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April 14, 2022

Source:  Tim Guido, Corporate Director, Performance Improvement, Sanmina | Manufacturing Tomorrow

Many industries such as the automotive, medical and semiconductor sectors must comply with third party standards to control processes, reduce risk and ensure quality during the manufacturing of products. Over the past few years, organizations have begun to embrace an even broader mindset towards risk-based thinking, motivated by the growing discipline of regulatory compliance and an increasing number of unexpected global events that have impacted their operations.

When manufacturers want to implement a new production line, they are examining all of the possible risks and scenario planning for every reasonable action that could either prevent or mitigate a risk if it materializes. Some people call this business continuity, risk management or disaster management. Nothing has brought these concerns more to top of mind than the past few years of dealing with trade wars, the pandemic, extreme weather and supply chain shortages.

Risk Management Checklist

Risk management is about the practical investment in preventative and mitigating measures that can be tapped during a crisis. There are four main areas to consider when building a risk management or business continuity program:

Risk Assessment. The first action to take is to put a stake in the ground in terms of what could go wrong at each plant, whether it happens to be a fire, earthquake, chemical spill, cyber attack or something else. This will vary for different regions. The possibility of an earthquake impacting operations in California is much higher than in Alabama. An act of terrorism may be more likely to happen in certain countries versus others.

Let’s say a manufacturer is setting up a new production line. The first step would be to complete a risk assessment form that spans different areas – Employee Health and Safety, Finance, HR, IT, Operations and Program Management. Based on the guidelines provided, the person completing the form identifies possible issues and potential impacts – this could be anything from production or shipment delays to impacts to employee health and safety. Then a threat rating is assigned between 1 and 5 for both occurrence and impact, with 5 being a critical situation that warrants the most attention.

Then, preventative and mitigating measures are determined based on factors that could contribute to the adverse event. Are there inadequate controls, lack of monitoring or poor training that might add to a problem? Could these areas be improved to either prevent or lessen the potential impact? While an earthquake isn’t preventable, an organization could retrofit their building and upgrade IT systems to ensure that people are safe and can still perform their job duties if a temblor hits.

Incident Management & Business Recovery Planning. Building out all of the details for incident management and business recovery is essential, if not glamorous. A contact list needs to be created so that a key lead can contact all affected employees, customers and suppliers during a disaster. Getting customers and suppliers in the loop early could enable them to become part of the solution. A call notification script should be drafted that provides consistent communications to impacted parties and decisions need to be made about whom gets told what in certain scenarios. Checklists and drills should also be included, such as how to safely clear employees from a facility.

 

Internal Audit Checks. Once the business recovery plan is drafted, it should be audited annually. This ensures that the right action plans are included and the correct project leaders and backup leads are identified and verified. Each section, such as advanced planning, revision histories and recovery priorities, must be evaluated as part of the audit to ensure that there’s a solid plan in place and that all participants are properly trained and on board with the approach.

 

Test Exercise. Every plant should run through a drill for their highest-priority emergencies to evaluate preparedness. They must be able to prove that there’s a data IT recovery capability and have a rough idea of what can be done for a customer in the scope of the test exercise. If work needs to be moved to another location, are they able to confirm the backup plant’s capacity and a timeline for the transfer? Do they understand the open orders that need to be transferred? How does the detailed recovery plan work in terms getting operations back up and running?  For each action, what would be considered a success and how soon? A sample objective would be to get access to a site within one hour and have at least 80 percent of the team notified within the hour of a situation.

After running a drill, evaluating its effectiveness and making improvements to the plan and communicating it to the team should occur. If actions such as getting access to the site, notifying the team, understanding orders, getting alternate facility confirmation and knowing the right customer contacts can all be demonstrated during the exercise, then the majority of functional activities are ready to go, even if the actual crisis requires some fine tuning of processes. Just like the overall plan, the test runs should be performed at least once a year to verify their continued relevance.

Preventing Problems Before They Happen

At Sanmina, we are seeing increasingly robust expectations for risk management programs across the markets that we serve. Customers are more eager to get involved in understanding the details of these plans than ever before and are considering them an integral part of their manufacturing strategy.

