Episode 16: Business Needs Process Science

Hello, and welcome to ProcessTransformers, the podcast that talksabout business transformation at theintersection of processes and AI.



For those of you who havelistened before, welcome back.



And if you're new to theshow, thanks for tuning in.



My name is Lukas Egger, and I'mhead of innovation at SAP Signavio.



I'll be your host for today's episodetitled Business Needs Process Science.



And I'm very honored and thrilledto introduce today's guest, Jan vonBrocke, Professor of InformationSystems and Business Process Management



at the University of Münster andDirector of the European ResearchCenter for Information Systems.



Jan, thanks for being with us today.



Thank you very much, Lukas.



I'm absolutely excited to be here.



I feel honored.



Can't wait to get the discussion going.



I love the title.



Business needs process science.



Then let's jump in.



You actually coined thephrase "process science".



Why is it important that businessknow about process sites?



So I think process science kind ofis the first time that we really putprocess in the center of science, we



make it the object of interest andwe study it from various angles.



In former times, what we have seen wasthat various different disciplines,they would have picked up on process; so



computer science did, information systemsdid, management did, psychology did.



And they all had this look into, atsome point, realizing it's actually soimportant to study the dynamics of what's



going on, say, in a system or in a supplychain or in an organization, but theyalways did from the disciplinary angle.



And so the initiating idea of processscience really is to establish aninterdisciplinary science which



puts process in the center andinvites contributions from variousdifferent disciplines to really.



accumulate knowledge on processWe like also even to push it sofar that we say it's not even



interdisciplinary, but it's rathersomething like post disciplinary, right?



Because it doesn't startwith the discipline.



It goes beyond the notion ofdiscipline, but it puts the phenomenoninto the center and then inviteswhoever can make a contribution.



This is also why we think it's superexciting from research, but alsofor business, because we will end upwith a way more consistent, coherent,



and comprehensive understandingof the process in an organizationand beyond an organization; which,



even regarding tremendous andsuper impressive contributionsboth from research and practice...



Honestly we've all come to realize that wemay have not arrived at that point so far.



There is nothing to not like aboutbusinesses incorporating moreperspectives, but let me playdevil's advocate for a second here.



Business could say, " We are inthe business of making money.



It sounds nice, but what does itactually really help me with?"



What is it that you would sayare we currently missing out?



If we don't think about processes, ifbusinesses don't take other perspectivesinto account, what will they change?



I think there are a number ofways to it, and let me try toelaborate on that a little.



There's the one element is that we hopeto get a bigger picture because oftenwhat we consider is a process when wethink of the processes in organization.



It's very determined by our pastexperience of what we have referred toas a process or what is in a system.



And what we have come to realizeis that this of course is limited.



It's like we hold a hammer andeverything looks like a nail, right?



So we are looking for these things we caneasily model in a BPMN process model, forinstance and which, as you say, run into



the organization, and which are assumedto make money or drive the business.



But where are the boundaries, forinstance, so where does a business processor a business start and where does it end?



Thinking of sustainability aspects, forinstance, and we know the ESG reporting.



It's super relevant for somany organizations these daysand causing actually realchallenges and opportunities.



It's super important to not only lookinto kind of what drives money, or whatis the business in the old terms, but



it's super important also to embracewhat are the societal, economic, andenvironmental impacts of what we do.



And after we look into the businessprocess first, and then we counter,so that we look into environmentalaspect as an afterthought.



Why not try to look into the seriesof events and actions all togetherand then identify relations between



what we are doing in business and whatwe are causing in the environment?



If we look into these series of eventsin one go and all together, we mightbe able to discover relations we havenot been able to discover before.



And now, I think an obvious questionwould be, this I think makes processscience super timely at the moment.



that's the second very characteristicaspect, would be how are weinvestigating process, right?



One could say, yeah, we have investigatedthis, and some people may have hada very comprehensive view already.



I think a key fascination comes fromwhy we refer to that as scienceis that we actually take data.



we do not only talk about processand draw pictures of process, butwe collect data in order to reallydescribe and capture what is going on.



And this, very much relates to thisnotion of what we have come to reallylook in terms of process mining, right?



So we capture event data, we have eventlog data, and now we can analyze it andsee how a process actually really runs,and at what events actually have occurred.



