It is alre­ady a deca­de when the idea of Indu­stry 4.0 (or “Indu­strie 4.0”) was intro­du­ced on Han­no­ver Fair in 2011. This is a good time to sum­ma­ri­ze what hap­pe­ned sin­ce that time and check the chal­len­ges that were obse­rved during implementation.

Befo­re we go to chal­len­ges, let’s remind what Indu­stry 4.0 is, and what is going to pro­vi­de to manu­fac­tu­rers and all people.

Indu­stry 3.0 made, and is still making, a big revo­lu­tion in many indu­stry sec­tors. Shi­fting manu­fac­tu­ring to the digi­tal world, by intro­du­cing com­pu­ters on a pro­duc­tion (e.g., Pro­gram­ma­ble Logic Con­trol­lers), invo­lving robots, and boosting auto­ma­tion, has alre­ady incre­ased per­for­man­ce dra­ma­ti­cal­ly. So why sho­uld we take ano­ther step? The bet­ter question is rather: “why not to move on with new level?”.

Ima­gi­ne that all machi­nes in your fac­to­ry could com­mu­ni­ca­te with each other, can col­lect data to one com­mon pla­ce and the­re are mecha­ni­sms that allow you to ana­ly­ze tho­se data and, based on them, sup­port the deci­sion-making pro­cess. By easy inter­fa­ces all inte­re­sted employ­ees and con­su­mers are well inte­gra­ted with this vir­tu­al world and can, in an easy way, adapt to the­ir needs. This is what Indu­stry 4.0 is going to provide.

Why is Indu­stry 4.0 Impor­tant in Manufacturing?

The­re are seve­ral key reasons and bene­fits why manu­fac­tu­rers would like to adopt to Indu­stry 4.0:

  • Cost Effi­cien­cy:
    • Decre­asing down­ti­me, redu­cing a cost of sup­ply­ing quali­ty parts, by using Pre­dic­ti­ve Main­te­nan­ce stra­te­gies; Solu­tions for Machi­ne Moni­to­ring or any other new technologies
    • Reduc­tion of the per­cen­ta­ge of defects or shrin­ka­ge in the fac­to­ries, sin­ce it will be possi­ble to test the pro­to­ty­pes in a vir­tu­al way and the assem­bly lines will be opti­mi­zed even befo­re they beco­me operational.
  • Gre­ater pro­duc­ti­vi­ty and bet­ter mana­ge­ment of resources
    • Pro­per inte­gra­tion of all kind or reso­ur­ces also with people will boost a pro­duc­ti­vi­ty and impro­ve a wor­king condition
  • More effi­cient deci­sion-making based on real information
    • Pro­per ana­ly­sis (sup­por­ted by Arti­fi­cial Intel­li­gen­ce, Machi­ne Lear­ning, Pre­dic­ti­ve Main­te­nan­ce, Big Data solu­tions) of data coming from many machi­nes, many dif­fe­rent fac­to­ries or even enti­re indu­stries can help in taking the right deci­sions and moving pro­duc­tion in a right direction.
  • Ope­ra­tio­nal Agility.
    • With tech­no­lo­gies pro­vi­ded in the con­text of Indu­stry 4.0, manu­fac­tu­rers sho­uld be able react quic­ker to fluc­tu­ating demands, new pro­duct trends, the skills gap, and other unpre­dic­ta­ble chal­len­ges. With the right tech­no­lo­gy in pla­ce, manu­fac­tu­rers have a gre­ater like­li­ho­od of suc­cess­ful­ly pivo­ting, when expe­rien­cing adver­si­ty. It sho­uld incre­ase fle­xi­bi­li­ty, to achie­ve mass pro­duc­tion (and per­so­na­li­zed in real time).
  • Reta­ining Customers
    • Cur­ren­tly we can obse­rve a trend of incre­asing expec­ta­tions of con­su­mers aro­und servi­ce and pro­duct quali­ty. To meet the­se rising demands, manu­fac­tu­rers will be for­ced to adopt tech­no­lo­gy, to sup­port custo­mi­za­tion, pro­duct deve­lop­ment, after-sales servi­ce, and more.
  • Direct com­mu­ni­ca­tion betwe­en clients and orga­ni­za­tions, which means that we can bet­ter under­stand what custo­mers need.

