CAN MACHINE-TO-MACHINE COMMUNICATIONS BE USED TO IMPROVE CUSTOMER EXPERIENCE IN A SERVICE...

CAN MACHINE-‐TO-‐MACHINE COMMUNICATIONS BE USED TO IMPROVE CUSTOMER EXPERIENCE IN A SERVICE ENVIRONMENT?

Shaun West, Dominik Kujawski and Paolo Gaiardelli

ABSTRACT Purpose: The purpose of this paper is to identify ways in which Machine-‐to-‐Machine (M2M) communication can be used by product-‐based manufacturing firms to deepen and broaden the service aspects of their customer value proposition. The expectation is that an improved customer value proposition leads to improved customer experience, and through this to improved customer retention. Design/methodology/approach: The approach taken has been two-‐fold:

1. a literature review to understand what is available in a B2B environment; 2. obtaining initial feedback from surveys and interview with equipment owners and operators,

suppliers of condition monitoring systems and other stakeholders to understand the different value propositions.

It was considered important to widen the horizon of ‘condition monitoring’ to provide as many different ways to improve the customer experience as possible. The literature review was undertaken based on the broader definition of condition monitoring. The review was not limited to the academic press but expanded to include trade journals and websites. The M2M impact on human-‐to-‐human interactions was also considered. Over 15 interviews with stakeholders were undertaken so that their perception of the value proposition could be understood. All were from the B2B environment and with interests, of some form, in high-‐value equipment. This required detailed segmentation based on how data was consumed – each segment had different outcomes that concerned them. Findings: M2M can be used within the internet of things to improve the customer experience. However there are many risks and negative aspects that limit the possible gains:

• the ‘customer’ may not understand what they actually need; • loss of personal interactions can lead to a perception of a lower level of value; • clear customer/use segmentation must be undertaken; • each customer persona must have a clear value proposition; • there must be transparency in the data collection; • the data collected must be used openly for root-‐cause-‐analysis rather than defensively to

protect warranty positions; • the data can be used to support new product and service development.

Originality/value: This remains a new area for development for many manufacturing firms in the B2B space. The technology is proven yet there are numerous firms that are unable to monetise the monitoring they undertake for their customers. The value of this paper is that it develops a process to support the application of M2M monitoring by identifying key tasks to help firms develop an effective customer value proposition. Keywords: Servitization, internet of things, value proposition, customer experience, technology communication.

West, Kujawski and Gaiardelli

4th International Conference on Business Servitization (ICBS 2015) November 19-‐20, 2015, Universidad Rey Juan Carlos, Madrid, Spain 2

1 INTRODUCTION For many years, machine-‐to-‐machine communication has been growing in the industrial product market. Today the terms “Industry 4.0” and “Internet of Things” are terms that are often used. The promise of the technology is that with data collected from the equipment and machines communicating directly with each other manufacturing processes will become more efficient. This has already been seen in the case of CAT’s fleet monitoring system (CAT, 2015); a fleet monitoring solution for lorries (Aston, 2015) and has also been used in many process industries successfully (OSISoft, 2015). The data collected has in some of these cases been used to deepen and broaden the service aspects of the customer value proposition delivered by these firms. The firms can design their service delivery systems to meet the outcomes desired by their customers and in some cases then to integrate their processes into the processes of their customers. This, according to Neely (2008), increases the degree of customer integration and leads to increased customer retention. To deliver advanced services (Bains et al, 2011) it is often necessary to have operational and technical data from the equipment. GE Energy Services has been very successful with this with its contractual services for both industrial and aero gas turbines; Rolls Royce similarly. In both cases, the firms can move to an hourly fee structure as they have operational and technical data on the machines for which they are providing services. Understanding the equipment operation and condition means that they can drive productivity in the equipment, typically through moving to condition-‐based maintenance. This increases their customer's equipment availability by reducing the need for equipment inspections. To provide a move to risk-‐based maintenance on large equipment requires significant data but also requires close co-‐operation between the key parties. The hypothesis is that for M2M to be successful it must be predicated on improved customer engagement, which is based on effective communication. This means that the data collected must be converted to information that generates discussion and action. This paper will examine this topic through a literature review, survey and interviews and make recommendations on how customer integrations can be improved based on M2M communications.