And in that sense, we also mean it thatwe want to capture data beyond the ERPsystem: say, also from the environment;say, from room climate; say, from the



body; and take event data on arousal,or flow states, or excitement, orcognitive load, stress, what not.



And try to bring this into a morecomprehensive analysis of whatwe consider to be the process.



Looking into the effects, certainactions will have on the business,the environment and also on the



people in order to be able todesign eventually better processes.



What is the core of process science thatyou feel like are the biggest missedopportunity or that gets people excited?



So what I can already hear is:"The 'processes' are essentiallyhow businesses are run, right?.



That's how we do ourinvoicing, our financials."



There's rules and steps to how weget stuff done in the business.



We call them 'processes'.



Great.



There is "science", meaning 'weunderstand, we learn, we gather data'.



We just don't go with our guts,but we have a reliable scientificmethod of dealing with it.



I guess what I would want to know iswhere do you see like maybe the firstapplication or the first point of



attack for process science as somethingnew and something that helps businessto do things maybe differently?



Often when we talk about processes.



People throw in a lot ofdifferent concepts, right?



You're right.



Absolutely.



And I think the key thing, killerthing is the truth value, right?



That is from all conversations we arehaving, that's really super exciting.



We started process science studies inindustry, we looked into an NA partnercompany, we looked into a simplerprocess, I think it was an order of cash



process, And my colleague said to methere in the industry, he said, yeah,Jan, I thought you'd get to that process.



It's a very linear, straightforward,very simple process.



It's seven steps.



It's one, two, three, two,seven, it's very linear.



we wanted to challenge that, Wetook a process science perspectiveonto it and said, okay let's, have



a look at the process event updatewhich you were able to collectduring the past two years, right?



And we found that actually, It wasn'tjust seven steps in the process,it was a 70, 000 variant space of



different ways of running through thissuper straightforward simple process.



And now compare the truth value,and the quantity of your decisions.



You assume it's linear, simple,straight jacket, seven steps process.



It's actually a mess of 10, 000different ways to run through it, right?



And now you ground your decision about theprocess on, seven steps straightforward,but it's actually the mess of 10, 000.



I think this is something which is,"Whoa", in a way shocking, This we knowfrom process mining, that this now is



ready to do, that it's easy to deliverit's from the shelf for organizations.



The next step, however, we call thisthe descriptive process science, right?



So we see, we describe, it'snot seven, it's 70, 000, okay.



And we don't stop there; the nextstep then is that we ask why?



"Why is it 70,000?



Come on.



Where is that?



Is that needed?



Is that slack?



where is the deviance?"



it's also the real research going onthere then, We try to investigate andsay, "Hey, what is driving deviance here?



Where is deviance adding value?



Where is it destroying value?



And where can we reduce devianceor where can we maybe alsoforeground, the value of it?"



So we really investigate into that,and I give you another example where weworked with a bank in central Europe.



It was super interesting.



It was a bank very much in thecryptocurrency business and they hadA lot of onboarding processes, likenew customers, onboarding those of you



familiar with banks know that KYC processcan be very time consuming to say theleast, that was the same with this bank.



And now this bank had a low codeplatform where they could easily modifythis onboarding process because theywere redesigning it all the time.



This bank approached us and said,



" We try to redesign thisprocess, but it doesn't work.



It seemed to not work, We changesomething, we think it's for thebetter; actually, after a week, wehave a feeling the process runs worse.



And then we change something againand sometimes randomly, it's reallybetter, but sometimes again, it's worse.



And again here, like we firsttook a BPMN model, how is thisonboarding process really running?"



and you could see where would be theareas where the process will most likelyget out of control between differentstreamlines of approving new customers.



But what you could not seein typical BPMN model, wouldactually be real, Processes, right?



How they really run and how thischaos and how this dynamics, to sayit in a positive way, really unfold.



And here again, we took two yearsof historic data, which was thereanyways, and we analyzed this andwe computed the process complexity.



And then it was like a healthcurve like heart rates.



We computed the process rates andwe could see where were the peaks.



And it started off with something Like 10variants very neat, and then it rocketedat some point to over 70, 000 variances,more than 90, 000 variances in complexity.