Items listed abo­ve are just a few exam­ples, but alre­ady, by just ana­ly­zing them, we can easi­ly see signi­fi­cant benefits.

What are the challenges?

Adap­ting to Indu­stry 4.0 is not an easy task and requ­ires time and a good stra­te­gy. Each manu­fac­tu­rer sho­uld pre­pa­re a solid, long-term plan, and needs to solve a long list of dif­fe­rent chal­len­ges to be suc­cess­ful and enjoy the bene­fits pro­vi­ded by Indu­stry 4.0. Some of tho­se trans­for­ma­tion-rela­ted topics would be:

  • New busi­ness models — the defi­ni­tion of a new strategy
  • Rethin­king your orga­ni­za­tion and pro­ces­ses to maxi­mi­ze new outcomes
  • Under­stan­ding your busi­ness case, under­stan­ding a pro­blem that you want to solve
  • Con­duc­ting suc­cess­ful pilots
  • Hel­ping your orga­ni­za­tion to under­stand whe­re action is needed?
  • Chan­ge management
  • Exa­mi­na­tion of com­pa­ny culture
  • The genu­ine inter­con­nec­tion of all departments
  • Recru­iting and deve­lo­ping new talent, tech­ni­cal skills
    • The needs requ­ired of the work­for­ce are evo­lving. Are your employ­ees able to keep up? When looking to fill open posi­tions, look for appli­cants who possess “digi­tal dexte­ri­ty” in that they under­stand both the manu­fac­tu­ring pro­ces­ses and the digi­tal tools that sup­port tho­se pro­ces­ses. Only with the right work­for­ce will busi­ness models be able to suc­cess­ful­ly imple­ment new tech­no­lo­gy and main­ta­in operations.
  • Data Sen­si­ti­vi­ty
    • The rise in tech­no­lo­gy has also led to incre­asing con­cerns over data and IP pri­va­cy, owner­ship, and mana­ge­ment. A com­mon exam­ple? To suc­cess­ful­ly imple­ment an AI algo­ri­thm, data is requ­ired to tra­in and test it. For this to hap­pen, the data must be sha­red. Howe­ver, many com­pa­nies are reluc­tant to sha­re the­ir data with third-par­ty solu­tion deve­lo­pers (what is quite under­stan­da­ble). Fur­ther­mo­re, our cur­rent data gover­nan­ce poli­cies for inter­nal use within orga­ni­za­tions are ina­de­qu­ate to sup­port cross-orga­ni­za­tio­nal data sha­ring. Data is a power­ful asset – make sure to keep it secure!
  • Inte­ro­pe­ra­bi­li­ty
    • Ano­ther signi­fi­cant issue is the lack of sepa­ra­tion betwe­en pro­to­cols, com­po­nents, pro­ducts, and sys­tems. Unfor­tu­na­te­ly, inte­ro­pe­ra­bi­li­ty impe­des com­pa­nies’ abi­li­ty to inno­va­te. Fur­ther­mo­re, sin­ce they can­not easi­ly “swap out” one ven­dor for ano­ther or one part of the sys­tem for ano­ther, inte­ro­pe­ra­bi­li­ty also limits options to upgra­de sys­tem components.
  • Secu­ri­ty
    • Thre­ats in terms of cur­rent and emer­ging vul­ne­ra­bi­li­ties in the fac­to­ry are ano­ther signi­fi­cant con­cern. The phy­si­cal and digi­tal sys­tems that make up smart fac­to­ries make real-time inte­ro­pe­ra­bi­li­ty possi­ble — howe­ver, it comes with the risk of an expan­ded attack sur­fa­ce. When nume­ro­us machi­nes and devi­ces are con­nec­ted to sin­gle or even mul­ti­ple networks in a smart fac­to­ry, vul­ne­ra­bi­li­ties in any one of tho­se pie­ces of equ­ip­ment could make the sys­tem vul­ne­ra­ble to attack. To help fight this issue com­pa­nies need to anti­ci­pa­te both enter­pri­se sys­tem vul­ne­ra­bi­li­ties and machi­ne level ope­ra­tio­nal vul­ne­ra­bi­li­ties.  Com­pa­nies are not ful­ly pre­pa­red to deal with the­se secu­ri­ty thre­ats, with many rely­ing on the­ir tech­no­lo­gy and solu­tion pro­vi­ders to sco­pe out vulnerabilities.
  • Han­dling Data Growth: 
    • As more com­pa­nies beco­me depen­dent on AI usa­ge, com­pa­nies will be faced with more data that is being gene­ra­ted at a faster pace and pre­sen­ted in mul­ti­ple for­mats.  To wade thro­ugh the­se vast amo­unts of data, AI algo­ri­thms need to be easier to com­pre­hend. Fur­ther­mo­re, the­se algo­ri­thms need to be able to com­bi­ne data that might be of dif­fe­rent types and timeframes.