2 METHODOLOGY This section describes the methodology applied in the study; it is broken up into the literature review, the survey and the interviews. 2.1 Literature review An in depth literature review was undertaken to assess the current state-‐of-‐the-‐art, this included a review of both academic literature and published examples in the industrial press. To keep the relevance of literature, the research and analysis was continuously carried out throughout the research. The scope of the literature review was:

• the value in ecosystems; • supply chain collaboration creating open innovation; • customer value; • sustainability through customer engagement; • decision making by converting data into information.

2.2 Survey A set of standard questions was created in a survey tool (SurveyMonkey) and distributed to stakeholders with an interest in industrial equipment. The range of stakeholders targeted ranged across asset owners, system suppliers, Original Equipment Manufacturers (OEMs), consultants and



technology investors. The survey was designed to be completed within 10-‐15 minutes to help with completion rates. The survey was broken up into the following sections:

• stakeholder analysis (eg, type of business, position in supply chain); • systems today (eg, Do they help you achieve the outcomes that are important for you? What

outcomes are you expecting from the equipment monitoring in terms of operations, maintenance?);

• issues associated with monitoring, warranty, and equipment operation; • issues associated with data ownership and information sharing; • issues associated with unplanned downtime; • an understanding of the gaps between what stakeholders expect and what is delivered

today. Each of the sections included an open question allowing direct feedback. The questions themselves were quantitative to enable analysis. The survey was distributed to the target stakeholders using direct methods (email) and indirectly (via LinkedIn topic area groups). The stakeholders questioned were expected to have a general interest or specific interest in machine-‐to-‐machine communication issues. 2.3 Interviews Based on the initial analysis of the survey results, an agenda for the follow up interviews was created. 15 follow up interviews were undertaken to gain a more detailed insight into the survey results. Each interview was scheduled for 45 minutes and consisted of the following questions:

• What are the best customer value propositions you have seen? • What are the negative aspects of monitoring? • Who should own the data? • How should data be accessed and shared? • Have you experience of spying vs transparency? • How does smart (remote) monitoring improve customer/supplier interactions? • Does it improve OEM/customer contact? • How could the contact be improved with the data flows? • Does the OEM get the data they need at the right time? How do 2nd tier OMEs get data? • How does the OEM use the data to improve their product? (eg, product development or

existing operations or maintenance?) • What do you learn from the data, what is the most surprising aspect? • Does the value outweigh the cost?

The interview data was then grouped into common themes to allow for analysis. Key lessons were distilled from the interviews and are presented in this paper.

3 RESULTS AND DISCUSSION This section lays out arguments from the literature and then moves into the finding based on the data collected and closes with a discussion. 3.1 Literature review In their shift to service business, manufacturers firstly focus on introducing technologies to increase the efficiency of their service operations (Agnihothri et al, 2002; Kowalkowski and Brehmer, 2008). This requires the redesign and standardization of service activities (Kindström and Kowalkowski, 2009; Brax and Jonsson, 2009). Then, as service orientation becomes more intense, digital technologies are incrementally leveraged to differentiate, extend and complement the company’s offer (Kindström and



