A dynamic model of learning in innovation process

A dynamic model of learning in innovation process Samuli Kortelainen 1, Kalle Piirainen 2, Markku Tuominen 3 1, 2, 3,

Lappeenranta University of Technology Faculty of Technology Management Department of Industrial Management P. O. Box 20 FIN-53851 Lappeenranta Finland 1 [email protected] 2 [email protected] 3 [email protected]

Abstract Managing the innovation process has become an important part of strategic decision making. Innovations are an important source of future revenue, which affect the profitability of the company in the long run. Innovation process has a crucial role in the process of converting organizational capabilities into competiveness and profit. Innovation process is also an important venue for exercizising organizational capabilities though and for the process of learning. This paper focuses on the interactions of the capabilities of the firm, process of innovation and learning, and the causality behind converting leaning and capabilities to profit. This paper presents a system dynamic model, which examines the effect of performance in the innovation process to the profitability of the organization and its capabilities. of the model is presented in causal maps to asses the relationships between different parts of the model and to provide understanding on the feedbacks that exist it the model. Two distinctively different feedback are found from our model, which offer different approaches to maintaining organization competitive. Keywords: Innovation management, New Product Development, Innovation Process, Learning, Resource Based view, System Dynamics

1. Introduction The importance of both efficient innovation process and the ability to get new products to markets in time has been highlighted in wide range of studies by the scientific community [e.g. Error! Reference source not found.]. The question has been approached from various different angles leading to diverse research field with many focus areas including NPD project management or innovation process management. The subject of technology management or innovation management has traditionally formed its own research track, but concepts like “dynamic capability” [36] and “core competence” [28] have caused this track to approach fields traditionally reserved for strategic management. This intersection of two major tracks provides the theoretic conceptual barriers that define the research area for this paper. The role of learning in the innovation process has been studied by e.g. Miller and Morris [24]. Also other literature on technology management, and especially knowledge management, emphasize the effect of organizational and individual learning on exploitation of knowledge to create new and successful products. Still, the practical linkages between the innovation process, learning and dynamic capabilities and especially the financial processes of the firm seem to remain somewhat arbitrary. The motivation behind the building of the model is to increase understanding on relationship between learning and innovation process efficiency. However intuitive the link between knowledge and resources of the firm and effectiveness of innovation process might be, the subject has escaped researchers due to immaterial nature of the problem and sometimes long causal loops between learning and realization of profits due to learning.

The examined literature presents a gap in demonstrating the effect of learning to the financial state of the company. The research problem in this paper is to examine the mechanism which interfaces learning to company profitability. To address this gap in understanding the role and implications of learning in innovation process this paper presents a model based on system dynamic approach. The model aims to create a conceptual linkage between organizational learning and discover the mechanisms and systems that lead to organization’s business profits. The modeling begins from conceptualization of the effects of learning in innovation process in a generic industrial firm. The model links the process of innovation to the financial performance of the firm through the product portfolio, and acts as a mediator for organizational learning to become ralized in profits. The paper starts by examining the conceptual background of competitive abdvantage, the innovationprocess and their link to learning. This conceptualization creates the base for the system dynamic model presented in the third chapter. The model and causalities between the modules are discussed and the last chapter presents an overview to the contribution. 2. Conceptual Background 2.1 Capabilities and learning Strategic management has been studied from various aspects, which has resulted in a wide range of different models and theories. These models have evolved according to change in dominant focus areas that have evolved from strongly market based view and positioning [27] to resource based theory (RBT) [Error! Reference source not found.] leading to knowledge based view (starting from Wernerfeld [40], Barney [2], Amit & Schoemaker [1] Grant, [14]; and others attributed for the Knowledge Based View of the firm, including the Cohen & Levinthal [3] as well as Kogut & Zander [21] who are also widely cited in the innovation literature). Underneath every theoretic framework is the reality that strategic management is ultimately decision making where each approach offers different logic for the decision makers. Each of these approaches has its dis-/advantages and loyal followers. RBT is selected as the dominant perspective for this paper. RBT suggests that organization’s performance is determined by its internal capabilities and resources [36, 14, 7]. In modern RBT the key concept is the dynamic capability which is defined as the concept of capabilities. The (dynamic) capabilities, coined by Teece et al. [36] are defined as a capability to use and develop new competencies for sustained competitive advantage over rivalling firms, or in other terms “a learned pattern of collective activity through which the organization generates and modifies its operational routines in pursuit of improved effectiveness”[36, 41, 8]. Competencies on the other hand, as Prahalad & Hamel [28] describe them, are distinct, hard to imitate skills, processes, structures or pools of knowledge, which are manifested in an ability to create disproportioned value, and advantage through for example rapid product changes and give opportunity to invest in new markets flexibly [28, 18] The Resource Based View (RBV),, emphasizes the importance of innovation in building organization’s resource base, which increases the importance of innovation management [15]. Innovation process is seen as a process that leads to both accumulation of firm specific resources and release of new products to the markets in form of resource deployment [16]. Learning is the mechanism which replenishes capabilities and is necessary to achieve and keep “privileged asset position” [7 p. 1506] over rivals. Knott et al. have also proposed similar conclusions [20]. Learning as a concept and as a process has been a subject for study in the strategic research, but has been limited due to problems in measuring knowledge stocks, capabilities, or the amount of learning and problems caused by long causality loops considering learning efforts and payouts. This connection between strategic management and 2

