Towards a Field-Data-Driven Productivity Analysis and Planning System for Industrial Service

Transcription

1 Towards a Field-Data-Driven Productivity Analysis and Planning System for Industrial Service Simone Turrin, Ralf Gitzel ABB AG, Forschungszentrum Service quality in a maintenance context can be measured through the factors that act as cost drivers for the total cost of ownership (TCO). Downtime and service cost are identified as main cost drivers in many TCO scenarios. Under this logic, service productivity and quality can be gauged by looking at service costs and the effect that service activities have on the reliability and life expectancy of equipment. This paper focuses on open issues and research directions in collecting and transmitting reliability and service data out of the field. The collected data should be analyzed to conclude proactively on service activities and to measure the actual quality and productivity of the service offered to the customer. 1. Introduction Ranging from mere spare part sales to advanced service solutions, industrial service has a huge impact on the overall availability, productivity and safety of strategic industrial sectors such as manufacturing, chemical industry, power production and transmission, or raw material extraction. Departing from the current reactive and preventive approaches, the future trend in industrial service is moving toward smart proactive service solutions. A proactive service approach consists of monitoring and fixing a component, product, or system before it fails and eliminating the source of defects and malfunctions to extend the life time of components, products and systems. Thus a proactive approach to industrial service enables increased customer satisfaction, reduction of service costs, generation of new service-related revenue streams and reduction of energy consumption and carbon footprint of service activities. However, customers are often facing the question which service offers to consider. In fact, customers are unwilling to buy services whose value they do not understand (cf. (Kindström, 2010)). While soft arguments can be used to convince a customer, an objective measure of service quality would be helpful to reach a rational decision about which services to use in a particular scenario. In this paper, we analyze several measures of service quality and productivity existing in the literature. We use these measurements as a basis for our own list of KPIs to use in the context of industrial service. Based on these requirements, we focus on the actual technologies and procedures to collect and transmit data from the field and 1

2 on the current open issues and research directions in developing a service productivity and quality analysis system for industrial service. In accordance with the main focus of ABB business, particular attention will be paid to power and automation technologies. ABB provides a full range of lifecycle services ranging from traditional spare parts and equipment repair, engineering and consulting, maintenance and field service, migration and updates to innovative remote monitoring and service solutions. In addition, ABB Full Service offers a globally supported long-term, performance-based partnership in which ABB commits to maintain and improve the production equipment. With a Full Service agreement, ABB takes over the responsibility for the engineering, planning, execution and management of an entire plant s maintenance activities. At this time, the development of a service quality and productivity analysis system is subject of research at the German ABB Corporate Research Center. The expected results consist, on the one hand, of a concept for a new generation of intelligent products able to collect and automatically monitor relevant field data. On the other hand, the enabler of proactive service offerings will be an integrated, scalable and modular system able to receive and to analyze automatically the collected data and, accordingly, to conclude proactively on service activities and to measure the actual quality and productivity of the service offered to the customer. 2. Measuring service quality and productivity Measuring service quality and productivity is not an easy endeavor. Due to its intangible nature, a service product cannot be measured with the same quality assurance methods as traditional (hardware) products (e.g. see (Fleisch; Friedli; Gebauer, 2005)). In many cases, the customer is even unable to understand the intended benefit of the service (e.g. (Kallenberg; Oliva, 2003)), i.e. what they gain even before considering any quality issues. In the context of proactive maintenance service things are further complicated by the fact that there is a stochastic element. Did a proactive measure really prevent a failure or was it wasted effort? In many cases, it is not possible to answer this question after the fact. Before we suggest solutions as to how to measure proactive service quality, we provide an overview of existing approaches to measure service quality in general. We start off with domain-independent solutions and progress towards methods from the field of industrial maintenance. 2.1 General service quality and productivity measures Papers that look at service quality and/or productivity use several different definitions as a foundation for their analyses. There are many overlapping and conflicting terms and definitions in the context of service productivity, the discussion of which is beyond the scope of this paper. (Rutkauskas and Paulavičienė provide a detailed discussion of the various measures such as productivity, efficiency and effectiveness of service (Paulavičienė; Rutkauskas, 2005)). We adopt a more pragmatic approach in 2

