ABSTRACT
Theories of manufacturing usually model knowledge as flowing into (and defining) production, but rarely is knowledge modeled as flowing out. This research develops a conceptual framework for an integrated, knowledge-based theory of production work. This framework demonstrates how production knowledge can effectively support evolving manufacturing core competencies while balancing the activities of production workers between the creation and maintenance of knowledge and the realization of products.
Production is conceptualized as involving tasks of operations, experimentation, and absorption (of external knowledge) with the direct goal of fostering both continuous learning and innovation and thus creating a truly empowered community of production workers. By further characterizing manufacturing knowledge as either operational or design-oriented, I use a simplified, integrated product development cycle to demonstrate how the dynamic interaction of these knowledge sources positively contributes to the competency-building goals of a manufacturing firm.
In the spirit of this Knowledge Era in which intellectual assets and human capital are becoming re-appreciated, many thinkers have re-oriented the concept of work around knowledge. The development of knowledge and the creation of products can be processes fraught with conflicting choices; these processes can also be synchronized for effective competitiveness. This paper creates a conceptual framework for balancing the commitments of production workers to the creation of products and to knowledge development. This goal is facilitated by defining and analyzing the role of production workers and their knowledge in constructing evolving core competencies through knowledge flows both to and from the factory floor. Finally, this framework establishes the foundation for future efforts to quantify models for strategic analyses of knowledge development in manufacturing..
We first contextualize the dilemmas facing manufacturing by reviewing manufacturing's current position in some of today's Information/Knowledge Age thinking. The manufacture of goods has long been a mainstay of strong economies and continues to be a prime driver of export success (Dertouzos et al., 1990). In the information age and the knowledge era/economy the manufacturing function appears to decline in value, with services increasingly providing more of the value-added to manufactured goods (Quinn, 1993). But the significant, economic linkages between numerous service industries and manufacturing indicate that we are less entering an entirely new, service economy as we are shifting between phases of an industrial economy (Cohen & Zysman, 1987; cf. Hirschhorn, 1984; Collins, 1998).
A problem with authors such as Quinn (1993), Marquardt & Reynolds (1994), Davenport et al. (1996), or Roth (1996) is that they formulate no plan for extending the benefits of the new knowledge-intensity of work to production workers. In particular, Quinn (1993) states that companies can now "...move from a preoccupation with overseeing workers and machines to a position where they can leverage their true assets --- intellect, knowledge bases, and human skills --- much more substantially." This idea belies the reality that workers and their machines are valid components of a manufacturer's knowledge management systems and that "...human operators are still the most adaptable part of a system" Hoc et al. (1995). Additionally, Leonard-Barton (1995) clearly demonstrates that the physical representation of manufacturing knowledge through processes and machinery is an important source of learning.
Overall, there appears to be a pervasive assumption that competitive manufacturing knowledge can only come from academically advanced personnel (e.g. Mody, 1989). Much of current organizational learning thinking has been devoted to workers traditionally thought of as "professionals." Allee (1997) recognizes this "creeping elitism" in conceptualizations of knowledge work, and Collins (1998) also decries the elite nature of the theoretical development on knowledge work and knowledge workers. Even discussions of advanced manufacturing concepts, such as agile/flexible manufacturing (Kidd, 1994) and mass customization (Davis, 1987; Davidson & Davis, 1990; Pine, 1993), tend to focus primarily on the prowess of the design sources of production knowledge.
This paper moves counter to these trends by honoring those who, through heart, soul, and body, conceive, sculpt, and produce our material world; the submission of this paper was inspired by Collins (1998). Thus, I empathize with Jerry Jasinowski of the National Association of Manufacturers when he declares that "...[pundits] said that we had moved into the post-industrial age, that it was time to abandon manufacturing and embrace services as the nation's economic salvation. This was to be an age where making things, manufacturing, no longer would count for much" (Jasinowski, 1995). Manufacturing still matters and still warrants the kind of critical analysis presented here.
