro™ can do for your business.

May 26, 2021

Industry 4.0: Strengthening Maintenance and Reliability

Industry 4.0: Strengthening Maintenance and Reliability

Industry 3.0 pioneered the idea of automated mass manufacturing, enabling the efficient production of millions of similar goods. As the process’s pace and size grew, labor became an impediment, ushering in an era of outsourcing. Manufacturers rapidly outsourced labor-intensive operations to countries with lower labor costs.

Machines are increasingly driving production processes rather than humans, emphasizing the importance of maintenance and reliability teams.

Maintenance and reliability teams ensured that equipment met efficiency metrics over decades of service during Industry 3.0. Machines were the means of development, and technicians ensured that they worked properly.

The only drawback to this strategy was that it pitted repair and reliability teams against other development departments. For instance, if a computer was required to be shut down for recommended repairs, production would be halted as well. Though machine health could improve, production output decreased in the short term.

Considering the innovation and cutting-edge technology that Industry 4.0 uses, it’s clear that future maintenance and reliability teams will look very different from those in the past.

Manufacturers now face new demands. Usually, Industry 4.0 is synonymous with cutting-edge technologies such as artificial intelligence (AI), the Internet of Things (IoT), machine learning, and advanced analytics. But the real reason for their adoption is to drive manufacturing innovation in response to large shifts in market demand in the twenty-first century.

Consumer demand is much more diverse now than it was before, owing to the internet. A viral moment can literally cause demand to spike (or plummet). People demand more customization and sustainable production practices. When all of these factors are considered, it becomes clear that manufacturers now lag behind by 20-30 years.

Demand, sustainability, and cost pressures are all compelling manufacturers to adapt — now, not later. 

Maintenance and Dependability in a Changing World:

The irony of Industry 4.0 is that while technology defines and drives it, each advancement increases the need for human input. The fully integrated factory is only a few years away.

In the near future, maintenance and reliability teams will continue to play a critical role in development. Historically, these teams were primarily responsible for planned maintenance and required repairs. That is no longer the case in Industry 4.0, where teams must optimize efficiency while remaining attentive to conflicting demands and shifting forces. Operations and maintenance teams alike will need to consider asset health holistically — both in terms of what they require to accomplish their strategic goals and how best to accomplish them.

Makoro™’s dynamic learning system takes operations management a notch up, where sets of validated recommendations with very high relevance and confidence score can be replicated across multiple locations to derive the maximum value. So instead of sharing data as reports and dashboards, manufacturers and operators share recommendations/prescriptions across locations.

Aligning Reliability and Maintenance with Industry 4.0:

Currently, maintenance and reliability teams are unprepared for the critical role they will play in development. They lack the technologies necessary to track machine health in real-time across several locations. Additionally, the use of siloed systems keeps maintenance and operations disconnected.

For all of these factors, maintenance and reliability teams would need to fully rethink their approach to manufacturing. To start with, recognize the critical role of maintenance and reliability teams in all aspects of factory operations. Second, begin equipping certain teams for the future roles they will fill. Whether by increased preparation, technology, or talent, or a combination of the three, maintenance and reliability teams will need additional resources to handle the additional responsibilities.

Makoro™’s Recommendations Dashboard tracks asset performance metrics and correlates them to recommendations in real-time. Leaders who have embraced Makoro™ have achieved more than 11% improvement in the agility of their operations.

May 18, 2021

Four ways asset OEMs, owners & operators can remove roadblocks to delivering intelligence

Four ways asset OEMs, owners and operators can remove roadblocks to delivering intelligence

This and the last year has been eventful for all sectors, but the pandemic has been particularly devastating for the manufacturing sector, which relies on the delicate balancing of supply chains to work effectively. As one of the first industries to adopt robotics, automation, and IoT, the sector has a long history of technological advancement. Manufacturers have access to an enormous amount of data. 

But less than 11% of the data is used to drive business decisions.

IDC  survey found several impediments to companies extracting value from their data. 

The fundamental one is the lack of integration of the technology infrastructure. For instance, although the growth of IoT devices at the edge of manufacturers’ data networks has been rapid, these devices are disconnected from manufacturers’ headquarters system. Data collection points are dispersed and uncoordinated, making it hard to use data to achieve desired business outcomes such as increasing throughput. Furthermore, this still leaves Information and Operations leaders driving siloed decision making.

Deriving intelligence from data is another roadblock. Manufacturing lags behind other sectors in terms of multi-cloud and hybrid cloud adoption, and the sector also has a lower likelihood of having a central data centre. As a result, data is either discarded rather than being backed up for long-term storage or stored in long-term storage but never analyzed. Managing the storage of collected data and deriving intelligence out of it in a timely manner to drive business decision making is the second most significant problem, behind making the data available.

