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ThinCats, the alternative finance specialist, is making £100m available to fund and support dynamic manufacturing businesses across the UK, and help take them to the next level of growth.
The £100m of funding in partnership with Hennik Edge, the networked advisory team for manufacturers, will see ThinCats use its UK-wide network of Origination Managers to support those companies in the manufacturing sector that require a level of capital to take their businesses forward.
John Mould, CEO of ThinCats.
ThinCats states that it has an excellent track record of lending to UK manufacturers. John Mould, CEO at ThinCats comments: “This is great news for fast-growing manufacturing firms. Since 2011 we have lent £20m to businesses operating in the manufacturing space, with 73 loans servicing 50 different companies.
“With this much-needed funding, and with the expertise of Hennik Edge, we can look to raise the pace of our lending even further.
“ThinCats specialises in providing funding that, in many cases, the high street banks cannot. Whether it’s for working capital, acquisitions, asset purchase or refinancing, we will help to ensure that manufacturing continues to be the lifeblood of the UK economy by supporting growth across the sector.”
Steven Barr, Managing Director at Hennik Edge said: “We’ve heard from frustrated manufacturers who need a different kind of finance from what’s on offer in the high street. This new release of £100m of funding, backed by ThinCats, offers a great alternative for ambitious, growing SMEs.”
One manufacturer that has seen significant benefit from working with ThinCats is Gainsborough Silk, the historic Sudbury textile weaver, which produces fabrics for Royal palaces, state buildings and grand residences across the world. ThinCats provided a working capital loan of £500,000 to the firm after it had been starved of investment over a 20-year period. The loan helped Gainsborough Silk ramp up production capacity with the purchase of additional looms and contributed to the modernisation of its dye house.
John Mould added: “Gainsborough was not the simplest credit story, but we liked their market position and management. We were pleased to have been able to structure a deal that raises the investment capital the business requires and offers attractive returns to ThinCats investors.
“It’s a success story that this £100m of funding will replicate many times over. We’re looking forward to working with other ambitious manufacturers over the coming months and years.”
Manufacturing is going through a technological resurgence that is transforming the modern factory. Today we are seeing data-driven factories, with an unstoppable integration of connected systems and devices.
Gartner predicts that 25 billion Internet-connected things will be in operation by 2020 with close to $2 trillion of economic benefit globally, and the advantages of Industrial Control Systems (ICS) and Supervisory Control and Data Acquisition (SCADA) systems being integrated into the IoT is proving its potential in improving efficiencies, innovation and profitability.
However, at the same time this increased connectivity is also opening up entry routes for cyber criminals, leaving manufacturers open and vulnerable to a multitude of exploits and unauthorised access attempts. This is yet further complicated by the increasingly complex network of third parties across the supply chain that are employed to manage many aspects of their operations, with some having several hundreds of external parties accessing their systems in a typical week, according to Bomgar’s 2017 Secure Access Threat Report. In addition, more than half of the respondents of the report said they had sole responsibility for managing third party access into their infrastructure, and one in five admit to offering three or more access routes to their vendors, making an already big job even more challenging.
IBM’s 2016 security intelligence survey revealed that manufacturing is now one of the most frequently attacked industries, second only to healthcare. In addition to the increased risk of attack via third parties, Bomgar’s report also revealed that insiders, such as employees or contractors, also pose a significant threat to security. The survey found that 67 percent of IT professionals believe an insider data breach is the primary security threat for them, and a further 73 percent were also concerned that they could lose sensitive company information within the next 12 months.
The race to connect devices and transform processes and procedures offers many competitive advantages to manufacturing organisations, however the security stance should not be compromised and must therefore be incorporated in projects from the outset. The detrimental impact a breach could have on the public as well as an organisations’ reputation, far outweighs the benefits that can be achieved.
It only takes one employee or one third party to leave an organisation vulnerable, and with the continuation of high-profile data breaches, many of which are caused by compromised privileged access and credentials, it’s crucial that organisations control, manage, and monitor all access to their environments to mitigate this risk.
Adding to this pressure, all manufacturers are under immense pressure to have effective security and breach response capabilities in place ahead of the 2018 EU General Data Protection Regulation (GDPR) deadline. The EU GDPR brings consistency to the current data protection laws across EU member states and provides guidance on how any EU citizen data should be stored as well as how companies must respond in the event of a data breach. In the short term it is key to minimise some of the more common security weak points such as password sharing and poor employee on and off-boarding strategies to support ongoing GDPR initiatives.
Security must therefore be built into the ethos of any organisation and its projects, where workforce training, regular security assessments and specific policies and procedures are included and understood by the whole organisation.
