IZVIRNI ZNANSTVENI ČLANEK - ORIGINAL SCIENTIFIC PARER Prejeto/Revised: Februar 2013 Popravi jeno/Revised: April 2013 Sprejeto/Accepted: April 2013 SYSTEM THINKING IN MANAGING TECHNOLOGICAL INNOVATION SYSTEMS IN MANUFACTURING COMPANIES Sistemsko razmišljanje pri upravljanju tehnološkega informacijskega sistema v proizvodnih podjetjih Timothy O. Oyebisi African Institute for Science Policy and Innovation, Obafemi Awolowo University, lle-lfe, Nigeria tooyebisi@yahoo.com Abiodun S. Momodu Centre for Energy Research and Development, Obafemi Awolowo University, lle-lfe, Nigeria abiodun.momodu@yahoo.com Se gun O. Olabode Lagos State University, Faculty of Management Sciences, Department of Business Administration and Management Technology, Lagos State, Nigeria osegunben@yahoo.com Abstract This paper discusses models for managing Technological Innovation System (TIS) in a manufacturing setting. It reviews the existing six models for managing TIS: Technology Push, Market Pull, Coupling Innovation Process (CIP), Functional Integration Innovation Process (FIIP), System Integration and Networking Innovation Process, and System of Innovation models. Major drawbacks of the model for managing TIS in manufacturing settings were identified. System thinking approach was then proposed as a suitable alternative for addressing these drawbacks. The basic principle of system dynamics on which system thinking is hinged is used to explain the proposed model. However, understanding and using this model are premised on the availability of knowledge and skills in computer modeling and simulation software (e.g., Ithink, Vensim, and Powersim). Key words: Nigeria, technology, innovation, Technological Innovation System model, manufacturing, simulation, system dynamics, system thinking. prrT- Naše gospodarstvo / Our Economy Vol. 59, No. 3-4, 2013 pp. 50-57 DOI: 10.7549/ourecon.2013.3-4.05 UDK: 001.895:007 JEL: 032 Povzetek Članek obravnava modele za upravljanje tehnološkega informacijskega sistema (TIS) v kontekstu proizvodnje. Preučuje šest obstoječih modelov za upravljanje tehnoloških informacijskih sistemov, t.j. tehnološko vzpodbujene inovacije, tržno vzpodbujene inovacije, proces združevanja za inovacijo, funkcijska vključitev v proces inovacije (FIIP), sistemska vključitev in mrežni proces, model sistemskega inoviranja. Članek identificira glavne pomanjkljivosti modela za upravljanje tehnološkega informacijskega sistema v kontekstu proizvodnje. Kot ustrezno alternativo reševanja teh pomanjkljivosti predlaga pristop sistemskega razmišljanja. Za razlago predlaganega modela je uporabljen osnovni princip sistemske dinamike, na katerem temelji sistemsko razmišljanje. Vendar pa razumevanje in uporaba tega modela temelji na dostopnosti znanja in spretnosti za uporabo programske opreme za modeliranje in simulacije, kot so med drugim Ithink, Vensim in Powersim. Ključne besede: Nigerija, tehnologija, inovacija, tehnološki informacijski sistem, model, proizvodnja, simulacija sistemska dinamika, sistemsko razmišljanje T m 1 ■ i iv O. Ovi T i, Ai'. ! n S. M' ¡Vi< >[îu, S •-. n O. O i ai ■ : Sï'ii m Tu ii: ng in Mananin:- Ti ais.; ai Inn; vai 1 Symim in Man . a' ïhhn-- O 'M an i 1 Introduction The phrase technological innovation system is a combination of three key words: technology, innovation, and system. Many definitions of technology abound in the literature. For example. Burgclman (1983) defines it as a production process, a key competitive factor, an applied science, a specific process, a core competence, a dynamic capability, knowhow. and improved quality oflifc. It is the current state ofhumaiii-ly's knowledge of how to combine resources to produce the desired result, solve problems, fulfill needs, and satisfy- wants. It can also be regarded as the purposeful application of information in the design, production, and utilization of goods and services and in the organization of human activities (Busi-ncssDictiorary.com. 2013). The word technology can be used to rcfer to the making, modification, usage, and knowledge of tools, machines, techniques, crafts, systems, and method of organization in order to solve a problem, improve a preexisting solution to a problem, achieve a goal, handle an applied input/output relation, or perform a specific function. Meanwhile, innovation has been defined as a process from idea generation to commercialization, bringing the idea or invention to the market as a new product, process, or service through the phases of idea generation, research and development, product development, marketing, and selling a new product or service (Du Preez & Louw, 2008). Innovation is also regarded as the commercial and practical application of ideas or innovations (Troll. 2008: Vajoncn. 