Ageing infrastructure is often the cause of the slowdown. For instance, data management may find it challenging to incorporate the latest sensor varieties on various factory machines. Or, the ageing infrastructure may not be capable of handling the volume of connected assets entering the facility. As a result, plants often use ad-hoc processes to link and manage assets in the absence of an underlying infrastructure that enables comprehensive management.

Additionally, a skills shortage exists. Manufacturers are confronted with an ageing workforce and a shortage of qualified workers willing to work on the shop floor. Due to the scarcity of new talent, manufacturers can only invest in upskilling current employees – which is not a bad thing on the surface but is not a long-term plan on its own. If skilled workers are the future of manufacturing, a dearth of sufficient skills is one of the most difficult obstacles to overcome.

Makoro™ alleviates these barriers to adoption.

  1. Makoro™ has an open architecture and can be deployed in any configuration. It can be on a fully managed Makoro™ Cloud (managed by us) or in your infrastructure in a BYOI (Bring your own Infrastructure) model or in any cloud and edge configuration.
  2. Makoro™ can work with a wide variety of assets and IT and OT systems. If the system has data in any form, Makoro™ can tap into that data to demonstrate value. You do not have to “big-bang” upgrade your current assets and/or infrastructure.
  3. Once deployed and connected to your assets and OT and IT systems, Makoro™ continuously delivers intelligence based on your data, so your data is not just stashed away without analysis.
  4. Natural language recommendations from Makoro™ augment your workforce. It can capture tribal knowledge, rapidly onboard new employees and enable them to be productive from day one.

#MakoroAI applies the power of #RecommendationEconomy to #SmartManufacturing, augmenting decisions related to the management of critical assets, so you can manage assets more efficiently with a lower total cost of ownership.

Ignoring the intelligence that your data could provide means passing up opportunities to expand, innovate, and boost your bottom line. Investing in intelligent operations will always have a transformative return on investment, but you need to start somewhere and start soon enough. 

It is time to start collecting data and deriving intelligence. All in a single thread, and not as separate stages of the project. And then repeat this thread as frequently as you can.

Sign up for our 10-day challenge to see how Makoro™ can benefit your business.

May 6, 2021

Predictive analytics: Transforming data into Recommendations

Predictive analytics: Transforming data into Recommendations

Predictive analytics technologies are being used to revolutionize manufacturing operations across all sectors. Not only manufacturers but these technologies are also being used by Asset Owners to derive values based on their data instead of relying on specific OEM’s to deliver “proprietary” value. Throughout the pandemic, these technologies have also been assisting Asset Operators in managing the lifecycle of critical assets distributed across several geographic locations.

Historically, assets of various types – from those on factory floors to those in the field – have exclusively relied on calendar-based management. This meant that decisions were taken, work orders were scheduled, and pieces were ordered simply because a calendar said it was time, rather than because they were actively managed to provide value.

Reactive asset management is costing businesses millions of dollars. The magnitude of the actual impact is even unknown to most asset owners and operators.

The other issue is the siloed focus on maximizing the throughput of an asset, rather than a focus on building end-to-end intelligent operations that cut across different OEMs.

Predictive and prescriptive analytics technologies leverage artificial intelligence (AI) and advanced analytics to develop intelligent operations. These technologies allow manufacturers, owners and operators to predict the operational impact to an asset, the line, the floor or to a location. Here is how Makoro™ leverages predictive and prescriptive technologies in building intelligent operations.

Makoro™ Edges connect with assets and devices on the front end and communicate with Makoro™ Hub on the back end. It transmits machine and process data such as flow rate, oil and water temperatures, vibration measurements, and machine operating hours.

After that, this data is combined with data from different operations and business sources. This is accomplished through Makoro™ Bind, the connector’s interface. After that, it is analysed using a combination of market rules and Makoro™ Mind’s predictive, Al-powered algorithms. While Makoro™ predicts a possible issue, it also does a root cause analysis of the issue. Further, it proactively generates maintenance requests, schedules and assigns the requests, and makes duration, fix and parts recommendations. As data flows through the Hub, Makoro™’s continuous intelligence delivers “unexpected” predictions, insights and recommendations.

Makoro™ learns and evolves as customers engage with the recommendations. And gradually establishing a “golden” operations workflow in which asset management actions are data-driven, traceable, and have been validated by the customer’s workforce.

Through deep-domain artificial intelligence, Makoro™ delivers a rapid time-to-value for customers. It results in higher asset uptime, lower cost of operations, lower maintenance costs and higher workforce adoption.

The ability to anticipate is the competitive advantage in the fast-paced #Industry40 adoption and Makoro™’s #PredictiveRecommendations give you that edge.