And then it went down again.



And then there were like, this developmentand at that point we saw, okay, there is alot of deviance and that was descriptive.



That's what you get out of mining.



But then the question was in termsof process science, why is this?



And then we conducted archivalanalysis, conducted interviewsand tried to research, here,May 2022, there was this peak.



What happened in that sort of time frame?



And so we really did this research behindit, and we found they actually wanted toonly fix a few bugs, but then they created



more bugs by, not being aware of the widerconsequences of what they were doing.



And that of course created tremendousvalue, which one would have neverseen only on a process model basis.



We really needed to look into thosedynamics and the instances of theprocesses to then better understand,



and then based on this understanding,and this explanatory process side;so we have descriptive is the first



layer, second layer explanatory,and then it's prescriptive, whichkind of would be the normative.



Then now, what we do is we identifypatterns of when a process is supposedto, or has shown in the past to reallygo wrong, or to be healthy, right?



Or run into a specific type of problem andthat we call processory patterns, right?



So we identify those patternswhere there is a healthy oran unhealthy process patients.



And also then we can do a diagnosis,like what's wrong with this process,if it's very close to this pattern.



And we can have somethinglike an early warning system.



And that's the normative bit that inthe future, my vision is that processeswill be put under control of an AI.



They will be put under a shield ofAI, which is based on digital tracedata, constantly analyzing patterns,and analyzing how far instances of the



process would come to such a pattern inorder to then send a warning and say,Hey, if you continue the process likethat, in two weeks, it's going to crash.



Or, this change you are at the momenttrying to do, that will most likelylead to an increase in complexity.



Is that what you want?



Or do you want to decrease it?



Then I would have threesuggestions for you.



That's I think, the ultimate, the northstar of where we are hoping to go to.



There are so many wonderful thingsand a lot to unpack, but I want tojump back a little bit because you



said we want to get to the truthvalue of our processes, right?



And I guess in this, at leastin the social sciences, truthis in the eye of the beholder.



So my question is for process science,is the goal to understand the reality andthe cost of processes from a descriptiveperspective, or is there something



more like we want to give differentperspectives on the truth and we havemore people who can participate and wecan optimize for different things, right?



Because the truth for, let's say aphysical system, is easy to describe.



It's whatever it does.



But with a business,you can make trade offs.



You can say, 'Hey, I want to be resilient.



I want to change a lot.



I want to jump on AI, or I just want tocompete in this and not that market'.



So if we now think about process scienceand truth, where do you want this to go?



And where would you see thetruth of process science,



Wonderful, Wonderful.



So that's a such great pointand thank you to clarify this.



So, Typically, we wouldnot seek truth, right?



For the obvious reasons you stated,because there might not be truth at all.



in the end, we're probably not.



You know, there's also in research,there's this big sort of differentstreams of behavioral research, which welook more into tools and then developing



theories, or there's design scienceresearch, which would try to developutility as opposed to tools, right?



And kind of very naturally, I'd say thatmy work personally would be much morealways on the design science perspectiveto create something of utility, while



at the same time, of course, wewelcome contributions from colleagueswho are looking for truth, right?



So as long as they can demonstratethen also to their work that theycan add to the body of knowledge on



our understanding of the mechanisms,how dynamics would unfold...



What I meant earlier was truthvalue is a different thing.



I think Deming is quoted forsaying; "Without data, you are justanother guy with an opinion", right?



Or you also said in God, wetrust all others must be data".



That's kind of the truthvalue I mean here, right?



So if you have a conversation withsomebody in an organization abouttheir, say, onboarding process asin my example, and you walk in there



and say, the onboarding process, youknow, it's very complex and maybe theredesign we did last week wasn't so good.



you know, a certain impression or youwalk into the room and said, ' Look.



here is how we kind ofrecorded the process to run'.



Now you tell me, just, you know,you, create this transparency, thisvisibility and say, 'Okay, now,is that good or is that not good?



Do we want it like that or not?



Do we want to change anything?



Have any ideas?



Yeah.



It's not your opinion, butIt's more evidence based.



So you create some sort of evidence,which of course, again, comes withthe assumption that if you record



events, that events would say somethingrelevant about a process, clearly.