Kowalkowski, 2009, Belvedere et al, 2013). This can be the case of remote monitoring systems, diagnostics & prognostics, reporting & analytics services that are bundled with the product to raise the quality of customer support and get competitive advantage. However, as suggested by Harmon et al (2011) firms can also exploit technologies to design radically new solutions and create discontinuous-‐breakthrough innovation. In use oriented service offerings, smart services are focused to provide any-‐time-‐anywhere access to the specialised resources (products, skills, applied knowledge), in either individual or shared consumptions, in order to enable the value creation process (eg, customers create value-‐in-‐context). The role of technology as an enabler of servitization is recognised by many authors as essential (Neely, 2008; Storbacka, 2011). In particular, both Neely (2008) and Bains et al (2011) confirm that it is a requirement equipment for advanced services where “pay-‐per-‐unit” is applied. The convergence of data availability and information processing technology boosts value creation, because technology adoption requires a redesign and a standardization of operating processes. Thanks to the enabling technology, a better visibility of the asset in use (in terms of operating conditions, time in use, and location) is available. This allows to speed up service activities, improve equipment design and operation behaviour and reduce, at the same time, service delivery costs (Lightfoot et al, 2011). The shift from “you are what you own” to “you are what you can access”, the emergence of collaborative consumptions (Botsman and Rogers, 2010), internet facilitated sharing (Agrain, 2012) and access based economy (Bardhi and Eckhardt, 2012), as well as a market getting more fluid, facilitating connection and share resources (Chandler and Vargo, 2011), supported by the improvement of product reliability and availability, enabled by mobile devices and appliances for employees and customers of service division (Fano and Gershman, 2002), information systems that enable field operations (Kowalkowski et al, 2014) rather than condition monitoring systems (Turunen and Finne, 2004), gives the opportunity to introduce new business models. These are characterised by a changed notion of asset ownership and management. In addition, the easy access to real-‐time information provides also the opportunity to develop a better understanding of customer behaviours, easing the development of smart solutions, that are “fundamentally pre-‐emptive rather than reactive” (Allmendinger and Lombreglia, 2005, p.2). Finally, technology enables comprehensive vertical and horizontal information sharing and coordination in all directions between department, divisions and network partners supporting the implementation of the product-‐service strategy (Martinez et al, 2011; Auramo and Ala-‐Risku, 2005). A large amount of research dealing with technology-‐driven service innovation in service business has been undertaken to understand how smart service initiatives reframe competitive landscapes. The literature review of the literature on this topic reveals the existence of different perspectives taken into consideration and briefly described in the following. The key themes of:

• value is in the ecosystem; • supply chain collaboration creating open innovation; • customer value; • sustainability through customer engagement; • systems must help the owner/operator to make the right decisions, technical info then

supports business decision making. will now be developed further in the following sections.



3.1.1 Value is in the ecosystem According to Iansity and Levin (2004) the metaphors of keystones and ecology are helpful to think about the business environment of a company. Iansity and Levin concluded that the loose networks of suppliers, distributors, technology providers and other “components” of the ecosystem affect and are affected by the creation and delivery of a company’s own offerings. Each member of an ecosystem shares the fate of the whole network regardless of its strength. As Clarysse, et al (2014) affirmed (as cited in Zahra and Nambisian, 2012) ecosystems are organized as complex networks of firms whose integrated efforts are addressing the needs of the end customer and there is a growing consensus that provide companies with resources and information to navigate in constantly changing compititive environment. Jacobides and MacDuffie (2013) said that the hardest companies to replace in the value chain are the integrators of system. Iansity and Levin (2004) present two ingredients that are part of success within the business ecosystems. First, business ecosystems consist of a large number of loosely interconnected participants who are dependent on each other for their own mutual performance. Every of the participants has its core competence which together with others allow to constitute value while individual efforts have no value outside the collective effort. The second vital element is the need for a “keystone” company that ensures each member of the ecosystem remains in good health. Indeed, such a firm must develop new capabilities as partners orchestration and management of network dynamics (Kindström and Kowalkowski, 2014). As Galateanu and Avasilcai (2014) concluded that the value co-‐creation in business ecosystems can be realized by establishing different types of relations where the technological changes have a major impact on value creation. Indeed, servitization forces changes to traditional buyer supplier relationships (Bastl et al, 2012; Saccani et al, 2014) The new trend that is Industry 4.0 might be the key influencer of the value drivers in the business ecosystem. (Bechtold et al, 2014) state the smart services and smart products will increase the scope of manufacturers value creation activities. Especially manufacturing companies based in high-‐cost countries need to leverage this opportunity to sustain competetive edge and drive growth. In such a context, as stated in (Bechtold et al, 2014) vertical and horizontal integration based on digital technologies allows companies to drive value through transparency and process automation. Connected supply chains allow identification all along the production process, which enable manufacturers to be more responsive to change requests. Thus, the maximum level of transparency can be established over the whole supply chain. This will form a centerpiece for operation excellence in any Industry 4.0 strategy. The "Ecosystem: people, machines and software,” (2015) website states that the Industry 4.0 ecosystem consists not only of smart factories and intelligent products, it also includes people. It is a question of allowing people to perform high quality and creative work and provide them with opportunity to achieve a work/life balance with just as much flexibility as the production systems of the future that people will control. 3.1.2 Supply chain collaboration creating open innovation According to Mathuramaytha (2011) today almost all organization are in the process of adopting the supply chain activities and make them competitive. Collaboration is the driving force behind effective supply chain management and improves performance. It may share large investments, pool risks and share resources, reasoning growth and return on investment. Both intra-‐firm and inter-‐firm collaboration is crucial for servitization (Neu and Brown 2005) and is part of the open innovation paradigm defined by Chesbrough et al (2007).



