Autonomous cars might be road ready in less than five years, but it will take another decade before the general public are allowed to buy them, according to Ford’s head of research, Ken Washington.

The comment adds to the growing fear that automakers will not sell autonomous vehicles to customers, instead relying on ride-sharing and shuttle services. There are rumors that Ford will use its FordPass service for ride-sharing or launch a new platform soon.

See Also: Industry split on when first commercial self-driving vehicle will be ready

Ford CEO Mark Fields said customers will be able to purchase autonomous cars by 2025, making Washington’s new estimate of 2026 to 2031 rather conservative.

“It’s really hard to guess and predict the pace of the technology,” said Washington at SAE WCX World Congress Experience. “Our current view is the adoption rates will be relatively gradual.”

Still skipping Level 3 autonomy

Ford has already confirmed it will skip Level 3 autonomy, the mid-level between full human and driverless control. That was seen by some as a decision to avoid customers taking control of the vehicle. It will instead shoot for Level 5, which would revoke all human control.

The company stepped up its investment in the self-driving industry last month with the acquisition for Argo AI for $1 billion, to be paid over five years. It looked like a major acquisition of talent from Ford, to keep up with Google, Tesla, and other tech firms.

At the event, Washington insisted that the auto-industry are not behind when it comes to autonomous tech innovation. He also said that tech firms are now looking for auto partnerships, to “bring it home.”

Companies in the manufacturing sector for years have been striving for lean production or processes to create more efficient operations. One of the latest trends in technology, the emergence of the Internet of Things (IoT), could give lean efforts a major boost.

Lean manufacturing, a systematic method for eliminating waste within a manufacturing system, is based on the concept of making obvious what adds value by reducing everything else. It’s a management philosophy that stems mainly from the Japanese manufacturing sector, and specifically Toyota Production System, which focuses on the reduction of waste to improve overall customer value.

Lean encompasses a set of tools that help in the identification and steady reduction of waste. And as waste is eliminated, quality improves and at the same time production time and cost are reduced. The ultimate goal of lean is to get the right things to the right place at the right time and in the right quantity, in order to achieve perfect workflow while minimizing waste and being flexible.

The Internet of Things involves the linking of physical objects such as devices, consumer products, vehicles, corporate assets, buildings and other “things” via the Internet. These “smart” objects are embedded with electronics, sensors, actuators, software and network connectivity that allow them to gather and share a variety of data and respond to control messages.

The IoT enables connected objects to be sensed and controlled remotely via an existing network infrastructure. This connectivity creates opportunities for a more direct integration of physical objects with digital systems. The potential benefits include increased efficiency, improved product development and enhanced customer service—to name a few.

The potential scope of IoT is enormous. Research firm Gartner Inc. has estimated that 6.4 billion connected things were in use worldwide in 2016, up 30% from 2015, and 5.5 million new things were being connected every day. The firm forecasts that the total number of connected things is forecast to reach 20.8 billion by 2020.

In the enterprise, Gartner considers two classes of connected things. One consists of generic or cross-industry devices used in multiple industries, and vertical-specific devices found in particular industries. Cross-industry devices include such items as connected light bulbs and building management systems.

The other class includes vertical-specific devices such as specialized equipment used in hospitals and tracking devices in container ships. Connected things for specialized use are the largest category, but this is quickly changing with the increased use of generic devices, Gartner says.

Taking lean to the next level

Within the context of building IoT-based manufacturing solutions, IoT opens up all kinds of possibilities, such as the ability to monitor the performance of products after they have been purchased to ensure adequate maintenance and customer satisfaction, optimizing supply chain logistics and streamlining the distribution chain. Information about product usage can be fed back to companies so that they can analyze the data to make improvements in design and production.

With this constant exchange of data, combined with the new automation technologies that are emerging and advancement in data analytics, manufacturers can achieve the dream of the truly “smart factory”.

IoT intersects with lean methodology and has the potential to take lean to the next level. The information gleaned from connected devices, including users’ experiences with a variety of products, can be fed back to instrumented factories to provide unprecedented opportunities to enhance manufacturing processes and reduce waste.