And maybe then, we have thisconversation because we work with allscience people, the psychologists,



we work with medical people, wework with various different people.



It's actually a fantastic challenge toagree on essential concepts, such as whatis a process, or what is an event, or is



an event actually, what is a good thingto record if you want to record a process?



Do we have discrete or is, thewhole thing about discrete processesor continuous processes, right?



We are used to have kind of very discreteperspective on processes in our sort ofcomputer science and information systemsand also off site, say, background.



But actually, if you look into what'shappening here now in our conversation,this is not very discreet, is it?



It's very flowing.



It's very evolving, you know, andone thought gives another thought.



So we had this challenging conversationwhere we thought like, 'Hey, isthere at all something like a display



process or wouldn't not all processesbe very continuous by nature?



And we only make that display to wherewe look at them?' And having suchconversations, of course, is fantastic.



This is also, I'd say what's happening onthe platform, on this inclusive platformof process science perspectives and inthat regard, you're completely right.



We would not assume thatthis is true what we do.



It's not better, but it'sjust another, another.



what it is, it is coming withthe intent to evidence to data.



But then again, data canbe of very different forms,very various sensors, events.



I love events for theintegration potential.



You can capture as an event thatyour heart rate exceeds a certainlimit, you could capture as an eventthat a customer walks by your store.



You can capture as an event that, youknow, it starts to rain if you wanted to.



So I love this in terms of integratingvarious different data sources.



But the whole idea wouldbe, of course, very open.



And at the moment, we arefascinated about digital traces.



That's also I'd say, what fuelsprocess science a lot in terms of notonly the needs, as we talked aboutalready, that is super important to



better capture dynamics, but also theopportunities we have these days, whichgives us a new sense, gives us a neworigin to, in a way, study processes.



I really like that you brought up theanalogy of, for instance, health data,because I think there is an analogy there.



If you ask people, no matter inwhat condition they are, most peoplewould say, 'Ah, I know there's someshortcomings, but overall I'm healthy'.



Most people will believe thattheir current state or whoeverthey compare themselves with, thatthey're pretty much well off, right?



So I guess one importantquestion is, are we all healthy?



Meaning from the perspectiveof process science?



Because you mentioned a bank and lookingat their onboarding; when you go topeople who apply process science, who



work with you, does everybody believethey're healthy and every time they'reshocked how bad their vitals are?



Or what is your experience, andin what ways would that thenlead to the next steps, right?



Because it could be that process scienceis a really big thing and we have neverhad such a big opportunity, or it could



be just, let's say a small increment,right, that is just healthy to do.



where do you see the industrygoing and and your experienceswith healthy patients?



I think what one could really sayis that most people we work withare absolutely surprised and amazed



about what's really going on intheir organization and around it.



for instance, now, we do not only lookinto inside the organization, we look intocustomer journeys, customer experiences,



and also customer feedback, link that backto certain instances of a process, right?



Identifying cause effect relations there.



And I think it's probably pretty muchwith, the analogy you also refer to,like if you think, okay, you're basicallyhealthy and that's a good thing.



Also, that's probably beanother discussion, right?



So that's kind of another dimension,kind of this being more brutal to, ormore interested and probably also moretransparent like in sports, right?



I really do measure and I'm open.



and it's also a matter of what do Ido with the measurement results, amI communicating that company wide?



Would that have any effecton my salary or my career?



Or is it something like, 'Ilove this thinking of personaldigital assistance, right?



Some, tool going back to you andsaying, 'Jan yesterday was a great day.



you kind of outperform ed your standards.



and also we realized', I don't know,'you had a longer lie in or youwoke up early or realized you went



on a run in morning time, or youtook the bike to work not the car'.



so you'd be giving this feedbackinformation to help us learn and improve.



I think that's the thing, I'd say,every organization will benefit alot from, and this is also in all



cases we have looked into, andthere have been ample opportunities.



Then however, it's probably also alittle bit like with sports again.



Like if you ever started a Fitbit deviceor a smartwatch first weeks, you wouldhave been amazed like, Oh, ah, 10,000 steps, that's what I should do.



And then you go, ah,15, 20, maybe I do more.



After a while, you kind ofhave changed your behavior.



All right.



You've come used to it.