As stated by DeAngelis (2014) using sensors to monitor manufacturing equipment and the environment is nothing new, but using those sensors to communicate with other equipment and automatically feed data is one of the newest frontiers. In Figure 1, there is presented a business scenario that shows intelligent communication system between different parts of the value chain within Industry 4.0.

Figure 1 Typical business scenario in the Internet of Things (Schönthaler, 2015)

Figure 1, presents communication between supplier, carrier, shipper, producer and his customer. As stated in (Schönthaler, 2015) this digital transformation of the value chain provides the supplier with insight to the inventory directly on the shelf, so proactive actions are possible. From this new way of collaboration arises. According to Siebenmorgen (2015) a fundamental step in the direction of Industry 4.0 is the digital modelling of the value chain, where a large number of users networked through cooperation platform benefit. Siebenmorgen underlines that the trust of all companies involved must be gained, otherwise no Industry 4.0 business model will be successful. Even smart services initiatives favour new forms of collaboration and cooperation, in certain cases, rivals are asked to collaborate (coopetition). Indeed, Smart services initiatives are likely to reshape the competitive landscape and change the traditional industry boundaries. 3.1.3 Customer value Anderson et al (2006) explains the importance of customer value that they value forces suppliers to focus on what their offerings are really worth to their customers. The paper described a systematic method to help with the development of value propositions to that are meaningful to their target customers. With M2M services customer value must continue to be developed, in fact, “smart services” encapsulates more than just mere technology. This concept also refers to a more customercentric view and strategy, that transform that technology into a value added services from the customer’s point of view according to Reinartz and Ulaga (2014). According to (Osterwalder and Pigneur, 2002) value is created through use, a reduction of the customer’s risk or by making his life easier through reduction of his efforts. Capturing the value can be during value creation, purchase, consumption, its renewal ot its transfer. The value and price level can be compared to one of the companies competitor’s. To deliver the right value the target customer needs to be defined, the means to reach and communicate with him, as well as the relational strategy to establish with customer. Campbell et al (2011) state that “advances in technology, especially information technology, and widespread use of the Internet, can be viewed as a catalyst that facilitates the shift



in the traditional service boundary between provider and customer towards either self-‐service or super service”. However, while services supporting the products (SSP) can be easily standardized to offer a “digital version”, services supporting the customers (SSC) always show a big deal of variety due to people interactions and customer-‐specific situations. Thereby, it is said that “technology may not be appropriate in the context of an SSC business orientation given that these services are directed at the client and customized rather than to the product and standardized” (Antioco et al, 2008, p. 351). 3.1.4 Sustainability through customer engagement As well as Park et al (2012) suggest, digital technologies integrate and combine product and services in different ways, to deliver a product-‐service systems that brings also social and environmental benefits Tukker (2004 and 2013). Most marketers think that interacting as much as possible with customer will allow them to build strong relationships with the customer (Freeman et al, 2012). Not all of the customers want to have relationship with the brand; it is essential to determine different expectations in different target groups. Also, interaction do not build relationships -‐ shared values build them. The shared value is a belief that both brand and consumer have about a brand’s higher purpose and philosophy. The more interaction is not always better, instead of continuous demanding of customer attention try to reduce the cognitive overload consumers feel for the brand (Freeman et al, 2012). As stated in Bloem (2014) the best example of engagement are applications that are directly related to interaction with blue-‐collar members of staff or end users, through measuring and regulating, maintenance and software upgrades. For example, Philips allow consumers to operate lamps as they wish and in this way get data to implement their tasks much more efficiently. This allows Philips to be connected with the customer 24/7, expand user experience through improved human-‐machine interaction and products are a part of the end-‐to-‐end ecosystem. Figure 2 presents sustainable customer engagement model that can be achieved when company makes the relationship with the customer visible, tangible, empowering and emotional through all phases of product and service consumption. Deloitte (2014) report presents sustainability as both a valuable risk-‐management tool and long-‐term contribution to the bottom line. Sustainability as a value proposition is still waiting to be implemented in many corporate strategies and that is for potential leveraging customer engagement. It allows to increase customer loyalty, advocacy and repeat conversions. A potentially engaged customer generates significant premiums in terms of money, profitability, and revenue and relationship growth, for the following reasons:

• transparency engagement framework refers to efforts where business effectively informs the consumers of the sustainability performance of a specific product.