Product Creation Versus Knowledge Development: A Conflict In Manufacturing
Motivating this analysis is the difficult struggle between abstract knowledge and concrete products. Traditionally, manufacturing has consisted of a production workforce which need only worry about adhering to strict commands. Both academia and industry have increasingly recognized over the past few decades that this model is insufficient for global competition in a dynamically changing market and technological environment (Wheelwright, 1987; Hayes, et al. 1988). The emphasis on quality circles, total quality management, continuous improvement, and worker empowerment has released some expressive and innovative potential, but it has generally not relieved production workers of many of the exigencies of making products. The conflict between creating products and developing knowledge in manufacturing companies appears unavoidable.
Manufacturing knowledge is usually measured using models of learning-by-doing (LbD). LbD occurs as a direct result of experience gained through product creation. This is a necessary but not a sufficient part of a knowledge development program. Nilsson (1995) correctly explains that LbD is a process that occurs after an innovation has been transferred to production workers. I call these progenitors of manufacturing knowledge, the design sources of knowledge (DSK). Accordingly, I collapse the world of manufacturing knowledge into design and operations. Nilsson (1995) sharply highlights the existing dichotomy: "Since the time of Adam Smith, students of technological change have tended to under-emphasize the role of workers' skill innovation as a source of technical innovation and to highlight the role of 'philosophers,' that is, of scientists, engineers, and inventors." Production workers best become legitimate sources of innovation from a systemic viewpoint of task allocation amongst a range of activities: operations, experimentation, and absorption (especially of design knowledge). I will expound upon these activities in the sections to follow.
March (1991) describes one organizational conflict as the struggle of an organizational learning system between exploring new knowledge versus exploiting existing competencies and capabilities (cf. Levinthal & March, 1993; Miller, 1996). Organizations tend to be biased towards exploitation (March, 1991), and this tendency is clear in production. The relentless push for reducing cycle times, increasing throughput, and improving quality means that production commonly proceeds under pressure for immediate results, leaving little time for exploration. As a result, manufacturers tend to designate specialized design functions staffed largely by technical professionals external to operational sources of knowledge, creating divisions between design and operations. However, a good learning cycle contains a positive feedback loop where new knowledge leads to new products and vice versa. Thus, investments in both processes can reinforce each other by bi-directionally conducting knowledge between design and operations. If production workers do not participate in the exploration process, their strong bias towards exploitation will make them increasingly resistant to the innovative changes ordered by the DSK. They need to have control over their own learning needs and think through and even plan these needs when possible (e.g., see Cusimano, 1995). This empowering methodology represents an initial step in managing the product/knowledge conflict on a systemic level.
Dertouzos et al. (1990) demonstrate several advantages of knowledge development for production workers: workers can organize their own work better, less reliance on engineers for handling production contingencies, and increased capability of the entire production system (cf. Hayes et al., 1988; Karakekes & Currier, 1995). Most importantly, these authors note that the resulting egalitarianism in the organization enhances the innovating of production workers and the overall flexibility and responsiveness of the organization. These observations are evident in a host of modern manufacturers that have instituted such concepts --- examples include Chaparral Steel (Leonard-Barton, 1995), NUMMI (Adler, 1993), Honda (Hayes, et al., 1988), and companies included in my on-going field research.
Differentiating Learning-by-Doing from Innovation
Knowledge development in manufacturing stretches along a continuum from (1) learning-by-doing (LbD), through which lessons are learned from experience and applied within the existing production apparatus, to (2) innovation, through which the production system is itself challenged and altered. LbD is typically modeled using a learning curve mapping the decline of per-unit production costs or labor hours as a function of cumulative production (a proxy for experience). Solow (1997), and similarly Spinnler (1997), model learning as bounded using a non-zero asymptote. Solow (1997) cleverly equates LbD with continuous improvement, and he demonstrates that continuous improvement is the progress down the learning curve whereas innovation is a reduction of the lower bound on learning. Innovation, then, introduces a new production regime.