It’s vitally important to understand potential risks, evaluate the scope and effectiveness of an action plan and cultivate a living risk management process that is periodically reviewed and updated. It’s also critical to instill a preventative mindset within an organization’s culture because it’s not always an intuitive thought process. While fixing a problem in the moment may be beneficial, it’s important to build a mindset that’s not just about corrective thinking but a proactive approach that identifies potential root causes that could help prevent or lessen a problem that may occur in the future.

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April 5, 2022

Source: Jon M. Quigley | Value Transformation LLC

There is no silver bullet.

Cost of Manufacturing

Businesses are constantly under cost pressures, this includes manufacturing.  Material and labor cost can be a significant cost in the product.  We use value engineering techniques to ensure that material costs are as low as we can while still meeting the performance and quality objectives of the customer and the organization.  Besides the labor costs, automation can improve quality.  I shake when I hear people use the words always and never.  People are not that repeatable; automation can lead to a very repeatable outcome if done appropriately.

Automation is usually not an all or nothing proposition.  It is possible to automate some of our manufacturing line and still require people in support of the manufacturing line.  The level of investment to automate balanced against the level of human talent available to keep the manufacturing line working.  Up front investment of capital, juxtaposed against hourly rate to work the line, and both often exist. That is where things can become difficult.  We may believe that automation is the savior for reducing the associated labor costs, but unless the line is fully automated, we will still have people working the line.  This can present some challenges.

Modern lines are not only automated, but can also perform the measurements, not just on some of the parts, but all the parts and automatically.  This reduces the cognitive and physical load upon the team members. This is perhaps a good thing, but does not help when it comes to engaging the team.  I have heard managers say just this, and is one of the reasons for writing.

Team Engagement

Have you ever been part of a team? I do not mean what most refer to as team, but those rare occurrences when we are thoroughly engaged and more importantly, so too our team mates.  If you have had these rare moments, you understand the power of team engagement.  A business makes progress not through technology, but through the creativity and talent of the team members.  It takes these things to determine the best approach to automation.  No problem here.  However, what happens to the level of engagement for those remaining on the manufacturing line?  I have been interested in employee engagement for years now, a good place to view employee engagement over the years, is the Gallup Workplace Engagement.  I have been following this poll for many years, and some of this is quite scary.  Their poll breaks down into the three categories below[1]. 

Engaged – work with passion and emotionally attached to organization. Generating new ideas and consistent performance to move the organization forward.
Not engaged – put their time in, but no passion or energy into their work.
Actively disengaged – unhappy and resentful, they spread negativity within the organization.

Lack of engagement limits the available creativity and talent; this is not the way to continuous improvement or competitive advantage. 

Team members do not have the same demand for their attention on the manufacturing line as those lines that are largely manual.  This reduction of focus on the manufacturing line should be a good thing in that it provides time for our team to explore other improvements and creative ideas.  Yet in discussions with managers of manufacturing lines we find that keeping the team engaged to be difficult.  Reduced stress due to a high degree of focus on the line, should make it possible to improve the organization at large.  It is in everybody’s best interest to find ways to engage our team members beyond the focus of the automated manufacturing line.

What can we do?

It is not in our organizations best interest and longevity to have unmotivated talent.  The level of automation of the manufacturing line may place limits the amount of team engagement maintaining a manufacturing line.  If the line is not extensively automated, we may be able to improve it through the creativity and talent of our team, engagement.  If we can improve the line, we can use a host of techniques and develop a culture of continuous improvement constant learning.  If the line automation is significant but not entirely, we can focus the talent supporting the line on other areas of the organization.

Operations improvement (logistics)
Process improvement
Quality improvement
Value Stream Mapping
Value Engineering (or other product / process cost improvement)

The approach need not be any of these, or any other formal regiment. The point is to find a motivating objective, and develop an environment that will not only allow, but value team exploration and learning.  In fact, there are good arguments for moderating the formalism, making easier for the team to explore alternatives.  If increased engagement can be obtained through some home-grown approach that will be the best solution.

