Why Do Software Bugs Continue to Plague Products?

By | Automotive, PLM, Strategy | 4 Comments

Pesky Software Problems Plague Many Products

It seems not a day goes by without reading about yet another software bug that inflicted a catastrophic (or, at times, just ridiculous) malfunction on an everyday product.

In these conversations about software quality problems, the auto industry is often singled out. Indeed, consumer complaints about vehicle software systems have been growing steadily over the past several years, and numerous automakers, including Volvo, Nissan, and Volkswagen, have  initiated large  recall campaigns to remedy software defects. Even Tesla, that usually gets immediate praises for almost everything it does, isn’t immune from releasing faulty software controlled systems (although Tesla does a superior job in fixing software defects via over-the-air updates).

But not only cars suffer from software malaise. General Electric’s refrigerators, too, require software updates to remedy errors that hamper the appliance’s most basic operations, and Samsung’s connected fridge allows hackers to steal a consumer’s Gmail login information.
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Metamorphosis of Narcissus (Salvador Dali, 1937)

Digital Transformation and the Internet of Things

By | Internet of Things, IT Strategy, PLM | No Comments

The Internet of Things Enables Digital Transformation

I recently returned from a trip to Munich, Germany where I spoke at the Product Innovation Congress. In additional to the usual product innovation, PDM and PLM topics, this year’s Congress highlighted technologies, business strategies and use cases that use the Internet of Things (IoT) as a means to create a digital thread of product information to support product creation and service strategies.

As you might expect, a discussion on IoT and, in fact, any technology to drive enterprise digital transformation can generate a frustratingly wide range of definitions and, consequently, vague characterization of its benefits and economic justification.

During a panel discussion, I suggested that digital transformation isn’t simply a matter of establishing a new IoT-centric enterprise architecture. Rather, I maintain that we should look at the impact of the information and the digital thread enabled by the IoT in the context of organizational operations and decision making processes. Here is the approach I propose. You can use this to identify opportunities and assess potential value in a way akin to a maturity model. Read More

2016: Trends, Predictions and Opportunities

By | Automotive, PLM, Service | 5 Comments


The Industrial Internet of Things (IIoT): Still More Talk Than Walk

The torrent of breathless headlines, rosy economic predictions and novel business ideas will continue in 2016, but, overall, there will be more talk than walk. At the same time, the promise, even if overly optimistic, is real, meaningful and worth pursuing, and the number of companies exploring it will continue to grow.

Excitement over IIoT, bolstered by increased corporate budgets, will continue to fuel new initiatives and projects, although they will be mostly driven by the lines of business, resulting in one-off non-scalable implementations. Lack of adequate standards, and sometimes the availability of multiple of standards will contribute to the proliferation of “limited edition” IIoT.

Security and privacy concerns will linger.  Demonstrations of hackable devices and vulnerable industrial networks, especially with the United States presidential election campaigns that keep cybersecurity and rogue forces in the public eye, will impede large scale implementations, especially of public IIoT systems.  Companies that offer robust methods for securing the IIoT will get the attention of investors and industrial companies alike.

In 2016, mature companies will finally get over the addiction of counting connected devices as a proof of the economic impact of the IIoT.  They will begin to focus on the value these connected devices can bring and the data analytics and decision support systems necessary to harvest this potential. 2016 will bring opportunities in predictive analytics, machine learning and complex decision support methods. I certainly expect to see the continuation of the wave of acquisitions in this space, most of which of early-stage companies with no customer base to speak of and debatable market valuation.

Thomas Koulopoulos maintains that the IIoT “will alter the nature of business in ways that will make the industrial revolution look like a speed bump on the road towards automation.”  We are still at the beginning of the journey, but heading in the right direction.

Service Lifecycle Management (SLM) Gets Its iPhone Moment

Service used to have a lackluster image of yet another cost-of-doing-business function. Yet, all of a sudden, service is cool. IIoT vendors are quickly adopting the jargon of SLM and talking about equipment uptime, unscheduled maintenance, MTTR, and truck rolls as enthusiastically as when they peddle network equipment and analytics software.