innovation management enables combination of knowledge management concepts such as knowledge and learning with strategic decision making. In this paper learning concerns organizational learning, as discussed in more detail below. 2.2 Process of NPD Research on innovation process has intensified as the importance of new products has increased to trend of shortening product life cycles. Many different innovation process models have been proposed but waterfall/stage-gate type process is the most common, although some writers also propose a cyclical model where the design, prototyping and testing go through several quick cycles during the development [31]. In this paper innovation process is approached through the well-know sequential process, dividing the process in to two main stages (Figure 1): Front End of Innovation (FEI) and New Product development (NPD) [17]. Phase I - Market knowledge - Technological knowledge - Technological and market capabilities - Existing projects

Inputs

- Idea generation (market, technology and cost orientation) - Idea assessment (attractiveness/risk) - Alignment with present portfolio

Phase II - Product concept definition - Market analyses - Product planning - Product architecture and specifications

Front End of Innovation

Phase III

Phase V

Phase IV

- Platform concepts - Product development - Product concepts - Design reviews - Service concepts - Industrial design - Product specifications - Project outlines

- Prototype testing - Market tests - Finalization of design - Preparation for production

- Market launch - After market activities

Product Development and Launch

- Commercialized offerings: - Products and services - After sales/life cycle services

Outputs

Figure 1. Innovation process divided to FEI and NPD stages (adapted from Herstatt & Verworn [17]) The role of FEI is to produce ideas to new product concepts for the actual development work and to select ideas which fit to organization’s strategy. Implementation of these first stages has proven to be challenging due to abstract nature of the work. The work done in FEI creates the groundwork for the whole innovation process and its importance to the performance of whole innovation process has been highlighted by many researchers [19, 32]. The role of NPD process it to develop the screened concept from FEI to a ready project launched to market. In many cases NPD is seen as a linear process where the understanding and functionality in the project increases as it progresses on the process and the process is managed though screening points or gates [5, 37]. The development is also generally the phase where substantial commitments in time and monetary terms are made, and the products are screened in the different stages of development. 2.3 System dynamics System dynamics are based on Jay Forrester’s industrial dynamics, where the chase is to model behavior of entities through relations, delays and feedback [10, 11]. A complex environment can be modeled one entity at a time by picturing the relations and effects between the actors, adding feedback and time delays one entity at a time [34]. By definition, a model is a simplification of a real problem, often described in the language of mathematics. Thus, the modeling approach also has certain error sources; the first pitfall is deciding what in fact are the relevant parameters that need to be included in the model, the second is the choice and forming of the decision model and the third is of course interpretation. System dynamics have been used to some extent to model different situations in the innovation process. The system dynamics based research on innovation process has traditionally concentrated mostly to two main focus areas: 1) Understanding the flows in the innovation process on operational level modeled with high level of detail with the focus on project implementation and project management [9, 25, 22] and 2) macro level, where the 3