2006). It can be the development of new customers" value through solutions that mccl new needs in new value-adding ways. This is accomplished through more effective products, processes, services, technologies, or ideas that are readily available lo markets, governments, and society. The Technological Innovation System (TIS) is a scientific field of innovation studies to explain the nature and rale of technological change (Smits, 2002). Caisson and Stanki-cvvicz (1991) defined TIS as a dynamic nclwork of agents interacting in a specific economic/industrial area under a particular institutional infrastructure and involved in the generation, diffusion, and utilization of technology. The purpose of analyzing TIS is to evaluate the development of a particular technological field in terms ofthc structures and processes that support or hamper it. TIS can be analyzed in terms of its system components and/or its dynamics. Base on the discussion thus far. TIS can be defined as the totality of know-how by which organizations produce new products, processes, and systems, resulting in sustaining or repositioning them in the emerging competitive global market. It includes those products that emerge. Thus, it means that the very survival of an organization, not to mention profitability or breakeven, depends largely on the proper management of TIS. Therefore, all efforts must be made to ensure that TIS is effectively managed in organizations. It is expedient lo look at the models by which TIS has been managed since the beginning of the Industrial Revolution lo situate our current thinking in the right perspective. Six models have been identified, each representing different generations (Du Preez & Louw. 2008: Tayaran. 2011): 1. Technology Push Model 2. Market Pull Model 3. Coupling Innovation Process Model 4. Functional Integration Innovation Process Model 5. System Inlcgration and Networking Innovation Process Model 6. System of Innovation Model 2 Technology Push Model The first-generation model representing the technology push theory is a linear model. This theory was the first pu-blically articulated thought of the founding fathers of management The theory was based on a simple linear process by which new products from organizations go to the market based on scientific and technological advances. Consideration was not necessarily given lo the market situation, which might be expected to be a period marked by big jumps in scientific and technology advancements. Scientists and tcchnologisls were primarily interested in the transformation of ideas into inventions. The inventors were not necessarily concerned with the ultimate commercial applications of their inventions. Figure 1 shows the Technology Push Model, where the consumers in the market arc Ihc recipients of the outpul of scientific research. Figures la and lb show the traditional and modern Tecli no log}" Push Models, respectively Figure la: Traditional Mode! 'technology Push Mode! INDIVIDUAL IDEA GENERATION INVENTION ADAPTION IMPROVED DESIGN AND —► PRODUCTION DEVELOPMENT CONSUMERS Figure lb: Modern Technology Push Model RESEARCH AND DEVELOPMENT NEW PRODUCT IMPROVED ->- DESIGN AND —>■ DESIGN AND — PRODUCTION — CONSUMERS DEVELOPMENT DEVELOPMENT As indicated, innovation is directly dependent on basic and applied research. Therefore, the management of innovation was limited to the management of the activities of the inventors and researchers. 3 Market Pull Model The market pull theory was the next stage in the understanding of the management of TIS. This model emphasizes the need to explore the market well before the commencement of the production of an innovation. It takes the position that a product is newly produced as the market dictates. Emphasis is placed on the consumers' specifications, to which the manufacturers have to respond positively. The model ensures a receptive market once the appropriate technological innovation is developed. Figure 2 shows the Market Pull Model. Figure 2a shows the Market Pull that does not necessarily involve the users in the generation of ideas whereas Figure 2b takes customers into consideration in the development process. This model, which is an alternative to technology push, proposes that the stimulus for innovation is societal needs or a section of the market. The technology push and market pull models are considered linear and were prevalent until the 1960s. 4 Coupling Innovation Process (CIP) Model The CIP model is a combination of the push-pull theory and was predominant in the 1970s and 1980s. Although it views innovation as a sequential process, it is not considered continuous, as in the push and pull models. It is made up of interdependent stages of inclusive feedback (see Figure 3). Figure 2a: Market Pull Model that does not involve users in the generation of ideas Figure 2 b: Market Pull Model that involves customers in the development of ideas Figure 3: CoupIing'Innovation Process (CIP) Model (Adapted,from Du Preez & Louw, 2008) T M 1 ■ i iv O. Ovi T i, Ai'. ! N S. M' ¡Vi< >[îu, S •-. n O. Oí AI a : S -i M M TM iK N'A IN Mana;-IN- Tiaiik; A ;<-: M INN; VAI 1 ^ SYMIM IN MAN . A1 -ii IHN- - G -M ^N i Close interaction occurs among internal and external mechanisms, including the market. Of course this model is not linear; il consists of interdependent stages with feedback such thai innovation is represented as a sequential process with limited functional integration. 