Conclusion

Competition does not end because we have automated our manufacturing line.  True competitors are constantly striving to improving.  Developing a line that balances the capital investment along with the labor burden at time t=0 (start of manufacturing) does not necessarily mean the end improvements.  This is a fact to which suppliers to the automotive Original Equipment Manufacturing (OEMs) are all too aware.  From experience, OEMs go to their tier 1 suppliers and “request” cost reductions annually.  Also from experience, these decreases in sale price are often contractual obligations.  Even when the production runs are 10 or more years long with little or no additional investments post launch.  Continuous improvement does not end when we have automated the line.  Creativity does not come from automation, but from our team and continuous exploration.  The on-going concern is best served not through an over reliance on automation, but the talent, creativity, constant learning, and engagement of the team members.  Not apathy.

Jon M. Quigley PMP (204278) CTFL is a principal and founding member of Value Transformation, a product development (from idea to product retirement) and cost improvement organization established in 2009. Jon has an Engineering Degree from the University of North Carolina at Charlotte, and two master’s Degrees from the City University of Seattle as well as two globally recognized certifications. Jon has more than thirty years of product development and manufacturing experience, ranging from embedded hardware and software through verification and process and project management. Jon has won awards such as the Volvo-3P Technical Award in 2005 going on to win the 2006 Volvo Technology Award.

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Click Here to Access the Full Webinar

With increasing pressures on pricing, speed, individualization, and sustainability brought on by customers and competitors, already- intricate supply chains & manufacturing processes are becoming more and more complex.

A resilient business needs digitalized manufacturing operations, customer service and supply chains to run with speed and flexibility.

Lean manufacturing (also known as lean production, just-in-time manufacturing and just-in-time production, or JIT) is a production method aimed primarily at reducing times within the production system as well as response times from suppliers and to customers. Central to the concept is the elimination of waste or activities which add no value to the process. And this in turn provides a basis for operational excellence by standardizing processes and creating a culture of continuous improvement by monitoring, proactively maintaining equipment and empowering employees.

Industry 4.0 refers to the digital transformation of industrial processes through Industrial Internet of Things (IIoT) and cyber-physical systems – smart, autonomous systems that use computer-based algorithms to monitor and control physical things like machinery, robots, and vehicles.  

In this workshop, we shall discuss and showcase use cases where Industry 4.0 meets lean manufacturing – with the aim of increasing operational efficiency!

SPEAKERS:

Sujit Hemachandran – Sr Lead, Industry 4.0 Digital Transformation, SAP Labs
Sujit is a technologist who specializes in understanding and helping customers adopt technologies and applications. He is currently engaged with SAP customers in the discrete and process sector to help them on their Industry 4.0 and industrial IoT journeys. His experience with SAP software includes strategizing and developing various enterprise technologies and platforms, such as mobile, integration, IoT, and cloud communications.

Ben Hughes – Industry 4.0 Hub OT Specialist, SAP Labs
Ben has 18 years of engineering experience in a wide range of industries. His experience includes working with many manufacturers to deploy automation solutions on plant floors, developing automated residential lighting systems, and developing custom control systems for the US Navy and Coast Guard. During his time off, Ben enjoys spending time with family, travelling, and reading, and is a volunteer with his son’s boy scout troop.

Jack LaMaina – Industry 4.0 Hub Specialist, SAP Labs
Jack graduated from The College of New Jersey with a Major in Business Management and a Minor in International Studies. Early in his career at SAP, Jack supported the Mission Control Center before taking on the role of a Solution Advisor for SAP’s Customer Experience Suite of solutions. Jack supports SAP customers by bridging the gap between business and technology, turning complex problems into value creating opportunities for customers by showing the power of SAP software in support of customers’ digital transformation goals.

Matt Ruff – Industry 4.0 Hub Specialist, SAP Labs
Matt graduated with an engineering degree and spent his early career learning the ins and outs of cocoa & chocolate processing. Matt has ?5+ years of consulting experience with SAP’s Manufacturing Portfolio & is a previous owner of SAP’s Model Company for Connected Manufacturing. Currently, Matt is focused on optimizing and integrating business processes and showcasing SAP’s strengths to customers.

Vivekananda Panigrahy – Industry 4.0 Hub Specialist, SAP Labs
Vivek is a full stack developer of SAP Business Technology Platform with 9+ Years of Experience with a strong focus on Design Patterns, Solution Architecture and Problem-Solving Skills to the best of Customer Success. Currently, Vivek supports SAP’s Digital Supply Chain Industry 4.0 Innovation Hub team helping customers throughout their Digital Transformation Journey.