• the partnership engagement refers to improving sustainability by inviting customers to participate actively in partnership with the third-‐party organization.

• the life cycle engagement is when business strives to engage customers in parts of the entire life cycle of a specific product.

• the collaborative engagement platform refers to business applying modern network technology to create with customers shared value.



• Figure 2 Sustainable Customer Engagement by (Deloitte, 2014)

3.1.5 Systems must help the owner/operator to make the right decisions, technical data then

supports business decision making McAfee and Brynjolfsson (2012) state that managerial decisions are greater than technical challenges starting with the role of the senior executive team. The most critical aspect of big data is the impact on how decisions are made and by whom. A successful and effective company puts information and the relevant decision right in the same location. Expertise is not often where it used to be due to create and transferred information. Maximization of a cross-‐functional cooperation allows the right usage of data. The idea of the right decision-‐making process lies in delivery the right data to people who understand the problems and who have problem-‐solving techniques to effectively use them. Rowley (2007), uses the DIKW-‐hierarchy (Figure 3) as a model to allow data to be translated into information, knowledge and eventually wisdom. Only with information can management actions be taken.

Figure 3 Translation of data into information to support business decision making

3.2 Survey and interview results The survey population was 32, from which interviews were conducted with 15 stakeholders representing a range of industry players:

• 20% were OEMs with 24% being engaged in OEM services; • 20% were equipment operators with 41% being involved in equipment maintenance

services; • 20% of those who responded were asset owners, a further 7% were pure financial investors; • 30% provided consulting services.



The total numbers add up to more than 100% as many of the firms were engaged in more than one activity. This confirms that the population that responded provided a wide view of the stakeholders in the equipment value chain. The supply chain analysis confirmed that both equipment and service sales were made directly to the end user and indirectly via a contractor. This is common in many industrial equipment markets (Rosenbloom,2007) where new equipment sales follow a different channel to service sales and the channel develops on the phase of the project. When asked about the types of systems respondents were using and how successful these were in supporting their achievements, the two least reliable systems were acoustic (11% response rate) and video/photo analysis (11% response rate). Interestingly there was a contradiction in that photo/video analysis was one of the most valuable fault finding tools, reflecting that it is used largely in an interactive way during planned (62%) and unplanned (42%) inspections. Acoustic emission analysis was found not to support outcomes successfully yet was often (50%) used in fault-‐finding. The most positive outcomes were found to be from the operational data (28%) and vibration analysis (26%). Vibration analysis was often (53%) used in fault-‐finding, whereas operational data was not used as frequently in fault-‐finding (33%). Both methods scored highly in remote and continual measurement (>42% of respondents). Performance data, something that combines many data feeds, supported outcomes 23% of the time and was used to support fault-‐finding with an expectation for the data to be collected continually (50%). 3.2.1 Operations and maintenance considerations Operations and maintenance have a major impact on the outcome of any operation. For this reason, there were a group of questions around these topics and how monitoring can assist the asset owner to achieve their desired outcomes. Warranty fulfilment is closely associated with the new installation of equipment. This can be, as has been discussed, a direct sale to the asset owner or indirect. Nevertheless, the OEM has warranty and performance obligations and there are also operation and maintenance requirements. For warranty and equipment operation, within all responses equal value was given to (80-‐75%):

• ensuring the equipment is operated and maintained correctly; • feedback on how equipment is actually used; • detailed understanding of equipment life consumption; • improving plant performance.

There are outcomes from monitoring the normal operation of the equipment, the three most important were:

• increased use of proactive maintenance (89% important/very important); • improved equipment efficiency (88%); • stable operation of the plant (73%).

These points are associated with getting more out of the equipment and reducing the costs, which leads to a lower per unit cost of production. When asked about the maintenance outcomes that were important the three most important were:

• a desire to move to condition (or risk) based maintenance (78%); • to undertake targeted/opportunity maintenance (75%); • to drive down the cost of maintenance (74%).

