1 Introduction

The fast-paced technological evolution of ourtime leads to equally fast changes in the businessworld, from efficiency gains through processimprovements to the redefinition of long-estab-lished business models. In this context, a key tech-nology, which accelerates this technological evo-lution, is artificial intelligence (AI) including its sub-domain of machine learning. Machine Learningmethods allow the training of models from dataexamples instead of tediously having to define theinput-output relationship of the models manual-ly. For instance, such models can be applied to makedecisions and predictions. While this technologyhas been cost- and time-consuming in the past, ithas now reached a level of maturity where broadindustrial application is feasible in many scenarios(VanThienen, 2016).

As AI constitutes a universal technology – like

the steam engine or electricity in the past – it isbound to influence many sectors of industry wellbeyond its origin (Brynjolfsson, 2017). Consequent-ly, also the process industry is already being influ-enced by this trend and therefore evaluates howto harness the arising opportunities. For example,predictive asset management has been adoptedby major players in order to maximize asset uti-lization or minimize unplanned downtime. Further-more, the monitoring of a plant can be made moreefficient by the digitization of a plant’s control data.Additionally, monitoring of production processescan be enhanced with pattern recognition in orderto assess influences on batch consistency or to pre-dict deviations from the production process beforethey could occur. Finally, downstream data frompoints-of-sale can be used to forecast the demandof customers and to plan a responsive schedule inproduction (VanThienen, 2016).

However, at the same time several barriers are

Practitioner’s SectionSuccessful data applications: a cross-industryapproach for conceptual planning

Robert Jenke** Dr. Jenke Consulting, Process Automation & Data Science, www.jenke-consulting.de,

mail@jenke-consulting.de

DOI: 10.17879/38129709602; URN: nbn:de:hbz:6-38129709797

Journal of Business Chemistry 2018, 15 (2) © Journal of Business Chemistry

Successful data applications: a cross-industry approach for conceptual planning

71

Data-based solutions powered by artificial intelligence (AI), and especially its sub-domain of machine learning, are a key driver of today’s fast-paced technological evo-lution. In the process industry, barriers for many organizations to apply this techno-logy are missing know-how for conceptual planning as well as lack of economic fea-sibility studies. However, companies risk to lose their competitive position by notapplying this technology. In this article, we describe the CRISP-DM model as a con-ceptual planning approach. In addition, we provide practical advice based on expe-rience in other industries how this technology can be applied in the process indus-try. Here, the steps process analysis and data understanding are key success factorsin order to develop economically viable use cases. An implementation strategy shouldinclude an agile environment to develop ideas fast and with little risk before trans-ferring working solutions to the requirements of the operational business. A com-bined bottom-up / top-down approach of knowledge distribution and pilot projectscan help organizations to successfully embrace this technology in their operationalbusinesses, overcome associated fears and organically seize company-individualopportunities that arise.

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currently hampering the use of digital technolo-gies like machine learning, as Stoffels and Ziemerpoint out in an article of an earlier issue of this jour-nal (Stoffels and Ziemer, 2017): Among those bar-riers are

1) Unclear benefits and/or lack of economic eval-uations and 2) Missing know-how on methods for analyzingand adapting processes.

Yet, it is imperative for companies to understandthe impact that this important technological changehas on their business model, their operations andtheir competitive landscape. As with any new tech-nology, identifying and defining use cases is notstraight forward. At the same time, they need tofulfill at least two preconditions:

1) the application must be technologically feasi-ble and 2) backed by a solid business case.

If these preconditions are not fulfilled in the begin-ning, many companies eschew investments due torisks involved. However, there is also considerablerisk associated with ignoring the changes aheadand thereby risk becoming the next Kodak (Antho-ny, 2016).

In this article, we aim to make the followingcontributions:

1) We present methods and approaches concern-ing data applications that have been successfullyapplied to other industries. More importantly, thiscontribution shows what can be learned and trans-ferred to the process industry. In this, we introducethe cross-industry standard process for data min-ing (CRISP-DM) model, which is a generic frame-work how to approach data mining problems. Wediscuss key issues in depth based on experiencefrom other industries.2) We discuss possibilities to overcome the previ-ously mentioned risks and barriers, i.e. implemen-tation strategy and integration into the organiza-tion on a strategic level, in order to increase theoverall ROI of investments in this field.

