Smart asset management as a service - VTT · 2020. 6. 4. · Juhanko et al., 2015). The importance...

Smart asset management as a service

Toni Ahonen, Jyri Hanski, Teuvo Uusitalo, Henri Vainio, Susanna Kunttu, Pasi Valkokari, Helena Kortelainen & Kari Koskinen

Deliverable 2.0


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PREFACE ...................................................................................................................................................................4

1. INTRODUCTION .................................................................................................................................................5

2. IMPACTS OF DIGITALIZED ASSET MANAGEMENT..........................................................................................7

2.1 IoT transformation pathways ........................................................................................................................8

2.2 Understanding decision-making as part of service development ................................................................9

2.3 Perceived value and key performance indicators .......................................................................................11

3. OPPORTUNITIES OF ANALYTICS ...................................................................................................................13

3.1 Machine learning in industrial services .......................................................................................................13

3.2 Analytics for maintenance service planning ...............................................................................................15

4. BUSINESS MODELS IN THE DIGITALIZED BUSINESS ENVIRONMENT ........................................................18

4.1 Identification of new service opportunities and sources of customer value ..............................................18

4.2 Digital transformation of asset management business models ..................................................................20

5. DIGITALIZATION ENABLES CIRCULAR BUSINESS .......................................................................................23

5.1 Services promoting circular economy ........................................................................................................24

6. DEVELOPMENT OF SMART ASSET MANAGEMENT SERVICES ....................................................................26

7. NEXT STEPS .....................................................................................................................................................28

CONTENTS

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A number of industrial companies have recognized the oppor-tunities and needs for transformation towards a service mindset with digital capabilities. The transition from bilateral partnerships towards business ecosystems is also challenging companies to think differently. While many successful examples have already been seen from the forerunners, building the capa-bility to integrate, analyze and exploit various sources of data and to create new services for effective asset management requires attention.

SmartAdvantage is a joint project of research organizations – VTT Technical Research Centre of Finland and Tampere Uni-versity of Technology – and industrial partners. The project develops methods and models for digitalized asset manage-ment with a focus on identifying opportunities from previous research.

VTT also coordinates a circular economy related research project, Data to Wisdom – Approaches Enabling Circular Economy (D2W), funded by the Finnish Funding Agency for Innovation (Tekes). While the vast majority of existing corpo-rate and academic research and development is focused on the role of material flows in developing new business models,

the D2W project focuses on information flows in the circular economy. While SmartAdvantage aims to identify and develop approaches for extracting value from data in the industrial con-text, D2W also focuses on the conversion of data into wisdom that can be used to pilot and implement new circular business models. A strong connection and need for collaboration between the two projects has thus been identified and the partners of these projects have been brought together to innovate within the areas of digitalization and circular business models. This deliverable will also present the results of that work.

The authors wish to thank SmartAdvantage industrial part-ners Chiller Oy, Delete Finland Oy, Huurre Finland Oy, Pesmel Oy and SW-Development Oy, for their active collaboration throughout the project. The project is funded by the Tekes Research Benefit program, VTT Technical Research Centre of Finland, Tampere University of Technology and the partic-ipating companies.

23.2.2018AUTHORS

PREFACE

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Digitalization is expected to disrupt many industrial sectors and blur industry boundaries (OECD 2015, WEF 2014). It has the potential to radically change the way people work and compa-nies operate. Digitalization offers companies new opportunities for asset management services. It offers tools to develop new kinds of services through new channels and business logics. Novel digital technologies such as platforms and advanced analytics have the potential to disrupt service design, delivery and evaluation processes.

The current focus in service development is on agile meth-ods and experimentation. The agile development approach originates from software development (Abrahamsson et al., 2002). This approach has the potential for quickly producing so-called minimum viable products (MVP), services that have only basic functionalities. MVPs can be quickly and relatively economically tested and modified in practice. Product and ser-vice ideas that do not show potential in agile testing can also be discarded quicker in comparison to more traditional prod-uct or service development processes. However, in focusing solely on agile development, companies may lose the strate-gic focus of their development activities. There is a need to connect the strategic level, the customer’s decision-making

context and the business model approach to support service development. This deliverable aims to present focal approaches, methods and frameworks that can be utilized in the develop-ment of digital asset management services.

The SmartAdvantage project has provided deliverable 1, which brought together the findings of the first funding period of the project. The present deliverable continues that work and thus encompasses the results of the 2nd funding period of the SmartAdvantage project as follows:

• The first part discusses the use of analytics approaches and methods from the perspective of different levels of decision-making and analytics

• The second part presents examples of analytics in order to demonstrate the opportunities related to data-based services

• The third part discusses the evolution of the business model of digitalized asset management services

• The fourth part aims to build a holistic picture of the combina-tion of circular economy, digitalization and asset management in order to better understand future requirements.

• The fifth part concludes the report by introducing a combi-nation of methods and models for digital service business development.

1. INTRODUCTION


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The SmartAdvantage project is carried out in close collabora-tion with the following industrial partners.

DELETE FINLAND

Delete is an environmental full-service company operating in the Nordic countries. It provides cleaning, demolition, recycling and waste-processing solutions for different applications. As a one-stop-shop, Delete can provide entire service chains with an extensive range of equipment and competent experts to support the business operations of its customers. The target of Delete is to be a forerunner in the industry in introducing innovative solutions to its customers’ needs. Delete looks to digitalization for support in creating new customer value and resource efficiency and pays attention to the scalability of services and the creation of new business models. Research collaboration in SmartAdvantage is focused particularly on the renewal of business models.

HUURRE FINLAND

Huurre Oy is an international refrigeration services company with a vision of becoming a global leader in selected markets by improving the efficiency and safety of global cold chains. Huurre has over ten years of experience in developing its digital offering and continuously promotes the introduction of novel technologies. Huurre, Tampere University of Technology and VTT have jointly initiated a project aimed at achieving energy efficiency improvements through novel analytics and creating novel business models for new digital services.

PESMEL

Pesmel is a global supplier of highly automated internal logis-tics, storage and packaging systems for the metal, paper and converting industries. Pesmel has developed the Pesmel FlowCare system which will in the future provide a collabora-tion platform for Pesmel and its customers in order to ensure a high standard of customer service. Research collaboration in the SmartAdvantage project has paid particular attention to developing accuracy in customer-specific maintenance planning and the application of simulation and algorithms for developing efficiency in internal logistics.

CHILLER

Chiller is one of Europe’s leading manufacturers of energy-ef-ficient and optimized air-conditioning solutions. Chiller designs and implements solutions for high-demand projects based on its high-level expertise. Collaboration in SmartAdvantage has focused on developing capabilities to utilize life cycle data more efficiently in the provision of Chiller’s services for selected cus-tomer segments. Furthermore, its value proposition and related business models are being developed.

SW-DEVELOPMENT

SW-Development operates in Finland and Sweden and special-izes in improving the profitability of supply chains. The company focuses on improving its customers’ production and logistics processes by utilizing intelligent simulation and optimization systems. These increase the accuracy of strategic, tactical and operational planning, thus enabling more efficient utiliza-tion of resources to achieve competitive advantage. The case study conducted in SmartAdvantage focused on the creation of new customer understanding for use in the development of new services and business models.