Fill out the form to access the full webinar






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March 29, 2022

Source:  Jane Marsh, Editor-in-Chief at Environment.co | Manufacturing Tomorrow 

In the U.S., manufacturing new goods accounts for nearly a quarter of all carbon emissions. By keeping goods in use for as long as possible, businesses can both cut down on their emissions and create economic opportunity.

Circular economy practices that prevent goods from going to landfills – by encouraging reuse or recycling – can help. However, these practices aren’t always easy to implement. Right now, brands are experimenting with Industry 4.0 technology that may streamline the circular approach.

How Technology Can Help Businesses Build the Circular Economy

Developing a circular economy will require a variety of different practices and new business strategies. Technology may make these practices much easier to implement.

For example, design for reusability or recyclability is one way for businesses to keep goods in the economy. If a device or product is easy to reuse or break down into recyclable components, both individuals and businesses may be more likely to reuse or recycle.

Design for recycling isn’t a new concept, but it can be challenging to implement for some devices. New design tools and design automation technology may help make design for recycling much more practical.

Some recyclers and manufacturers are also using Industry 4.0 technology like AI to streamline recycling or the design process. The pattern-finding abilities of AI can help manufacturers create designs that are more recyclable.

In other cases, circular economic practices may look similar to the preventive maintenance that many businesses already perform. Vendors of yard equipment, for example, often recommend certain end-of-year maintenance practices that can keep tools working well.

In other industries, manufacturers can work with their customers to encourage preventive maintenance practices, which can keep tools and equipment running for much longer.

These practices can have benefits for both customers and manufacturers – customers get a product that lasts longer, and manufacturers can develop a reputation for creating reliable tools. Technology like maintenance scheduling tools and equipment management systems may help both manufacturers and customers keep on top of essential maintenance.

Put together, these circular economic practices and technologies may help a wide variety of businesses reduce their carbon footprint or adopt more environmentally responsible policies.

It’s no secret that many major corporations struggle with environmental stewardship. Businesses like Ikea, Apple, Walmart, and Microsoft have all come under fire for policies that generate excessive carbon emissions or exploit vulnerable ecosystems.

Circular economic practices can help these businesses – and businesses of all sizes – adopt greener, more sustainable practices.

These Businesses Are Already Using Technology to Create a Circular Economy

While ideas about the circular economy continue to develop, some businesses have already begun experimenting with advanced technology as a building block for the circular economy.

One major adoptee of the circular approach to manufacturing is Cisco, a multinational technology company best known for its networking and cybersecurity solutions.

Katie Schindall, leader for the circular economy at Cisco, recently spoke with the magazine Tech Monitor about how the company is using technology to develop its own circular economy. According to Schindall, the right systems can have a significant impact.

“Optimising manufacturing processes for maximum reuse and tracing the embedded emissions in components and materials are both information problems that data and automation can help to address.”

Cisco isn’t the only company using modern industrial technology to develop its circular economy.

Ikea, for example, has recently rolled out a new buyback program for used furniture – which could help offset some of the environmental impacts of manufacturing new furniture.

Many footwear brands, including Puma and Adidas, are beginning to experiment with shoes made from fully recycled polyester. Fashion company H&M is exploring both fully recycled clothing materials and the use of recycled food waste in manufacturing clothing.

New Technology May Help Drive the Circular Economy

Sustainability is likely to become even more important in the future – and younger consumers, in particular, want to shop with sustainable brands.

Because manufacturing new goods is typically a carbon-intensive process, businesses can make themselves much more sustainable by building a circular economy.

Almost any practice that keeps goods in the economy can help. Recycled materials, buyback programs, and even initiatives that encourage preventive maintenance can all help businesses reduce their carbon footprint and create new economic opportunities.

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Fog computing extends the concept of cloud computing to the network edge, making it ideal for internet of things (IoT) and other applications that require real-time interactions.

Published on NetworkWorld By   Jan 17, 2018

Fog computing is the concept of a network fabric that stretches from the outer edges of where data is created to where it will eventually be stored, whether that’s in the cloud or in a customer’s data center.

Fog is another layer of a distributed network environment and is closely associated with cloud computing and the internet of things (IoT). Public infrastructure as a service (IaaS) cloud vendors can be thought of as a high-level, global endpoint for data; the edge of the network is where data from IoT devices is created.