modeling focuses on the dynamic factors of innovation system [23, 39] or on the perspective of national competence and national innovation systems [12]. Roberts [30] also outlined a third field 3) “interrelation between the R&D effort and the total corporation” [30 p. 279], which has been studied to lesser extent. System dynamic research that sets between the two main focus areas, on the third field, can be seen to be the strategic level, where hypothesis how strategic decisions affect the dynamics of systems can be evaluated. During recent years the use of system dynamics in strategic issues has emerged [13, 29, 42], but still Warren [38] highlights the lack of system dynamic based research. Out of these previous contributions Zott [42] is focused on the same problem area as the model presented in this paper, where the effects of strategic decision making are evaluated by simulating the long term profits from the innovation process. 3. System Dynamic model linking learning to revenue through innovation The model presented here is set to weave existing theoretical frameworks to a single system. The model builds on four major modules as presented in Figure 2: Innovation process, products at market, cash flows, and capabilities. The division to these modules is done based on both theory and functionality. Each of these modules is an entity that has certain internal dynamics and can be integrated to other modules through somewhat standardized interface. The modules also present different bodies of research, the knowledge based view has been concerned with the capabilities and competitive advantage, innovation management with the process of NPD and management accounting with profits, but multidisciplinary efforts are few and far between. Capabilities module

Innovation process

Cash module

Marketing capability FEI Cash deposit Technological capability

NPD

Sales revenue

Process capability

Fixed costs

Products at market Variable cost Good products

Mediocre products

Bad products

Figure 2. The basic modules in the model 3.1 Assumptions in the model The main assumption in the model is that it presents a generic industrial organization with an internal, not networked, NPD organization. The model describes a self standing firm, which forms a semi-open system; the product ideas flow to the process from whatever channels and the products are launched to steady consumer markets, from where they exit after a predetermined average lifecycle period. The markets cannot be saturated by the NPD effort and outside factors or the firm’s actions do not affect prices or demand. Organization is also seen to work on consumer markets where it is able to choose its product selection without any obligatory requirements.

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Second main assumption is that there is always an element chance present in NPD. This is brought to the model through adding a probability of wrong decision to each decision making situation. The underlying idea is that the organization with better capability is more likely to make right decisions than competitors thus leading to better profits in long run. However, even the best organization might still make process errors. Process error is defined here to mean a situation where organization makes a decision that is not optimal. In real life identification of process error might be hard or even impossible to realize, but this can be done in simulation settings. The concepts and projects in the innovation process are divided to good and bad ideas on the basis of their true value. This decision is based on making the model more simple and small additional value gained from treating each part of the phase independently. The logic is that an idea in itself is not seen as good or bad, and in the initial screen the feasibility of the ideas is assessed, so the ideas entering FEI are all feasible product ideas. As the business case and concept is developed further, the ideas develop to good or bad concepts, and in the screening of business cases the commitment to development is made and the ideas are either developed or sacked. For the managers concepts are seen similar so that the manager cannot be sure which of the concept will eventually turn out to be good. The true value of the project has effects to its possibility of becoming a high earning product. Good projects can lead to any of the product categories where, but bad projects can lead only to mediocre or bad product. The actual definition in which groups the project ends is again defined by probability. With initial values good project has the highest likelihood to become mediocre product and the lowest probability to become bad product and with bad projects the highest probability is to end up as bad product. These probabilities are evolving in the model due to changing capability levels caused by learning and erosion of capabilities. Learning is assumed to be a function of decision making. Each decision in the NPD process forms an increment of learning, as the decision makers act upon their knowledge and learn the consequences of the decisions. The firm, or the people within, learn by executing their tasks and observing the results, which results in knowledge accumulation and building of asset stock. The firm’s ability to deploy resources is dependent on decision making and the effectively is dependency of the human condition of the individual responsible for decision making. Cohen and Levinthal [3] named the ability to recognize opportunity and proper action, as ‘absorptive capacity’, which dictates the ability to receive and assimilate information and to identify relevant and valuable pieces of knowledge. In this paper, the definition of learning is knowledge accumulation through action. The model assumes that the capabilities are deployed through decisions, where the decision maker makes distinctions, classifications and acts upon those [26]. 3.2 The structure of the model The role of the innovation process module is to develop new products to the markets and it is the central piece on the model. The process of NPD as presented in figure 1 above starts from the idea generation, where the initial product possibility is identified. In general NPD the ideas come from various sources and forms and in large quantities. For a general NPD organization as Stevens & Burley [35] found on their UKbased innovation survey, for each 3000 “raw” ideas 125 ideas are developed to product concepts in the FEI phase, nine of which become development projects and on average a little fewer than two enter the markets. The FEI in the model starts after preliminary idea screening and comprises the concept development and screening. At the final concept screen the idea can be dropped to the idea bank or can be forwarded to development. This is also the point where the good and bad concepts are distinguished from each other. The concepts that are killed go to concept bank and those accepted progress to the product development phase. A 5