And maybe then you look intoother aspects probably, whereyou would like to improve.



and so this, I think could be somethingsimilar, but I think this openness towardswhat we also refer to as behavioralvisibility, having this end to end



transparency, but also coming with thisethical and human and constructive wayto deal with this and to work with this.



I think this is something whereI see a huge paradigm shift, andcoming back to your question , isthis marginal or is it a big thing?



I think it's a paradigm shiftreally, it's a paradigm shiftwhich will change management andthe way organizations behave.



Just to give you another example, inprocess management, we, we talked aboutall this, you know, process governance andyou have kind of the process owners and



managers and these armies of people, andthey have your annual summit, or you mayhave your audit on processes, and thenthe process police is coming and looking



into what's going on in the processes,like, in a very, very monolithic,very static, very, very huge...



I envision process management to be waymore shop floor like in the future, right?



People responsible for a process cometogether on Mondays, have a look atthe data, say, okay, that's good,keep this in the process, that's bad.



Get that out of the process, you know, andthen, and then, okay, run for it, yeah?



And then on Wednesday, they justmeet again and say, okay, let'shave a look at the data again.



Has this improved?



It's not improved yet.



So I think it will be way moresensitive and adaptive to change.



And I think this is also, Again,probably coming to these twokey drivers for process science.



I'm not sure if I'vementioned them already.



I think the one is the realneeds to be adaptive to change.



I mean, that has never beenas strong as it is today, andit will be so in the future.



So the capability to respondto change and deal with changeand capitalize maybe on change.



That'd be the core competencyof the future company.



That's the need side.



And now we also have the meanside, that for the first time,really, we have the sense that



we can look into those dynamics,and we can capture digital traces.



And we can not only capture them we cancompute them with AI, and we can playthem back to people through bots andpersonal assistants and great dashboards.



So I think it's a unique opportunity now.



What makes process science also so timelythat this need and means comes together.



That's, I think, fueling this, butit will bring a paradigm shift.



And I think we will not go backto just talking about processes.



Ambitious organizations, they will also,they want to see that it's just like witha doctor, you go to a doctor and looksat you, say, ah, take the green pill.



And you feel like, 'Wait a minute.



Like you haven't even measuredmy temperature', and so we wouldjust not accept that anymore.



Do you think that the competitiveedge of, let's say an organizationthat really takes process science veryserious, will be the ability to adapt?



Or do you think that once you'rein the habit of having your fitnessdevice and doing all of this, thatthe efficiency will be the focus?



Because there's two, not conflicting,but two different dimensions.



what do you think is the biggestopportunity there, right?



Why, businesses who take processscience serious will be betterthan maybe other businesses.



Oh, absolutely, and, I thinkit's really great to say that.



First of all, I think we haven't talkedabout that much, but my understandingalways is there are different types



of processes, it's something we'veinvestigated decades of researchinto, that context matters and that



not all processes are alike, which isa huge problem in most organizationswho treat all processes alike.



And they all treat them sort ofwell structured and recurring,and at least they assume that.



And they apply the methodology whichhas been designed for that kind ofprocesses also to other processes.



And then they are surprised why peoplewho are supposed to use this are not asexcited as the people who try to sell it.



We've developed actually, Ishouldn't probably take too muchtime talking about that, but I



think it is really super importantand I can share material on that.



We've developed measurementdevices to better understand whatis the context of my business.



And together with Hinty, the manufacturingcompany, Fastlink Technology, we workedon context matrix in the end, right,and we have two dimensions, very simple.



It's a process frequency, can below or high, and it's a processvariability, can be low or high.



And now you have a high frequency,low variability process.



That's what all assume.



And that's, you know, wheremost stuff is developed for.



And that's, however, what I'd saywill come out of the PowerPlugin the future, which is, verymuch, standards will be automated.



That's sort of the automation process.



We call that performance processing.



Sort of the complete oppositeof that would be you have lowfrequency, high variability.



Wow, this is now how to develop newprojects or services or products,how to develop new strategies,



you know, how to innovate, becausethat's also the innovation cluster.



And of course, you see, it's superdifferent what makes such a processand such a cluster successful.