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2. IMPACTS OF DIGITALIZED ASSET MANAGEMENT

The digitalization of manufacturing has been ongoing for decades and has had a growing impact on economies and industries worldwide. At the core of the most recent wave of development are increasingly smart, connected products and services that produce up-to-date information on their status, and the Industrial Internet of Things (IIoT) that combines intelli-gent machines, analytics and the people using them (Evans & Annunziata, 2012). Table 1 presents the main elements of an industrial IoT system. Assets enabled with sensors and com-munications capability generate data and share this data. Its key characteristic is the real-time availability of data, ensured by network technologies and cloud-based infrastructure. Data is interpreted using sophisticated analytics tools. This enables people to base their decisions on analyses provided by the IIoT resources.

TABLE 1. INDUSTRIAL IOT EFFECTS (FROST & SULLIVAN, 2018).

Intelligent assets

Machines and other assets ena-bled with sensors, processors, memory and communications capability. Intelligent assets gen-erate data and share information across the value chain.

Real-time data availability

Data communications between assets and other entities using network technologies and cloud-based computing infrastructure.

Analytics and applications

Analytics and related software enhance asset and system optimization. Predictive analytics used to reduce unplanned downtime.

Power of the people

People have access to more data, improved analytics tools and better information. Decisions are increasingly based on the analyses produced by these resources.

New intelligent products require the creation of a drastically new multi-layered technology infrastructure. This technology stack consists of different platforms, programs, networks, ser-vices, processes, and actors (Porter & Heppelmann, 2014, Juhanko et al., 2015). The importance of information platforms is increasing in the manufacturing industry. Platforms are uti-lized in the development and maintenance of new products and processes in order to maintain compatibility with previ-ous, present and future product generations and ecosystems (Juhanko et al., 2015). Collecting information and offering digital services through a platform is a source of competitive advan-tage for manufacturing companies.

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”Bottom-up” IoT focusing on operational

benefits inside a company

”Customer oriented” IoT focusing on new

functionality and services for users

”Digital twin” oriented IoT coupled with

advanced machine learning, AI, and

simulation

”Value chain oriented” IoT focusing on

benefits in logistics chains across

companies

2.1 IOT TRANSFORMATION PATHWAYSThe industrial internet transforms companies, their value net-works, business and ecosystems in several ways. Two distinct pathways can be distinguished: innovations by incumbent firms who gain operational benefits and business value (disruption from within) and new entrants to the market, e.g. from media or banking expanding their business (disruption from outside) (Mäntylä, 2017). Figure 1 illustrates the possible scopes of digital solutions in companies.

The “Customer-oriented” IoT refers to the development of digital services. Using the data collected from the installed base of machines and infrastructure items is an important application of the industrial internet. Companies are able to access data streams from machines and infrastructure items located anywhere. Fleet-based information may be used, for instance, for assessing the performance of a single machine compared to the current and previous performance of the entire fleet (Lee et al., 2015).

With digital applications, sensor and process information can be extensively used to support decision-making related to the use and maintenance of production systems. To deliver advanced products and related services, manufacturing com-panies should deploy solutions that enable remote monitoring of product or asset location, condition, and use. These solutions allow actions to manage maintenance, repair, field operation, and improvements to product or service design (Baines & Lightfoot, 2013).

Entrants from other sectors – 3rd party players – have also found a growing market for knowledge-based services. For example, Travis Kalanick (CEO of Uber) said “If we can get you a car in five minutes, we can get you anything in five min-utes”, meaning that they are prepared to re-distribute roles in manufacturing ecosystems by creating new and interesting information and platforms (Choudary et al., 2016). Digitaliza-tion may disrupt “traditional” fields of industry in crucial and unforeseen ways. In addition to the technological challenges of intelligent assets and real-time data transmission, machinery

suppliers face new knowledge requirements regarding data analytics and, in particular, understanding of the customer business environment, as this knowledge can be used to sup-port customer decision-making.

The “Bottom-up” IoT – refers to the operational bene-fits that could be realized by IoT solutions inside a company. With industrial internet sensors machines, processes and ser-vices continuously produce information that can be refined as knowledge for real-time decision making and used to automate work – among other applications. Digitalization is considered as a tool to increase turnover, reduce costs, improve the effec-tiveness of capital spending and renew business.

Digitalization changes the value chains and blurs the tra-ditional borders inside and between companies. It enables the outsourcing of almost all current business processes and opens up new business opportunities in data-based services, logistics and production (BMWi, 2015). The new forms of value creation require manufacturing companies to combine novel

competencies and resources from inside and outside the com-pany into new business ecosystems.

The “Value chain oriented” IoT has its origin in the German Industrie 4.0 initiative that focuses on improving the efficiency and interoperability of industrial supply chains (Acat-ech, 2013). Industrie 4.0 aims to create more value chain transparency. It emphasizes the role of autonomous manu-facturing systems but also the competencies of the people using those systems. Production and logistics processes are integrated intelligently across company boundaries to make manufacturing more efficient and flexible. Comprehensive real-time information enables companies to react to the availability of certain raw materials early on, for example. Production pro-cesses can be controlled across company boundaries to save resources and energy. Flexible production and transparency in the value chain enable companies to produce customized products and at the same time reduce manufacturing costs.

The “Digital twin” oriented IoT connects the real, dig-ital and virtual worlds of production and serves to bridge and link real-world entities and their digital representations, i.e. “digital twins”. The physical asset and its digital twin thus form a cyber-physical system (CPS) (Lee et al., 2015). In future, a digital twin will be created already in the product design phase from CAD models and data contained in the PDM system (Juhanko et al., 2015). The digital twin could be shared with supply chain partners, thus offering joint access to real-time data and speeding up the design, product manufacturing and ramp-up phases. A digital twin is maintained synchronized with the evolving behavior of the physical systems and offers up-to-date information on the asset status, also allowing simulations and predictions of future behavior. The “Digital twin”-oriented IoT has applications in many other sectors beyond industry. For instance, the built environment and construction sector generates and utilizes a plethora of digital data, data models, analytics and ICT, with the digital and physical worlds inter-linked throughout the asset life cycle.

FIGURE 1. PERSPECTIVES OF IOT IN COMPANIES (MÄNTYLÄ,

2017).

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2.2 UNDERSTANDING DECISION-MAKING AS PART OF SERVICE DEVELOPMENTAsset management decisions, including investment, operation and maintenance, are affected by several indo- and exogenous factors (Hastings, 2010; Komonen et al., 2012; Liyanage, 2012):

• changes in demand, the competitive environment and products

• economic stability• climate change• compliance with requirements• technological development• acquisitions• changing operating practices and requirements• wear and aging• economic, technical and environmental obsolescence

These factors highlight the need for thorough understanding of the decision context (decision-making situations and deci-sion-making at different organizational levels) in a company, in addition to technical analytics expertise.

2.2.1 The maturity of decision-makingFigure 2 present the levels of analytics from backwards looking descriptive and diagnostic analytics to forward looking predic-tive and prescriptive analytics. When moving from methods that try to answer the question “what has happened” towards “what will happen” and “how can we make it happen”, the value and difficulty of the method increase considerably.

However, this classification does not take into account the different decision situations and, thus, the different levels of decision-making that the analytics method may support. Knowledge provided by descriptive analytics may support strategic decisions, for instance major investments, whereas prescriptive analytics may provide new foresight into operative decision situations, such as maintenance actions.

