VY_FIMECC-S4Fleet_esite_valmis-low

1. 2015 FIMECC S4Fleet Business Intelligence for Real-Time Decision Making

3. Table of contents Business Intelligence Framework �� 5-6 Business Intelligence Architecture ��7 Competitive Intelligence �� 8-9 Customer Intelligence, Sales and CRM ��10-11 Financial Intelligence ........................................................................................12-13 Fleet Management ............................................................................................14-15 HR Intelligence ..................................................................................................16-17 R&D Measurement ...........................................................................................18-19 Supply Chain Measurement ��20-21 Strategic Decision Making ��22-23 3

4. “FIMECC S4Fleet BI sub-project, is developing a BI framework to facilitate real-time strategic decision making of solution providers” 4

5. Business Intelligence Framework 5

6. Areas of interest Business Intelligence Framework for real-time strategic management New theory for real-time strategic management Measurement framework and measures for data collection Utilization of BI knowledge in solution provider organizations Outcomes BI framework for improved real-time decision making BI practices and capabilities for solution providers Identification of key BI measures Marko Kohtamäki Professor Department of Management marko.kohtamaki@uva.fi +358(0)44 971 0432 Business Intelligence Framework Contacts Where the companies are collecting and storing more information than ever, the question of development, utilization and implementation of the knowledge has become particularly relevant. This sub-project focuses on developing a framework utilized for collecting, developing utilizing and implementing BI knowledge in the top management team and business unit levels. Where the existing research on strategic decision making provides a vast amount of knowledge on the processes and contents of company strategy, much of this knowledge has been developed before or during the early days of digitaliza- tion and the internet. The traditional strategy theory does not provide necessary frameworks, practices nor tools for real- time strategic decision making. New theory development is needed for fast strategy utilizing the newest solutions for BI, and big data analytics. BI User Interface theme concentrates on developing a framework for Business Intelligence at the top management team level and a new approach to real-time strategic decision making. “The traditional strategy theory does not provide necessary frameworks, practices nor tools for real-time strategic decision making.” 6

7. Business Intelligence Architecture 7

8. Competitive Intelligence Areas of interest The role of CI tools and practices during the service transition. How does CI support strategy formulation/ implementation, transform information into knowledge, and ensure knowledge sharing and organizational learning? How does CI impact the speed and accuracy of problem/opportunity identification, the exploitation of new opportunities, and the reconfiguration of organizational routines? Competitive intelligence (CI) uses processes, tools, and advanced skills to collect, interpret, and internally share and exploit the intelligence related to customers, suppliers, competitors and government. Therefore, CI is critical for manufacturers moving toward customer solutions. The CI theme of our project focuses on how external information is collected, analyzed and transfor- med to actionable intelligence through BI applications. Outcomes How CI data collection and knowledge utilization enables strategic decision making at the top management team level (TMT). Delineating links between firm performance and CI practices. Identify advantages and shortcomings of the key CI practices, and attempt to offer new measures for potential process design. Contacts Rodrigo Rabetino Assistant Professor Department of Management rodrigo.rabetino@uva.fi +358(0) 29 449 8451 8

9. “CI is critical for manufacturers moving toward customer solutions.” 9

10. Customer Intelligence, Sales & CRMCustomer Relationship Management (CRM) is a management approach that aims to create, develop, and enhance relationships with strategically targeted customers. CRM is utilized in order to maximize customer value, corporate profitability, and thus, shareholder value. From a strategic viewpoint, CRM is not merely an IT solution. CRM involves a synthesis of strategic vision: A corporate understanding of the nature of customer, the utilization of the appropriate CRM applications and information management as well as high-quality operations and service. CRM theme of this project aspires to increase the knowledge on customer data gathering and quality as well as the factors that enable companies to do reliable forecasting. Areas of interest CRM & Sales measures used by management level The role of Customer Relationship Management practices in leading industrial companies Customer data gathering & usage in sales processes and the fit and implementation of CRM systems Outcomes Increased understanding on the CRM related knowledge collection and utilization in sales processes Increased understanding on firms way of managing sales processes and CRM activities Identification of key CRM -measures Ossi Cavén Project Researcher Department of Management ossi.caven@uva.fi +358(0) 29 449 8698 Customer Intelligence, Sales & CRM 10

11. “CRM theme of this project aspires to increase the knowledge on customer data gathering and quality as well as the factors that enable companies to do reliable forecasting.” 11

