News & Updates
September 21, 2021
To assist the rapidly increasing e-mobility sector, as well as the energy transition, climate change, and sustainability, all of these initiatives require novel solutions. Additionally, alternative propulsion concepts must be developed to considerably cut CO2 emissions.
That is why advancements in battery manufacture are becoming increasingly important.
However, which batteries and production techniques are capable of meeting escalating demand and stringent specifications? How can manufacturers maximize the sustainability, efficiency, and quality of their plants while automating their processes? New comprehensive automation concepts, such as those based on artificial intelligence and robotics, are critical. Additionally, these notions should consider market requirements, as well as the demands of policymakers and the battery industry. Thus, what are the current trends and future approaches in battery manufacturing that will enable European companies to seize the lead in energy storage for electric vehicles?
Battery makers must excel in terms of quality, increased efficiency, and resource optimization in order to compete better. As the field of e-mobility advances, more advanced battery technologies are being developed. It ensures the vehicles’ durability, safety, and operation. Lithium-ion pouch batteries suit a number of critical current industrial and automobile manufacturing needs. However, pouch cell manufacturing is more complex and time-consuming than cylindrical battery manufacturing. This, in turn, puts unique demands on manufacturing processes. Manufacturers are looking to artificial intelligence to assist with their manufacturing processes. For instance, AI can assist in optimizing machine efficiency and ensuring defect-free output.
Combining artificial intelligence, sensing, control, and robotics delivers higher quality.
While battery producers must react to the market’s ongoing evolution, they demand production processes that can be altered more quickly and flexibly than ever before to suit changing requirements. Combining artificial intelligence, sensing, control, safety, and robotics into a single automation platform enables manufacturers to meet customer criteria for product quality and predictive maintenance while also reaching critical sustainability goals.
Additionally, assistance with integrated battery cell inspection solutions, as well as solutions for electrode and battery module manufacture, can assist in streamlining the testing and providing end-to-end traceability throughout the battery cell’s life.
The technology ensures sustainability.
Battery manufacturers and suppliers require a dependable partner that can give theAsset Performance Managementm powerful technology and relevant advice from a single source in order to be inventive, adaptable, and future-oriented. A holistic, artificial intelligence-based solution can assist industries in reducing waste. When used in conjunction with an intelligent warehouse system and mobile robotics, firms can significantly increase process efficiency and productivity.
Simultaneously, battery cell quality – including capacity and battery life – should be covered sustainably by a manufacturing and lifecycle control solution that is backed up by an in-line inspection system. This solution must take into account all stages, from manufacture to usage and recycling. By applying these technologies, businesses take a critical step toward the future of sustainable industrial and battery production.
Recommendations based on data maximize utilization of production resources.
Today’s batteries as well as their production generate massive amounts of data. Makoro™ provides a range of device and enterprise system connectivity protocol options, and a recommendation system powered by digital performance twin, enabling manufacturers to translate the data generated by the manufacturing processes into a wealth of real-time recommendations. Leveraging these recommendations in battery production helps manufacturers improve their productS and production systems efficiently. They are able to create new business opportunities based on recommendations based on aggregated, correlated, and analyzed operational data.
As a result, battery manufacturers achieve::
- Improved productivity of existing plants
- Reduced inventory and throughput time
- Maximum utilization of production resources
Research indicates that proactive asset recommendations result in an 11% – 13% reduction in raw material usage and a potential savings of up to 15% – 18% in energy utilization.
August 17, 2021
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
- 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?
August 9, 2021
Recommendations from Makoro™ deliver improved asset and process performance consistently. But instead of looking at data and making decisions yourself, the system provides a simple prescription of actions to be taken based on deep analytics. This improves consistency in decision-making and ensures compliance.
Decision-making is transparent as all actions on recommendations are tracked by the system and learned from to make future recommendations better.
Makoro™ connects to any source of data, including data from log files, and excel spreadsheets, and the data that has been sitting on your flash drives.
Makoro™ offers simple SaaS pricing (per asset per month per user) for customers – asset, equipment, and process manufacturers, owners, and operators.