As consulting firm Deloitte has stated, “in operating the existing business, IoT and analytics are helping companies to connect a diverse set of assets. This results in efficiency gains throughout the manufacturing process.”

The firm describes a number of areas in which efficiencies can be added. One is through the acceleration of planning and pre-manufacturing. The processes of choosing suppliers, considering risk and managing material costs can be fine-tuned through the interconnectivity IoT and analytics bring, Deloitte says.

“Analytics can deliver insight to help companies gain a better understanding of customer preferences and desires, potentially resulting in improved predictability and performance in the marketplace,” Deloitte says. “Understanding the products, and the specific features, that are being purchased allows companies to plan production to meet market needs.”

Another potential benefit of IoT is streamlining the manufacturing process, which is changing dramatically as more companies incorporate IoT and analytics capabilities. “Predictive tools and machine learning allow potential problems to be identified and corrected before they occur,” the firm says. “The value of lean manufacturing and just-in-time processes like Kaizen and Kanban improves exponentially” when intelligence obtained via IoT and analytics can be applied.

And a third area where IoT can add value is in improving post-manufacturing support and service. In the past, Deloitte says, manufacturers often lost track of their products once they were sold. Now, because of new levels of connectedness and the greater insights provided by IoT and analytics, manufacturers can gather information from their customers effectively while improving service and support in the aftermarket.

The benefits of IoT for lean manufacturing extend well beyond processes within a single organization. IoT can help optimize the interaction of manufacturers and their business partners, enhancing the flow of materials along the pipeline based on more accurate data on product demand and usage. An IoT service creation and enrichment platform such as Accelerite Concert can go a long way in making such collaborations happen.

Manufacturers will be able to fully realize production efficiencies that were extremely difficult and in some cases impossible to achieve through traditional, manual processes.

Dean Hamilton, Senior Vice President and General Manager of the Service Creation Business Unit, Accelerite

The vital need for analytics

Organizations that successfully leverage the Internet, mobile technology, business analytics, digital performance dashboards, and integrate other enabling technology with strategic improvement end up with a much more advanced version of lean and continuous improvement in general, according to Terence Burton, president and CEO of The Center for Excellence in Operations Inc., a management consulting firm.

Enterprises “need a higher order paradigm of lean to benefit from these complex emerging technology-enabled innovations in business models, rather than suffer the inevitable waste creep and margin erosion,” Burton says. “The Internet of Things will undoubtedly play a large role in evolving lean to a higher order, enterprise-wide and technology-enabled paradigm of improvement.”

The potential benefits IoT can deliver for manufacturers stem from improved availability of timely and precise data. The ability to instrument, at low cost, almost every aspect of the manufacturing process and to deliver that data quickly to business stakeholders via the Internet is already transforming business operations and business models. But the promise of an evolved “higher order paradigm of lean” is entirely dependent on manfacturers’ ability to derive meaningful insight from data.

As valuable as IoT data can be for manufacturers’ lean efforts, it’s important for them to keep in mind that having enormous volumes of information will not necessarily be of help if they don’t have a timely and effective way of analyzing the meaning and context of the data.

Only advanced analytics and artificial intelligence (AI) technologies (such as machine learning and predictive maintenance), combined with the flexibility, processing and storage capabilities of cloud computing, will give manufacturers the ability to optimize IoT data and leverage it as part of their lean methodologies.

The smart factories of tomorrow will need to deploy a next-generation, cloud-based, big-data analytics platform that enables them to use newly acquired information to the fullest. The platform should be capable of analyzing structured as well as unstructured data, both at-rest (in databases) and in-flight (from streaming data sources) and include a single tool for data acquisition, storage, transformation, AI and visualization.

Manufacturers need to be able to drill down into IoT data via easy to understand dashboards, so they can find patterns and detect anomalies that can directly contribute to creating more lean operations. They need to be able to quickly identify useful correlations and make inferences that can lead to enhanced processes.