These points are associated with the desired outcome of a lower total cost of ownership.



What outcomes are expected in terms of supporting unplanned downtimes? The most important aspects here were:

• to support opportunity-‐based maintenance (77%); • to improve problem solving (74%); • to allow safe operation when equipment is damaged (69%).

These points are associated with minimising lost production associated with unplanned downtimes. 3.2.2 Data sharing and ownership The consensus view was that the data should be owned by the equipment owner but shared within the ecosystem. The interviews highlighted this to be a very emotional issue for the equipment owners as they considered that the data (technical, operational and commercial) was commercially sensitive. In interviews, they were also concerned that the data should be shared and used within the ecosystem, provided they understood the purposes for which it was being used. In details the three most important aspects were:

• Information/output/reporting from the system needs customizing (80%). • The data is commercially sensitive (66%). • The equipment owner should own the data (60%).

Interview responses confirmed the ownership of data was an important issue. Several of the interviewees stated clearly that the data had commercial value and that ownership must be vested with the equipment owner and not the OEM. Further views here suggested that the firm doing the measuring should own the data and another said it depends on who takes the risk. In contrast to the data ownership question, there was general agreement from the interviewees that faster, better and cheaper solutions could be generated by the ecosystem when the technical, operation and commercial data were shared. The use of data and the anonymity of data remained key concerns. 3.2.3 Descriptions of customer value propositions and value for money The utilities and OandG firms provided some of the most attractive examples of customer value propositions, typical themes being:

• maintenance – maintenance cost out, moves to risk-‐based maintenance; • advanced services – underpinned by monitoring, we could de-‐risk our service contracts; • operations – data showed that the OEM damaged the equipment during commissioning;

operational technical data helps increase speed of troubleshooting; value comes from a holistic view; we use the combined data for our business reporting and optimization.

When asked in the survey if the monitoring system that was used supported the desired outcomes: only in 33% of the responses did the system provide all of the data that was required. This clearly shows that there the value propositions are not matching the expectations. Yet owner/operators were providing examples of positive value propositions and had a desire to continue using and developing the technology. 3.2.4 Negative aspects of monitoring There were a number of negative aspects that were in contradiction to each other. This suggests a weak fit between today's problem and solution and that therefore a clear value proposition has not yet been identified. This was typically found when the OEM chose a marketing “push” to sell the technology, with the owner/operator considering that the technology was being forced upon them.



Data overload was clearly a problem for some and related to the integration of the systems and the relevance of the data presented. Questioning on these issues, there was a preference for one management-‐level system that could present the data in a more relevant way for those consuming the data. Base-‐level concerns about fault reporting, data security and a reporting/controlling vs transparent approach were described and discussed separately. Here there were concerns from equipment owners/operators about the OEM spying on them, yet there was also an expectation of pro-‐active OEM support. The OEMs also had a concern that owner/operators did not want to “expose their stupidity”. A Liquefied Natural Gas (LNG) plant Operations and Maintenance (O&M) team member said they “Need to know what is needed by whom and why”. 3.2.5 Improving customer/supplier interactions and the sharing of data The consensus view from the interviews was that sharing data should improve customer/supplier interactions. How to do this is part of the value proposition; however, the findings were that:

• it should be proactive so that the OEM can be ready to help with trouble shooting or spares; • information must flow in both directions, allowing one set of data to be used to help improve

the quality of trouble shooting; • joint problem solving helps to mature the relationships and encourages more interactions at

different levels; • sharing of resources helps to drive out cost yet risks deskilling staff.