On the one hand, it's the efficiency,as you mentioned, right, with theperformance process, but the efficiency



of the innovation process, okay,efficiency is never really bad, butit's not the game changer, right?



The game changer is, will you beeffective given certain constraints?



And, the power is not in the layoutof the process, but it's more inthe people and the problem solvingcapabilities of your actors, right?



Probably including also machine actors.



And certainly you would manage suchprocesses pretty much the oppositeway as performance processes.



And then there are ' low low', lowfrequency, low variability, which werefer to as reliability processes.



You have a recovery plan or, you know,something happens and you need toplay to the rules, but Here you haveto challenge you to remind people how



this is working at all, because nobodywould remember because it's happeningonce a year, maybe, or even less.



And then you've got to have the realbeast cluster, which is 'high, high',which is you have high frequency.



And you also have hyper reliability,which in our partner organization,Hilti, was very much a market openerbecause you you have loads of sales



and customer contacts, but then invarious regions around the world, verydifferent ways to engage with customersor potential customers would have evolved.



And now we see from this example, this canbe a good thing that can be really needed.



It can also be a bad thing.



We don't know, right?



And so here is specifically thiscluster where we try to identify, wetry to detect and challenge also thisvariability in order to manage complexity.



And see, where is thisdeviance adding really value?



And where can we reduce this deviancefor the benefits of efficiency?



So sorry for the long answer, but Ithink it's important to understandas you mentioned, this different



modes of this different objectivesyou might have that very much comesalso from the nature of business.



In some areas, process science willdeliver this efficiency really,really really strong because wecan automate processes better.



We can develop also moreadaptive processes which candeal with more deviants as theywere able to deal with before.



But then in the other clusters, likein the agility cluster, we call this'high high' cluster, they're enabled a



lot to identify this deviance and thenalso to send and respond to change.



I think it's a super insightful way ofthinking about what a business does:to break it up in the way you just didabout frequency and in variability.



I think one question that for mecomes up immediately is -- becauseyou mentioned AI before, and right now



AI is all the hype and every businessleader needs to have an opinion on it.



Do you think this will help ordo you think it will make it evenharder to do process science?



So in a way.



Putting AI now into the mix, breakingup all processes, putting mayberesponsibility to intelligent systems.



You mentioned that you would hopethat in the future AI can help to be achaperone or a guardian of processes.



Do you think this will make it easier,harder, or the jury is still out?



What is your take on AI when youthink about process science?



I think, first of all, ofcourse, AI is super exciting.



Then we should not overestimate it.



I hear this too much actually, really.



And that very often people say AI,they don't even actually talk aboutAI in terms of, what they think of.



AI has become a synonym fordigital a certain extent.



And so I think it's veryoverused, that's that.



Then I think AI really isas good as the data we have.



And so with me, the fascination isreally with the foundations, that wecreate this data pool and we develop



this richer understanding aboutprocess relevant data and that we startcollecting that and integrating that.



And that does not necessarily relatetoo much to AI, again, also extraction.



And you know, we, we've done it maybefive times and talk about this alittle, we've done super cool projectsat developing chatbots for process



management, feeding into large languagemodels with AI algorithms and sothat also can be really, really nice.



but first of all, connecting this,process relevant data on this descriptivelayer, I think that does not necessarilyneed to relate too much to AI.



But then, of course, I think in termsof analyzing this data, AI gives usnew power, and again it allows us to



do things we, without AI, would notbe able to do At this scale also.



But also when I say that, for me, itis always clear that we would not justhand it over to AI and let AI manage all



processes, but according to this humanin the loop thinking, we would alwayshave a human machine collaboration.



In which more, and more devioustasks also tor asks where, accuracyor handling loads of data or



having really high computationalperformance, Would be computer based.



Why?



Then there's a conversation with studentsand together we will manage processes.



So I think there's the bot helpingus and the algorithm helping usin the whole team doing business



process management, which will nottake away the responsibility at all.



I'm a big supporter of that.



We need to get the governancestructure really clear first.



When we approach processes all together.



We need to know what processare we talking about.



We need to make people responsible.



And then we need tosupport them with tools.