In fact, SLM is one of the most meaningful and credible use cases of IIoT. IIoT-enabled products offer innovative service business models and elevate SLM to become a strategic business function rather than an unavoidable operations tax. Furthermore, connected devices offer a practical platform for advanced analytics, augmented reality (AR) and similar “cool” technologies.

IIoT is reinventing traditional service. Service seems to finally nearing its iPhone moment.

In 2016 and beyond, service VPs and business leaders must use this momentum to educate the rest of corporate management on the financial and operational opportunities afforded by IIoT-based SLM in order to lead further investments in service technology.

Connected Cars: Battle for the Screen

To the dismay of Detroit and other global automakers and suppliers, industry outsiders, from Tesla to Google to Uber, continue to challenge the status quo and have an increasing influence on the future of connected cars, infotainment systems, and consumer engagement. This will not change in 2016.

But the second half of the decade will be a challenging time for companies such as Google and Tesla that will have to crystalize their position and prove the commercial viability of their disruptive technologies and business models. The more obvious examples are Google getting robotic cars into commercial operation and Tesla managing capacity demand and maintaining profitability in global operations. Although different, both of these examples focus on establishing effective manufacturing supply chains.

More new cars will have connectivity than not. But the having the capability to connect doesn’t guarantee consumers will embrace it.  Cost, security and privacy concerns and, above all, weak and irrelevant customer value and engagement models lead many customers to opt out or simply not use the service.

There will be new highly advertised demonstrations of vehicle hacking, further distancing mainstream buyers from connected cars.

But at the heart of the problem is an outdated connected car model that requires consumers to pay again for services they are already receiving on their smartphone at a much higher quality and ease of use.

With connectivity becoming a standard feature in new cars, the infotainment systems market is becoming yet another battleground for market share between Apple and Google (Microsoft has probably missed this opportunity for good.)  There’s no doubt that the size of the market for Apple CarPlay and Android Auto is attractive: more than 80 million new cars are sold worldwide every year. But more importantly, controlling the content and user experience of the car’s dashboard screen, means that you maintain persistence presence in the life of the always-connected consumer. In fact, this is the only reason Google and Apple are interested in this space.

OEMs should be mindful of the importance of the consumer’s digital identity and seek balance between connectivity and a few data services they need to own, and those areas where an open platform offers better and more cost-effective answers to consumers’ demand. Connectivity will soon become an expected commodity. OEMs that do not offer an open, responsive and safe infotainment platform will lose the battle on the screen and to consumers’ hearts.

Transforming Product Innovation and Development

Digital technology and in particular software as a source of new business growth and innovation is having a transformative effect on product design, manufacturing, operations and service across most industrial sectors.

PLM vendors will continue to lead the digital transformation crusade, rekindling a space that has gotten used to an awkward status quo between Autodesk, Dassault Systèmes, PTC and Siemens PLM at the top, PLM offerings from Oracle and SAP, and a number of much smaller yet influential smaller vendors. The efforts to create differentiation and grow market share in a quasi-stable market will increase in 2016. Expect more activities and growth—both organic and acquisitions—in Industrial Iot, the digital twin, augmented reality (AR), 3D printing, service lifecycle management (SLM), analytics, and more.

Leading PLM companies will continue to redefine and stretch the traditional definition of PLM, breathing a new life into PLM concepts that until now were mostly on paper, and technologies that were unable to articulate a credible use case. I am afraid, however, that this will produce more of those dreaded PLM conference talks in which the opening sentence is “let me give you my definition of PLM.”

While 2016 will brings new functionality to the PLM portfolio, one of the most critical gaps in product innovation and development is not going to be closed. The rush of PLM companies to acquire functionality (often in the form of overvalued early stage companies with no customer base to speak of) only adds to the fragmentation of an already complex product development process, fueled by myriad tools and many Excel spreadsheets. This is an opportunity for PLM and ERP companies to establish leadership by integrating the disparate tools and synthesizing data from multiple enterprise tools and data repositories to optimize product related decisions.