well performing FEI will produce higher portion of good ideas compared to bad ideas that will have effects on the probabilities of success during next phase of the process. The development phase normally comprises several stages from the product design to testing the product and launch planning. During the development stage, the product design and manufacturing is planned. The next stage is testing and trials, where developed product is tested and validated. The plans are implemented and first batches are produced, from which prototypes are taken to field testing and validation. The model does not intentionally distinguish the stages but simulates the generic process where some projects are killed during the process, which end up in the project banks. As the projects proceed in the process they are screened between each stage and in an ideal situation only bad projects are terminated and good projects get launched to markets. The decision making of managers is tied to the number of ongoing projects at NPD compared to the resources available for the phase. Ongoing projects in innovation process lead to cost where projects in NPD stage cost is significantly higher than of those projects in FEI stage. If the number of projects increases over the optimal level of projects the performance of the process starts to decline, which leads to problems in getting products ready to markets, a phenomenon also known as resource saturation in the realm of computing [e.g. 33]. Resource saturation will decline the progress of all projects until the number of projects will be lowered. The ability to keep the amount of projects in control is dependent on organization’s process capability. As the final stage in the development the validated product undergoes a launch to the markets. The process output is on average a little under two products in a year, one of which will be successful in the markets, in other words a star product. Due to inefficient development or errors of judgment during the NPD, change of customer preferences and different other factors, roughly half of the launched product end up as mediocre or downright failure in the market. The “products at market” module controls the flows of each product group. The product module is divided to three categories: bad products, mediocre products, and star products. The modules responsibility is to control to which category either good or bad product ends up. This flow is controlled through probability that is derived from organizations capability and controlled by project source (the NPD process). Once in market the product life cycle is controlled again by probability, which leads to situation where products are likely to spend the wanted time at the markets, but it is possible that a single product can either be at the market longer or shorter time. On the case of bad products managerial kill decision is also added that helps organization to get rid of unwanted products at market. The main variable in the cash flow module is the cash deposit, which counts the cumulative cash flow in the model. The money flows are controlled by sales revenue, fixed costs, and variable costs. Sales revenue is created from products at market, with good products creating the greatest revenue and bad products creating negative result. Fixed costs are generated from the basic resources available in NPD stage. Variable costs are generated from ongoing development projects in both FEI and NPD stages. The capabilities module has three main variables: Market, Technological and Process capabilities. Capabilities reflect the relative capability level compared to industry average and they affect how well organization can do innovation work. This division can be seen to relate with Cooper’s division of product development process activities to marketing and technological [4, 6]. The level of competencies is determined by the difference between capability erosion and capability creation. Capabilities grow from either deployment through 6