3 that we look at various quality measures regardless of actual name and analyze them for their suitability to our task of objectively evaluating the worth of a proactive service offering. Input/output based service measurement: Anderson et al consider service productivity to be a firm s total sales divided by number of employees (Anderson; Fornell, Rust, 1997), presumably under the assumption that all the companies revenue is generated by service. This and other input-output-oriented definitions of service have the effect that increased productivity will lead to worse service, since less time is spent on each customer (cf. (Anderson; Fornell, Rust, 1997)). Thus, we argue that such a value is not helpful in tracking and improving the quality of service. Indirect quality measurements: Since quality is difficult to measure, several approaches resort to measuring quality through customer perception (e.g. (Berry; Parasuraman; Zeithaml, 1996), (Gummesson, 1994) or (Grönroos, 1982)). Zeithaml et al have developed a conceptual framework that measures service quality based on customer behavior. The basic premise is that quality impacts customer satisfaction, which in turn reflects in the customer behavior. The model measures customer behavioral intent to gauge customer satisfaction (Berry; Parasuraman; Zeithaml, 1996). The approach applies to service business in general and as such does not take into account technical values such as OEE or reliability values. Furthermore, it measures the perceived quality and not the actual quality. From the technical standpoint, it is hard for the service provider to take an objective measure of service quality. It is even harder for the customer who does not have access to broad statistical data and who acts on his subjective interpretation. 2.2 Quality of maintenance service Gebauer states the purpose of high end services is to lower the Total Cost of Ownership (TCO) for the customer (Fleisch; Friedli; Gebauer, 2005). Thus, one might argue that service quality in a maintenance context can be measured through the factors that act as cost drivers for TCO. In previous work, we have identified downtime and service cost as main cost drivers in many TCO scenarios (Dix; Gitzel; Stich, 2011). Under this logic, service quality can be gauged by looking at the effect a service has on the reliability and life expectancy of equipment. There exists some previous work on how to represent the impact of service on reliability parameters and thus the expected lifetime of equipment ((Wang, 2002), (Clements-Croome; Wu, 2005a), (Clements-Croome; Wu, 2005b), (Clavareau; Labeau 2009), (Wu; Zuo, 2010)). While these models focus on traditional preventive maintenance, they are abstract enough to be applied to more advanced measures that are taken to avoid failures. Essentially, these models allow the calculation of factors to the relevant parameters in the probability distribution function for failures and could be based on statistical data collected in the field. 3

4 3. Collection, transmission and analysis of field data As mentioned previously, industrial service quality can be gauged by looking at service costs and at the effect that service activities have on the reliability and, consequently, on the life expectancy of equipment. In this paper of the paper, we focus on open issues and research directions concerning the collection, transmission and analysis of reliability and service data out of the field. If available, these data could be used to conclude proactively on service activities and to assess the productivity and quality of service in terms of effectiveness, efficiency and performance. On the one hand, reliability data would help to assess field reliability 1 and, in the context of proactive service solutions, to make prediction on the life expectancy of components, products and systems. On the other hand, service data would be used to determine the influence of service activities on the life expectancy of components, products and systems. With the term reliability data all information concerning product failure is meant. In other words, reliability data are those data that may help answer the question How and how often do products fail?. Examples of reliability data are failure time, failure mode, downtime, and so on. Other data like location of the product, customer, industrial sector, manufacturer, etc. may be used to identify clustering structures in product failure. In addition, in this paper all information concerning product life at customer plants are considered as reliability data as well. In other words, all those data that may help answer the question How are products used?. That is effective product usage time and condition, environmental condition (temperature, air humidity, vibrations, etc.), and so on. This information may be then used to build predictive models on life expectancy of products based on their actual usage scenarios and as a consequence of service activities. With the term service data all information concerning service activities is meant. Answering the question How are products maintained?, service data are information about service activities and costs, reports, technicians, methods, planning and so on. ABB is manufacturer and service provider for a very wide spectrum of power and automation products. From a service perspective, these products are characterized by different installed base, market value, serviceability, accessibility and connectivity. Consequently different reliability and service data, also in terms of quantity and quality of data, are available. In addition, different data collection and transmission technologies and methodologies are currently in place. Possible scenarios range from reading handwritten service reports to continuous remote monitoring and service. What is reported in this paper is the perspective of a company like ABB that is, at the same time, product manufacturer and service provider. Nevertheless, some concepts 1 In the industrial sector, field reliability is defined as the reliability performance of a component, product or system in operation at customer plants. 4