5 Functional Integration Innovation Process (FUR) Model The FIIP Model has much to do with linkages and alliances between the upstream (supplier) and downstream (customer). The fourth in the scries of innovation models was developed in the mid-1980s based on the knowledge gained from the Japanese automobile and electronics industry. The model (Figure 4) takes into consideration the use of concurrcnt/simultancous engineering techniques in its different stages of innov ation process to design a parallel model instead of the sequential model adapted by the CIP Model (Tayaran. 2011). This method allows linkages of Ihc activities of operational groups at each stage through structural feedback mechanisms to connect the other stages. According to Zhang, Maniar, and Fire (2001), this model is functionally woven around a core project, making it easier to combine expertise from different fields of specialization and thereby reducing the cycle time/time spent on the innovation process in a product lifccyclc. An example of the fourth generation of innovation model is the Minnesota Innovation Research Program (MIRP) model, which explains Ihc sequence of core characteristics reflected as an innovative idea is transformed and implemented into a concrclc reality (Du Prccz & Louvv, 2008). Figure 4: Functional Integration Innovation Process Model (. Idaptedfrom Du Preez & Lottw, 2008) Marketing 1 1 1 1 1 R&D 1 1 1 1 1 Product development 1 1 1 1 1 Parts manufacturer (supplier») 1 1 1 1 1 Manufacturer i i i Á J Joint group meetings (engines/managers) Marketing Launch Figure 5: System Integration and,Networking Innovation Model (Adaptedfrom Du Preez & Louw, 2008) External Inputs: • Societal needs • Competitors • Suplier • Partnership • Distributor • Customers • Strategic alliances External Inputs: • Competitors • Suppliers • Distributors • Customers • University • Departments Marketing and sales Accumi / / ! Finance lation of ; '. ■ V Vv V knowledge Engineering and manufacturing J ! j 1 ■A'7 wer the time Research and development External Inputs: •Competitors • Suplier • Distributor •Customers External Inputs: • Scientific and technological developments • Suppliers •Customers • University • Departments 6 System Integration and Networking Innovation Process Model The fifth in the generation of innovation models originated in the early 1990s. It emphasized the need for continuous change and attempted to explain the complexity in the innovation process (Du Preez & Louw, 2008; Tayaran, 2011). This model develops a network that encompasses both internal and external stakeholders. Unlike other models that limit the external more to the customers, the fifth generation includes all relevant stakeholders (e.g., supplies, other firms). To take advantage of the influence of the external environment, effective communication with the external environment was developed. In this way, an innovation that is not useful to an organization at any point in time can be sold or licensed to another organization, thereby ensuring that the idea is not wasted. The use of computer simulation and other advanced technological tools has led to a quick response to the design and development stages and reduced prototyping costs in the innovation process. Hence, Tayaran (2011) suggested that efficiency and speed in this process are the key benefits derived mainly from continuous interactions across the innovation network. 7 System of Innovation Model The System of Innovation Model is the sixth generation of innovation models. Although adopted from Friedrich List's concept of the "National System of Political Economy" (1841), Lundvall was the first to introduce the concept of system of innovation in 1985, which gradually became popular, particularly among policymakers and innovation researchers, in the 1990s. By 1988, after studying the success story of the Japanese economy. Freeman labeled it "the national innovation system" because the model identifies the social and economic effects of the process that generate innovation across a nation (Du Preez & Louw, 2008). According to Chang and Chen (2004), this sixth generation of innovation models enables the system to have a clear understanding of the factors/variables that affect innovation and how they influence the process of innovation. Although this model takes care of much of the limitations of the previous models, the method of analysis is not dynamic. 