One OEM respondent went as far as saying that “…you should work 'open book' with the data…”. The move to outcome-‐based solutions with an alignment of objectives creates value in some cases. Embedding/sharing of resources was viewed positively by a number of the interviewees. There is an effort required by all parties to learn to work closely together, and focusing on high-‐level goals (e.g. total cost of ownership) rather than transaction cost was a key lesson. Sitting together in this way and understanding the equipment owner’s business objectives was considered important by many respondents. Getting people to do this requires effort and maturity. The OEMs working in joint data analysis centres with the owner/operator considered this a good approach as it could assist the combining of technical and commercial reporting, helping all parties to focus on improving operations or as one interviewee said “finding ways to use customer waste to generate value”. The consensus view was that second tier OEMs, unless suppliers of critical plant items, had a tough time getting access to the data they need when they need it. Here the system integrator was considered a key party in the ecosystem to support access; however a number of respondents mentioned that warranty and other contractual issues may create barriers. 3.2.6 Product improvement The use of the data collected to improve the product was considered important in the interviews. An investor said that it was a “must”, the owner/operators said that the OEMs were too slow to integrate what they learned into new product development or service upgrades. GE was considered as an OEM that took what they learned from monitoring and integrated it into both service upgrades and new products. The data should also be used to support changes to operations and maintenance (e.g. longer intervals between maintenance) based on both the technical and operational data. The only way this can be done is through closer working with the stakeholders within the ecosystem.



3.2.7 Lessons from the interviews from the use of monitoring technologies In balance, the interviewees said that there was value from using monitoring systems and that the ecosystem created more value than individual parties were able to do. This means that there must be significant integrations at a personal level within the ecosystem to allow this co-‐creation to take place. On an interpersonal level, a number of the interviewees stated, “once the parties start working together you start to get more trust”. A number also commented that the monitoring solution “ran the risk of being taken for granted”, in which case may lose it importance in the view of the owner/operator. This was because the system tended to focus on risk mitigation meaning that a failure was prevented. Other findings from the interviews were:

• Low cost sensors (video) have enough on-‐board computing power (investor); • Our flash dryer was having problems: it was found before it caused problems (utility); • Once you start working together you start to get more trust (LNG); • GE medial have a super value proposition for their equipment in hospitals (OEM); • Must work around the business solution and then the technical solution can be found

(system integrator); • Solution comes best from co-‐creation around the ecosystem (consultant); • The customer can pull you out of the problem (consultant); • A modern train can have 10M data points per trip – must be provided in an understandable

form (consultant). 3.2.8 Overview of the survey and interview results In summary, the main findings of the survey and the interviews were segmented into two themes, customer relationships and underlying considerations, listed in Table 1. Interview results suggest that the best solutions provided information to allow people to make the decisions, rather than the machines taking their own decisions based on pure technical data. A process in Section 3.3 below provides one possible framework to help OEMs to help integrate customer experience into the development and operation of machine-‐to-‐machine systems. Table 1 Main issues that can drive customer relationships and underlying considerations,

identified from the interviews

Customer relationships Underlying considerations • The ‘customer’ may not be able to describe

clearly what they need, yet many are able to describe the outcomes they are trying to achieve;

• Clear customer/use segmentation must be undertaken based on position in supply chain/ecosystem and the outcomes they are seeking;

• Each customer persona must have a clear value proposition, it is no long sufficient to have one value proposition for ‘customers’;

• Loss of personal interactions can lead to a perception of a lower level of value as customers take the service as the new norm.

• There must be transparency in the data collection and as GE say, a ‘single point of truth’, this means that every party in the ecosystem should use the same data source;

• The data collected must be used openly for root-‐cause-‐analysis rather than defensively to protect warranty positions, this requires trust between the players in the ecosystem;

• There are internal consumers of the data collected and this can support new product and service development, so the data (technical and operational) must flow down to them.



3.3 Process description Using the results of the survey and the interviews and integrating these with the best practices identified in the literature, the authors have designed a process to assist industrial firms to understand better the complexities of how to integrate these new technologies into their existing offerings to provide the customer with the value that they are expecting. This is not a simple task as every OEM exists in a different position in their particular ecosystem and this makes it more critical that the OEM comprehends the ecosystem, so that they can understand how and where the know-‐how exists. A proposed process is shown in Figure 4; this is developed further below.