2 CRISP-DM model

The CRISP-DM describes a generic frameworkfor data mining and knowledge discovery projects.It is used by the majority of experts in the field ofData Science (Marban, 2009). The approach cap-tures the essence of what is needed to successful-ly carry out a data mining project, but at the sametime it is easily transferable to any industry. The six

steps of the CRISP-DM are Business Understand-ing, Data Understanding, Data Preparation, Mod-eling, Evaluation, and Deployment (Shearer, 2000).It is worth noting that the steps are not necessar-ily carried out in a linear manner, as depicted in Fig-ure 1. The single steps are explained below (fordetails, see (Shearer, 2000)):

1. Business understandingBefore touching any data, it is very important tounderstand the business context in which this datawill be used. This phase concerns itself with ques-tions around the business objectives, success cri-teria, relevant business processes, and an assess-ment of the overall situation, in order to derivegoals and develop a first plan of the project. Thebetter this step is carried out, the more valuableinsights and outcome can be expected from theremaining steps.

2. Data understandingIn the second step, data is collected and an under-standing is developed. This includes describing thedata and what kind of information it contains. Also,quality of data should be assessed, e.g. consisten-cy and completeness. First hypothesis can alreadybe derived from data. Often, it is necessary to goback to step one multiple times, until a clear pic-ture of the interaction between business process-es, objectives, and data is gained.

3. Data preparationOnce a specific use case is defined, data must beselected based on the relevance to the project’sgoal as well as the technical limitations. Here, thedata must be usable. Data sets must be provided,which can be used to develop models. Quality canbe increased, for example, by removing outliers,handling missing values, and giving a proper struc-ture to the data.

4. ModelingIn this phase, tools like machine learning methodsare employed to train one or several models on theselected data. Usually, more than one method isavailable. This phase aims to identify the best fitand optimize any free parameters. This also includestesting the quality of the model, e.g. its generaliz-ability measured by the error rate on test data.

5. EvaluationWhile the model itself is verified in the fourth step,this step evaluates the suitability of the developedprocessing pipeline and model with respect to busi-ness application. Only when there are no criticalissues overlooked, the model can be deployed inthe next step. Too often, false assumptions require

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Successful data applications: a cross-industry approach for conceptual planning

Figure 1 CRISP-DM model (source: Shearer, 2000).

BusinessUnderstanding

Data Understanding

Data Preparation

Modeling

Evaluation

DeploymentData

going back to step 1 and to revise the businessunderstanding.

6. DeploymentWhen the suitability for real-life business applica-tion has been shown, the deployment phase aimsto transfer the findings from the data mining proj-ect to day-to-day operations, i.e. actually make useof the created models. In some cases, this might bea simple report, in others the implementation ofsoftware to track and analyze real-time data inorder to support the decision-making process ofthe organization.

In Data Mining more than anywhere else, afamous quote, that may or may not be attributedto Albert Einstein, can be literally applied for goodresults: “If I had one hour to save the world, I wouldspend 55 minutes understanding the problem and5 minutes trying to find a solution.” Challenges fre-quently arise in the definition of use cases, i.e. trulyunderstanding the problem. Therefore, understand-ing business and understanding data need to becarried out thoroughly and will have to go back andforth. We collected learnings from experience gath-ered in other industries and summarize these inthe following together with some practical exam-ples.

2.1 Process modeling and analysis

The step of understanding business is very muchabout understanding processes, i.e. identify tasks

and steps that data can improve, enhance or carryout more efficiently and reliably. Therefore, mod-eling tools like Business Process Modeling Nota-tion 2.0 (BPMN 2.0 (OMG, 2013)) are helpful to modelprocesses and develop a common understanding.Although the BPMN 2.0 notation consists of a largenumber of available elements, a handful of thebasic elements are already sufficient to significant-ly increase process understanding.Approaches to generate use cases can be versatile.A high-level approach is to focus on areas of thevalue chain that have an impact on operating andgrowing the business, as van Thienen et al. suggest(VanThienen, 2016). On a more practical level, itshould be looked out for the following cues to iden-tify high-potential areas for data and/or AI usagewhen analyzing processes:

1. High-volume tasks:Naturally, task that are frequently carried out oftenprovide a greater leverage for potential improve-ments. In these cases, even small time saving meas-ures or quality improvements can lead to a viablebusiness case for applying machine learning.