When developing services, companies should have a clear understanding of the situations where their potential customers might need their services and in what kind of situations they

can benefit from the services. Decision-making situations can be classified as reactive, real-time, proactive or strategic deci-sions. The decision-making situations vary according to the effect of the decision and uncertainty of the outcome. A cate-gorization of decision-making situations can help the service provider to see the potential service opportunities. Categori-zations have been made according to the organizational level involved and the time spent on the decision-making process (Sun et al., 2008), the timescale available for decision-making and the life cycle perspective (Kinnunen et al., 2015), and the frequency of decision-making situations (Kunttu et al., 2016a).

Kunttu et al. (2016a) classify decision situations into three categories according to the aim of the decision: daily operation

FIGURE 2. STAGES OF DATA ANALYTICS MATURITY (ADAPTED FROM DAVENPORT & HARRIS 2007 / GARTNER 2012).

Sophistication of Intelligence / Difficulty

What happened?

Why did it happen?

What will happen?

How can I make it happen?

Co

mp

etiti

ve A

dva

ntag

e /

Mat

urity

/ V

alue

Information

Optimization

Descriptive Analytics

Diagnostic Analytics

Predictive Analytics

Prescriptive Analytics

and maintenance to achieve strategic goals, development of current assets or functions to improve business profitability, and changing current assets or functions to improve business profitability. Decisions in the first category aim to achieve the goals, e.g. key performance indicators (KPIs), set for opera-tion and maintenance. They are made at the operational level. Tactical- and strategic-level decisions aim to improve business profitability. Tactical-level decisions concern the development of current assets and functions and include investment deci-sions or decisions to develop maintenance plans. Strategic-level decisions are related to major changes in assets or functions. Examples of strategic-level decisions include a new produc-tion line or the outsourcing of functions.

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2.2.2 Decision-making at different organizational levelsFigure 3 visualizes the different decision levels in asset management.

Operational-level decisions include optimizing operational and maintenance activities. Decisions on maintenance actions must typically be made rather quickly (timeframe from hours to days). The same kind of maintenance decision-making sit-uation can occur frequently. When decisions must be made quickly, there is no time to gather more data or plan struc-tured decision-making. On the other hand, there is no need for laborious methods because decision-making situations at

the operational level are typically not very complicated and the impact on business is seldom crucial. Despite their minor effect, it is crucial over the long-term that most operational level decisions are taken correctly. Due to the limited timeframe for decision-making, support for operational-level decisions should be well planned. Typical data-intensive services for the operational level include, e.g., remote monitoring and control, predictive analytics for asset performance, descriptive analyt-ics for causes and effects, failure detection, and elimination of unplanned breakdowns.

At the tactical level, asset management decisions may be supported by risk analyses that consider economic, tech-nological and environmental aspects. Operational data may support both tactical and operative decisions. The timeframe for decision-making is typically longer than at the operational level (from weeks to a couple of months). At the tactical level, the situation is more complex and the effect on business much greater. Wrong decisions can have a negative effect on business. Tactical-level decisions need more time, struc-tured methods to identify relevant alternatives, and selection of the optimum choice for the current situation. Similar deci-sion-making situations typically occur once a year or once every couple of years. For example, replacement investment decisions are made annually.

The vision, values and mission of the company affect its business objectives and constraints, which in turn affect its strategic asset management decisions. These decisions are supported by various methods and services such as scenarios, strategic analyses and technology forecasts. The frequency of strategic-level decisions is often low, and the timeframe for decision-making can be from months to years. An example of a strategic level decision could be moving into a new business, market or disruptive technology. This often leads to investment in a new production line or plant. Strategic-level decisions are complicated, include multiple uncertainties, and have a big influence on profitability. Strategic-level decisions require gathering a lot of data and the adoption of decision-making methods, but due to the uniqueness of these decision-mak-ing situations, support must be planned separately each time.

Vision, values and mission

Optimal use of assets to meet the business objectives

Strategic analyses: e.g. scenarios, technology forecasts, business intelligence

Risk analyses: economic, technological and environmental aspects, exploitation of operational data

Optimising operational and maintenance activities, exploitation of operational data

Business objectivesand constraints

Tactical levelobjectives and

constraints

Operative levelobjectives and

constraints

Strategic assetmanagement

decisions

Tactical assetmanagement

decisions

Operative assetmanagement

decisions

Allocation Information

Allocation Information

FIGURE 3. ASSET MANAGEMENT DECISION MAKING FRAMEWORK. ADAPTED FROM KOMONEN ET AL. (2006).

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2.3 PERCEIVED VALUE AND KEY PERFORMANCE INDICATORSOverall equipment effectiveness (OEE) is a commonly applied key performance indicator that summarizes the impact of availability, performance and quality. OEE represents a bal-anced approach that helps to align the activities in production, maintenance and logistics with a common goal to minimize downtime, disturbances and defects. Net total production time is less than planned as the “six big losses” hamper produc-tion. The “six big losses” notion is part of the Total Productive Maintenance (TPM) concept (Nakajima, 1989). TPM aims to eliminate six types of losses: failure of equipment, set-up and adjustment times, idling and minor stoppages, reduced speed of equipment, defects in process, and reduced yield (Figure 4). TPM strives for zero defects by understanding and con-trolling interactions between manpower, material, machines and methods that could enable defects to occur.

Table 2 presents some OEE figures from different industrial sectors. As the table shows, the manufacturing industries are lagging behind the process and paper industries in OEE. The manufacturing industry seems to be characterized by a high portion of unplanned maintenance work and a low portion of measurement-based maintenance. In the process industries, commercial condition monitoring systems are widely applied to provide trends and to generate alarms on machine condi-tion and process operations, and on abnormal and potential signs of premature failure (e.g. Marshall, 2002; Holopainen et al., 2005; Galar et al., 2014).

As the OEE concept is not standardized, there is no “right” reference value and the measurement practices across indus-tries vary. For this reason, the figures in Table 2 should be considered only as indicative. In most industries the OEE values are below those of the world class performers, leaving space for improvement. The OEE figures also indicate the potential to increase production without major capital investments by improving availability and performance in some industrial sec-tors, such as manufacturing. In one case example, systematic

TOTAL TIME SIX BIG LOSSES

OEEFACTORS

Theoretical productiontime per day

Planned productiontime Prod.notplanned

Actual prod. time Availabilitylosses

Speedlosses

Net prod. time

OEEvalue

Qualitylosses

Net total inproduction

Loss in production Total loss

Scrapped units

Rework

Idle time, short stops

Reduced prod.speed

Machine malfunction, unplanned downtime

Changeover, adjustments

AAvailability

QQuality

OEE=A x P x Q

PPerformance

FIGURE 4. OVERALL EQUIPMENT EFFECTIVENESS (OEE) AND THE SIX BIG LOSSES.

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efforts to improve OEE improved the availability performance by 4–8% percentage units (Valkokari & Rouhiainen, 2000), and in another case an implemented mobile strategy reduced major breakdowns, set up and adjustment losses (Jain et al., 2015). Industry 4.0 concepts allow direct and real-time measurement of OEE, improving accuracy compared with traditional means (Hwang et al., 2016).

