Production has been interested in efficiency for a long time and in the last few years, logistics has also become more and more interested in work productivity. In many companies, the most apparent improvements may already be done, either through lean or automation. So, what to do next? Is digitalization the next step?
Sometimes, trends and fashionable words remain a bit vague, even if they sound positive, if you do not familiarise yourself with them. For example, Industy 4.0 is a framework that is quite difficult to define clearly. In my mind, one excellent interpretation is by Aachen University. In it, we do not approach the matter through traditional digitalization methods but through the purpose, i.e. information. In the more traditional depictions, methods, digital environment, IoT, cloud solutions and so forth are discussed in conjunction with Industry 4.0. But the whole point is the purpose, not the tools. Janne Viinikkala from Leanware has coined the term pilipalisaatio, which he uses to depict the result of an aimless digitalization. Same goes for all enhancement and work. Why something is done is a more interesting question than how it is done and this is something the marketing sometimes forgets.
For example, a smart sensor is a completely useless investment if its operations do not advance the purpose. By itself, RFID is an excellent device for the implementation of wireless identification, when you have a operational reason for it. So, if we approach the term Industry 4.0 from information’s viewpoint, the matter becomes clearer and starts to sound like something you should strive for. In that sense, the road to Industry 4.0 consists of four steps.
The majority of companies have passed computerisation and networking by. That brings us to the first stage of the Industry 4.0 framework; we are striving for visibility in the current situation. At the start, the methods might include reports and, for those more advanced, operative systems (MES and WMS, for example). Some have already advanced to the next stage, analytics. The aim has moved to understanding and analysing the current situation. At the moment, only a few people can in the current situation predict what will happen, and even fewer have overall systems that can automatically optimise the overall picture. There is no shortcut to this stage and therefore the advancement has been a little slow, even though the subject matter is not new. So, you cannot optimise if you cannot predict what each change will do and you cannot predict, if you do not understand why things happen and you cannot understand, if you do not know what is happening. The first stages of digitalization are not pointless, they are necessary.
Artificial intelligence, which I refer to in the headline, is there to serve the purpose of predicting the future and of optimising the result within the limits of resources and load. I have used three factors of which the lasting efficiency in industry and logistics consists: efficient processes, functioning systems and daily management. When the processes and systems are in order, we at Leanware see the tools of daily management as an especially interesting area for improvement. For example, as a result of international research, the time management problems of work supervising have come up. Clearing things up and running around take up too much time and management and development too little. The researches have identified the situation of successful companies to be the almost complete opposite, which means that we can assume that they have time to both manage matters and people and develop processes. The researches also prove that the successful companies are further ahead in the digitisation of systems, which frees up time for development and management.
Daily management is an area that Industry 4.0 also aims to enhance with digitalization. With the help of artificial intelligence and analytics, we can free up work management’s time for developing processes and managing people. Nowadays, the predicting is often a gut feeling completely based on experience and guesswork and optimising some separate area causes more harm than good, if it does not take the overall picture into account. From this viewpoint, artificial intelligence and machine learning in the logistics area are starting to become very interesting tools. Already, the LeanwareWMS system optimises product placement through machine learning and we are adding in more directive and smart features for route optimisation and combining. This does its part in freeing up time from warehouse maintenance work and work planning. The system makes the maintenance optimisations automatically. In addition, there is another implementation area for artificial intelligence; resources and goals. The work managerial view into the progression of the day and resource needs brings much-needed transparency and predictability into the operations. In future, the systems can, based on the competence and performance of the personnel present, give estimates on the day’s revenue in relation to the load, on the achieving of the day’s goals and on the focusing of the resources. In October, as a part of this work, we were the first in logistics to start POC testing gamification with a customer (ALSO Finland Oy), of which further information is available on the gamification blog. For efficiency and learning, the combining of production information with competence and training opens interesting viewpoints into the flexible processing of different situations.
Naturally, we at Leanware want, as the industry’s bellwether, to research new methods and implement them immediately. So, watch this space!