12. Financial Intelligence Financial intelligence refers to collecting and assimilating financial information thorough the organization to enable rigor and accurate real-time decision making in executive board level. The role of finance has traditionally been strategic as CFO is typically also corporation’s executive vice president. However, the nature of financial information has been changing from the historic focus towards future orientated. Thereafter, understanding the root causes and predicting the future have been highlighted recently. On the other hand, manufacturer’s transition from products to solutions has forced companies to critically review their established financial KPIs as the new business and earning logics emphasize different factors such as customer profitability and retention rate and life-cycle profits and costs over revenues, scale advantages and fixed costs. The financial intelligence theme highlights how industrial solution providers use BI systems to collect, assimilate, analyze and exploit financial information for improved real-time decision making. Moreover, the theme focuses on how financial information can enable executive board to sense and seize business opportunities and reconfigure company’s resources more effectively. Contacts Tuomas Huikkola Project Researcher Department of Management tuomas.huikkola@uva.fi +358(0) 29 449 8448 “Theme focuses on how financial information can enable executive board to sense and seize business opportunities and reconfigure company’s resources more effectively.” 12

13. Areas of interest The role of BI systems to collect and assimilate financial data How BI systems can be utilized in predicting and sketching future scenarios? How BI systems facilitate solution provider’s dynamic capabilities i.e. how BI systems facilitate firm’s ability to sense, seize and reconfigure its resources? Outcomes What type of BI systems solution providers apply to increase their financial intelligence in case companies How financial intelligence is orchestrated across the organization effectively in case companies How BI systems are utilized in implementing strategy in case companies What financial KPIs solution providers use to measure their organizational performance 13

14. Fleet Management Remotely collected fleet data has been used for various purposes in manufacturing companies for more than two decades. During the on-going decade, the industrial internet megatrend has brought the topic into mainstream business discussion. Technology has developed to facilitate, and is no longer a bottleneck for, the development of innovative industrial services and solutions. Areas of interest How remotely collected fleet/environment data is utilized in operational/strategic decision making in industrial companies at the moment? What kind of manually collected fleet data is utilized at the moment? What kinds of development needs exist: How remotely and manually collected fleet data can be combined to provide optimal support for both supplier and customer decision making? Outcomes Holistic understanding about how remote data and manually collected qualitative data can be combined effectively to be utilized for strategic and operational decision making in both supplier and customer companies. Contacts Mathias Hasselblatt Project Researcher Department of Management mathias.hasselblatt@uva.fi +358(0)44 712 3030 Remote data enables radical innovations in industrial service business, but to fully exploit its potential, combining it with manually collected qualitative data is needed. The fleet management research in this project aims to create a holistic understanding how combined fleet information can be utilized more effective manner in strategic and operational decision making in both supplier and customer companies. 14

15. “Remote data enables radical innovations in industrial service business, but to fully exploit its potential, combining it with manually collected qualitative data is needed.” 15

16. HR Intelligence Areas of interest Strategic human resource measures utilized by the top management team The role of strategic human resource management practices in leading industrial companies Talent, competence and performance management practices affecting work force engagement and various forms of performance Jesse Heimonen Project Researcher Department of Management jesse.heimonen@uva.fi +358(0) 29 449 8550 Contacts The role of human resource (HR) related knowledge utilization in the top management team decision making is recognized as a growing area of interest in the search for alternative sources for competitive advantage. Especially in industries shifting from pure product business into more service oriented offerings, firms are forced to re-evaluate and develop the existing human resource base. Advanced abilities to measure and interpret human capital related knowledge contain potential to further develop and effectively manage valuable internal assets that are difficult to imitate by competitors. HR Intelligence as one of our central themes in the ove- rall BI framework focus on HR practices that drive firm performance. Outcomes Increased understanding on the advanced HR related knowledge collection and utilization enabled strategic human resource management Increased understanding on firm performance driving talent, competence and performance management practices Identification of key HR related measures 16

17. “In industries shifting from pure product business into more service oriented offerings, firms are forced to re-evaluate and develop the existing human resource base.” 17

18. Research and development (R&D) activities have direct impact on the competitive advantage for technology firms. It has been widely recognized that strategic investments in internal R&D functions as well as collaboration with external R&D partners has become critical in developing successful product innovations. On the other hand, developing innovations can be risky and costly; research has shown that only one of four R&D projects is successful. Due to the high failure rate of product innovation as well as increasing investments in R&D, measuring R&D performance is essential for managerial decisions. Due to its complex and knowledge intensive nature, performance and cost efficiency of R&D work is far more difficult to estimate or measure than production cost. However, to analyze performance and success of R&D investments, relevant metrics that can be used as managerial basis to obtain relevant and objective information for strategic decision making concerning R&D function are essential. Contacts R&D MeasurementAreas of interest Research on success factors and performance metrics in new product development and success Developing metrics for R&D in terms of efficiency, performance, competences, resources and external dependence Outcomes Identification of key performance measures (KPIs) for R&D function and product innovation success Practical and objective tools to support decisions related to R&D outsourcing, insourcing or offshoring decisions in the firms. Iivari Bäck Assistant Professor Department of Management Iivari.back@uva.fi +358(0) 44 712 3127 18

