The Internet of Things (IoT) is not something you will experience as such itself. What you will see is that more and more objects become connected. If you are manufacturing products, you will be negotiating with providers of connectivity and in the very near future with any tiny part of the artefact that you are intending to build.
We are at the beginning of a manufacturing revolution, a five-year cycle as we can see from what happened to the products itself in the supply chain. In the 2013 Introduction to the Springer book Enabling Things to Talk one of the things that struck me most was a meeting with Prof. Dr. Michael ten Hompel, Managing Director at Fraunhofer-Institut for “Materialfluss und Logistik”, who described the consequences for something as “solid” as logistics:
“The logical consequence of the Internet of Things is not just a new philosophy of how we can control our production and logistics. It completely changes the paradigms of conventional supply chain management. Within the Internet of Things the supply chain will be created in real time: Entities, consisting of objects and a piece of (agent based) software, generates the resulting supply chain ‘on the move.’ Therefore the sequences of operations are not predicted. This leads to a new understanding of how to handle our logistic management which won’t be a supply chain (!) anymore.”
He was referring to what happened to the products, the 'goods', as an entity it makes the supply chain transparent, visible and controllable, enabling intelligent communication between people and cargo. The manufacturing revolution that #IoT enables and makes inevitable will start in earnest by enabling this kind of intelligent communication between all individual parts and a product that is forever in beta, as it exists in real-time, and as such can be updated and personalized to the task at hand or the preferences of an enduser.
This, like Internet of Things itself, is an ontological shift. You will need a different kind of mindset then the one that got you to the place where you are now, wherever you are. A kind of reset? Yes, and hopefully without the breakdown or depression (What am I losing? Why is this happening, I cannot change myself or my ways of working every month!)
This shift is already happening. A June 2016 PricewaterhouseCoopers (PwC) report, found electronic manufacturing services (EMS) companies offering new services moving into "new models of joint design manufacturing (JDM) and outsourced design manufacturing (ODM).” “What Is the Biggest Challenge Facing the Manufacturing Industry Today?” the 2017 Industry Week Salary Survey, asked. In the category of 21-29 year olds a reply was "balancing the roles of machine and human interaction in intelligent manufacturing environments.” In the category 40-49 this response of a "female, Environmental Health and Safety management, 11-15 years in manufacturing." is worth quoting in full: “I think it is lack of flexibility, meaning that manufacturers don't seem interested in changing their product, approach, etc. in order to keep up with changes in the world. I understand that people do not like change but if we don't change approaches and even products, we will never adapt to the world as it is now. In my industry, more time is spent blaming government, laws, and people than figuring out with a way for the company to remain innovative and relevant in the current economy.” In the 50-59 and beyond category remarks point to the lack of new business models and the still prevailing focus on quick wins: “The biggest challenge is the raising pressure to manufacture with less and less cost in order to be able to compete with the low salary countries. As long as this is not being recognized by government and companies and companies only follow the idea "How can I get rich fast?" more and more manufacturing jobs will disappear.”—male, IT/IS management, 11-15 years in manufacturing. As well as: “Although it has varied in degrees of the years, I still think the biggest challenge is the low return on the capital invested for most manufacturing entities. Not very attractive to Wall Street nor the current individual expectation to get rich quick.”—R&D/product development, aerospace & defense."
Seemingly, the overall political and economic situation is not favorable to innovation and investment. According to the 2017 Industrial Manufacturing Trends: "Many will take a wait-and-see approach, delaying capital expenditure investments until more clarity on actual policies emerges", as "Global demand for manufactured products is growing at a snail’s pace. Output is expected to increase just 3.1 percent in 2016 and 3.4 percent in 2017, according to the International Monetary Fund."
Actually, this only refers to the situation the experts above are complaining about. It does not reflect upon the entirely new business models and models of manufacturing itself that are on the verge of happening. Very few companies are foreshadowing the trends, but it is not a surprise that one of the most interesting ones stems from a background combining interest in how machines learn (deep learning) and how people learn. Landing.AI is a Silicon Valley startup founded by Andrew Ng. He oversaw the development of Stanford University’s main MOOC (Massive Open Online Courses) platform. According to a recent Zinnov report, The Spring of AI: Leveraging Collective Disruptor Insights, the US is the dominant innovation hotbed for “AI start-ups led by the Bay Area, building an AI-first future with $10B in acquisitions, 300+ patents and 30K AI talent.” All techno-ecosystems are leveraging AI into their data-lakes, predictive maintenance and Big Data analytics, in itself the logical consequence of the Turing computing evolution that not just facilitates but creates new potential and statistical realities. The hardest part in the Enigma/Ultra journey was not cracking the code as such but maintaining a moral compass and ethical balance in deciding which actions that were ‘knowns’ could be acted upon and how without alerting the ‘competition’ that their knowns were common good. Having the data and having the information might have meant having knowledge at some point in the isolated room of ‘full clarity’, but putting that knowledge to actual situational use leading to a defined ‘win’ or ‘advantage’ for that same set of actors, were and are two different things entirely. Especially because it becomes clear to all in the ecosystem that there are losers too, sometimes known to you before the losers know themselves. Dealing with that kind of clarity and focus without a systemic vision and a set of principles is impossible as the process of optimization, efficiency and transparency will always take you out as an actor in the near-final iteration of digital transition. The ‘extra’ we are installing with every step along the way always becomes more insightful as the position that installed and kick-started the ‘innovation’ project.