2. High-value decisions: Similarly, decisions with a high inherent potentialto influence large areas of the organization arepromising candidates for intensive data-usage. Inthese cases, additional efforts may be well justi-fied to make the right business decision.

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3. Media discontinuity: Despite increasing efforts to digitize, many process-es still involve media discontinuities, e.g. printingof documents or form, switching from online work-flows to phone calls or emails. While sometimesthis is obviously not avoidable, e.g. sending a hard-ware product to the end-customer, steps withinthe process that go back and forth between digi-tal and analog workflows are good candidates forapplying automation.

4. Time consuming steps: A strength of AI methods is to process large amountsof data fast without any errors. This often comple-ments human abilities and can lead to significanttime savings. Thus, it is worth focusing on the annoy-ing and tedious tasks.

5. Bottlenecks:Restructuring processes around bottlenecks canincrease overall efficiency. A solution can be paral-lelization of sub-tasks carried out simultaneouslyby a machine in the background.

As with any new product or service, it is impor-tant to take on a “Customer centric” mind-set anddevelop the solution this way. Here, the customercould also be an internal one. In the following, welike to illustrate this approach with an examplefrom practice.

Example: In one project, we analyzed the inter-nal processes of the sales department. One of theplanning steps consumed half a day on averageand was carried out regularly. The organization wasgrowing substantially, so that the process neededto be rethought for scalability. In a workshop, wemodeled the current state of the process and ana-lyzed possibilities for restructuring including theautomated processing of data. We were able toeliminate all media discontinuities by fully digitiz-ing the planning process through a web-applica-tion. Large parts of the data were then gatheredand processed automatically. Overall, this led tosignificant time savings of up to 80% and furtherincreased quality and transparency of this process.

2.2 Data understanding and preparation

The second important step is to understand andprepare the available data. Both technical and eco-nomic feasibility of a use case depend strongly onthe data available, which makes use cases highlyindividual according to a company’s data situation.Therefore, several topics need to be taken intoaccount when aiming to use data successfully. Putin simple terms, the following factors influence thepotential of your data and how well you can build

models from it. They can be considered as levels ofdata needs building on each other. Each levelrequires a certain maturity of the previous level:

1. Data sources and typesFirst, where does your data come from and sincewhen are you recording it? Is it a structured SQL-Database, a sensor-stream, a manually filled spread-sheet-file, or something else? Data can be struc-tured or unstructured. For example, a free text doc-ument like a project report or an invoice in pdf-for-mat is considered unstructured data. If you have aform like a contact form on a website, then eachfield (e.g. name or email address) has a particularmeaning. This represents structured data. Unstruc-tured data is much harder to process and requiresmore steps to prepare the data.Data types regard to the kind of data. For example,this can be text, numerical, or categorical. The lat-ter refers to a fixed set of values that the data cantake on, e.g. ‘True’ or ‘False’ or colors of a product‘red’, ‘green’, or ‘blue’.

2. Data qualitySecond, it is important to assess and understandthe quality of the data available. In particular, thefollowing questions are of importance: Are the val-ues complete over the whole data set or are theremissing values? How should they be handled andhow does this affect the quality of the application?Can the root problem be fixed or can the data beprocessed even when values are missing? Are out-liners present in the data? If so, the data might needto be cleansed. Many of these rather technical issuescan also point to issues in the process setup itself.

3. Data contentThird, it is important to understand what informa-tion does this data represent? With numerical data,descriptive statistics can be used to describe thedata (i.e. data distribution, histograms, etc.).Although this step may seem tedious and withouta clear goal at first, every minute spent is well invest-ed. It is a prerequisite to understand the data avail-able very well. Then, a judgment of the models con-cerning their suitability and application is possibleat later stage of the process. When digging into thedata, ideas for potential use cases can be generat-ed. In most cases, insights about operational andorganizational processes can be gained, which arenot expected at first.