learning-by-doing achieved in innovation process, or by basic research. The deterioration of relative advantage in capabilities is caused by learning by competitors. These variables are determinant on how firm can perform these activities. Loss of competence is faster when firm gains higher relative capability due imitation occurred in markets. This assumption can be based on e.g. work done by Dierickx and Cool [7] who characterize (intangible) assets, such as capabilities or R&D, as stocks, which are replenished and drained with different flows. The most important repercussion of this notion is that, as per the law of diminishing returns, asset stocks can not be bought in the sense that the flows can be adjusted but the stocks have to accumulate. Considering R&D or NPD, the knowledge and capability to execute operations deteriorate as the knowledge needed for action is drained through obsolescence, competitive imitation, and the relative advantage over rivals deteriorate and the ability to execute deployment of capabilities deteriorates over time as the routines which constitute the act of development [Error! Reference source not found.] deteriorate unless they are exercised. The logic in the model is that the capabilities affect the ability of the organization to develop good products. Learning in turn affects the capabilities. The learning function employs the logic of diminishing return so that the incremental learning from each decision is larger when the quantity of screening decisions is smaller. The capabilities, or the level of the knowledge stocks, affect the success rate of NPD and product launch. Adhering to the theory of absorptive capacity [3], when the firm has above average technical capability, it produces better product ideas as the decision makers are able to recognize technological opportunities better and develop novel solutions to fill customer need. Market capability grows from the stock of market knowledge and affects the ability to recognize the market need, in the model this translates to idea quality/feasibility, and it also dictates the ability to execute market launch. The process capability is basically the ability to exercise the market and technical capabilities of the organization. It affects what Cooper [e.g. 6] would call quality of execution in the process, that is, the concepts/projects are executed efficiently, bad projects are killed at the screens and so on. The actual knowledge is created in the process stages and release of knowledge is triggered by flow control between stages. Knowledge accumulation is greatest when correct decisions are made in the process and process error lead to smaller or even negative learning. The overall capability creation is controlled by a relative learning efficiency factor that can be used to simulate different levels of learning. The amount of learning is thus dependent on learning efficiency and number of stage shifts in the innovation process. If the firm is unable to finish innovation projects in form of project kills or ready products it will start to lose capability through inadequate learning. This ties innovation process performance to capability building. The capability levels are tied to the innovation process through the odds of positive occurrence. High capability level will increase the likelihood of good ideas and reduces the chance of process errors. High relative capability increases also the probability of creating a higher quality product that increases firm’s profits. The opposite is true when organization loses capability, which ultimately leads to lower profits in the innovation process. 4. Causality in the model The causal impact chain between learning and profitability argued in previous chapter is presented below in Figure 3. This figure contains the key dynamic elements in the model. The components have been grouped in modular groups and interfaces between different modules are presented by an intersection of module barrier and causal relationship line. Overall, the system is quite simple: the performance of FEI and NPD dictate the number and quality of products in the market. The number of products and the quality affect the profit, 7

and accumulated profit enable investment to expand the capabilities. The capabilities are eroded by competitive imitation and lack of deployment and replenished by learning. Learning in turn is a function of operations in FEI and NPD. The relative capability then affect the performance of NPD and FEI, and the cycle starts over. The figure contains many different feedbacks, which can be divided to two distinctive cycles: Group 1 includes loops that are tied to the profits and Group 2 includes loops that are tied to learning. These loops will be presented separately in this chapter. Investment in capability

+

Learning Learning by competitors

+

+

Innovation process module

-

+

FEI performance

+

+

+

Relative marketing and technical capability

Profit

+

NPD performance

+

+

+ NPD Management

Capabilities module

-

Products at market module

+ + +

Number of products at markets

Quality of products at markets Loss of process capability

Cashflow module

Figure 3. Causal loop diagram illustrating the NPD system In a more detailed inspection, Group 1 loop can be found from the outer edge of the causal map presented at Figure 4. The common factor for these loops is the idea that they are derived from increased profits from new products, which enables investment to new capability base. The effects of these investments lead to increased FEI and NPD performance, which has positive effects on both quality and number of products at market. This feedback is positive if organization is capable and does decide to invest in its capabilities, but will lead to negative feedback is investments are not made. This kind of situation occurs if organization decides to either educate staff or acquire capabilities through some other methods like outsourcing, licensing, or networking. Although this loop is considerably simple it has still many challenges for manager. Buying competence is not necessary the most efficient method in the long run because, depending on the implementation method, it does not give a real incentive for building the capability sustainably through deployment. Investment will most likely lead to a boost in performance, effect of which is lost when organization in not able to sustain high level of relative competitive advantage. Another major problem is how to implement such a resource investment. It is likely that the procedure of “transferring”competence in to the organization is not going to work as efficiently as possible leading to loss of the actual competence level of resource of entity that is bought. This effect or how to implementation in the organization is not however included to this model.