With the variety of hacking strategies combined with the integration of modern solutions such as the IoT and complex interconnected networks of suppliers, it is paramount that controls are put in place to manage and audit all access to both complex systems such as server based environments, but also to less complex devices such as routers and switches, including all insider and third-party access.
There are a few core steps the industry can take to securely manage access:
Manufacturers need to encourage the integration of multiple best-in-breed tools together with privileged session and privileged access management solutions as part of a robust security eco-system. These include detection tools, SIEM solutions, network segmentation and employee awareness initiatives to ensure they protect themselves from attacks through their connected devices whilst maximising the benefits available.
By implementing secure remote access, architecture and controls, manufacturers can prevent breaches and protect their corporate and reputational damage, ensuring innovation can prosper and regulations met.
Progress, the provider of application development and deployment technologies, has announced that long-time partner QAD is expanding its relationship with Progress and plans to deliver new value to its customers with the Progress DataRPM platform.
DataRPM, acquired by Progress in March 2017, uses automated machine learning to help manufacturing and industrial organisations harness the Industrial IoT, to detect and predict machine failures before they occur, dramatically reducing downtime and increasing asset operational efficiency. This solution is a key part of Progress’ strategy for helping its partners and customers build cognitive applications.
“Progress’ strategy for cognitive applications is well-aligned with our vision for the future,” said Carter Lloyds, Chief Marketing Officer, QAD, Inc. “QAD prides itself on the ability to offer the best full-featured manufacturing ERP software that minimises complexity, simplifies process and provides the agility and focus our customers require. We see the automated machine learning capabilities in Progress DataRPM as a significant opportunity for us to drive new value for our end customers in a fast, scalable and repeatable solution.”
QAD provides integrated business software for manufacturing companies and serves customers in more than 100 countries, including eight out of 10 of the top auto parts manufacturers in the world. QAD’s strategic agreement with Progress enables customers looking to take advantage of predictive maintenance and machine learning to leverage the DataRPM platform to improve their business.
“QAD has remained successful for nearly 40 years because they are forward-thinking and highly focused on their customers’ needs,” said John Ainsworth, SVP, Core Products, Progress. “The expansion of our strategic partnership will enable QAD to take advantage of our new cognitive anomaly detection and predictive maintenance capabilities. The future is cognitive applications, and with Progress, QAD will be prepared with solutions for its customers that will enable them to succeed and thrive.”
First announced in January, Progress’ cognitive-first strategy focuses on providing the technologies to enable customers and partners to quickly build modern business applications that are intelligent, adaptive and connected – creating better application experiences at dramatically lower cost.
LEADING ARTIFICIAL-INTELLIGENCE RESEARCHERS gathered this week for the prestigious Neural Information Processing Systems conference have a new topic on their agenda. Alongside the usual cutting-edge research, panel discussions, and socializing: concern about AI’s power.
The issue was crystallized in a keynote from Microsoft researcher Kate Crawford Tuesday. The conference, which drew nearly 8,000 researchers to Long Beach, California, is deeply technical, swirling in dense clouds of math and algorithms. Crawford’s good-humored talk featured nary an equation and took the form of an ethical wake-up call. She urged attendees to start considering, and finding ways to mitigate, accidental or intentional harms caused by their creations. “Amongst the very real excitement about what we can do there are also some really concerning problems arising,” Crawford said.
One such problem occurred in 2015, when Google’s photo service labeled some black people as gorillas. More recently, researchers found that image-processing algorithms both learned and amplified gender stereotypes. Crawford told the audience that more troubling errors are surely brewing behind closed doors, as companies and governments adopt machine learning in areas such as criminal justice, and finance. “The common examples I’m sharing today are just the tip of the iceberg,” she said. In addition to her Microsoft role, Crawford is also a cofounder of the AI Now Institute at NYU, which studies social implications of artificial intelligence.
Concern about the potential downsides of more powerful AI is apparent elsewhere at the conference. A tutorial session hosted by Cornell and Berkeley professors in the cavernous main hall Monday focused on building fairness into machine-learning systems, a particular issue as governments increasingly tap AI software. It included a reminder for researchers of legal barriers, such as the Civil Rights and Genetic Information Nondiscrimination acts. One concern is that even when machine-learning systems are programmed to be blind to race or gender, for example, they may use other signals in data such as the location of a person’s home as a proxy for it.
Some researchers are presenting techniques that could constrain or audit AI software. On Thursday, Victoria Krakovna, a researcher from Alphabet’s DeepMind research group, is scheduled to give a talk on “AI safety,” a relatively new strand of work concerned with preventing software developing undesirable or surprising behaviors, such as trying to avoid being switched off. Oxford University researchers planned to host an AI-safety themed lunch discussion earlier in the day.
