Big data has also reached materials management
Among the buzzwords that have recently filled the titles of the trade press, the terms Internet of Things, Industry 4.0 and Big Data stand out. And when I think of big data, I primarily associate it with the Googles, Facebooks and mobile apps of this world; data octopuses that collect all the information they can get their tentacles on in a morally bad way. Big data is, as Viktor Mayer-Schönberger from the Oxford Internet Institute characterizes it, “when insights into reality can be gained from a large amount of data that could not have been gained from a smaller amount”.
In my field of work, materials management, there are also large amounts of data in our companies that could provide us with insights for optimizing our materials management strategies that we would not gain from smaller amounts of data. Insights that companies lack today if they do not have the appropriate big data tools to analyze them.
In addition, the problem with such big data applications is that the causalities remain hidden in the large number of random correlations found. The forest of pseudo-truths hides the tree of knowledge; obfuscation is the neologism for this.
But let’s take a look into the future, which has already begun. The first tools for gaining strategic insights from the wealth of material management data and thus making planning and control processes more economical and efficient have long since emerged from the laboratory and have proven themselves in practice.
In order to avoid the problem of obfuscation, the core of the methodology consists of simulations that use empirical data to check how different settings of planning, control and scheduling parameters and master data affect the profitability of the material flow through the value chain. On this basis, the entire value stream through a supply chain can be strategically optimized and economically improved.
The approach is quite comparable with other simulation approaches, e.g. the crash simulation of newly developed car bodies in the CAD system. However, the value stream is not crashed against ideal-typical obstacles (= planned requirements), but against the real empirical requirements situation of the past. Such a materials management simulation does not replace the expert who can interpret the simulation results and draw conclusions from them. However, optimization processes are drastically accelerated, risks are significantly reduced, far better quality results are achieved and planning and scheduling processes can be massively automated.
On the one hand, the simulation results are mapped in scheduling rules. On the other hand, particularly dynamic parameter settings can be checked and automatically readjusted using simulation processes. The results in trading, procurement and distribution go further than in production. The companies Hansa-Flex (hydraulic system provider), Trost (independent automotive aftermarket) and STO (facade paints and external thermal insulation composite systems) in particular are far ahead of the sluggish masses.
No other field of activity in materials management offers such leaps in improvement. When do you jump?