Example: At one of our customers, productioncontrolling data was recorded in both EnterpriseResource Planning (ERP) system and Manufactur-ing Execution System (MES), but was not intensive-ly utilized. Before creating use cases to apply AI, theavailable data was inspected for its types, quality

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Successful data applications: a cross-industry approach for conceptual planning

and content. This exercise proved to be highly edu-cational for everyone involved. In fact, untappedinformation was identified, and a new detailedreport personalized for each foreman was designedand introduced as a result. This example illustrates,that effective solutions to enhance data usage canalso be quite simple.

2.3 Modeling and evaluation

In the data modeling step, the actual buildingof models using machine learning methods takesplace. Models are a representation of relationships.As shown in Figure 2, they map input data to anoutput. In the past, models were often created man-ually. For example, if-than-that-rules can make upthe relationship between input and output. Thestrength of machine learning methods is that theycan extract relevant information like rules fromdata automatically. By doing this, the model can belearned from existing data, i.e. input-output exam-ples from the past. Against common belief, thisstep consumes only a relatively small part of thetime – thanks to the versatile tools like softwarepackages available today. These tools facilitate theimplementation of machine learning methods totrain and evaluate models.Machine learning methods can be summarized inthree categories:

1) Supervised Learning, where the model outputis provided to the machine learning method fortraining. For example, Classification, Regression,and Feature Subset Selection methods belong tothis category.2) Unsupervised Learning, where the model out-put is unknown. That means, the methods have tofind structure and make connections by themselves.Clustering and Dimensionality Reduction are exam-ples here.3) Reinforcement Learning, where the model istrained independent of the output. Instead a trained

rule is reinforced by rewarding / punishing depend-ing on whether the output was right / wrong. Arti-ficial Neural Networks and Deep Learning can beassigned to this category.

For a given problem, the model can usually becreated by means of several methods. The chal-lenge for data scientists is to pick the appropriat-ed method (or a combination of methods) basedon their experience in order to develop the bestmodel. Models are trained based on examples.Therefore, it is important to consider the general-ization of the model very well, so that unseen exam-ples are also handled well by the model. Thus, thequality of the model is judged based on a so-called“test-set” of data that is put aside in the beginningof the modeling phase. The following example illus-trates how pattern recognition can help to improveprocesses of a plant.

Example: A plant’s control data from the pastcontains information about process deviations andwhen they occurred. Pattern recognition can beapplied to automatically identify such deviationsbefore they occur. This task can be framed as a clas-sification problem with two classes: “deviation” or“no deviation”. From past examples of plant oper-ations, relevant data features and rules to detectsuch deviations are extracted using machine learn-ing methods from supervised learning. Before thefinal model containing these rules is deployed, itsfit is evaluated using a second set of examples fromthe past.

2.4 Implementation strategy

The understanding of both process and dataprovides two important results. First, the technicalfeasibility as well as the potential benefits of a usecase can be judged. Second, the effort how to imple-ment the respective use case can be roughly esti-mated. This provides an indicator of economic fea-

Model OutputInputData

Machine LearningMethods and Tools

train

Figure 2 Machine learning methods allow to train models from data examples (source: own representation).

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sibility, which significantly lowers the risk of invest-ing disproportionate budgets. However, a consid-erable amount of ideas might still be disregardedat a later stage due to factors that cannot beassessed at this point. In such cases several key fac-tors help to reach a good ROI. In particular, meth-ods can be used that are already widely applied insoftware development and the lean start-upapproach, which focus on agile and iterative devel-opments (Ries, 2011). Risks of failure can be reducedby allowing multiple possible directions. Then,adjustments can be made based on the learningmade within the implementation process. Organ-izations should start with prototypes at a very earlystage to generate this learning. Tackle assumptionswith highest risks first, i.e. fail fast to learn fast!This, of course, requires a culture that is open-mind-ed towards failure.

Meanwhile, it is important to keep a clear focus.A strong and rigorous selection funnel is necessaryto weed out directions, which are not promising.Only projects should be continued that have provedto be successful by fulfilling the defined evaluationcriteria.