While business intelligence (BI) and data visualization tools are nothing new, current technologies often require the use of data analysts, BI developers and ETL developers before insight can be exposed to business users. The next generation of analytics tools, such as Accelerite ShareInsights will place more power in the hands of business owners and subject matter experts who fully understand the factory processes instead of data scientists and programmers. They also will be made accessible to factory operations teams and development teams, who can help provide an integrated flow of data to make products and processes more efficient.

Ultimately, the most significant transformation in how lean methodologies will be applied to smart factories will come from the use of AI to perform sophisticated forms of big data analysis that are impossible for human analysts. AI algorithms now drive semi-autonomous vehicles; recommend what we should watch on TV, read or listen to; recognize our speech patterns and faces; diagnose our illnesses and so much more.

These algorithms are not just capable of learning; they are also capable of detecting patterns, correlations and anomalies in large data sets that would go undetected by humans. They’re able to predict the behavior of complex, inter-connected systems and recommend the optimal course of action to accomplish a particular goal.

This type of capability will be especially important as manufacturers move toward product personalization, where products can be catered to specific users and predictive insight will be needed to configure production lines and supply chains in the most efficient manner.

The next generation of IoT analytics will place the power of AI directly in the hands of business stakeholder to drive continuous optimization. And AI-powered lean methodology will not simply be better at eliminating waste that inevitably creeps into complex systems; it will predict that waste before it occurs and take steps to ensure that it never does.

Manufacturing in the future will be about building the product the customer wants at just the right time, and together lean processes, IoT, big data analytics and AI will allow the smart factories of tomorrow to operate with unprecedented efficiency.

This article was produced in partnership with Accelerite. The author is Senior Vice President and General Manager of the Service Creation Business Unit at Accelerite.

Internet of Things (IoT) is changing the world around us. This may look like a bold statement to many, but that’s the truth. From elevators to planes, there will be 30 billion connected devices in the next three years. Clearly, IoT is moving fast, and tech giants are taking bold measures to constantly push the boundaries of what can be achieved with the IoT.

The recent Genius of Things Summit that was organized in Munich, Germany was a demonstration of how IBM’s unrivaled IoT ecosystem is changing the way we live, work, and play. Here are a few notable developments that caught my interest.

Digital twin

The traditional way of conceptualizing, designing, and developing a product is time- and resource-intensive. Digital twin is an initiative to improve the efficiency of the process by using cloud-based virtual image of an asset maintained throughout the lifecycle. The asset, in turn, remains accessible to every individual involved in the process, thereby allowing people to work collaboratively, reduce errors, and improve efficiency.

Airbus and Schaeffler are the two companies that are currently using digital twin engines and bearings. Airbus is using this technology to create a digital thread that allows different engineering divisions to collaborate. This way, in case a problem is discovered, the company can explore whether the product is due to inadequate maintenance, poor manufacturing, or a fault in the design.

Cognitive commerce

The ultimate objective of cognitive commerce is to offer a truly personalized service to customers based on their precise preferences. To achieve this, a wide spectrum of technologies is used, from speech recognition to machine learning.

Visa is working in collaboration with IBM to offer its customers the flexibility to make payments from any IoT connected device. This, in turn, will eliminate the need to carry sensitive financial information embossed on payment cards, thereby making the customer journey simpler, easier, and more secure.

Predictive maintenance

As the name implies, predictive technology analyzes the data collected using sensors to predict the maintenance needs of an asset. This, in turn, improves asset availability, reduces maintenance costs, and improved customer satisfaction.

SNCF, which is a leading freight and passenger transport service, has collaborated with IBM to connect its entire rail system to the IoT ecosystem. Using the data collected from sensors, SNCF will be able to predict repair and maintenance needs of its trains and tracks, improve the security and availability of its assets, and reduce the downtimes associated with unexpected downtimes.

While the Internet of Things (IoT) has the power to change our world, we are still at the beginning of the transformational journey that will revolutionize the way we live and work for the better. In the next few years, we can expect to see incredible advancements being made by tech giants, such as IBM and other companies.