And among this, toolkits of tools,I'd say AI certainly offers superexciting new opportunities to this



extent, in process science inparticular, I would love to see more.



process patterns, it's kind of shockingthat we still don't know that, right?



we do process work since decades, butnone of us would be able to say now,like a medical doctor, okay, here



are these 30 diagnoses, which could,you know, a process could have, andhere are the treatments, and this



is how we studied those treatmentsover the past decades, and we'vejust not come to that maturity level.



And it's kind of My hobby and apersonal vision where I would likein my own research to bring process



science is identify such patternsin order to support the discoveryof such contextualizations more.



There is one for me, obvious follow upquestion, because you are painting avery vivid and compelling picture about



people going to the truth, creating thematurity to understand, enabling othertasks and other technologies like AI.



But I guess the question nowis, what is the first step?



If there is a decision maker runninga business and they say, Hey, we wantto be real about process science.



What is the first best step in orderto get to, to have the data, to havethe understanding and not just do more



of the same, but gravitate towardsthe maturity of process science,as you just painted the picture.



Yeah, so I think important step is: Start.



You should start somewhere, andthen the second understandingis Start Somewhere Small, right?



So don't overthink itin the very beginning.



Take an exemplary area and tryapply these methodologies andthese techniques to that area.



Then third, well, once you've learnedthis a little, and probably the thirdand the second, they go a little bitin parallel: Develop use Case Strategy.



So that's what we are actually doingin our department at the EuropeanResearch Center with companies at themoment that we have to identify use



cases, which are value creating, andthat relates then to, what's on thestrategic agenda in this organization.



What's the context to the factors.



Some of you may know the businessprocess management billboard,and it talks about those areas.



we apply this in order to develop a custommade strategy, because you want to startsomewhere where you can actually havegood opportunities to be successful.



And that relates to, you needto have data, technically.



now if you think of these fourquadrants again, for instance,maybe choose one of those processes,which is in the agility cluster.



Don't go for one which is maybe in theinnovation cluster just because somebodyshows interest in it or don't go into



the reliability cluster or maybe theperformance cluster would work as well.



You would have the data there butyou don't have that reliability soit's not so exciting what you find.



but that's part of the methodologywe are applying that we startedwith a process, for instance, in theagility territory of high frequency.



So we have the data as you havehigh reliability, so you have aninteresting case also, if you areable to, make this more transparent.



Once you then have a couple of thoseuse cases laid out, start and doPrototypes in those together with the



people involved there, and make peopleexperience what process science can do.



That'll, in my experience, willlight a fire of people being, more,interested in say, can we be next?



I love it.



And I think we now talked a lotabout understanding and truth.



So for me, the last question to you isnot only understanding, but also changing.



What will be the one business processthat you would like to see change?



If you could just magically changeit, which one would you change?



Just, if you just find out, like...



Personally, I would love tosee text processes changing,



administrative texts, reporting,kind of self processing, right?



I would love them tobe in the performance.



cluster, or maybe, I would love themto be really, you know, automated,they are way too complex and, taking



too much of our attention in termsof the value creation, value.



I love to do something new andme personally now, right?



It's just as a personal comment, if Ithink of my own processes I'm involvedin, I think all these aspects need to



be done and you need to have solidadministration, but sometimes, I'dsay, all this documentation , and



admin, I think that would be verybeneficial, not only in terms ofefficiencies, but happiness for people.



It will grant us lifetimeand enjoyment if we could.



Just go to the beach, or for a run,or meet with friends, instead ofre documenting or keeping track of



stuff, which has happened, and thenmaybe argue why it has happened.



In the sense of process science, Ithink that could be automatically,in the digital traces.



Personally, I would be more thanhappy to just share this, and thenhave algorithms doing the rest.



I think there is no better note toend on other than say, " Processscience can lead to happiness".



Yeah, that's it.



I'm personally convinced



. So that's, the perfect note.



Jan, thank you so muchfor being with us today.



Super.



Thank you so much.



And with that, thanks for listening toanother episode of Process Transformers.



This podcast is brought toyou by the dedicated effortsand hard work of our team.



So a heartfelt thank you to BeyzaKartal, Dharmika Rathod, Reagan Nyandoro,Erica Davis, Abdurahman Hale and JulienThevenod If you have any questions



or comments, email us at processtransformers@sap.com, and until next timefor another transformative conversation.