The CRISP-DM model aligns very well with thislean approach. This approach will be illustrated bythe example below.

Example: One company holds monthly meet-ings, in which employees participate that are notinvolved in the actual project – similar to a “super-visory board”. Learning from the project regardingwhat works and what does not work as well as newideas are presented to them. The group then judgesthe value and feasibility of the projects and takesa decision to terminate directions that do not showenough potential with respect to the company’s

competitive position. At first, these elements may seem difficult or

sometimes impossible to incorporate in the devel-opment processes of companies from the processindustry, which are traditionally rather conserva-tive and have strict requirements in their qualitysystems. A method to solve this is depicted in Fig-ure 3 – similar to Google’s so-called moonshots1:This should be a parallel and isolated track to theoperational business. In doing this, it provides away to develop solutions to a level of maturity with-out a costly overhead and operational risks. Onlywhen a solution has proven to provide benefits itis transferred to the operational business. Thisapproach greatly decreases costs and risks associ-ated with such endeavors. If a company decides tonot establish such a department internally becauseof its size or budget, an outsourcing of such serv-ices to external experts can be considered.

2.5 Integration into organization

For the long-term success, it is vital to find asustainable way as an organization to embrace theAI technology and to embed it into the organiza-tion’s strategy. The subsequent adaptation of busi-ness processes also brings about change to theemployees, which play an important role concern-ing the integration of such technologies. The humanfactor and the topic of change are easily overlooked.Thus, human factors such as their behavior mustbe especially considered. Several fears are associ-ated with AI as the public discourse discloses. Forexample, its impact on job security and job descrip-tions. These fears must also be addressed within

Transfer Transfer

Operational Business

Figure 3 Development of innovative applications in a parallel environment (source: own representation).

1 Moonshot refers to projects that are similarly ambitious and unrealistic as landing on the moon seemed back in the early 1960’s, but that would constitute a significantadvance.

DevelopDevelop

Test

Test

Design

Design

Plan Plan

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the organization. One way to move forward is tofollow a combined bottom-up and top-downapproach:

Bottom-up: A key factor to get support from theworkforce for technological change is the distribu-tion of facts and knowledge. Employees should beencouraged to identify use cases by themselvessince they know their workflows and tasks betterthan anyone else. This includes the exhausting andrepetitive processes, which could be promising can-didates for automation. The ideas of employeesbecome a valuable source for process improvement,when they are equipped with the right knowledgeabout the technological capabilities and limita-tions. At the same time, they can be mobilized forthe change and a momentum can be created, ifthey are included at the beginning of the transfor-mation process.

Top-down: In parallel, pilot projects initiated bythe management serve as vivid examples in a famil-iar environment help to spread a realistic and clearpicture of what the organization’s individual needsare and showcase success stories as a generatorfor new ideas.

3 Summary and conclusions

Advanced and complex technologies like AIrequire knowledge and a well-structured approachfor conceptual planning and implementation. Withthe CRISP-DM model, an established framework isavailable, which can be used to guide projects thataim at the utilization of data in order to get valu-able insights. In this process, challenges frequent-ly arise regarding the definition of use cases. In thisarticle, insights from practical experience offerapproaches how to overcome these challenges.For the conduction of projects, modern and agiledevelopment methods such as the lean start-upapproach should be used. Strict evaluation criteriaconcerning the decision which projects should befurther pursued must be in place. Implementedcorrectly, this will ensure a high ROI across theseefforts. Further, a holistic strategy for integratingthis change into the organization is necessary toseize opportunities that arise organically.

Many companies are still reluctant to tackle thetechnological shift towards intensified data usageand artificial intelligence – they remain in a wait-ing position. Yet, it has to be considered whetherthe energy and resources spent in observing themarket might not be better invested in gainingfirst-hand experience by diving into the topic. Thefirst option leads to an inevitable time delay. Thus,the second option should be more appealing tocompanies. Especially, since both feasibility andeconomic viability are two company-individual fac-

tors. First practical experiences can be collected byjust diving into the topic and testing assumptions.

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