MakoroTM’s dynamic learning framework brings operations management to the next level, allowing sets of validated guidelines with extremely high validity and trust scores to be repeated across several locations to maximise value. So instead of sharing data as reports and dashboards, they share recommendations/prescriptions.

Makoro™’s Recommendations Dashboard tracks asset performance metrics and correlates them to recommendations. Leaders who have embraced Makoro™ have achieved more than 11% improvement in their operations agility.

To get started with Makoro™, sign up for our 10-Day Outcome Challenge.

April 29, 2021

Pandemic has accelerated Industry 4.0

Pandemic has accelerated Industry 4.0

Manufacturers are looking to digitize their activities to meet the demands of the pandemic. The aim is to create a single operating mechanism that spans all available resources on a global scale.

This necessitates consistent structures and standardization, which must be facilitated by emerging technology, agile processes, and data access. It is essential to provide global insights into plant performance combined with business background from enterprise systems.

The greater the amount and the variety of data that learning systems can ingest, the more accurate they become. Digital processes and the capacity to process vast amounts of data are essential components of digital transformation.

A key element of adoption for digital transformation initiatives is value demonstration.

A large percentage of pilots rot in the “Pilot Purgatory” because they do not demonstrate a clear business value.  Makoro™’s dashboard provides a continuous value demonstration of recommendations on specific business outcomes related to assets, maintenance, and workforce. These are quantified values that also act as incentives to continuous asset and process improvement.

Another impediment is workforce adoption.

Deployment of new tools often is too technology-focused. It does not put business capabilities in the hands of the existing operations workforce, the real catalysts who leverage their domain and process knowledge to derive value from these tools. Makoro™ is created for the operations workforce – for operators, maintenance engineers, floor managers, and operations executives. Makoro™ gives recommendations that are seamlessly integrated into their regular task workflows, making them easy to adopt.

We internally say that “Makoro™ solves the issues on the production line that keeps operators up at 8 o’clock on a Friday evening.” Do you really want to call the data scientists at that time to interpret data and drive decision-making? That’s where the natural language recommendations really make their mark.

A third barrier is the high cost of a scalable solution.

It is one thing to experiment with analytics – ingest data from a few machines, run experimental algorithms, and derive value (which is also a necessary step in the lifecycle) but building a scalable solution that works for thousands of assets and hundreds of devices per asset, and supports hundreds of workers across multiple locations needs well-thought-out vision, risk mitigation strategies, and investment plans. Makoro™’s Automated Operations and Continuous Intelligence scales up and down in a frictionless manner across devices, edges, and hybrid clouds to deliver the scalability that businesses need.

We should aim higher, but implementations must start smaller in scale, with an eye on delivering maximum impact. The rollout should be line-by-line and site-by-site, which mitigates risk and delivers value at each stage.

Want to know how Makoro™ can drive adoption, scale, and value in your business? Sign up for a demo.

April 28, 2021

Artificial Intelligence in conventional manufacturing

Artificial Intelligence in conventional Manufacturing

During these times of social and economic distance, the incorporation of Artificial Intelligence into conventional manufacturing, supply chain, and quality management is already proving to be a game-changer across verticals.

AI is not only the trigger, but it is also the wheels. One of the growth drivers in manufacturing, with growing competition and declining margins, is to reduce downtime and improve asset efficiency. However, current asset performance management products and solutions are flawed. As they only offer data views in many shapes and forms without actually recommending solutions.

As a result, asset management is costly, arbitrary, unreliable, and inefficient.

When it comes to managing asset performance, more than 78 percent of our customers tell us that data alone is insufficient. And as the volume and complexity of data increases, it is impossible to dig through data to make the best decisions. There is no transparency in the decision-making process. There is also no validation in terms of acceptance of the decision and its impact on the business outcomes.

Makoro™’s innovative fusion of digital performance twin, artificial intelligence, and recommendations engine extends asset performance beyond reports and dashboards. It delivers real-time recommendations that demonstrate value to business continuously.

Makoro™’s always-on, stable, and scalable asset performance solution is driven by #AppliedArtificialIntelligence and #PredictiveRecommendations. It gives you a competitive advantage in the fast-paced #Industry40 while lowering downtime and maintenance costs.

Schedule a demo to see how we do it.

April 24, 2021

Data is not Enough – An Introduction to Makoro™

Data is not enough - it's time to switch to Makoro™

Introduction to Makoro™ AI – how Makoro™ derives its name from the Bantu language and the foundational principles of Makoro™. The proliferation of connected assets makes it impossible to manage assets by viewing data, reports, and dashboards. Makoro™ manages assets through natural language recommendations, which operators, process engineers, and maintenance engineers can act upon without having to depend on data scientists.