OEE aims at maximizing utilization of the current, designed capacity. A “part optimization” or “trade-off”, e.g. production rate vs. product quality, may not yield the best solution. The system may, however, contain upgrade potential. Debottle-necking, process optimization or upgrading equipment with the latest technology may increase capacity. The impact of systematic and continuous improvement is schematically shown in Figure 10.

Digitalization and IoT solutions enable nearly unlimited data collection and data analysis in real-time, and novel data-based service offering. Effective production – as measured by OEE – requires equipment to be running at optimal speed during the planned production time and that the production process delivers the output according to specifications. OEE is impacted by maintenance-related services, services that help to encounter operational problems, and services that reduce quality deficits. Thus, OEE offers a way to identify, illustrate and measure the benefits of a service to the customer´s production efficiency as a whole. However, the possibilities of increasing production by reducing planned downtime or by upgrading production systems also offer service options. For the cus-tomer, each realized OEE percent increase confers an equal financial benefit is financially equal, but the cost of achieving the desired level can vary.

Source Keynon and Sen (2015) (cf. ABB Inc.)

Cheh (2014) Komonen (2005)

IndustryWorld class

OEE (%)

Top industry performers OEE (%)

OEE (%) in Swedish

companies

OEE (%) in Finnish

companies

Proportion of measurement-based maint.

hours (%)

Proportion of unpl. maint. hours (%)

Manufacturing 85 60 43-50 69 0.9 45

Process >90 >68 79 3.5 31

Paper 95 >70 80 7.9 23

Food industry 50 70 8.6 36

TABLE 2. OEE IN SELECTED INDUSTRAL SECTORS.

FIGURE 5. IMPACT OF LIFETIME SUPPORT ON PRODUCTION EFFICIENCY (KORTELAINEN ET AL., 2003).

OEE

100 %

Time

Continuous improvement of machinery, procedures and processes

Normal ageing, wearing, misuse, lack of preventive maintenance

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3. OPPORTUNITIES OF ANALYTICS

The SmartAdvantage – Data to Wisdom joint seminar on 15.9.2017 included discussion on the data requirements for applications in a circular economy and asset management. The discussion covered a good number of data sources in the industrial landscape. Several applications for the data were also identified.

The identified data sources can be divided into process information, measurement data, environment data, user data and tacit knowledge. Process plans, weather and other envi-ronment data, sensor readings from the process, and operator information together create process-wide situational aware-ness. Tacit knowledge is more difficult to capture, yet any additions from that realm deepen this awareness. While there is often a large amount of data available from at least some of these sources, it was found that it is not utilized very effi-ciently. This can be due to a lack of means to gather the data or to analyze it, or simply due to the lack of a strategy for the large-scale utilization of data.

The identified applications based on these data sources ranged from industrial service delivery to large-scale optimization

of processes or fleets. Application areas were identified both in the scope of technical solutions as well as organization development. Three categories were found: 1) On-line optimi-zation and minimization techniques and efficiency of usage; 2) Organizational memory, meaning the utilization of tacit knowl-edge and human-to-human communications such as emails and digital conversations; 3) Long-term optimizations up to lifespan level supporting product development and design. An important conclusion was that most applications benefit greatly from combining multiple sources of data.

3.1 MACHINE LEARNING IN INDUSTRIAL SERVICESMachine learning provides efficient solutions to some of these applications. Machine learning means algorithms that discover rules behind patterns in a dataset and then learn those rules and are able to apply them in new situations. Given enough training data to learn properly, machine learning algorithms are

able to apply what they have learned in either classification (deciding which predefined category the target of analysis is most likely to belong to), regression (predicting the next values in a sequence, such as a time series) or clustering (applying the learned rules to the training data itself and arranging it in ways that seem logical, thereby making it easier for humans to understand and other algorithms to learn). All of the machine learning application types include algorithms that are able to handle multiple data sources, creating the sort of combined information or knowledge that was seen as an important con-clusion of the SmartAdvantage-D2W seminar. In the first phase of SmartAdvantage several useful algorithms were identified and these were tested in practice during the second phase. The potential for adding value is great, since machine learning tools are in most cases open source technology and require only data, computational power and knowledge on how to use them, with the ability to leverage knowledge creation from multiple data sources.


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3.1.1 Data preprocessing issuesData coming from multiple sources must be unified before training the machine learning models. Timestamps and meas-urement frequencies must be matched and outliers and anomalous events removed. Some algorithms require the data to be scaled in certain ways. Then, the first machine learning tools can be taken into use. If the data is completely unstruc-tured, unsupervised learning methods can be used to split it into clusters and find patterns in it. In the case of known data, correlation matrices can be created and decision tree forests can be trained to identify the relative importance of the input variables. This information can then be used in selecting which data sources to use in training more complex algorithms. This is a time-consuming but very important step.

It should be noted that even data preprocessing and feature importance detection must be done with a good understanding of the process being analyzed. In the use of machine learning methods, correlation does not necessarily imply causality. The algorithms can, and will, also reveal things that are obvious to anyone who understands the physical system. While this can be used to verify the correct operation of the algorithm, it also means that using these methods blindly does not guarantee good results or important findings. Machine learning methods should be used in close cooperation with experts who under-stand the operation of the real-world system, both in designing the experiments as well as interpreting the results.

3.1.2 Optimization and prediction tasksOnce the data is cleaned up and the meaningful variables chosen, it is possible to train a wide variety of machine learn-ing models. The choice of algorithm mostly depends on the intended task as well as the amount of training data available. Many optimization and prediction tasks in industrial services fall into the category of regression and time series modeling. The simplest algorithms in this category are linear regressors and the most complex are deep neural networks of the recurrent type. The decision tree forests used in the data preprocessing step can also be used here. They are not as powerful as the deep networks, but require less training data and give a good initial model. Such a model can then be used either to predict future events or analyze the effects different decisions have on the operation of a process. They have the ability to model the effects of varied things, such as weather and production amounts, on the behavior of a machine. For example, energy consumption or remaining useful life can be predicted using multiple sources of data.

Machine learning models are by nature probabilistic and cannot compete in accuracy with properly constructed ana-lytical models. Their strength is that they are much easier to create; they only need proper training data and enough com-putational power to construct. They are also able to detect, learn and simulate phenomena that would be quite difficult to model analytically. For example, the effects of temperature or

DataGathering

• Process• Sensors• Environment• User

Tacit Knowledge inchoosing the data

DataPreprocessing

• Cleaning• Clustering• Choise of important variables

System Knowledge in evaluating the results

Model Training

• Choice of ML Algorithms• Testing• Creating the final models

System Knowledge in verifying the models

Analysis

• Energy• RUL• Scheduling• Efficiency

System Knowledge in intrepreting the results

FIGURE 6. THE STEPS OF INITIALIZING A MACHINE LEARNING BASED ANALYSIS.

humidity may lead to complex analytical equations that require a lot of computational power to solve, while on the other hand a machine learning model of the same phenomena will be much lighter to run and still achieve good enough accuracy. Figure 6 illustrates the steps of creating a machine learning model and the important things to consider at each step.