We live in a cyber-vulnerable world – a world governed by data. Data encapsulates almost every aspect of our personal and public life. It is heavily shared, distributed, stored and accessed, and it is constantly at risk. Recent mega-hacks, such as the ones on Target, Yahoo, and Ashley Madison, among others, demonstrate that leaking of personal information and misuse of our data are inevitable in a world that is becoming increasingly more connected and data-centric.

As concerning as these hacks are, we should be aware that at the end of the day, the risk is merely the misuse of data. Without question, attacks such as these can have devastating effects on people’s lives, but their outcome is limited to the virtual world and cannot touch the physical world, at least not directly.

See also: The U.S. VA protects your hacking cough from hacking

This is all changing now as our world is gradually developing into a cyber-physical one. Wikipedia defines a cyber-physical system as one where physical and software components are deeply intertwined. That is, physical systems control, and are controlled, by electronic components.

A good example of a cyber-physical system is a modern car, with approximately 60% of its costs associated with electronic engineering. There are often more than 100 electronic units in a modern vehicle that affect critical mechanical and physical components, such as brakes, the steering system and the door lock mechanism.

Another example is a manufacturing plant that is not only monitored by software, but to a large degree, is also controlled by it, often via remote wireless interfaces.

These are only two examples, but many others exist in countless industry verticals, such as intelligent transportation systems, medical devices and home automation — aka the smart home.

Not the same as IoT

A cyber-physical system is closely coupled with, but is not synonymous with, the Internet of Things (IoT). IoT devices are typically the controllers of the cyber-physical domain. They use one or multiple connective technologies (e.g. cellular or Bluetooth) and are governed by service providers or user applications on a mobile device. For instance, the iPhone application provided by your vehicle manufacturer enables you to unlock your car or start the engine remotely. The Amazon Echo smart speaker app that controls your home lighting is another good example.

What is common to these examples is that they allow us, as end users, to wirelessly manipulate physical functions.

Our control over these systems is terminated at the IoT controller (the vehicle telematics system or the smart speaker, respectively). The IoT controllers communicate with physical objects using two key elements – sensors and actuators. Sensors measure properties of the physical world (e.g. the temperature of a centrifuge) and report them to the controller. Actuators manipulate the physical world (e.g. keep the car in its lane) at the command of the controller.

The glue that connects all of this is software in the form of code and data, and it is software, not love, that makes our 21st Century world go ’round. Therein lies the problem – software code inevitably has bugs and design smells, which can potentially lead to serious security issues – what we would call exploitable.

The number of actual exploitable bugs per 1,000 lines of code depends on numerous factors, including what research you care to read. But even by the most optimistic research, the average vehicle, with some 100,000,000 lines of code, is likely to have a few thousand exploitable bugs – that’s what we call a hacker’s paradise.

Before we panic, get rid of our cars and revert to horse-drawn carriages, it is worth noting that cars are safe and that this number does not necessarily represent a few thousand ways in which your car could be manipulated to kill you as you are driving. To truly assess how vulnerable a system is to attack it needs to be thoroughly analyzed. There are many factors to take into account, such as attacker motivation and access to the target. Still, the overall concern is valid, and no less of a valid question is “how did we get here?”

My answer might catch you by surprise. I think that it was almost inevitable that we arrive at this point. History has shown, time and again, that markets are driven by features, with security lagging behind. When the first computers were connected to the Internet, only a handful of experts thought of the issues of its inherent insecure architecture. Of course, they were largely ignored because they didn’t contribute to the bottom line.

The result was that we gradually became acquainted with a world of mystical cyber-creatures, such as viruses, worms and malware. It took time for the industry to react, because it’s always harder to add security to a production environment. Gradually, antivirus programs and firewalls were invented and we managed to move on. PCs are not without their security issues, but the economy didn’t collapse and the sky didn’t fall.

The next wave of connectivity

A similar path could be traced as we moved on to the next wave of connectivity – smart mobile devices and the evolution of IT systems. The security guys are always blowing their whistles at the gate, shouting that we must build in security at the initial design stages, but market drivers are pushing for features, more of them and faster.