8 Limitations of the Models Considered All the models described thus far are based on analytical thinking, which is premised on optimization and econometrics. Optimization-based models are essentially equilibrium models that focus predominantly on the short-term performance of the system. However, this has not precluded the existence of a few long-term market analyses based on optimization techniques. The strength of these models is based on the assumption that resource allocation resulting from the market mechanism is equivalent to the minimization of the discounted, cumulated, operating, and investments costs over the considered period of planning (Olsina, 2005). On the other hand, econometric models are inherently descriptive, aiming at reproducing the actual observed market behavior regardless of whether it deviates from the ideal behavior described by the prescriptive models. Therefore, these models are data/statistics dependent, with the implication that they leave out many relevant variables, leading to the oversimplification of detailed complexity (Oladeji, 2005). In addition, these methods usually involve paradigms suited for systems that are linear, partly open or near equilibrium, with a short time horizon and are incapable of handling planning for quality in high technology facilities. The models also require detailed historical data and data reconciliation or a choice of details among alternatives at the detailed implementation stage of decision making (Oyebisi & Momodu, 2012). Tables 1 and 2 compare the stages and attributes of analytical and systems thinking, respectively. Summarily, table 3 shows the contrasts between analytical thinking and systems thinking. Table 1: Comparison of the Three Stages of Analytical and Systems Thinking Stage Analytical thinking Systems thinking (synthesis) 1 Take apart the thing to be understood Identifying a containing whole (system) of which the thing to be explained is a part 2 Try to understand the behavior of parts taken separately Explain the behavior or properties of the containing whole 3 Try to assemble this understanding into an understanding of whole Explain the behavior or properties of the thing to be explained in terms of its role(s) or functions) within its containing whole Source: Fasser and Brettner (2002) Table 2: A Comparison of the Attributes of Analytical Thinking and Systems Thinking Analytical thinking Systems thinking Focuses on structure (how things work) Focuses on function (why things operate as they do). Yields knowledge Yields understanding Enables us to describe Enables us to explain Looks into things Looks out of things Source: Fasser and Brettner (2002) 9 System Thinking Model The system thinking model follows the system dynamics principle, which makes it possible to analyze and synthesize both hard "figures" and soft "feelings" as variables of the entire TIS. Here decisions are premised on policy and Table 3: Analytical Thinking vs. Systems Thinking Analytic thinking (analysis of today) vs. Systems thinking (synthesis for the future) 1 We/they vs. 1 Customers/stakeholders 2 Independent vs. 2 Interdependent 3 Activities/tasks/means and 3 Outcomes/ends 4 Problem solving and 4 Solution seeking 5 Today is fine vs. 5 Shared vision of future 6 Units/departments and 6 Total organizations 7 Silo mentality vs. 7 Cross-functional teamwork 8 Closed environment vs. 8 Openness and feedback 9 Department goals and 9 Shared core strategies 10 Strategic planning project vs. 10 Strategic management system 11 Hierarchy and controls and 11 Serve the customer 12 Not my job vs. 12 Communications & collaboration 13 Isolated change vs. 13 Systemic change 14 Linear/begin-end vs. 14 Circular/repeat cycles 15 Little picture/view vs. 15 Big picture/holistic perspective 16 Short term and 16 Long terms 17 Separate issues vs. 17 Related issues 18 Symptoms and 18 Root causes 19 Isolated events and 19 Patterns/trends 20 Activities/actions and 20 Clear outcome expectations (goals/values) Source: Haines Centre for Strategic Management (2007) regarded as the software variable. The system dynamics principle involves the following: - Stock and flow diagramming, dealing with input and output from and to a source - Causal (feedback) loop diagramming, relating interconnections between actions - Time delays that affect behavior in the entire system - Simulation of model using computer The system thinking concept of managing TIS is depicted in Figure 6. The proposition is adapted from the canonical form of control system. The diagram shows both negative (balancing) and positive (reinforcing) feedback. The planning module represents the point where initiatives inputs are "mixed" and decisions are made and forwarded to the implementation module for actual action. The system works in a way as to monitor the rate of demand for organizations' products. This information is fed into the planning module, where it is compared with the expected level of consumer demand. If the demand is less, the reinforcing loop is affected whereas the balancing loop is used when the demand is higher. Of course, in business, the demand is expected to be higher—even higher than the set level. The information will assist in the planning stages for the right level/amount of technological capabilities, in- Figure 6: Modified Canonical Form of Control System