Value crea­ti­on in the pro­duc­tion indus­try today is cha­rac­te­ri­zed by capi­tal-inten­si­ve pro­duc­tion sys­tems which are high­ly auto­ma­ted. It is in the natu­re of things that a lar­ge quan­ti­ty of data ari­ses during pro­duc­tion. The­se data are often only used for their ori­gi­nal pur­po­se, con­trol­ling and regu­la­ting the pro­duc­tion pro­ces­ses, and are not archi­ved for other uses. “As long as the machi­nes are run­ning, nobo­dy real­ly loo­ks at this data”, says Jür­gen Wal­ter, Mana­ging Direc­tor of Data­tro­niq, mem­ber of the Indus­tri­al Data Space Asso­cia­ti­on sin­ce the sum­mer of 2016.

Loca­ted in Stutt­gart and Ber­lin, the com­pa­ny is cur­r­ent­ly working tog­e­ther with Fraun­ho­fer IOSB on the use case “For­ward-loo­king main­ten­an­ce and pro­cess-rela­ted qua­li­ty assuran­ce with IDS”. They have set them­sel­ves the task of using data to gene­ra­te spe­ci­fic inst­ruc­tions. “Our aim is to impro­ve the over­all effi­ci­en­cy of sys­tems by ana­ly­sing and eva­lua­ting the data”, exp­lains Wal­ter. Lon­ger ser­vice lives for the machi­nes, fewer inter­rup­ti­ons and if pos­si­ble no down­ti­me, will crea­te com­pe­ti­ti­ve advan­ta­ges for the com­pa­ny. And the­se impro­ve­ments in sys­tem avai­la­bi­li­ty are pos­si­ble if the lar­ge quan­ti­ty of data is not only gene­ra­ted but is made usable.

Jür­gen Wal­ter has found that many com­pa­nies do under­stand this new way of dealing with data in pro­duc­tion pro­ces­ses. But “it is still often dif­fi­cult to put this into prac­ti­ce.” Some­ti­mes the doubts about whe­ther the pro­mi­sed bene­fits will real­ly emer­ge are too gre­at. “This is why many com­pa­nies are wai­t­ing to see what hap­pens next”, says the Mana­ging Direc­tor of Data­tro­niq. Many of the machi­nes which are run­ning right now are not com­ple­te­ly set up to ope­ra­te in a digi­ta­li­zed pro­cess chain. “Even new machi­nes which have only been in ope­ra­ti­on for a year or so, may have been put out to ten­der up to four years ago and digi­ta­liz­a­ti­on was sel­dom part of the spe­ci­fi­ca­ti­on in tho­se days”, says Wal­ter.

That means retro­fit­ting the machi­nes, for examp­le with sen­sors, invol­ves making invest­ments. “Invest­ments which will be well worth it in terms of the bene­fits they crea­te”, Jür­gen Wal­ter is sure. Ano­t­her rea­son why com­pa­nies hesi­ta­te is that the huge quan­ti­ties of data that ari­se must first be col­la­ted and sys­te­ma­ti­cal­ly eva­lua­ted. “Basi­cal­ly the data is just raw data and has to be tur­ned into infor­ma­ti­on, ana­ly­sed and then trans­for­med into inst­ruc­tions.” Wal­ter exp­lains.

Many com­pa­nies can’t and don’t want to make the addi­tio­nal effort. This is whe­re exter­nal ser­vice pro­vi­ders like Data­tro­niq and the idea of Indus­tri­al Data Space come in. “If com­pa­nies issue lar­ge quan­ti­ties of data they want to be abso­lute­ly cer­tain that they are used for spe­ci­fic pur­po­ses”, says Wal­ter. Who will get my data? What will they be used for? And for what peri­od of time? And what access rights will they have? All of the­se ques­ti­ons are ans­we­red and regu­la­ted by Indus­tri­al Data Space. “Set­ting up and deve­lo­ping an Indus­tri­al Data Space is clear­ly the right step to take”, Wal­ter is con­vin­ced. Also whe­re ano­t­her aspect is con­cer­ned: name­ly when com­pa­nies collect their data and want to make them acces­si­ble to cus­to­mers in order to impro­ve the per­for­mance of their sys­tems and thus to docu­ment the qua­li­ty of their pro­ducts and the sta­bi­li­ty of their pro­ces­ses. “That crea­tes a huge com­pe­ti­ti­ve advan­ta­ge”, Wal­ter tells us. “And the Indus­tri­al Data Space pro­vi­des the secu­ri­ty we need for this data exchan­ge.