Makoro™ operates frictionlessly across Edge, Private, Public, and Hybrid Clouds, and Customer Data Centers, so it leverages customer’s infrastructure to the maximum.
Makoro™ now offers 4 capability levels – starting with Starter, through Intermediate, Advanced, and Pro, so customers can onboard and scale with ease depending on their requirements.
The 10-Day Challenge program is the right point to start and mitigate your risks in Makoro™ adoption.
July 1, 2021
Smart factory development is a top priority for manufacturers across all industries and sectors. It is a component of a business’s digital transformation, and it is critical to maintaining competitiveness while also meeting the demands of consumers and end-users, as well as B2B customers and regulators.
This is all part of Industry 4.0, the umbrella term for the revolution currently sweeping the manufacturing sector.
As with previous industrial revolutions, Industry 4.0 will evolve over many years and decades as new technologies and processes are developed, and as businesses, regulators, and consumers grow.
What are the critical smart factory developments worth examining today if you work in the pharmaceutical or medical technology industries?
Why is Industry 4.0 even more critical to the pharmaceutical manufacturing industry?
- Increased Productivity — Automating processes enables them to be completed more quickly and accurately. Reduce downtime through automation and the use of sensors and predictive systems that enable equipment to function autonomously.
- Improving the supply chain’s efficiency through integration – They are making it feasible to transition from batch to continuous manufacturing, a production approach that results in much less downtime and increased efficiency.
- Increased Quality – Increased automation and continual monitoring of your manufacturing plant will result in fewer mistakes and higher-quality final products.
- Risk Reduction – This follows naturally from the preceding two arguments – increased manufacturing precision reduces the danger of substandard items reaching end customers.
- Facilitates Regulatory Compliance – Compliance with existing and upcoming laws in the E.U. and other parts of the globe is made more accessible via the use of Industry 4.0 technology. To comply with the Falsified Medicines Directive, new track and trace systems and serialization solutions are presently being deployed.
- Improved Business Oversight – Industry 4.0 technologies enable real-time reporting, increased data collection, improved data analysis, and improved data presentation in usable formats.
- Development Of New Business Prospects – Industry 4.0 will also open up new commercial prospects for pharmaceutical businesses. For instance, Industry 4.0 technologies enable seamless and highly efficient end-to-end supply chain integration. This increases productivity, which helps your business immediately. A more connected and efficient supply chain, on the other hand, is also more scalable, opening up new potential for your organization. Covid-19, a quick-win situation.
- Increased Profitability – When all of the aforementioned are combined, the outcome is improved corporate profitability. Additionally, your firm will be more competitive and better able to address future difficulties and opportunities. Covid-19 has accelerated the advancement of biotechnology problems.
The pharmaceutical industry has only recently begun to adopt Industry 4.0 technologies, despite the fact that it has been using batch manufacturing for more than 50 years. However, the traditional batch process technique has been demonstrated to be lengthy: after each stage in the process, production is generally halted to allow for quality assurance testing of the material. Each break lengthens the lead time and increases the likelihood of defects and errors (FDA, 2016).
This encouragement comes at a critical time – we are entering an era of precision (personalized) medicine, “where medicines must be produced with unique characteristics and made available to people in need more rapidly.” (U.S. Food and Drug Administration, 2016).
To create customized medications, pharmaceutical companies no longer need to produce large batches but rather tiny ones tailored to a limited group of individuals who require a certain treatment in a specific dosage. Batch production is not the answer to these demands, but continuous manufacturing that is connected, smart, adaptable, and accurate.
Continuous manufacturing is used in the pharmaceutical business to move substances nonstop inside the same facility, eliminating wait times between process steps; the materials are fed via an assembly line of integrated components. Manufacturing that is continuous “saves time, decreases the risk of human mistakes, and enables a more agile response to market changes. It can operate for an extended length of time, potentially reducing the probability of medication shortages.” (U.S. Food and Drug Administration, 2017).
Pharma Opportunities 4.0
- Gains in economic terms, such as greater revenues as a result of reduced transaction costs
- Increased reliability and consistency in production and output, as well as higher-quality products
- Energy-efficient and ecologically friendly manufacturing systems
- Utilization of human and material resources effectively.