3.1.3 Example of energy consumption analysis using machine learningThe project case company Huurre Oy has collected sensor data from several of its clients over the years. A test case was created using data from one of these clients, with the aim of analyzing the energy consumption of a cooling system of a factory. The system has several hundreds of sensors installed and their measurement data is continuously gathered to a cloud server. Production data from the factory was also available in the form of production amounts. Corresponding weather data from the Finnish Meteorological Institute was also acquired. Operator data was not available; however, the process is largely automatic and operators do not have a large impact on it.

The data was preprocessed and correlation matrices were then created to give a general idea of which data items were the most meaningful for energy consumption. A deci-sion tree based random forest regressor model was trained on the data and then used to create a list of data items based

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3.2 ANALYTICS FOR MAINTENANCE SERVICE PLANNINGIn industry, one typical aim of data analysis is to identify abnor-mal behavior or other anomalies indicating a component failure. From the maintenance service point of view, this kind of data analysis is part of the service delivery. In addition, analyzing other types of data, including customer data, technical event data and cost data, can create information that supports the customer’s decision making and the service provider’s offer-ing definition.

The value of maintenance services is determined by a combination of several factors, and the outsourcing policy of a potential customer affects which value elements are the most important (Ali-Marttila et al., 2017). Thus, the service provider needs a set of tools and methods to identify the value that a

on their relative importance. These results were checked with experts who understood the real-world process thoroughly. With their help, the important variables were chosen and used in training a new random forest regressor model. This model was then able to simulate the energy consumption of the pro-cess satisfactorily.

This project increased the level of knowledge on system functions at every step, confirming the expert’s view of the importance of some variables while giving new information on the effects of production amounts on energy consump-tion. The model could then be used for examining the effects of different input variable configurations on energy consump-tion, creating optimization solutions that take into account the

How to define service offering for a potential customer?

1. Define maintenance environment of the site

2. Recognize current maintenance practices

of the site

3. Evaluate successfulness

of the site

4. Compare maintenance practices between the best

site and the new site

Environ. 1Environ. 2Environ. 3

FIGURE 7. THE MAIN STEPS IN RECOGNIZING DEVELOPMENT TARGETS BY BENCHMARKING.

mechanical system, the process and weather. For processes that consume a lot of energy, this type of optimization could bring considerable value to the customer or enable cooling as a service business model. Further work will include creat-ing better models of the process as well as testing predictive models based on the same machine learning concepts.

The key requirement for this modeling task was having enough training data. Therefore, in order to leverage the power of the machine learning algorithms in support of industrial pro-cesses, these processes must include sensors and data must be gathered. Of course, different types of data exist and can be analyzed with other methods.

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customer will gain and how that value is created. This chapter presents two practical databased methods that can be used in service purchase negotiations to support the definition of service content and to show the value of the service.

3.2.1 Benchmarking

Benchmarking is a widely used method that allows a com-pany to compare its own practices and processes to the practices applied in the best firms of the industrial branch. A typical objective is to find justified development targets – and to benefit from existing good practices in the industry. For the service provider benchmarking can be a tool to identify poten-tially weak points in the operation of a potential customer and provides input for detailed discussion and planning and prior-itizing development actions (Kunttu et al., 2017).

Benchmarking for service offering definition requires rel-evant data about the maintenance environment, applied maintenance practices and level of success. Maintenance environment data includes variables describing, e.g., the age of the technical system, required system availability, heaviness of the process, etc. This kind of data is needed because the benchmarking method is based on the idea that no absolutely best maintenance practice exists. The most effective mainte-nance practices vary according the maintenance environment (Komonen et al., 2011), which is essential to take into account when offering maintenance services.

Not all data needed for this kind of benchmarking is typi-cally available from CMMS or other industrial databases. The first step in developing a benchmarking system is therefore to determine the available data, the required data, and the steps needed to implement data collection. Collecting good statistical data for benchmarking purposes requires time and resources, but initial pilots or demonstrations can often be carried out with already available data.

3.2.2 Life cycle cost/profit calculationPotential customers are interested in the value of the offered service. Life cycle cost (LCC) and profit (LCP) calculations are tools with which to identify and discuss which monetary factors the service will affect. The objective of the calculation case is to determine whether to focus on costs or profits. The aim of LCC calculations is to optimize the total cost of ownership (Wood-ward, 1997), which makes it an appropriate tool for evaluating the monetary value of maintenance or a maintenance service. The main aim of the maintenance or maintenance service is to increase availability, i.e. production. Unavailability cost, i.e. the cost of lost production caused by unavailability, is an indirect measure of availability increase. Thus, it is essential to include unavailability costs in the calculations in order to gain a real-istic understanding of the maintenance value.

Obtaining realistic values for calculations estimating future costs is a challenging task. Analyzing maintenance data can give estimates of, e.g., time between failures, which can be combined with the cost of repair to calculate the expected maintenance cost per year. The service provider is highly unlikely to have a database comprehensive enough to cal-culate reliable life cycle cost estimates for a given customer case. Findings from data analysis can, however, be supple-mented by expert judgements from the provider’s own service organization and by customer-specific data collected from the customer (Kunttu et al., 2016b).

Detailed life cycle cost categories are highly dependent on the case in question, but in industrial cases, mainte-nance, unavailability and energy are typically the main cost of ownership categories. Engaging in discussion with potential customers about the cost categories most relevant to them is highly beneficial. Comparing the calculated life cycle cost of a case with and without the proposed service will further concretize the potential value of the service even if the figures are little uncertain.

3.2.3 Example of existing data exploitation Project case company Pesmel has for several years kept main-tenance reports on all preventive maintenance visits carried out by them. The company’s maintenance personnel fill out and save a fixed-form maintenance report, which includes the customer name, lift identification, current use hours and list of components inspected, cleaned, lubricated, adjusted and replaced during a maintenance visit (Figure 8). The aim of the report is twofold. For maintenance personnel, the report serves as a checklist to follow during a maintenance visit, while for the customer the checklist serves as documentation of conducted maintenance actions. In addition to this, the reports offer a potential source of valuable data on maintenance intervals, but

FIGURE 8. EXAMPLE OF A MAINTENANCE REPORT.

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so far Pesmel has not utilized this opportunity. A similar situa-tion can be seen in many other industrial service companies.

Combining all maintenance reports within a single dataset enabled data analysis providing information on, for example, the mean time between replacements and its variation between different customers (Figure 9). The maintenance reports did not, however, contain any data that could be analyzed to identify the reasons behind the differences in times between replace-ments. In this case, pure statistical data did not provide enough information for service development. Maintenance personnel have an important role in supplementing statistical data with inside knowledge, such as which lifts are comparable between each other and can be analyzed together.

FIGURE 9. EXAMPLE OF RESULTS CREATED FROM MAINTENANCE REPORTS.

Component X - mean time between replacement

2000

1800

1600

1400

1200

1000

800

600

400

200

0Lift 1 Lift 2 Lift 3 Lift 4 Lift 5 Lift 6 Lift 7 Lift 8 Lift 9

Tim

e b

etw

een

rep

lace

men

t [h

]

Industrial companies have been busily saving vast amounts of different kinds of data over the years, but with no clear plan for how to use it. Today, huge amounts of statistical data are being created offering a potential wealth of useful informa-tion, given the proper analysis. Although data gathering and cleaning for analysis can be laborious, it is likely to be quicker and cheaper than starting a new data collection project or process. It is better, then, to first turn to the currently existing data, examine its possibilities and then make a development plan for data collection based on the information needs.