5 are still valid for companies whose main business is exclusively focused on industrial service. 3.1 Reliability data As reported above, in this paper reliability data are classified in data concerning product failure and data concerning product usage Product failure Product failure is usually described by a failure time, a failure mode, failure conditions and an associated downtime. These data are used to assess field reliability and to gain knowledge about how products fail at customer plants. Field reliability is then expressed, according to the particular equipment, in terms of mean time to failure (MTTF), mean time between failures (MTBF), mean time to service (MTTS) and failure rate. A deeper insight on the significance of these quantities is beyond the scope of this paper. In few words, these quantities represent the life expectancy of products at customer plants. What is important to remind here is that these quantities are calculated by statistical methods. Consequently, data concerning a large installed base of similar product is necessary for a rational assessment of field reliability. Information availability: For a product manufacturer, during the warranty period information about product failures is usually available. For repairable products, this information is available to service providers after the warranty period as well. However, after the warranty period customers typically use service providers that are cheaper than the product manufacturer. For non-repairable products, no information about product failures is usually available after the warranty period. In fact, if a nonrepairable product fails after the warranty period, it is thrown away and replaced. Since the typical life expectancy of equipment is longer than the warranty period, from a product manufacturer perspective very little information about product failures is available. In this context, some customer incentive programs should be developed to achieve product failure information after the warranty period as well. Information collection, transmission and analysis: Information on product failure during the warranty period is contained in warranty databases. Customer complaint management systems and service reports are also source of information about product failures after the warranty period. Automatic collection of product failures (in terms of failure time and conditions) happens only in the case of remote monitoring. Typically, product failures are communicated by customers or service technicians with a relatively poor quality of data. Analyses of data concerning product failures in order to assess field reliability are performed by well-established statistical techniques. Open issues: In the following, some open issues concerning information on product failures are reported: Information is usually not available after the warranty period and the quality of the information is generally poor. For some specific products, the failure time is not exactly detectable. 5

6 Due to the small quantity of available data, information on product failure is affected by extreme sensitivity to missing and/or incorrect values. Due to the long life expectancy, data censoring methods play a fundamental role in the assessment of product field reliability. Different management systems and databases (e.g. warranty database, customer complaint management system, service reports database) should be integrated to facilitate the availability and accessibility of data Product usage Information about product usage at customer plants may be used to build predictive models on life expectancy of products based on their actual usage scenarios. From a service perspective, predictive models may help in planning and delivering proactive service activities. In addition, the effect of service activities on the life expectancy of equipment and, therefore, on the quality and productivity of service may be assess. For power and automation technologies, product usage scenarios are typically given by effective product usage time or cycles, usage condition (e.g. load, current, voltage), environmental conditions (e.g. temperature, vibration, air humidity) and so on. Information availability: Information on product usage is associated to sensor systems able to monitor and to collect specific quantities characterizing the usage scenario for a particular product. Due to the relatively high cost of sensor systems, data concerning product usage are usually available only for high-end products. Low-end products are, in some cases, only able to measure the effective usage time or cycles. Information collection, transmission and analysis: As mentioned before, data monitoring and collection is performed by sensor systems. In the context of data transmission, remote monitoring technologies are currently state of the art in many industrial sectors. Nevertheless, a main challenge is to improve the security of remote access connections. In some cases, product usage information is collected and saved in the product and can be downloaded on demand during service activities (for example as a consequence of alarms, warnings or product failures). Product usage information is used to build predictive models on reliability and life expectancy of equipment at customer plants. In this context, data mining and complex event processing (CEP) are two promising techniques whose capabilities are not fully exploited. Open issues: In the authors opinion, the main open issues related to product usage information are listed as follows: For some products (e.g. control systems) a huge amount of data concerning product usage is available. In this case, efficient data consolidation techniques should be developed. For low-end products, product usage information is usually not available since sensor systems are still too expensive. In some cases, customers do not want to share product usage information with product manufacturer (particularly during the warranty period) or service provider. 6