- Changes in work structure, with a greater emphasis on remote, flexible, and on-demand labor
Pharma 4.0’s Obstacles
- Gaps in infrastructure
- Policies and regulations that are out of date and do not take into account 4.0 Industry
- Ownership and security of data
- Transparency, confidentiality, and ethics
- Changes in the fundamental characteristics of innovation processes and their implications for competition and entry barriers
Take away points
We may now re-evaluate our industry’s accomplishments while simultaneously re-strategizing our future strategy toward offering high-quality therapy at scale and bringing fresh hope to humanity.
By strategically integrating the production process, we as an industry will be able to stay ahead of the problems and expectations on which the rest of the world is so reliant.
Predictive analytics enables you to foresee issues, minimize risks, capitalize on opportunities, and ensure that resources are directed appropriately.
Predictive analytics is already being used by pharmaceutical and medical device makers in the field of equipment maintenance. It is possible to forecast the effect of a variable on a piece of equipment, a production line, or a process using technology such as digital twins.
Makoro™ leverages #AppliedArtificialIntelligence and #NaturalLanguageRecommendations powered by #DigitalTwin in solving transformative problems in the manufacturing supply chain.
If you are still leading with reports and dashboards, you are falling behind. You are leaving money at the table. When you can shave off minutes and hours you spend making the right decisions on your assets.
Asset performance recommendations from Makoro™ deliver improved asset and process performance consistently through recommendations. These recommendations are backed by data and can be traced to the sources of data.
Makoro™ recommendations deliver direct operations savings and indirect compliance savings.
May 26, 2021
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 24, 2021
Manufacturers worldwide are under pressure to modernize their factory workforces, but 57 percent of manufacturing leaders report that their company lacks the trained staff necessary to support digitization plans, according to a Gartner, Inc. survey.
The survey found that manufacturers are currently experiencing a challenging phase in their digitization journey towards more intelligent manufacturing.
Connected factory workers rely on a variety of digital resources and data management strategies to enhance and incorporate their experiences with their physical and virtual environments, increasing decision precision, spreading information, and reducing uncertainty.
1. Change Management is the Most Difficult Task
Complexity in the organization, integration, and process reengineering are the most common barriers to implementing smart manufacturing initiatives. When taken together, these issues represent the most significant change management roadblocks.
83 percent of respondents believe that their leadership recognizes and supports the need for smart manufacturing investment. It does not, however, reflect whether the majority of leaders grasp the scale of change ahead of them – both in terms of technology and talent.
2. Intelligent Manufacturing Needs Technology
Businesses are beginning to recognize the importance and potential of smart manufacturing. However, implementing new innovations alone is insufficient. The most critical action is for businesses to recognize that this is about more than digitization, as it entails coordinating activities for capability growth, capability enablement, and human empowerment.
3. Human capital needed for Smart manufacturing
Factory staff must adapt in lockstep with technology and be prepared for future changes. In the long run, it is critical to embed a data-driven culture in manufacturing operations through governance and training – without choking employee innovation and imagination.
Makoro™’s Dynamic Learning technologies help retain tribal knowledge. This assists in onboarding new employees and provides them with the augmentation necessary to make them better at their jobs immediately,
Sign up for our 10-day challenge and see the performance.
May 18, 2021
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.
- 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.
- 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.
- 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.
- 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 17, 2021
The current IoT landscape is highly OEM-centric – it is heavily OEM-data-biased and at first services the interests of the equipment manufacturer. The end customers, the asset operators, plant owners, and process owners, have been sharing their operational data with their OEM equipment suppliers in return for insights into asset conditions and improved maintenance. And depending on the strategies deployed by the OEM, the end customers and operators often have valid concerns about the credibility of these insights delivered by the OEMs. And more often than not, it is impossible to get a true understanding of the approaches that led to the insights. (Take the over-simplified example of auto-manufacturer-recommended oil change every 3,000 miles.)
However, the end customers are increasingly demanding improved equipment and process reliability across their whole asset/equipment estate. This estate includes operational assets from multiple OEM providers across the entire set of manufacturing and medical laboratory processes, across multiple geographies, with multiple technologies and wide variations in equipment age. And every organization needs a systematic and coordinated set of activities and practices which sustainably manages all its assets’ performance, risks and expenditures over their life cycles for the purpose of achieving the organizational strategic plan.