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Utilizing opportunities of digitalization inthe development of new services

Streaming analytics and tools

Data transfer follows therequirements of standards

Concept for fleet asset management

Data sharing and common KeyPerformance Indicators (KPI)

Customer value and service experience

Benchmarking foridentifying best practices

Development of knowledge intensive

services

Roadmap for industrial internet applications

Strategic tools for digital disruption

4. BUSINESS MODELS IN THE DIGITALIZED BUSINESS ENVIRONMENT

4.1 IDENTIFICATION OF NEW SERVICE OPPORTUNITIES AND SOURCES OF CUSTOMER VALUEDigitalization offers several new service opportunities for the operation and maintenance of asset fleets. Companies need competencies in analytics and understanding of the custom-er’s business to utilize these opportunities. The challenge for companies is to identify untapped opportunities that are rel-evant to a large enough number of paying customers. In this chapter, we introduce approaches that support the identifi-cation of new digital service opportunities. In addition, they provide a means for deeper understanding of customer’s activities and potential for value adding services. We argue that a holistic view of the customer’s processes supports the development of the service provider’s product and ser-vice portfolio. Figure 10 shows the focal elements related to the development process of digital services. We particularly highlight the importance of customer knowledge and under-standing of customer value creation being utilized throughout the service development process. FIGURE 10. IDENTIFICATION AND UTILIZATION OF DIGITALIZATION OPPORTUNITIES FOR NEW SERVICE DEVELOPMENT.

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Our approaches for the identification of customer needs and understanding customer value include:

• Roadmap for industrial internet utilization and applications• Value map for circular economy opportunities• Analysis of circular economy requirements for service

potential• Tools for customer task identification and business modelling• Reliability and criticality analysis• Life Cycle Cost and Profit models (LCC/LCP)• Total Cost of Ownership models (TCO)• Optimization of Overall Equipment Efficiency (OEE)• Benchmarking in identifying targets for improvement• Analysis of history data

In efforts to understand the potential and impacts of digitaliza-tion, asset owners, original equipment manufacturers (OEMs) and service providers have established their roadmaps for the adoption of digital technologies. Roadmapping for digi-talization and analysis of the value potential related to circular economy as well as comparisons of the customer organiza-tions’ and service provider’s roadmaps and plans are among the key tasks to be done when starting to identify concrete steps towards digital services.

Reliability and criticality analysis may be applied to identify where maintenance and investments could result in best value for money and reveal bottlenecks in the customer’s processes and the potential for digital services. Life cycle cost and profit, TCO and OEE models provide additional information in terms of efficiency and economic measures. Data regarding large fleets may be utilized to provide benchmarked knowledge and act as a gap analysis for the customer.

Large amounts of operations and maintenance data have been collected in recent years; however, use of this data has often been limited. Relatively small investments in the anal-ysis of maintenance history data, for instance, may provide significant support in identifying where maintenance is most needed, where the most costs originate and where the most potential lies in O&M.

The value proposition and understanding of how value is created in customers’ processes are at the core of the digital service business model. This begins with a vision of the tech-nology, process change or service with which a positive impact on the customer is to be achieved. A structured approach is needed here from the outset; however, there is room for iter-ation regarding the value proposition throughout the business model development process. It is widely acknowledged that the value proposition is among the most challenging aspects of creating the digital business model. One of the challenges is understanding what we want to get out from the large

GAINS

CUSTOMER JOBS

PAINS

GAIN CREATORS

PAINRELIEVERS

PRODUCTS &

SERVICES

FIGURE 11. A VALUE PROPOSITION CANVAS (FIGURE ADAPTED FROM OSTERWALDER ET AL. 2014).

amounts of data. Together with the abovementioned meth-ods and tools, the value proposition canvas may be used to develop and analyze customer value.

The value proposition canvas is used to structure and bet-ter understand customer needs and feasible ways to react to them. It may be considered as a complementary approach to the business model canvas, which puts together the differ-ent pieces of the whole business model. Figure 11 presents the elements of the canvas and illustrates the key objective of determining how customer jobs, gains and pains should be connected to the company’s offering.

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4.2 DIGITAL TRANSFORMATION OF ASSET MANAGEMENT BUSINESS MODELSTraditionally, manufacturers’ have focused on producing a physical product and capturing value by transferring owner-ship of the product to the customer through sales. The owner of the product is then responsible for the costs of servicing the product. Digitalization allows a radical change of this traditional business model. The manufacturer, through access to product data and the ability to anticipate, reduce, and repair failures, now has the ability to affect product performance and optimize their service. This provides opportunities to create new business models for capturing value. These new business models can, e.g., supplement the traditional ownership model by offering new service efficiencies to customers. Another possibility is to offer the product as a service. The manufacturer then retains ownership and takes full responsibility for the costs of product operation and service in return for an ongoing charge (Porter & Heppelmann, 2014).

Table 3 presents the results of the project workshop in which the impacts of digitalization on business models were discussed.

Digital products and services require rethinking of the value proposition. The whole life cycle of products and services must be considered. Digital enables new ways of selling per-formance instead of products, as it is possible to monitor the use of products. Optimization of customers’ processes is an area where digitalization opens new opportunities. Data ana-lytics provides information that can be used in improving the efficiency of processes.

Customer relationships change from individual transactions towards continuous partnerships. New customer segments can be considered with digital products and services. It is important to jointly with customers to define and agree the

Key partners Key activities Value propositionCustomer relationships

Customer segments

• Partners that are needed to deliver the value proposition

• Utilizing the knowledge of ecosystem partners

• Cloud services

• Analytics providers

• ICT companies

• Customers as partners

• Specialization in core competences

• Operating as part of an ecosystem

• Continuous training of personnel

• Models for sharing benefits within the ecosystem

• Contract

• Optimization of operations

• Focus change from a product to life cycle management

• Selling performance

• Use based pricing

• Available 24/7 globally

• From reactive to proactive approach

• Optimization of customers’ operations

• Covering intangible values (e.g. image, environment)

• New customer segments

• From one-time transactions to continuous relationships

• Customer relationships based on trust

• Jointly agreed KPIs

• Understanding customer needs

• New customers

• Customers interested in utilization of data

• Extending customer base

• Changing role of users

Key resources Channels

• Data

• Analytics

• Availability of data from partners

• Competent personnel

• Open APIs

• Direct channels to customers

• Global reach

• Automatization of order process

Cost structure Revenue streams

• Costs related to collecting, storing and analyzing data

• Dynamic cost structure

• Purchased services

• Personnel

• Continuous cash flow

• Revenue from life cycle services

• Pricing based on shared benefits

• Selling data to third parties

TABLE 3. DIGITAL TRANSFORMATION IMPACTS ON BUSINESS MODEL CANVAS.

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KPIs that will be monitored to ensure the quality of service. Digitalization also provides new opportunities for reaching cus-tomers through direct channels, also globally. Furthermore, digitalization enables the automation of processes related to customer interactions, e.g., by automating orders based on data received from production.

Digitalization provides new opportunities for continuous cash flow and revenue from life cycle services. Pricing can be based on shared benefits. Additional revenue can be acquired from selling data to third parties. However, costs related to collecting, storing and analyzing data need to be taken into account, and services related to, e.g., data analytics or cloud services may need to be purchased.