19. “To analyze performance and success of R&D investments, relevant metrics that can be used as managerial basis to obtain relevant and objective information for strategic decision making concerning R&D function are essential.” 19

20. Supply Chain Measurement Ilkka Sillanpää Assistant Professor Department of Management ilkka.sillanpaa@uva.fi +358(0) 40 777 7167 The main purpose of supply chain (SC) measurement is to get information for top management’s needs, but also several kinds of SC measures are needed at every management and operational level. SC should be measured because of the management interest in measuring how efficient SC is. Measuring is also needed when supply chain management (SCM) is going to be developed. Developing SCM needs a qualified measurement system for measuring SC performance. SC performance measurement systems play important role in manufacturing firms in operations and in business strategy implementation. Performance measurement system provides information for the monitoring, control, evaluation and feedback functions for operations management. It can be a driver for motivation, management action, continuous improvement and the achievement of strategic objectives. Contacts 20 Areas of interest There should be several kinds of measures used in SC performance metrics: balanced approach, strategic, tactical and operational levels and financial as well as non-financial measures. SCM could be measured at various management or operation levels. The main research questions are: How can the performance of the supply chain be measured? With which indicators can the performance of the supply chain be measured? How do the indicators selected represent the supply chain? Outcomes The goal is to deepen knowledge in supply chain performance measurement in manufacturing industry and to develop supply chain performance measurement framework for companies.

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22. “This research theme sheds light on strategic decision making, strategic cognition and cognitive models in in Finnish solution provider firms.” 22 Strategic Decision Making The existing strategy research is mostly concentrating on processes and contents of strategy work. Far less attention has been placed to real-time strategic decision making processes, which is knowledge acquisi- tion, decision making and knowledge implementation. Relevant measures, tools and processes are at the center of absorptive capacity and organizational agility. This research theme sheds light on strategic decision making, strategic cognition and cognitive models in Finnish solution provider firms by utilizing both qualitative and quantitative research designs. Focusing on the cognitive factors of decision makers, the study intends to understand the utilization and implementation of knowledge in decision making in different organizational levels. In short, researchers search for practices, processes and tools that facilitate strategic learning in organizations. Areas of interest How BI knowledge is utilized in strategic decision making? How strategic knowledge is implemented in the organizations? How managerial cognition frames strategic decision making? Outcomes a) Enhanced understanding of strategic decision making in different organizational levels b) Increased understanding of utilization of BI information in strategic decision making c) Knowledge on strategic cognition, cognitive maps and mental models influencing strategic decision making Suvi Einola Project Researcher Department of Management suvi.einola@uva.fi +358(0) 40 563 0507 Contacts

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24. University of Vaasa Wolffintie 34 PL 700, 65101 Vaasa tel. 029 449 8000 uva.fi facebook.com/vaasanyliopisto youtube.com/UniversityOfVaasa twitter.com/univaasa instagram.com/univaasa pinterest.com/univaasa Interested in collaboration with us? Project Lead Customer Intelligence, Sales & CRM Fleet Management Production / Project Measurement Supply Chain Measurement Competitive Intelligence Financial Intelligence HR Intelligence R&D Measurement Strategic Decision Making Processes Marko Kohtamäki Professor Department of Management marko.kohtamaki@uva.fi +358 (0) 44 971 0432 Ossi Cavén Project Researcher Department of Management ossi.caven@uva.fi +358 (0) 29 449 8698 Mathias Hasselblatt Project Researcher Department of Management mathias.hasselblatt@uva.fi +358 (0) 44 712 3030 Valeriia Boldosova Project Researcher Department of Management valeriia.boldosova@uva.fi +358 (0) 40 448 9153 Ilkka Sillanpää Assistant Professor Department of Management ilkka.sillanpaa@uva.fi +358 (0) 40 777 7167 Rodrigo Rabetino Assistant Professor Department of Management rodrigo.rabetino@uva.fi +358 (0) 29 449 8451 Tuomas Huikkola Project Researcher Department of Management tuomas.huikkola@uva.fi +358 (0) 29 449 8448 Jesse Heimonen Project Researcher Department of Management jesse.heimonen@uva.fi +358 (0) 29 449 8550 Iivari Bäck Assistant Professor Department of Management iivari.back@uva.fi +358 (0) 44 712 3127 Suvi Einola Project Researcher Department of Management suvi.einola@uva.fi +358 (0) 40 563 0507

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