Makoro™ illustrates the power of #AppliedArtificialIntelligence in solving transformative problems in the manufacturing supply chain.

#MakoroAI #CodeDataIO #PredictiveRecommendations #NaturalLanguageRecommendations #PredictiveAssetManagement #ContinuousIntelligence

April 19, 2021

Makoro™ AI Predictive Asset Management

Makoro™ uses applied artificial intelligence to solve transformational problems in manufacturing supply chain

Makoro™ uses applied artificial intelligence to solve transformational problems in the manufacturing supply chain.

Makoro™ AI differentiates itself in Enterprise Asset Management and Asset Performance Management spaces through its AI-powered predictive asset performance management for asset manufacturers, owners, and operators. Makoro™ AI lets you connect to all operations and IT data sources and transforms data to recommendations in minutes – recommendations that power higher asset uptime, lower maintenance costs, and higher workforce engagement.

Makoro™ illustrates the power of #ArtificialIntelligence applied to solve the deep-domain problems of the manufacturing supply chain.

#MakoroAI #CodeDataIO #PredictiveRecommendations #NaturalLanguageRecommendations #PredictiveAssetManagement #ContinuousIntelligence

April 16, 2021

The story of Makoro™ AI

The Story of Makoro™ AI

April 14, 2021

The changing face of Asset Management

Asset Performance Management

Effectively managing asset performance has been an unrealized opportunity for asset-intensive industries. As a result, industrial downtime caused by equipment and process failure in just about every industry is still causing an ever-increasing and significant impact on manufacturing EBIT, production, and quality.

Assets and equipment are not being replaced or maintained optimally as they should be, which results in higher risks, larger costs, and adverse impacts on balance sheets. As we emerge from this terrible Covid crisis, this trend of “sweating the asset” is becoming more and more prevalent

Managing asset performance is a foundational capability for asset-intensive industries as they seek to make their operations more efficient, reliable, and safer. However, many organizations still see asset management as simply keeping track of asset data or more advanced maintenance, leading to several siloed applications and failing to demonstrate clear business value. In fact, fewer than 5 percent of companies have achieved an asset performance program that helps to optimize maintenance, operations, and asset investment decisions to achieve financial results. (can you add the source of this data?)  

“The way we look at manufacturing is this: the U.S.’s strategy should be to skate where the puck is going, not where it is.”—Tim Cook, CEO, Apple Inc., 

 

Yet too many businesses are stuck with where the puck is – in terms of siloed views of the current state of asset data in form of graphs, charts, reports, and dashboards.

 As we enter a new age of value creation through Industry 4.0 technologies, opportunities to take asset performance to the next level emerge through connected assets, the Industrial Internet of Things (IIoT), and artificial intelligence and advanced analytics technologies. For organizations investing in digital transformation, proactively managing asset performance should be a critical component of the strategy; Industry 4.0 revolves around innovations for operational excellence, which cannot be achieved without a proactive strategy towards managing assets.

However, merely implementing APM software will not drive operational excellence that Industry 4.0 envisages or drive the ROI that businesses strive for. Instead, the real value of asset management can be realized only when data from systems across the business are connected and correlated – from enterprise resource planning systems to quality, maintenance, inventory management, and operations systems, and including real-time data from assets. Because asset performance is affected by a gamut of parameters coming out of real-time health, operations, and enterprise systems, companies that fail to take a holistic view of asset management will all too often fail to harness its full value.

Connecting in real-time to assets and other sources, aggregating and correlating the data is by no means simple. But that is the first step in creating an asset management strategy that will deliver a complete understanding of your assets. Benefits include better worker safety and asset availability, less unplanned downtime, and improved throughput, leading to a reduction in overall costs.

Additionally, a holistic approach to asset management also reduces reliance on tribal knowledge. Since asset insights are based on data, they will augment your new workforce as your experts retire, not to mention the benefits of increased employee engagement and developing a data-driven, proactive culture.

 But it is the predictive intelligence that is layered on top of the data that gets the leaders ahead of their competition. Rooted in their own data and based on models trained in that data, leaders can predict maintenance activities to postpone or prevent asset failure. Prescriptive technologies take businesses even further along in their journey of operational excellence, giving them the ability to prescribe operational changes to alter how equipment performs. 

“Your data can do a lot more than what you think.”

Bring in connectivity. Put your data to work. But be quick in delivering intelligence based on the data. Always integrate the intelligence gains into your process flows. While data is fundamental, integrated intelligence vastly increases your ROI. Do not fuss about accuracy, scalability, or performance. Get started. Deliver value and improve continuously.  Because asset management as you know it is changing. Intelligent asset management is where the puck is going.