A Secure and Effi­ci­ent Indus­trial Process using Predic­tive Maintenance


As the indus­trial appli­ca­ti­ons for IoT and machine to machine commu­ni­ca­tion are beco­ming common­place, indus­tries need to ensure the costs of deploy­ing, main­ten­ance and admi­nis­tra­tive over­heads do not outweigh the poten­tial bene­fits. Exis­ting indus­tries seek migra­tion stra­tegy towards Indus­try 4.0 ready systems. This could mean having hete­ro­ge­neous control systems, new machine models working in tandem with previous models and ensu­ring higher level of data secu­rity.  How can we ensure effi­ci­ent data aggre­ga­tion so all models seam­lessly commu­ni­cate with each other?

Consider a manu­fac­tu­ring plant that packa­ges medi­cal equip­ment with an exis­ting Super­vi­sory Control and Data Acqui­si­tion (SCADA) system. When an error is detec­ted on the robo­tic pack­ing and palle­tiz­ing unit, how will this affect produc­tion output?

Data­sets used

Rely­ing on state of the art SCADA systems is a reac­tive approach and could lead to loss of time and reve­nue. A predic­tive main­ten­ance solu­tion gathers the data logs from the SCADA units and corre­la­tes it with other exter­nal data­sets such as tempe­ra­ture, number of units produ­ced, anony­mi­sed error reports from simi­lar models and plan­ned produc­tion sche­dule. Apply­ing machine lear­ning algo­rithms on the aggre­ga­ted data­sets will help effi­ci­ent fore­cas­ting and prepa­ra­tion of remedy actions even before they appear on your SCADA systems. Such an approach is feasi­ble with a data marketplace.

Adva­neo Data Marketplace

When deal­ing with sensi­tive data in a manu­fac­tu­ring indus­try, privacy and secu­rity of data beco­mes para­mount. An inter­nal market­place (i.e. the Adva­neo data market­place) ensu­res no data is trans­por­ted outside your inter­nal network. Device manu­fac­tu­r­ers can receive meta­data (stan­dard data descrip­tion) before deci­ding which data­sets will be rele­vant. Further­more, the Adva­neo Data Market­place serves as a broker to ensure hete­ro­ge­neous data systems, equip­ment with diffe­rent data formats can seam­less exch­ange data without the need for yet another addi­tio­nal black box. The Adva­neo data market­place offers:

Inter­nal Market­Place: A custo­mi­sed inter­nal market place is desi­gned for your unique scena­rios. This further ensu­res descrip­tion of data­sets available to sales, marke­ting, logi­stics & trans­por­ta­tion, returns and new orders, can all be visi­ble on a single inter­nal plat­form. No need to upload company data to some cloud service. Having 100% data gover­nance is more important than ever before.

Closed User Groups: In a speci­fic produc­tion cycle, the option to create a Closed User Group (CUG) helps more visi­bi­lity and coor­di­na­ted plan­ning among ever­yone invol­ved inclu­ding commer­cial, design, produc­tion moni­to­ring, quality control and deli­very units. Data­sets are made visi­ble to all requi­red teams and the CUG can be ended at the expi­ra­tion of the produc­tion sprint.

Secu­rity and Privacy: Majo­rity of smart machi­nery used in indus­try export data directly to manu­fac­tu­r­ers in form of system log files error log reports. This is a high risk and loss of privacy control. The data market­place broker ensu­res data sove­reig­nty and control of all data gene­ra­ted by your machi­nes within your premi­ses. By having control, if, when and how a direct exch­ange between your systems and the equip­ment vendors or any third party occurs or not.


As all compa­nies begin their migra­tion towards smart indus­try or indus­try 4.0, gene­ra­ting new data to help decis­ion making process is vital. Howe­ver only compa­nies who focus of proac­tive stra­te­gies based on predic­tive system will gain the most bene­fits. As data gene­ra­ted in your facto­ries beco­mes trade secrets, data privacy and secu­rity cannot be over­em­pha­sised. The Adva­neo data market­place is desi­gned to ensure a perfect balance between data driven indus­trial revo­lu­tion and highest possi­ble level of secu­rity and privacy.