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Contributions of our algorithm team to our consulting services

On how to deal with the ‚Human Factor‘ 

Summary:
Every consultant worth his fee soon recognizes that consulting is about humans, not about numbers
– or is it?
Indeed, according to our experience, obstacles in the smooth flow of businesses are rarely monocausal. Its not ‚the market‘, ‚the competition‘, ‚the globalization‘, at least, not alone. Almost always there is the so-called ‚human factor‘, be it in your organization or somewhere else.
Nevertheless we, as do our clients, like very much dealing with numbers. We all like the illusion of control, numbers offer. Comparability, benchmarking, having something defined, something easy and crystal clear, to calculate with and to count on. So our algorithm team thought about this discrepancy between the understandable wish and the obvious reality and came over with an interesting approach.

First, it’s not ‚the people‘, it’s what they do, how they do it and why they do it – which is, as a matter of fact, very close to being the people themselves, but seems easier to grab somehow.
Second, people are not acting in a vacuum, but are inter-acting with someone ore something (aka their context) – and this is, where good or bad things happen.
Third, for that sort of thing, a model already exists! ‚My printer is not talking to my notebook‘. Sounds familiar? And the technician says ‚it’s an interface problem‘, and indeed, it is:
Interfaces, the separating and, at the same time, connecting spaces between entities, where the magic happens.
Since there are well known methods on how to detect and measure technical interface failures, our team tried to abstract from them and to apply them to the border zones between people and ’something‘ in our client’s companies.
QSSA was born (from the German ‚Quantitative SchnittStellen Analyse‘, being Quantitative Interface Analytics‘ in English). Quite a lot of work had to be done nonetheless, to adopt those technical principles to the ’soft factors‘ and their conditions. Here our team received great support and inspiration from Ms. Jakubaszek, at that time student in Economic Geography at the University of Tübingen, Germany, doing an internship  at [thstech].
After months of refining the algorithms, testing and re-testing methods, it meanwhile has shown its value in many business cases. 
More recently (2018/2019), the method has been extended by Ms. Jakubaszek, to include the principles of abstract spaces and distances, coming from Economic Geography, thus giving additional value to QSSA.

 

QSSA: what, how and why?

Summary:
QSSA is a method, used in translating the so-called ’soft parameters‘ that occur within an organisation’s environment and that are relevant for the smooth flow of business therein, into numbers. Numbers that are quantitatively comparable to one another.
The necessary data can be extracted from quantitative interviews and be then transferred into a subject- and company-specific, multidimensional matrix. The values themselves are normalized and a set of calculations is conducted on each value in relation to all the other values in the matrix.
The resulting quotients (row-quotient, column quotient and over-all quotient) lie within a range of -1 to +1 and form a quantitatively comparable number.
These numbers can not only be compared within the same setting (organisation), but also between different settings (organizations), thus e.g. allowing for benchmarking and further inspection.
QSSA can both be used to capture a snapshot of a given situation and/or to monitor changes over time. They therefore also provide an instrument of assessing the outcome of  measures taken.

On how to use this method in assessing the quality of interactions within the socalled ‚interaction spaces‘ aka ‚interfaces‘, we strongly recommend the perception of the masterthesis by M. Jakubaszek „Untersuchungen an Schnittstellen in einer wissensbasierten, standort-übergreifenden Organisation“ at Universität of Tübingen, March 2019.

Further methods, we apply, include MAS and aCA-simulations, combined with pattern recognition by ANNs and ML.

In case you’d be interested in this method and want to know how it can be applied in your organization’s environment, do not hesitate to contact us.