Another area to watch for in 2016 is the proliferation of predictive analytic applications, especially in the general context of SLM. Implementing an industrial-strength predictive maintenance system is a difficult task.  But the rewards are significant. Initially, it will be difficult to recognize the leaders of the pack, in part because of the loose fashion in which some vendors define “predictive”, the potentiality long implementation time, and the tendency of this type of projects to die a slow death rather than declare failure.

Product companies should envisage how the digital transformation will focus and improve the efficiency and success rate of innovation. For the many product companies who will have pursued this transformation successfully, there will be many companies (and VCs) that fall victim to the eye candies enthusiastic software vendors dangle in front of their eyes.

Knowledge Based Product Development

By | Internet of Things, IT Strategy, Manufacturing, PLM | No Comments

Lack of ongoing insight into product operation and the interaction with users is a major contributor to the persistence of a knowledge gap that plagues many product organizations, reduces efficiency and stifles innovation.

What drives this knowledge gap? A number of general trends impact companies in most industry sectors and product types.

  • Technology complexity and, in particular, the increased reliance on complex embedded control software.
  • Elongated and fragmented supply chains that support global operations.
  • Meeting global markets demand results in lower volume of configuration-specific product instances.
  • Accelerated egress of an aging workforce from the workplace.

The impact of the growing knowledge gap is most recognized and frequently discussed in the context of equipment maintenance and repair, which is a knowledge intensive activity. Another area were closing the knowledge gap has significant benefits is engineering change management.

Organizations often speak about the need to collect and formalize “tribal knowledge” to close the gap between the knowledge needed to perform a complex takes or reach a decision, and the knowledge and experience the person performing this task has access to.

In a previous article I discussed the severe myopia that exists in many product companies: most engineering organizations lose sight of their products once they are sold or installed in the field. In some manufacturing companies this happens even earlier, during the transition to manufacturing engineering and before the product enters volume production. Once a product is in use, there is only a trickle of information in the form of service records and warranty claims. But many organizations dismiss critical maintenance and warranty information as merely operational and cost of doing business and aren’t leveraging its full potential.

Knowledge Based Product Development

Product organizations cannot have a true and complete view of a product and how customers are using it unless they can continue to observe it while it is in use. They must monitor and analyze products throughout their lifecycle, gauge their performance, quality and uptime, how users are interacting with the products, and how well they meet market expectations overall.

This rich multidisciplinary insight extends beyond design information. It includes multifaceted data from a variety of sources and disciplines and includes manufacturing, supply chain, filed operations and service and maintenance. The data can be aggregated from multiple sources, including real-time data such as IoT, maintenance record and customer experience.

The aggregation, classification and analysis of this data provides critical insight to embellish and enhance an organizational knowledge library for subsequent iterations of product design, manufacturing process engineering, and service planning.  reinforced by analytics, case-based reasoning (CBR), and similar tools to collect, analyze and vet information.

Product organizations cannot afford operating with blinds that prevent visibility to downstream processes. They should not ignore the value of information collected throughout the product lifecycle. They must establish knowledge processes, governed by PLM software, to maximize the utility and benefit of product lifecycle information.


Elephant Monument for Champs-Élysées (Jean-Jacques Lequeu, 1758)

Design for IoT or Design by IoT?

By | Internet of Things, M2M, Manufacturing, PLM | No Comments

The Internet, mobile devices and cloud computing are drastically changing manufacturing. Software control systems embedded in smart devices and connected machines form a network of sensors and actuators that exchange information autonomously and can monitored and controlled online.

The potential impact of the Industrial Internet of Things (IIoT) and Industry 4.0 are undisputed. While industry pundits and technology vendors may dispute the exact size and growth rate of the IIoT, there is a general consensus that the space offers significant business value and will be transformative indeed.

Are the smart connected devices—or the “things”—that form the IIoT the same machines and devices we know and understand today, only connected? Or do we need to invent new paradigms to define system functionality and product architecture in order to realize the value of the IoT? Read More