What are the solutions?

Unfor­tu­na­te­ly, the­re is none, a com­mon solu­tion for all manufacturers.

Each manu­fac­tu­rer can build its own solu­tion adap­ted to the fac­to­ry from scratch. We could assu­me that this will be pret­ty cost full ope­ra­tion and will requ­ire a lot of main­te­nan­ce thro­ugho­ut many areas.

The­re are also exter­nal pro­vi­ders of many com­po­nents that may sup­port the who­le process.

The cen­ter of the IIoT infra­struc­tu­re is a good plat­form, that helps with the inte­gra­tion pro­cess of all machi­nes wor­king in the factory.

Sour­ce: Gart­ner Magic Quadrant for Indu­strial IoT Platforms

Befo­re the selec­tion of the right one the­re is a need to ana­ly­ze how much its sup­port our busi­ness needs, e.g.

  • what kind of com­mu­ni­ca­tion pro­to­cols it uses e.g., MQTT, OPCUA, Ether­CAT, EtherNET/IP, etc… and how easi­ly it can be inte­gra­ted with machines?
  • What is the secu­ri­ty level?
  • What kind of ana­ly­sis may pro­vi­de, and which deci­sion-making pro­cess may support?
  • What is the level of inte­gra­tion with alre­ady exi­sting con­su­mer appli­ca­tions, ERP sys­tems, Mobi­le appli­ca­tions, etc…
  • What is the main­te­nan­ce level?

The good infor­ma­tion is that the­re are IT / Softwa­re com­pa­nies that spe­cia­li­ze in such trans­for­ma­tion and sup­port manu­fac­tu­rers on dif­fe­rent levels like:

  • Iden­ti­fy­ing the busi­ness needs
  • Selec­ting and inte­gra­tion of the platform
  • Imple­men­ta­tion of requ­ired embed­ded, web, mobi­le softwa­re that make all parts run­ning together

Did you know?

Code­lab gained exten­si­ve IIoT expe­rien­ce in a mul­ti­tu­de of indu­strial refe­ren­ce pro­jects span­ning indu­stries such as injec­tion mol­ding, glass, (intra-) logi­stics, con­struc­tion, che­mi­cal and medical.

Code­la­b’s IoT Cen­ter of Com­pe­ten­ce offers full ran­ge con­sul­ting for your indu­strial IoT pro­jects. Whe­ther you start from scratch and need an archi­tect, want to retro­fit and con­nect an exi­sting base, need some­one to opti­mi­ze, secu­re and test your code — con­tact our experts Hubert Kamin­ski or Tomasz Brzo­zow­ski for a solu­tion briefing.