REINFORCING EXPECTED DEMAND LEVEL => ^ CONSUMER DEMAND vcslmcnl. produclion and linkage capabilities, and other resources to be put into place to maintain the desired level of product demand. Employing the system dynamics principle offers several advantages. First, all conceivable factors influencing innovation. including the front end of innovation (FE1) and new product design and development (NPDD). arc clearly monitored and controlled. Second, the cost, risk, and resources arc effectively controlled and managed. Third, the system can be modeled on the computer to allow for simulations using simulation software application packages such as lthink. Vcnsim. and Powcrsim. Fourth, the issue of non-linearity, which the other models cannot address, can be effectively tackled. Finally, knowledge creation occurs in the system as every computer simulation would be part of the database for the organization. 10 Conclusion The use of systems dynamics as a major tool of systems thinking is highly useful and most desirable for managing T1S in manufacturing settings if the necessary knowledge and skills in computer modeling and simulation are available. References 1. Bain. R. (1937). Technology and State Government. American Sociological Review. 2(6). 860-874. http:// dxdoi.org/10.2307/2084365 2. Burgclman. R.b A. (1983). A process of model of internal corporate venturing in the diversified major firm. Administrative Science Quarterly, 1(5). 223-224. http://dx.doi org/10.2307/2392619 3. Carlsson. 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Innovation management.and.new product development (4T|1 ed ). Harlow, England: Pearson Education Limited. 19. Vajonen. A. (2006). Management of early phases in innovation process: A case study of commercializing technology in a small enterprise (Master's thesis). Helsinki University of Technology. Retrieved from http:// cn.Yvikipcdia.org/wiki/lcchnology transfer 20. Vcntosa. M.. Balillo. A.. Ramos. A.. & Rivicr. M. (2005). Electricity market modelling trends. Energy Policy, 33. 897-913. http://dx.doi.org/10.1016/j.cnpol.2003.10.013 21. Zhang. H.. Maniar. J. M.. & Fire. A. Z. (2011). Inc-iniRs: Functional intro-interrupted miRNA genes. Genes Development, 2.5(15), 1589-1594. http://dx.doi.Oig/10.1101/ gad.2058711 Timothy O. Oyebisi is a professor of technology management at the African Institute for Science Policy and Innovation, Obafemi Awolowo University, Ile-Ife, Nigeria. He holds a PhD in electronic and electrical engineering and a MBA from the same university. His research thrusts are industrial technology management and information technology management. His current research efforts are in industrial system modeling, project risk management, and entrepreneurship development. Timothy O. Oyebisi je profesor za tehnološki management na African Institute for Science Policy and Innovation, Obafemi Awolowo University, Ile-Ife, Nigeria. Na isti univerzi je pridobil doktorat s področja elektronskega in elektro inženir-stva ter magisterij s področja poslovnega upravljanja. Njegovi raziskovalni interesi so predvsem na področju industrijsko-tehnološkega managementa in managemen-ta informacijskih tehnologij. Trenutno se osredotoča na modeliranje industrijskih sistemov, management projektnih tveganj in razvoj podjetništva. Abiodun S. Momodu, Research Fellow at the Centre for Energy Research and Development, Obafemi Awolowo University, Ile-Ife, earned his PhD in systems modeling and energy management from the same university. His research interests include systems and energy planning, environmental management, and entrepreneurship development. His current work focuses on modeling synergy between solid fuel production techniques and environmental protection. Dr. Abiodun S. Momodu je znanstveni raziskovalec na Centre for Energy Research and Development, Obafemi Awolowo University, Ile-Ife. Na tej univerzi je tudi pridobil doktorat iz sistemskega modeliranja in energetskega upravljanja. Njegovi raziskovalni interesi vključujejo načrtovanje sistemov in energetsko načrtovanje, okoljski management in razvoj podjetništva. Njegovo trenutno delo vključuje modeliranje sinergije med tehnikami proizvodnje trdih goriv in varstvom okolja. Segun O. Olabode is a lecturer at the Department of Business Administration and Management Technology, Lagos State University, Nigeria. He earned his BSc and MSc in industrial and production engineering from the University of Ibadan, Oyo State. He is currently working on his PhD thesis entitled "Development of Model for Managing Technological Innovation in the Nigerian Manufacturing Sector." Segun O. Olabode je predavatelj na Department of Business Administration and Management Technology, Lagos State University, Nigerija. Na University of Ibadan, Oyo State je pridobil diplomo in magisterij iz gospodarskega in proizvodnega inženirstva. Trenutno pripravlja doktorsko disertacijo na temo "Razvij modela za upravljanje tehnološkega inoviranja v nigerijskem proizvodnem sektorju".