Is the same happening in the IoT world? Recent data suggests that this is indeed the case. So, you might be comforted by the premise that we let the dynamic of technology run its course, adding in security as an afterthought, the same way we did in the past. However, there is one very important observation, one which I would like to be the takeaway of this article – the tolerance for error falls dramatically every time we become more dependent on software.

We have managed to overcome loss of data on our personal computers and hacked phones. We struggle with colossal data breaches to credit cards databases and our most personal information. Will we be able to cope with a real mega-hack that compromises, or worse yet, irrevocably harms, our physical world?

A better idea might be to invest in security by design. Those experts might be right this time around.

VB Profiles Connected Cars Landscape

This article is part of our connected cars series. You can download a high-resolution version of the landscape featuring 250 companies here.

Is industrial IoT new or old?

It’s a more complex question than you think. If you’ve experienced the advertising barrage of the past decade, you might have concluded that large industrial companies are just discovering the power of data. Ports are being rigged with sensors and software to optimize boat traffic. Cities are saving millions by switching to smart LEDs that cut energy and reduce maintenance demands. People are scurrying up and down escalators in an artsy blur.

The underlying message is that the 19^th century has suddenly discovered the 21^st.

But when you dig into it, you’ll soon discover that data—and systems animated by data—have been an essential player on the factory floor for decades. The first industrial robot, Unimate, went live in a General Motors facility in 1959. SCADA systems for controlling industrial equipment have been around so long many have forgotten, or never learned, what the acronym stands for. Oil and gas producers played a big part in bringing technologies like GPS and data visualization to the mainstream.

Here’s another fun fact: the average age of a transformer in the U.S. is now around 43 years and some are 70, or well beyond the 35-year length of their warranties. (The picture you’re looking at is a collection of pipes and pumping equipment from a wastewater treatment center in San Francisco. The equipment dates back to the 1950s. It’s no longer in use but it’s still in place.)

So what’s the right answer? Both.

See also: Industrial IoT set to turbocharge lean manufacturing

What we are seeing in industrial IoT is a marriage of young and old. Manufacturers aren’t racing out to replace aging equipment with intelligent new models. Instead, they are grafting on IoT gateways and wireless radios to effectively get the same (or better) results with far fewer headaches. Old becomes young again.

Take, for example, J.D. Irving. The 135-year old Canadian conglomerate is the fourth largest supplier of frozen French fries, produces paper for magazines like Vogue and plants approximately 20 million trees a year in Canada’s largest reforestation project.

It also makes toilet tissue. At its factories, a large roll of luxuriously soft paper goes in one end of a piece of equipment called a log saw and out comes a cascade of identical six-inch rolls. Even though they are located in factories, however, logs saws are “remote,” i.e. the information inside the control system is effectively landlocked. Replacing the existing system to would have cost close to $31,500, mostly due to labor and cabling.

Adding wireless sensors ran $9,600, says Keith Flynn of RtTech Software, which collaborated with J.D. Irving on the project. As a result, the company can now do things like vibration analysis, preventative maintenance or peak power management. Wireless also opens up options for things like managing traffic for the automated forklifts.

Likewise, rail operators are looking at ways of rigging rugged wireless sensors onto freight cars to prevent mishaps and conduct forensics better.

Not everything is a retrofit

Not every situation is a retrofit. Dell monitors power consumption and equipment health of its micro-modular data centers with IoT gateways. Think of the micro modular as your neighborhood Netflix outlet. Carriers and content providers will install these to locate the most popular videos (or business documents) closer to users to cut down lag time and telecommunications costs.

Are retrofits the answer to everything? No. Many of these assets were put into place decades before computer viruses were weaponized. Installing gateways means reviewing and tightening security policies. Companies will also have to go through the process of determining whether they want to keep and analyze most of these data locally and in-house, or whether to outsource it.

Still, the declining cost and increasing sophistication of sensors and algorithms, along with the growing portfolio of analysis platforms, mean that wireless upgrade will increasingly become the de facto choice. It’s simply an easier way to experiment.

And that means that a lot of equipment nearing retirement age will get a new lease on life.

The author is a technical analyst at OSIsoft.