First of all: Industry 4.0 has a lot to do with technology, computers, software, machines, the Internet and intelligent data analysis. These relationships are not unknown, but have been decisive in the industry over the last 30-40 years. We remember how the computer (often a 286 AT) pushed the mechanical typewriter out of the office step by step… and with it everything that belonged to that machine at that time, from Tipp-Ex (with the special smell of solvents) to carbon paper and ink ribbons. The first modems followed suit, which “audibly” connected the office with the Internet and data services. And shortly thereafter, discussions started as to whether and who really needed a color monitor: “Honestly? A color monitor? What’s that good for?”
So changes in the way we work / with what we work are not unknown to us – we tend to forget how much the user’s experience with an interactive system has changed.
Industry 4.0 – Change is not new, but essential
Today is about “smart“ in smart manufacturing. This word represents a revolution in connectivity of industrial components and the related digitization of interactions and information that has to be faced by the industry. Well, whether we are dealing with a revolution or merely an evolution, that leads to a shift of paradigms, is open for interpretation – it will depend how industry, human beings and the society is going to deal with it. To outline the subject of smart manufacturing and UX, I‘m stating some hypotheses – some of those will be known to the inclined reader:
- Users expect the same level of high quality of use in work systems as in systems that are used privately.
- Users are less willing to accept poor quality of use in work systems.
- Users tend to trust new work systems less than privately used systems (e.g.: the fear at work to reveal / document own opinions or activities – but in contrast to that having no fear to post a photo of the delicious, lactose-free cappuccino at the beach bar in Büsum).
- Smart manufacturing requires the reorganization of existing job profiles.
- Smart manufacturing requires a new strategy of communication between people and machines, as well as between people themselves.
What now? This last hypothesis doesn’t sound like smart manufacturing, digitization or innovation at all! Is smart manufacturing going to regulate me, in which way with whom I am allowed to talk to?
Does some mysterious smart manufacturing committee with equally mysterious headquarters in the caves of the Swabian Alb defining a language code for digitization with many as many loan words from English, Denglish and official German as possible? Let’s then only talk like: “Within the framework of the project applicant’s entry, the increase of efficiency for mobile phones and portable use will be realized within the framework of the motion “Super-Push-4-Market” for innovations of Man-Robot-Interaction under the following key KPIs:” (I’ll quit, it hurts me too…).
No, that would be too stupid. Throwing around terms that sound fitting is too obvious in craving for acceptance but also for missing the point. Cheap, faking an non-existent consensus and, naturally, free of any meaning – such is the way smart manufacturing becomes an empty shell, void of substance and purpose. Happens so often, diminishing opportunities and renders the subject uncontrollable.
But we are facing also another challenge: Within the context of smart manufacturing we locate a topic like “”interpersonal processes and communication” in system ergonomics and human resources.
Quite a few managers have been surprised by this topic – isn’t digitization just about technology & networking? No, in fact, as with all issues in which humans play an essential role, the ergonomic dimension of human-system interaction is at stake. Whenever machines, intelligent bots and assistants take over work from humans, when they take over communication channels, or when consensus building is based on data and algorithms, the character of the interpersonal communication has to change fundamentally. Instead of exchanging information about individual activities within a task or data quality, giving work instructions or obtaining corrective information, human-system communication is being transformed into a channel of metainformation. This means that information between people will include not so much the product but the system at a higher level of abstraction. An example from the “Stone Age” of the computers should illustrate this. In those “good ol’ days”, a user had to use a command line to tell a system which individual, specific parameters a command (SORT) needs to produce a special result:
SORT /R /+2 d:\samplesort.txt /O d:\output.txt
As of today, the same result can be achieved via natural language in a digitized environment – the method of sorting is communicated, no longer a multitude of commands:
„Sort the entries in Samplesort backwards after the 2nd letter and save the result to the file Output.”
This range between a regulated, formalized and very reduced command language and the natural, living language can also be summarized by the following example:
For the DOS operating system of the 1980s, there were 78 words that acted purely as instructions. The predecessor CP/M knew only 32 commands in version 3. In the command prompt of Windows 10, however, there are 85 commands available. The so-called PowerShell (the “command line version” of the Windows 10 operating system) provides the user with 485 commands that can be used in conjunction with a scripting language.
On the other hand, there are the natural, spoken languages, whose vocabulary has completely different dimensions. In English between 500 000 and 600 000 words, in German around 500 000, in French around 350 000 words.
Fig1. Example commands of an assembler code. This abstraction of commands to program a system led in earlier forms of human-machine communication de facto to the exclusion of “laymen”
Let speech do its job
Language alone does not solve the problem of a new human-machine communication. Language without context is pointless and not appropriate for tasks. Not only the obvious homonyms like bow, fluke, or bank are meant. Human language is wonderfully inaccurate… and allows interpretations and adaptations to the different life situations.
Some examples to illustrate this:
“What will the weather be like tomorrow?” – that’s a simple question. However, through our learned social information in the context of a conversation, we fill in important (but missing) information. The reference to the place is not explicitly mentioned… Is the question aimed at the current location in which the questioner finds himself? Or is the question a more general one? On the other hand, we assume that the respondent knows which day is today and which day is tomorrow – so the date may be assumed to be a known quantity.
“Which offer is better?” – Very exciting, because this short question is not so easy to answer if we don’t know the criteria for “better”. Should we weigh offers according to price, performance, availability, etc.? And how do we assess the individual characteristics? What is important to me, the questioner?
Tasks that are beyond 0/1, Yes/No, On/Off, True/False require a clear semantic reference. Language makes the major contribution here. But we need to think beyond speech recognition. If we understand the meaning of language as a means of conversation, the human-system interaction is no longer limited to the means of naming things or as “command”, but as a form of dialogue of speech and answer.
Using language as reference for next generation interaction technique, we can also determine the loss of control of third parties over human-system interaction: If I offer an user 10 commands to interact with a system, I can be quite sure that only these 10 commands will actually ever be used. Each deviation leads to an error and non-performance of a task. However, if alternatives to the command (e.g. direct manipulation with a pointing device, synonymous commands, etc.) are possible, I can no longer be sure which way the user has chosen to solve a task. Therefore some solution providers are striving for some kind of formalization of interactions, which is offered in very different interaction technologies due to current technological market conventions. The result of this rift between formalization and variation of interaction techniques may result in assistance systems, which, under the pretext of “support with the most modern methods”, represent nothing else than commands that produce, even enforce, a clear result.
In summary, in the impression of increasing flexibility of interaction techniques, the image of an extreme context-dependent design of human-system interaction emerges. Thus, “the software” can be detached less and less from the overall system and put as independent unit next to the work situation. Instead, the interfaces between man and system, through which communication and interaction take place, become more granular and filigree. The technologies that are applied continue to evolve, become more “intelligent” and require strategic planning of what is intended, not what is feasible. Within the framework of a company’s UX strategy, these technologies are to be understood, planned and implemented as technical competence within that organization in order not to be driven by digitalization, but as designers of it.