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+

Investment in capability

Innovation process module FEI performance

+

+

+

Relative marketing and technical capability

Profit

+

NPD performance

+

+ NPD Management

Capabilities module

Products at market module

+ +

+

Number of product s at markets

+ Quality of products at markets

Cashflow module

Figure 4 Group 1 feedback loops Group 2 loops tie the innovation process performance to accumulation of organizational resources. These loops can be found in the middle of the causal map and the form between innovation process stages organizational capabilities through the process of learning (Figure 5). The loops form the basis for sustaining organizations competitive resource position. A steady stream of ongoing and finished products will lead to constant level of learning that can be used to compensate the erosion of relative capability caused by advantages achieved by competitors and lack of deployment of own capabilities. Learning

+

+

Innovation process module

+

+

Relative marketing and technical capability

FEI performance

+ NPD performance

Capabilities module

Figure 5. Group 2 feedback loops When comparing these different loop back groups, certain differences can be found. Group 1 offers quicker opportunity to add to organization’s competence base than group 2 that builds on long time performance of the whole process. The upside of group 1 is that it can be done to change organizations performance rapidly. However group 2 offers a long time period opportunity where learning-by-doing offers organization to expand its competence base organically. The risk with group 1 is that if organization seeks certain performance level it may be jammed to a vicious cycle, constantly pumping cash to capability investment, when the spirit of RBV is more developing in developing and deploying the unique resources of the company to gain competitive advantage, not to ‘buy’ revenue and stock price through mergers, acquisitions and licensing for example. However, capability investment allows a

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company to hit the ground running, so to speak, and to gain a base for unique capabilities through wise investment. 5. Discussion This paper has investigated the interfaces of NPD, organizational learning and capabilities and cash flows in industrial organizations. The contribution is the formalization of these separate modules to a system dynamic model, which is presented in the paper. The model assumes that the organization has a NPD function, which develops new products to insatiable consumer markets and gains profits, which can be used in investment. The capabilities of the firm affect the performance of NPD and also the profits of the organization. The capabilities develop through investments and learning in the process. In short, the answer to the research problem is that, based on theoretical conceptualization, the main interface of learning and business profitability is through new product development, which also acts as an important venue for learning. Conclusion from the system description is that NPD forms a virtuous cycle, as the profits grow and the organization learns from each new project, which in turn raises the probability to commercial success. The model is firmly based on RBT, notably in Dierickx and Cool’s [7] theory on asset stock accumulation. Combining the innovation process performance and accumulation of capabilities opens an interesting combination, which increases the importance of project management capability as it is not only to important in producing products rapidly enough to markets but it has also effects to organizational capability. The process capability is in key position when it comes to cashing in the technical and market capabilities. As noted above, learning and capability are not worth much by themselves unless they are operationalized through operations such as NPD. What the model tells about learning is that learning can raise capabilities of an organization, but translates to money only indirectly through deployment of these capabilities. Learning and learning organizations have been touted here and there, but the fact of the matter is that learning needs to be operationalized. Learning efficiency forms a shunt for learning in an organization. Absorptive capacity and other constraints of the human condition affect how well the opportunities presented in the processes are used and how much they affect the organizations capabilities. The key attribute when comparing loops 1 and 2 is the rate or the efficiency of actual learning, as investment and deployment are not alone sufficient in explaining variations in real-life companies. Acknowledgement of different learning efficiency levels raises the question of investing to learning capabilities of the organization. Learning efficiency can also be an important explaining factor for differences in the performance of real companies. The conceptual model would predict that organizations with similar structure and throughput would behave identically in the markets, but difference in absorptive capacity and other constraints may account for this difference. Learning efficiency also raises the consideration whether to invest on capabilities or try to develop them through deployment, in practice if learning efficiency in a company lags significantly In this paper the authors have developed a system dynamic model and presented it for examination of face-validity, that is, presented the theoretical background and structure of the model for critical examination. The future work will include validation and building of the model for actual simulations. Also some consideration presented above, such as the issue of learning efficiency, will be on the list of further improvement. The implications of this research are mostly theoretical, concerning learning and its effects for profitability in 10

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