Fog computing is the idea of a distributed network that connects these two environments. “Fog provides the missing link for what data needs to be pushed to the cloud, and what can be analyzed locally, at the edge,” explains Mung Chiang, dean of Purdue University’s College of Engineering and one of the nation’s top researchers on fog and edge computing.

According to the OpenFog Consortium, a group of vendors and research organizations advocating for the advancement of standards in this technology, fog computing is “a system-level horizontal architecture that distributes resources and services of computing, storage, control and networking anywhere along the continuum from Cloud to Things.”

Benefits of fog computing

Fundamentally, the development of fog computing frameworks gives organizations more choices for processing data wherever it is most appropriate to do so. For some applications, data may need to be processed as quickly as possible – for example, in a manufacturing use case where connected machines need to be able to respond to an incident as soon as possible.

Fog computing can create low-latency network connections between devices and analytics endpoints. This architecture in turn reduces the amount of bandwidth needed compared to if that data had to be sent all the way back to a data center or cloud for processing. It can also be used in scenarios where there is no bandwidth connection to send data, so it must be processed close to where it is created. As an added benefit, users can place security features in a fog network, from segmented network traffic to virtual firewalls to protect it.

Applications of fog computing

Fog computing is the nascent stages of being rolled out in formal deployments, but there are a variety of use cases that have been identified as potential ideal scenarios for fog computing.

Connected Cars: The advent of semi-autonomous and self-driving cars will only increase the already large amount of data vehicles create. Having cars operate independently requires a capability to locally analyze certain data in real-time, such as surroundings, driving conditions and directions. Other data may need to be sent back to a manufacturer to help improve vehicle maintenance or track vehicle usage. A fog computing environment would enable communications for all of these data sources both at the edge (in the car), and to its end point (the manufacturer).

Smart cities and smart grids Like connected cars, utility systems are increasingly using real-time data to more efficiently run systems. Sometimes this data is in remote areas, so processing close to where its created is essential. Other times the data needs to be aggregated from a large number of sensors. Fog computing architectures could be devised to solve both of these issues.

Real-time analytics A host of use cases call for real-time analytics. From manufacturing systems that need to be able to react to events as they happen, to financial institutions that use real-time data to inform trading decisions or monitor for fraud. Fog computing deployments can help facilitate the transfer of data between where its created and a variety of places where it needs to go.

Fog computing and 5G mobile computing

Some experts believe the expected roll out of 5G mobile connections in 2018 and beyond could create more opportunity for fog computing. “5G technology in some cases requires very dense antenna deployments,” explains Andrew Duggan, senior vice president of technology planning and network architecture at CenturyLink. In some circumstances antennas need to be less than 20 kilometers from one another. In a use case like this, a fog computing architecture could be created among these stations that includes a centralized controller that manages applications running on this 5G network, and handles connections to back-end data centers or clouds.

How does fog computing work?

A fog computing fabric can have a variety of components and functions. It could include fog computing gateways that accept data IoT devices have collected. It could include a variety of wired and wireless granular collection endpoints, including ruggedized routers and switching equipment. Other aspects could include customer premise equipment (CPE) and gateways to access edge nodes. Higher up the stack fog computing architectures would also touch core networks and routers and eventually global cloud services and servers.

The OpenFog Consortium, the group developing reference architectures, has outlined three goals for developing a fog framework. Fog environments should be horizontally scalable, meaning it will support multiple industry vertical use cases; be able to work across the cloud to things continuum; and be a system-level technology, that extends from things, over network edges, through to the cloud and across various network protocols. (See video below for more on fog computing from the OpenFog Consortium.)

Are fog computing and edge computing the same thing?

Helder Antunes, senior director of corporate strategic innovation at Cisco and a member of the OpenFog Consortium, says that edge computing is a component, or a subset of fog computing. Think of fog computing as the way data is processed from where it is created to where it will be stored. Edge computing refers just to data being processed close to where it is created. Fog computing encapsulates not just that edge processing, but also the network connections needed to bring that data from the edge to its end point.

[ Related (NetworkWorld): What is edge computing and how it’s changing the network ]

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Based on actual users’ experience with IoT platforms, here are the leading features and functionalities potential users should be looking for.