7 The security of remote access connections is of primary importance, above all for strategic industrial sectors like chemical industry, power production and transmission, or raw material extraction. Even if some promising techniques exist, building reliable, robust and valid predictive models is still a challenge. 3.2 Service data Service data are information about service activities and costs, reports, technicians, methods, plans and so on. By an analysis and comparison of service data with the current field reliability and life expectancy of equipment at customer plants it is possible to assess the productivity and quality of service in terms of effectiveness, efficiency and performance. Availability, collection and transmission of service data depend on a large extent on the geographical, operational and organizational structure of the service provider. The rest of this paragraph mainly reflects the current ABB service structure. Nevertheless, a similar operational and organizational service structure is common to (almost) all global industrial service providers in power and automation technologies. Information availability: Data concerning service activities are usually available on a local level and in a very poor quality. The typical scenario is represented by a handwritten service report that, in many cases, is even not completely filled out. In addition, the composition of service reports, the information contained in the reports and the language used to fill out the reports usually vary across the different service local divisions. Information collection, transmission and analysis: Service data collection and transmission is assigned to service engineers and technicians. Service data are then usually saved locally. By data mining and CEP techniques, clustering structures and relationships between service activities, service costs and equipment field reliability may be revealed thus providing a basis for a reliability-based mathematical formulation of industrial service quality and productivity KPIs. Open issues: Two main open issues characterize the research directions in the context of service data: As mentioned above, service data collection is characterized by poor quality data and non-standard procedures. Consequently, the main issue is represented here by a global standardization strategy for service reports. The systematic use of mobile devices by service technicians during service activities could support the standardization of service reports and strongly improve the quality of service data. Assessment of industrial service quality and productivity should be based on the mathematical formulation of service quality and productivity KPIs based on service activities (in terms of service time, number of service dispatches to fix a failure, etc.), service costs and on the effect that service activities have on the actual field reliability of equipment at customer plants. 7

8 4. Conclusions In the first part of this paper, several measures of service quality and productivity existing in the literature are reported and their pertinence to industrial service was discussed. As a conclusion, it can be stated that, from an industrial service perspective, service quality can be gauged by looking at service costs and at the effect that service activities have on the reliability and life expectancy of equipment. Consequently, in this context, collection and transmission of reliability and service data out of the field are of primary importance. Database integration, small amount of available failure data, customer commitment to share information about product usage and security of remote access connections represent the main research topics to be address in the short term. In the context of reliability and service data analysis, data mining and complex event processing techniques may provide the basis for the mathematical formulation of industrial service quality and productivity KPIs, defined considering the effect of service activities on the actual life expectancy of equipment at customer plant. In conclusion, these techniques may help service providers in planning and offering proactive service solutions and the related KPIs may help customers in reaching a rational decision about which services to use in a particular scenario. References Berry, L.; Parasuraman, A.; Zeithaml, V. (1996): The behavioral consequences of service quality. Journal of Marketing, vol. 60, pp Anderson, E.W.; Fornell, C.; Rust, R.T. (1997): Customer satisfaction, productivity, and profitability: differences between goods and services. Marketing Science. Kindström, D. (2010): Towards a service-based business model Key aspects for future competitive advantage. European Management Journal. Fleisch, E.; Friedli, T.; Gebauer, H. (2005): Overcoming the service paradox in manufacturing companies. European Management Journal. Kallenberg, R.; Oliva, R. (2003): Managing the transition from products to services. International Journal of Service Industry Management. Wang, H. (2002): A survey of maintenance policies of deteriorating systems. European Journal of Operational Research, vol. 139 (3), pp Clements-Croome, D.; Wu, S. (2005a): Optimal maintenance policies under different operational schedules. IEEE Transactions on Reliability, vol. 54 (2), pp Clements-Croome, D.; Wu, S. (2005b): Preventive maintenance models with random maintenance quality. Reliability Engineering and Safety Systems, vol. 90 (1), pp

9 Clavareau, J.; Labeau P. (2009): Maintenance and replacement policies under technological obsolescence. Reliability Engineering and System Safety, vol. 94 (2), pp Wu, S.; Zuo, M.J. (2010): Linear and nonlinear preventive maintenance models. IEEE Transactions on Reliability, vol. 59 (1), pp Paulavičienė, E.; Rutkauskas, J. (2005): Concept of productivity in service Sector. Engineering Economics, vol. 43 (3). Gummesson, E. (1994): Service quality and productivity in the imaginary organization. 3 rd International Research Seminar in Service Management. Grönroos, C. (1982): Management and marketing in the service sector. Research Reports, Nr. 8, Swedish School of Economics and Business Administration. Dix, M.; Gitzel, R.; Stich, C. (2011): Life cycle cost model for distributed control systems. atp Edition, vol.5, pp Authors Simone, Turrin, Dr.-Ing. ABB AG, Forschungszentrum Life Cycle Science Wallstadter Str. 59, Ladenburg, Germany simone.turrin@de.abb.com Ralf, Gitzel, Dr. ABB AG, Forschungszentrum Life Cycle Science Wallstadter Str. 59, Ladenburg, Germany ralf.gitzel@de.abb.com 9