Figure 12 introduces the results from a joint workshop of the Data to Wisdom and SmartAdvantage projects regarding the evolution of digitalized business models of asset management. It was observed that companies differ in their preparedness for digitalization and their ways of adopting new technologies. This applies to both customer organizations and OEMs. Forerunner OEMs already have analytics and IoT technologies integrated in their portfolios; however, a large proportion of companies have not yet achieved significant results related to digitalization.

Ecosystems

Optimization of production systems

Efficient purchaseand use of resources

Maintenance and Overall Equipment

Efficiency

Common view on the earning and value sharing models (value-based models in the network

Process and maintenancedata integration

Maintenance (e.g. CCMS, CBM) and operations data gathered

Minimization of operating costs withclear KPIs

Transaction based operations

Support and optimization of operations as a service

Applications of Digital twin simulationsthroughoutsystem lifecycles

Self-optimizing systems

Clear KPIs for value sharing

Autonomous factories

Increased automation in white collar work

Efficient and integrated utilization of data producedin human interaction

Automated work processes Automated processes

Identificationof operating conditionand optimization

Simulations in design and training

Outsourced maintenance services

2018 2022

FIGURE 12. ADOPTION OF DIGITALIZATION IN LIFE CYCLE SERVICES

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The following drivers are recognized as the top influencing factors:

1. Increase in automation level

In many industries the path to autonomous systems follows numerous steps, with automation first increased through auto-mated work processes, then parts of production processes and finally a transition to full autonomous systems. The speed of change is highly dependent on the complexity of the pro-cess in question.

2. Integration of different data sources to be exploitedThe use of different sources of data in a systematic way by integrating, e.g., maintenance and operations data in a plant is currently limited. Different pieces of data have previously been collected for certain purposes, cross-functional analyses have remained limited and examples of plant-wide thorough utilization of data are few.

3. Quick strategic experiments

Companies are currently willing to carry out rapid experi-mental trials to see if a technology or business model works in practice and to quickly gauge its real potential. This mini-mizes the risk of failure and also supports the organization’s efforts to better understand business opportunities. Strategic guidelines are still strongly needed to serve as a framework for such rapid experiments in order to help ensure that solu-tions are found that most fit the strategy and hold the most promise of success in the market. A company needs to have a clear vision of what is expected of its R&D investments. It is believed that quick experiments may not only be limited to incremental technological changes but may enable significant changes in the business.

4. Integration of business and process domain knowledge with novel technology capabilities in ecosystems.Combinations of different expertise are needed. For instance, in order to derive value from the data, solid understanding is needed of the phenomena studied or the production process given support to. The OEM’s key success factors are knowledge of the product and access to fleet data. On the other hand, the OEM needs to have adequate customer understanding and capabilities to create a profitable business model. Better man-agement of customer interfaces is needed and contact needs to be built at different levels with the customer organization.

Digitalization has already affected design processes, and the application areas of simulations are strongly expanding from training purposes to providing an effective basis for design and life cycle support (digital twins).

Despite the rapid development of digital technologies, busi-ness models have so far remained largely transaction-based. However, there seems to be mutual understanding that val-ue-based models earning and value sharing models will increase their importance in the future, specifically in the future business networks. Capabilities and tools are required, such as measurements for value sharing and models for life cycle costs and profits.

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5. DIGITALIZATION ENABLES CIRCULAR BUSINESS

Circular economy can be understood as “a regenerative system in which resource input and waste, emission, and energy leakage are minimized by slowing, closing, and narrowing material and energy loops. This can be achieved through long-lasting design, maintenance, repair, reuse, remanufacturing, refurbishing, and recycling” (Geissdoerfer et al., 2017). Digital technologies are considered as a key enabler for circular economy (e.g. Ellen MacArthur Foundation (EMF), 2016). This is not surprising, as activities such as maintenance, recycling and repair have been supported by digital technologies for decades.

However, the connection between digitalized services and circular economy is often rather vague. Frameworks describ-ing circular economy business and operational models (e.g. EMF, 2012; Lacy & Rutqvist, 2015; Bocken et al., 2016; Sitra, 2017) focus on operational-level activities. At the operational level, questions related to maintenance of the assets needed in service delivery are only limitedly considered. Tactical and strategic levels are generally not considered. The frameworks are, however, a good starting point for supporting the devel-opment of circular business.

To answer to these challenges, the SmartAdvantage and D2W projects connect the concepts of digitalization and cir-cular economy, focusing on the technical cycle of circular economy (e.g. EMF, 2012) and the life cycle management of assets. The projects offer concrete examples of digitalized asset management and life cycle services.

Five main archetypes of circular economy services are listed below (Sitra, 2017; Sitra, 2015; Lacy and Rutqvist, 2015):

• Product life extension. Increasing the value from invested resources, providing as long as possible useful life, and maximizing profitability over the life cycle of assets.

• Product as a service. Retaining ownership of an asset and offering it to customers as a service. The company offering the product has an incentive to optimize the utili-zation and life cycle of the asset.

• Sharing platform. Providing a means to connect asset owners with individuals or companies interested in using them to boost asset profitability.

• Renewability. Renewable, recyclable or biodegradable inputs are used as substitutes for linear ones.

• Resource efficiency and recycling. Finding value in all material streams. Sources hiding in organizations’ production outputs and discarded assets are recaptured and reused.

Digital assets have the potential to transform the way assets and material are manufactured, used and reused by monitor-ing performance, redefining maintenance, developing design, improving components and products, and extending the use cycle (EMF, 2016). The EMF (2012; 2016) has analyzed exist-ing circular economy solutions based on their archetypes and main value drivers. Three types of knowledge were identified as the main value drivers: location, condition and availability of assets. As seen from the table, digitalization has the potential to enhance all circular economy archetypes.

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TABLE 4. CIRCULAR ECONOMY ARCHETYPES AND MAIN VALUE DRIVERS. ADAPTED FROM EMF (2016).

Circular economy archetype Location of assets Condition of assets Availability of

assets

Product life extension Replacement service of broken component, optimized route planning

Predictive maintenance and replacement of components, changed use patterns

Improved product design from usage information, optimized sizing, supply and maintenance from use patterns

Product as a service Optimized route planning, swift localization of assets

Minimized downtime, precise use of input factors

Automated connection of available asset with next user, transparency of available space

Sharing platform Optimized route planning, swift localization of assets

Minimized downtime, precise use of input factors

Automated connection of available asset with next user, transparency of available space

Renewability Automated distribution systems for biological nutrients

Automated condition assessment

-

Resource efficiency and recycling

Enhanced reverse logistics, automated localization of assets on secondary markets

Predictive remanufacturing, accurate asset valuation and benchmarking, accurate decision-making for future loops

Improved recovery and reuse of assets, digital marketplace for secondary materials

5.1 SERVICES PROMOTING CIRCULAR ECONOMYThe SmartAdvantage and Data to Wisdom companies together with project researchers identified life cycle services that pro-mote circular economy (Figure 13). The focus of this work was on technological cycles (e.g. machines). To support the ideation process, the ideas were placed in a circular econ-omy matrix. The axes of the matrix were closing the resource loops and extending product life. The identified services were mainly data-intensive. The group highlighted the crucial role of digitalization and digital assets in the implementation of circu-lar economy solutions. The main categories of digital circular economy services were:

1. Identification and utilization of side streamsThere are numerous ways of identifying and utilizing new side streams with economic potential. Examples of circular econ-omy based services include utilizing excess heat from cooling processes instead of wasting it and utilizing construction waste from demolished buildings in the construction of new build-ings at the same site. Inconsistency in production volumes is a challenge for utilizing side streams as some application areas require consistent availability of raw material.