Article published on NetworkWorld by , Contributor, Jan 16, 2018

As an IoT platform and middleware analyst, I am asked constantly about the benefits of IoT platforms and “what makes a great IoT platform.” In response, I often ask these curious inquirers if they’ve ever used IoT platforms themselves. Walking on the edge is exhilarating, but having hands-on insights, data and expertise on how to survive the journey is even better.

What do users actually experience when they use IoT edge platforms?

IoT edge computing is a technology architecture that brings certain computational and analytics capabilities near the point of data generation. IoT edge platforms provide the management capabilities required to deliver data from IoT devices to applications while ensuring that devices are properly managed over their lifetimes. Enterprises use edge platforms for factory automation, warehousing/logistics, connected retail, connected mining and many other solutions. With IoT platform revenue slated to grow to USD63.4 billion by 2026, IoT edge is one of the most highly relied upon enterprise IoT platform approaches.

Enterprises spend a tremendous amount of time completing edge-related IoT platform activities. According to hands-on tests of IoT platforms in MachNation’s IoT Test Environment (MIT-E), the majority of an enterprise user’s edge-related time is spent creating visualizations to gain insight from IoT data. 35% of a user’s time is spent creating dashboards with filtered alerts. And a combined 16% of a user’s time is spent viewing sensor data for an individual device (8%) or a group of devices (8%). Data from an IoT platform are critically important, so the ability to assemble dashboard sensor data and alerts are key – expect to spend a lot of time doing it.

Since the edge is critical for enterprises deploying IoT solutions, we’ve identified the top five user requirements of IoT edge platforms, based on IoT platform users’ experiences with these platforms.

1. Pick a platform with extensive protocol support for data ingestion

To seamlessly bring data from devices into the edge platform, enterprises should choose leading IoT platforms that support an extensive mix of protocols for data ingestion. The list of protocols for industrial-minded edge platforms generally includes brownfield deployment staples such as OPC-UA, BACNET and MODBUS as well as more current ones such as ZeroMQ, Zigbee, BLE and Thread. Equally as important, the platform must be modular in its support for protocols, allowing customization of existing and development of new means of asset communications.

2. Ensure the platform has robust capability for offline functionality

To ensure that the edge platform works when connectivity is down or limited, enterprises should choose leading IoT edge platforms that provide capabilities in four functional areas. First, edge systems need to offer data normalization to successfully clean noisy sensor data. Second, these systems must offer storage to support intermittent, unreliable or limited connectivity between the edge and the cloud. Third, an edge system needs a flexible event processing engine at the edge making it possible to generate insight from machine data when connectivity is constrained. Fourth, an IoT edge-enabled platform should integrate with systems including ERP, MES, inventory management and supply chain management to help ensure business continuity and access to real-time machine data.

3. Make sure the platform provides cloud-based orchestration to support device lifecycle management

To make sure that the edge platform offers highly secure device management, enterprises should select IoT platforms that offer cloud-based orchestration for provisioning, monitoring and updating of connected assets. Leading IoT platforms provide factory provisioning capabilities for IoT devices. These API-based interactions allow a device to be preloaded with certificates, keys, edge applications and an initial configuration before it is shipped to the customer. In addition, platforms should monitor the device using a stream of machine and operational data that can be selectively synced with cloud instances. Finally, an IoT platform should push updates over-the-air to edge applications, the platform itself, gateway OSs, device drivers and devices connected to a gateway.

4. The platform needs a hardware-agnostic scalable architecture

Since there are tens of thousands of device types in the world, enterprises should select IoT platforms that are capable of running on a wide range of gateways and specialized devices. And these platforms should employ the same software stack at the edge and in the cloud allowing a seamless allocation of resources. Platforms should support IoT hardware powered by chips that use ARM-, x86-, and MIPS-based architectures. Using containerization technologies and native cross-compilation, the platforms offer a hardware-agnostic approach that makes it possible to deploy the same set of functionalities across a varied set of IoT hardware without modifications.

5. Comprehensive analytics and visualization tools make a big difference

As we’ve already discussed enterprises should choose IoT platforms that offer out-of-the-box capabilities to aggregate data, run common statistical analyses and visualize data. These platforms should make it easy to integrate leading analytics toolsets and use them to supplement or replace built-in functionality. Different IoT platform users will require different analyses and visualization capabilities. For example, a plant manager and a machine worker will want to access interactive dashboards that deliver useful information and relevant controls for each of their respective roles. Having flexibility in analytics and visualization capabilities will be essential for enterprises as they develop IoT solutions for their multiple business units and operations teams.

