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.