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Valu

e in

clo

sing

res

our

ce lo

op

s

Value in extending product life

Locality of resource reuse

Side streams, for instance construction waste and energy

Identifying and utilizing side

streams

Excess heat, ”waste”

Platform solutions

Markets for used materials, equipment and competencies

Repair, maintenance, spare parts,

reuse of equipment

Optimization and resource

efficiency

Servitization, outcome based services

Better availability of information from previous life cycles

More information about materials and more pure materials for new life cycles

Solutions based on customers needs

Condition monitoring, simulation, whole logistics chain, total optimization, optimization of energy use and maintainability

Design, training and consulting (circular economy know-how)

Designing services according to the principles of flexibility, modularity Predictive

maintenance, learning systems

Fitted into production planning

Shortening supply chains



FIGURE 13. MAIN GROUPS OF DIGITAL CIRCULAR ECONOMY SERVICES.

2. Optimization and resource efficiency

Automation of information streams could support optimization and resource efficiency by, for instance, shortening supply chains. A range of optimization and resource efficiency related services were identified, such as condition monitoring, sim-ulation and optimization of energy use and maintainability.

3. Repair, maintenance, spare parts and reuse of equipmentAt the design phase, companies should consider the end-of-life of the products and possible new life cycles for the products, components and/or materials. The flexibility and modularity of the products supports reusability goals. In addition, attention should be paid to the quality and correctness of assumptions and data with respect to meeting real customer needs. The group raised the issue of improving after sales and secondary market opportunities for equipment and devices.

4. Design, training and consultation services related to service types 1-3.Implementation of circular economy from strategic decisions to operational level actions demands more competencies, tools and knowledge at the operational level. Training and consulta-tion were considered as means for supporting implementation. Circular economy should be a crucial part of the activities of the company as a whole. The focus should be on improving working methods and how the operating models support and improve circular economy.

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6. DEVELOPMENT OF SMART ASSET MANAGEMENT SERVICES

Digitalization offers opportunities to develop new kinds of ser-vices through new channels and business logics. Novel digital technologies such as platforms and advanced analytics have the potential to disrupt service development, delivery and evaluation processes. Agile methods and experimentation have become the mainstream of service development. They are quick and relatively cost-effective means for testing new data-based asset services. However, as stated earlier, when focusing solely on agile methods, companies are at risk of losing the strategic focus of their service development. The strategy, service portfolio development and business model need to be integrally connected to service development.

In this deliverable, we have presented methods and frame-works to support the development of digital services based on data from installed base of machinery, equipment and infrastructure. In Table 5, we position the methods and frame-works within the model for service information requirements (McFarlane & Cuthbert, 2012). The methods and frameworks are positioned according to the main elements of service (customer need, specification or use, the service offering and the supporting infrastructure). Additionally, we present ques-tions that companies should consider when developing digital asset services.

Main elements of

service

Methods and frameworks Questions

Service need Roadmapping and analysis of the changes in the business environment, market and technology (Sections 2.1 and 4.3):

What are the drivers and future needs? What are the foreseen technological, market etc. developments? When do the developments take place? What are the most crucial developments for us?

Identification of new service opportunities and sources of customer value (Sections 4.1, 5)

Which are the customer’s processes and their bottlenecks and improvement opportunities? Which key performance indicators may be influenced by the services?

Understanding customers’ decision-making context, decision situations, Decision-making levels (Section 2.2)

Are the customer requirements understood? What are the future customer needs? What are the customers willing to pay for? What potential decision-making situations should be supported by refining the relevant data? Can we provide new understanding with the data for, e.g.: • maintenance strategy and implementation • overall equipment efficiency development • investment assessments and decision-making • improvements in energy efficiency

TABLE 5. FRAMEWORK FOR SUPPORTING DEVELOPMENT OF DIGITAL ASSET SERVICES (APPLIED FROM MCFARLANE AND

CUTHBERT, 2012).

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Main elements of

service

Methods and frameworks Questions

Service specification

Analytics (Section 3) Are the customer requirements correctly specified and satisfied? Who collects, controls and owns the data? Availability of information to support the service? How should data be managed in a service network with multiple partners? What are the costs and benefits? What kind of platform should be selected? What type of analytics are we using?

Value proposition formulation (Section 4.1)

What distinguishes us from the competition? How are our products and services packaged to meet the expectations of potential customers?

Service offering Business model formulation (Section 4.2) and understanding the key features of circular economy related business models (Section 5) Analytics according to use case requirements (sections 3.2.3 and 3.3.3)

Which capabilities are needed? Who are we working with? Has risk assessment been satisfactorily completed? What service support is required? Is the resourcing within cost? From where and how are we found? Which case-specific analytics tools, methods and models are required?

Service operation

Business model canvas (Osterwalder & Pigneur 2010, Section 4.2) Measurement and assessment; KPIs (Section 2.3)

How should the system be operated to optimize value creation? How do we measure the value created by the services? Do we understand the supported functions? Is the system reliable and resilient? Is the related legislation and regulation fully understood? Do we need to improve the service infrastructure?

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7. NEXT STEPS

In this deliverable, we have presented a range of methods and frameworks to support the development of smart asset management services. The importance of understanding the customer’s decision context is highlighted. This includes under-standing the decision situations they are facing at the specific organizational decision-making levels. Novel analytics enable the development of solutions that better answer customer needs. Furthermore, for a smart service to be successful, selection of the right business model is crucial.

The work in the SmartAdvantage project will be continued until the end of 2019 in close collaboration with our industrial partners. Our aim for the next steps of the project is to uti-lize the presented methods and frameworks in practice and co-develop them further with the industrial partners. Particular attention will also be paid to promoting capabilities to provide digital services in the global business environment.

From the strategy perspective, we aim to support com-panies in moving towards deeper understanding of decision situations and organizational decision-making levels and a more

holistic understanding of the customer’s decision context. The presented frameworks can be used as tools to support com-panies’ smart service portfolio development and to pinpoint which decision situations and organizational levels their ser-vices should target. Circular economy is a rising trend that may have a major impact on the manufacturing industry and soci-ety as a whole. Taking the goals and requirements of circular economy into account at an early stage of product, service or business development gives companies a competitive advan-tage. For established companies, services and products it may be difficult to change operating models to pursue the goals and requirements of circular economy.

From the analytics perspective, we aim to further develop the analytics solutions and machine learning algorithms to support, for instance, energy optimization and maintenance service planning. Utilization of maintenance and operation related history data and benchmarking are examples of areas for smart service development. Analytics based services are developed and tested in selected case studies.

From the business models perspective, we aim to iden-tify business model archetypes for smart asset management services. We present methods and frameworks that sup-port the development of smart asset management concepts. Approaches for identification of new service opportunities and sources of customer value are tested in practical case stud-ies. The aim of business model development is moved from value proposition to a more holistic view of the business model including costs and benefits.

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