Enterprises worldwide are using IoT to increase security, improve productivity, provide higher levels of service and reduce maintenance costs. As they seek to adopt IoT solutions to improve their critical business processes, they should conduct hands-on usability tests to understand edge platform capabilities. Keep watching as more and more enterprises start walking on the edge.

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It may be time for the U.S. government to step in to coordinate security standards across all the players that participate in creating the internet of things, Frost & Sullivan says

Article published on NetworkWorld by , Senior Writer, Jan 15, 2018

Thanks to the Mirai botnet attacks, few people in the world of tech need a reminder that IoT devices remain a serious threat to enterprise networks. Still, more than a year after the botnet made headlines worldwide, IoT security remains mostly an idea, rather than a reality.

Such is the scope of the problem that Frost and Sullivan IoT research director Dilip Sarangan argues for governmental intervention. Sarangan says that, because the responsibility for IoT security is diffused across device manufacturers, network providers, software developers and many others, it’s difficult for the industry to make progress on all-encompassing standards.

“The only entity that has the ability to actually dictate what the minimum threshold is, unfortunately, is the U.S. government,” he said.

The difficulty in creating overarching standards mostly has to do with the fact that any given IoT implementation has a large number of moving parts, each of which may be administered by different organizations, or even by third parties. For example, a set of medical devices provided by company A connecting to a network provided by company B, running an application, originally written by company C and residing in company D’s cloud.

“Everyone talks about it like they’re going to provide end-to-end security, and there’s actually no way to do that,” said Sarangan. “You have no control over a lot of parts of an IoT solution.”

Network visibility

From the networking side, Sarangan said, there are plusses and minuses to most of the options available to any given IoT implementation. Cellular networks, for example, tend to be a lot more secure than Wi-Fi, ZigBee or the other wide-area options, but a company will probably have much more limited visibility into what’s happening on that network.

That, in and of itself, can be a security issue, and it’s imperative for the carriers to provide more robust device management features in the future.

“What type of device it is, what type of information it’s supposed to send, where it’s supposed to send the data, what you are supposed to do with that data – until you know all of that, it’s hard to be completely secure,” said Sarangan.

Improved network visibility is key to preventing worst-case scenarios like malicious actors accessing power grids and Internet infrastructure, but so are common-sense measures like air gaps.

“You have the hacks happening, but the hacks haven’t been significant enough to where you’d worry about it,” he said. “The other side of it is that a lot of critical infrastructure – let’s say a smart grid – is on private networks.”

A sea of IoT devices

A lack of quality control and the presence of a host of very old devices on IoT networks might be the most critical security threats, however. Decades-old hardware, which may not have been designed to be connected to the Internet in the first place, let alone stand up to modern-day security threats, creates a serious issue.

“You have over 10 billion IoT devices out there already … and a lot of these devices were created in 1992,” noted Sarangan.

Moreover, the huge number of companies making IoT-enabled hardware makes for a potentially serious problem where quality control is concerned. Big companies like Amazon and Microsoft and Google make headlines for their smart home gizmos, but the world of IoT is a lot broader than that.

China, in particular, is a major source of lower-end IoT devices – speakers, trackers, refrigerators, bike locks and so on – and it’s not just the Huaweis and Xiaomis of the world providing the hardware.

“[There are] hundreds of mom-and-pop shops out there developing hardware that we don’t necessarily know whether to trust or not – these are devices that are getting on unsecured Wi-Fi networks,” said Sarangan. “That’s already a security threat, and a large portion of Americans don’t actually protect their routers.”

Indeed, hidden backdoors have already been found on some such devices, according to The Register.

Posted on Posted in Digital security, Digital transformation, IoT, What's on Tagged with ,

Article written by Brian Krebs, published on KrebsOnSecurity the 18th Jan. 2018

Most readers here have likely heard or read various prognostications about the impending doom from the proliferation of poorly-secured “Internet of Things” or IoT devices. Loosely defined as any gadget or gizmo that connects to the Internet but which most consumers probably wouldn’t begin to know how to secure, IoT encompasses everything from security cameras, routers and digital video recorders to printers, wearable devices and “smart” lightbulbs.