Given these conditions, reliance on OEM recommendations not only leads to fragmented asset management strategies across the estate, but these recommendations have mostly proven to be the least effective for assets with medium to high criticality.
End customers and asset operators require clear and accurate recommendations on process optimization. Unfortunately, many consider this information to be their proprietary data, their playbook, their operational cookbook, and this is confidential to their ecosystem. In addition, many manufacturing or process operations are competitive and thus deferential to their product offering and cost.
There is a trend in which end-customer business models move to a lower-cost model, with the lower deployed operational resource. However, employed process technology generally is increasing in complexity.
Makoro™, therefore, recommends and delivers continuous improvement in quality and asset/equipment total through-life cost while maintaining high process availability (OEE) and delivering a reduction in downtime based on the end customers proprietary data from their whole ecosystem while still sharing nominated data with selected OEM providers.
Makoro™ is a technology for the end-user, the asset operator.
Makoro™, by design, works with multiple and simultaneous data sources, protocols and connected and non-connected devices, legacy operational, and maintenance data. You do not have to have IoT fully deployed with every edge device reporting to get immediate value.
We sell directly to the asset operators, through process consultants and also to OEMs that recognize any IoT platform must support all the process stakeholders while protecting the intellectual proprietary of the end customer process.
We also licence our technology via API to extend the capability offering of already deployed Enterprise Asset Management (EAM), Field Service Management (FSM), Asset Performance Management (APM), Computerized Maintenance Management (CMMS) and Enterprise Resource Planning (ERP) systems.
Too many people tell you where the problem is, but shy away from recommending solutions. This is where Makoro™shines – we not only pinpoint the problem but also recommend solutions.
Makoro™ lets you take control of the proprietary data you collect from your line and your manufacturing process devices. You decide what you wish to share with the OEMs providing your equipment maintenance.
Sign up for a 10-Day Outcome Challenge to learn more about what Makoro™can do for your business.
May 14, 2021
Manufacturers will never build in the same way again as a result of Industry 4.0 – the Fourth Industrial Revolution. Manufacturers across all industries are digitizing their businesses to discover new, more cost-effective ways to source materials, run production lines, serve customers, and analyse their results for continuous improvement.
As with any significant change, Industry 4.0 may appear challenging to manufacturing professionals unfamiliar with its implications. Indeed, numerous technologies may play a role in the current revolution. The challenge is to determine which of these technologies have the greatest potential to transform the way you conduct business.
Artificial intelligence, additive manufacturing, and blockchain are three technologies that are shaping up Industry 4.0.
As with the majority of Industry 4.0 technologies, artificial intelligence, additive manufacturing, and blockchain all have one thing in common: they are cloud-based. This means you’ll have easy access to the data storage space and computing power required to analyse the massive amounts of data generated and consumed by these technologies.
Artificial intelligence (AI) assists you in resolving issues by learning from patterns in your data that you might not have noticed on your own. How is this accomplished? By combining automation and machine learning for the purpose of analysing massive amounts of data. This enables you to identify undesirable trends in machine downtime, pinpoint periods of high scrap rates, and use these insights to make more informed profit-maximizing business decisions.
Additive Manufacturing has grown in popularity even outside the manufacturing sector, with the majority of people referring to it by another name: 3D printing. Manufacturers are only beginning to explore the full range of additive manufacturing applications. Several researchers have combined machine learning and generative design to generate alternative part designs.
Blockchain technology improves transparency from raw material procurement to final product delivery and confidence at any level of the industrial value chain. Combining blockchain technology and the Internet of Things aid in resolving issues such as provenance, counterfeit identification, and asset tracking in supply chain management. It also enables new approaches to maintenance (such as digital service agreements) and faster turnaround times.
At Makoro™, we use AI & advanced data analytics to make asset performance management recommendations in the natural language of the user so they can easily understand and act upon the recommendations. This enables faster, better and compliant decision making.
April 28, 2021
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.