Figure 4 Proposed process description to assist OEMS to develop a customer value proposition

for M2M communications

3.3.1 Where do you sit in the ecosystem and who bring what value? The purpose of this element is to provide context for the OEM so that they understand where they sit within the ecosystem. They can then understand who and what they can influence. More importantly when it comes to joint problem solving, they can then identify the parties who may be able to support them to create a solution for the owner/operator of the equipment. This is an open innovation paradigm (Chesbrough et al, 2007) in that the solution is developed with the help of external partners. 3.3.2 Do you understand your customer's gains and pains? Within Service Design (Tripp, et al, 2013) empathy mapping is an important activity to gain a fuller understanding of your customer. Here it has been seen that many OEMs have complex supply chains and ecosystems and therefore understanding key stakeholders becomes increasingly important. Users outside the key target group of the system may have an interest in the information that the data from such systems represents. Consumption of the information must (Rowley, 2007) be in a form that creates action; this means that the data must be transformed into information relevant to the person consuming it. 3.3.3 Do you understand the customer’s outcomes and their influencers? How easy is it for the OEM to understand the outcomes that the customer is expecting? This may explain why so many of the respondents were only partially happy with remote monitoring. The outcomes or goals that the owner is seeking must be translated into a form that is relevant and controllable within the environment of the monitoring (Bostsman and Rogers, 2010). The relationship between the technical issues and the commercial implications are a key demand from the owner/operators of the equipment. 3.3.4 Can you clearly describe the customer value proposition? The owner/operators that were interviewed were better able to describe the customer value propositions they were expecting than were OEMs. Marketing theory says that the seller must be able to describe the value proposition and Osterwald (2002) has provided a format to assist OEMs to

Where do you sit in the

ecosystem and who brings what value?

Do you understand

your customer's gains and pains?

Do you understand the customer's

outcomes and their

influencers?

Can you clearly describe the

customer value proposition?

Can you describe clearly

where the customer’s

value accrues?



do so. Nevertheless, the clearest descriptions of customer value propositions were from the owner/operators. This suggests that customer pull will bring the technology to the market. 3.3.5 Can you describe clearly where the customer’s value accrues? As with the point on describing customer value propositions, this is very important. It is specific to the different stakeholders and was again best described by the owner/operators.

4 CONCLUSIONS The survey and interview data were generally in agreement with the literature: the owner/operators were looking for support with new M2M solutions that would increase the interactions between the key stakeholders. The expectation was that joint problem solving would increase the speed of problem resolution, reduce costs and create better solutions. This is in agreement with the open innovation concept of Chasebrough et al (2007) and Doblin (2015) who recommend increased customer engagement in innovation. This is also supported by Freeman et al, 2012 and Deliotte (2014) where the customer experience and shared values were considered as a key sustainability aspect. The degree of customer engagement must increase in order for M2M systems to deliver the customer value propositions they offer. Loss of personal interactions can lead to a perceived lower level of value. Engagement should be on a more individual basis, where each customer persona must have a clear value proposition. Customers of data include all of the active players in the ecosystem, so an understanding of what each customer requires needs to be actively made. This is particularly true in an environment where the customer may not understand what they actually need. Consumers of the data could be in OEM product development as well as other suppliers in the ecosystem. Data itself has a value, and many stakeholders should be able to access the data. There should be transparency in the collection and future uses of the data. The best relationships were developed from data that was transformed into information and used collaboratively for root-‐cause-‐analysis, rather than defensively to protect warranty positions. The data should include the operational data as well as the technical data from the machines.

5 RECOMMENDATIONS To address the conclusions, the authors have some recommendations that any firm that is creating an M2M solution for its customers should consider during the development of the customer value proposition:

• identify who are your customers in the ecosystem and understand the outcomes they value; • segment your customers in terms of the outcomes they are seeking and create for each a

persona with a clear value proposition with clear identification of where value is created; • find ways to engage with the customer, as experience is important in creating sustainability

and the loss of personal interactions can lead to a perception of a lower level of value; • wherever possible, the data collected must be used openly for root-‐cause-‐analysis rather

than defensively to protect warranty positions; • remain open and transparent with data collection and the use of the data; • there are internal consumers of the data that is collected and this can support new product

and service development.



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ACKNOWLEDGMENTS The authors would like to thank the Lucerne University of Applied Sciences and Arts and the university of Bergamo.

AUTHOR CONTACT DETAILS Dr Shaun West Lectuere for Product and Service Innovation Wirtschaftsingenieurwesen | innovation, Lucerne University of Applied Sciences and Arts, Switzerland Email: [email protected] Phone: +41 79 770 5986

Paolo Gaiardelli Assistant Professor Department of Engineering University of Bergamo Email: [email protected] Phone: +39 035 2052385

Dominik Kujawski Student, Masters in Science and Engineering Luzern University of Applied Science and Art Email: [email protected]

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