Throughout 2016 and 2017, attacks from massive botnets made up entirely of hacked IoT devices had many experts warning of a dire outlook for Internet security. But the future of IoT doesn’t have to be so bleak. Here’s a primer on minimizing the chances that your IoT things become a security liability for you or for the Internet at large.

-Rule #1: Avoid connecting your devices directly to the Internet — either without a firewall or in front it, by poking holes in your firewall so you can access them remotely. Putting your devices in front of your firewall is generally a bad idea because many IoT products were simply not designed with security in mind and making these things accessible over the public Internet could invite attackers into your network. If you have a router, chances are it also comes with a built-in firewall. Keep your IoT devices behind the firewall as best you can.

-Rule #2: If you can, change the thing’s default credentials to a complex password that only you will know and can remember. And if you do happen to forget the password, it’s not the end of the world: Most devices have a recessed reset switch that can be used to restore to the thing to its factory-default settings (and credentials). Here’s some advice on picking better ones.

I say “if you can,” at the beginning of Rule #2 because very often IoT devices — particularly security cameras and DVRs — are so poorly designed from a security perspective that even changing the default password to the thing’s built-in Web interface does nothing to prevent the things from being reachable and vulnerable once connected to the Internet.

Also, many of these devices are found to have hidden, undocumented “backdoor” accounts that attackers can use to remotely control the devices. That’s why Rule #1 is so important.

-Rule #3: Update the firmware. Hardware vendors sometimes make available security updates for the software that powers their consumer devices (known as “firmware). It’s a good idea to visit the vendor’s Web site and check for any firmware updates before putting your IoT things to use, and to check back periodically for any new updates.

-Rule #4: Check the defaults, and make sure features you may not want or need like UPnP (Universal Plug and Play — which can easily poke holes in your firewall without you knowing it) — are disabled.

Want to know if something has poked a hole in your router’s firewall? Censys has a decent scanner that may give you clues about any cracks in your firewall. Browse to whatismyipaddress.com, then cut and paste the resulting address into the text box at Censys.io, select “IPv4 hosts” from the drop-down menu, and hit “search.”

If that sounds too complicated (or if your ISP’s addresses are on Censys’s blacklist) check out Steve Gibson‘s Shield’s Up page, which features a point-and-click tool that can give you information about which network doorways or “ports” may be open or exposed on your network. A quick Internet search on exposed port number(s) can often yield useful results indicating which of your devices may have poked a hole.

If you run antivirus software on your computer, consider upgrading to a “network security” or “Internet security” version of these products, which ship with more full-featured software firewalls that can make it easier to block traffic going into and out of specific ports.

Alternatively, Glasswire is a useful tool that offers a full-featured firewall as well as the ability to tell which of your applications and devices are using the most bandwidth on your network. Glasswire recently came in handy to help me determine which application was using gigabytes worth of bandwidth each day (it turned out to be a version of Amazon Music’s software client that had a glitchy updater).

-Rule #5: Avoid IoT devices that advertise Peer-to-Peer (P2P) capabilities built-in. P2P IoT devices are notoriously difficult to secure, and research has repeatedly shown that they can be reachable even through a firewall remotely over the Internet because they’re configured to continuously find ways to connect to a global, shared network so that people can access them remotely. For examples of this, see previous stories here, including This is Why People Fear the Internet of Things, and Researchers Find Fresh Fodder for IoT Attack Cannons.

-Rule #6: Consider the cost. Bear in mind that when it comes to IoT devices, cheaper usually is not better. There is no direct correlation between price and security, but history has shown the devices that tend to be toward the lower end of the price ranges for their class tend to have the most vulnerabilities and backdoors, with the least amount of vendor upkeep or support.

In the wake of last month’s guilty pleas by several individuals who created Mirai — one of the biggest IoT malware threats ever — the U.S. Justice Department released a series of tips on securing IoT devices.

One final note by the author (Krebs): I realize that the people who probably need to be reading these tips the most likely won’t ever know they need to care enough to act on them. But at least by taking proactive steps, you can reduce the likelihood that your IoT things will contribute to the global IoT security problem.