In today’s digital landscape, asset management systems are becoming increasingly sophisticated, introducing new data sources and a data-driven approach: weather data, asset information and sensor data from equipment can be integrated and fed to machine learning algorithms to predict and prevent failures and disruptions.
As a result of the new available data, more sophisticated approaches to Predictive Asset Maintenance (PAM) can help organisations in reducing downtimes, avoiding reactive maintenance costs, reducing preventive maintenance costs and improving customer satisfaction.
The traditional approach to maintain assets is mainly around preventative maintenance with strict maintenance regimes, standard inspection cycles and renewal policy based on the lifetime of the asset calculated using theoretical engineering expertise. While such approaches are generally good at reducing downtime, they can also be inefficient, inflexible and are generally associated with high costs.
A machine learning approach to PAM enables organisations to design maintenance policies based on real time data and to make efficient decisions about asset renewal and maintenance. Engineering knowledge is still key, captured in the model definition stage and through feature engineering, for example understanding what the key features are as well as different types of failures and consequences. However, unlike the traditional approach, a machine learning enhanced PAM system benefits from data in real time and captures high-volumes of data to improve processes over time, based on historical information.
Increasing use of sensors in equipment and the availability of more data is creating ideal environments for machine learning to thrive and drive more business value from PAM systems. However, businesses will only look to invest in machine learning if they can see immediate Return on Investment (ROI):, it is difficult to prove value from machine learning models without making a strong case with evidence based savings calculations.
So how do we take the next steps towards machine learning with PAM systems? And what are the business benefits companies can hope to achieve?
Businesses need to wake up to the potential of machine learning driven PAM systems in terms of utilising asset data. The challenge is that the value of machine learning models is not transparent and the cost benefit analysis is not easy to conduct, therefore, business stakeholders are often reluctant to invest in machine learning driven PAM systems.
That’s where data scientists and PAM experts step in. They should acknowledge the importance of proving real business value to the key stakeholders. It is insufficient to provide a good machine learning model that accurately predicts the probability of a failure and data scientists need to develop an actionable insight for the business.
Given the importance of ROI evaluation, data scientists need to communicate the benefits of machine learning through developing optimised maintenance plans. The optmised maintenance plan is the maintenance policy for renewal and repair of assets based on probability of a failure and prioritisation calculated using machine learning models. It also needs to consider business specific requirements including the number of available repair crews and maintenance costs. This actionable insight would bring measurable benefit to the business and can be effective in reducing the communication gaps between data science and business stakeholders.
Machine learning based PAM systems can deliver significant transformational and business value to various industries. Here’s how:
A logistics company wanted to be able to predict container ship engine component defects using sensor data. Predicting and avoiding ship engine components from failing saves the shipping companies for idle and unproductive time worth millions of dollars. Traditionally, real time streaming data requires an expert who understands the engine to monitor it. There is some level of automation to traditional preventive maintenance of this asset as alerts can be raised based on rules to detect “abnormal” states of an engine based on engineering knowledge.
Machine learning models were developed to predict failures with ten-day lead time. The models were trained on historical sensor data such as temperature of the engine and vibrations. These models enable an automated processing of large amounts of sensor data and accurate reporting of a probability of an engine component failure within 10 days.
The PAM model’s outputs can be used to setup an automated system to raise alerts each time the engine is in risk of failure. The main benefit of implementing such a system is to reduce unexpected downtime by raising preventive alerts and reducing the cost spent in manual inspections by relying more on sensor information – and applying this data-driven approach.
A global train manufacturer wanted to improve the servicing of trains and reduce downtime to improve customer satisfaction. The organisation wanted to use PAM models to understand the root cause of failures, as well as to give its Operations teams time to react to train disruptions, thus minimising downtime. With better machine learning models, maintenance costs would ultimately decrease and the manufacturer’s supply chain would be optimised by ordering parts exactly when required.
The PAM models were built using a range of data sources such sensor data from various components, historical maintenance data, weather and geo data. The models were used to understand the most critical components and influence factors of downtime. This insight helped to inform management of failure risk, and eventually the team could design predictive models to respond to this risk in an automated way.
PAM with machine learning models enable organisations to take a data-driven approach to managing asset failure risk while improving efficiency and testing current engineering assumptions about the assets. However, to reveal its full potential, PAM must be adopted on a wider scale – therefore it is important to show its benefits to business leaders using interpretable and actionable insight. One example is to make this extra step from reporting a probability of a failure to designing an optimised maintenance plan. This would provide clear evidence of the benefits to the business and promote the adoption of PAM with machine learning models.