Although the learning curve has undergone numerous empirical tests (Yelle, 1979), Dutton and Thomas (1985) assert that traditional learning curve analyses do not clarify the origins and nature of learning processes. Learning curves inherently assume a standardized manufacturing process (Abernathy & Wayne, 1974), and Jaikumar and Bohn (1994) demonstrate that learning curves can also mask problem-solving strategies that are mere patches but not fundamental solutions. Cohen & Levinthal (1990) correctly assert that LbD means a firm gets better at current activities, but does not create the diversity needed for innovation. Thus movement along a learning curve says little about how production can accomplish on-going innovation and extended problem-solving.
The implication is that continuous improvement is relegated to progress within the confines of the overall existing production paradigm. An innovation alters the paradigm itself and introduces completely new production possibilities. Continuous improvement can increase the quality of products, but innovation makes it possible to redesign attributes and functions of the existing product or to create new and different products. Continuous improvement tends to be more deterministic while innovation is more stochastic. Huber (1991) implicitly questions the usefulness of making such distinctions, but with regards to building a knowledge-based conception of manufacturing work, the distinction is quite instructive. Long-term sustainability comes from deftly combining innovation and subsequent continuous improvement with timely repeats of this cycle, and finally translating these cycles into evolving core competencies.
Due to the time constraints on production noted earlier, a significant amount of learning occurs during operations, but time to reflect, experiment, and collaborate are critical for validating lessons and for creating new ones. An effective learning cycle consists of plan, act, reflect, change, and plan again with the stage of reflection being critical to achieving learning (Redding & Catalanello, 1994; Nonaka & Takeuchi, 1995; Marquardt & Reynolds, 1994). But typically, production workers are limited to the "act" part of this cycle and other manufacturing functions, the DSK, control the other components.
To improve the knowledge development of production workers while balancing the needs for product output, the production portfolio of tasks should contain three activities: production/operations (all activities which directly manipulate a product), experimentation (purposeful discovery of knowledge specifically separate from production/operations), and absorption (acquisition of knowledge external from one's direct actions). I call this the TPT for "tripartite production tasks." Dutton & Thomas (1985) suggest a similar methodology of knowledge development: LbD, research and development (learning-by-studying), and absorption from other firms. To extend the possibilities of LbD and to mitigate the disadvantages listed earlier, production workers must be given the opportunity to experiment and absorb (design) knowledge separate from immediate production concerns. The cooperation between operations and design is called "expansive system development" by Norros (1995), and companies such as Advanced Micro Devices have experienced some success with similar initiatives (Karakekes & Currier, 1995). Hirschhorn (1984) cites research findings which suggest that, without access to theory (design knowledge), operators only seek and utilize information to the extent it is relevant to resolving problems identifiable from past experience; again, knowledge development limited to LbD.
One method for increasing the time for experimentation, reflection, and absorption is to reduce product cycles below that of competitors and use the resulting slack time for knowledge development. However, in the drive to cut production costs or simply increase output, an organization usually drives out this slack needed to spark innovative activity amongst its production workers (Abernathy & Wayne, 1974; Klein, 1989). Klein (1989) shows how the relentless pursuit of productivity and efficiency can constrict their freedom to express their innovating potential. Thus, a successful knowledge development program must seek to leverage slack time into learning and innovating time that pays off over the long-run. Companies such as Sun Hydraulics are unique in their ability to remain profitable and allow their workers the freedom to determine production schedules and their pursuits of learning (Kaftan & Barnes, 1991; Henderson, 1997).
To clarify this process for managing the product/knowledge conflict, we must now understand the relationship between production work and knowledge.
Knowledge-Oriented Organizing Principles for Manufacturing
There are three, well-established propositions in the literature that are directly relevant to this work. First, continual growth in targeted knowledge assets enhances a company's ability to provide competitive products. This proposition suggests that the product/knowledge conflict is manageable. Second, an organization is designable to support and encourage this growth in knowledge. The key to this proposition is that as more agents increase their knowledge capacities and share and gain knowledge, whole new associations and structures will likely emerge to evolve the organization into new competencies and capabilities. Even amidst such organic, or evolutionary, processes some structure and focus must be provided for the system to function properly (Eisenhardt and Tabrizi, 1995; cf. Lounamaa & March, 1987; Adler, 1993). This structure usually appears in the integrated product development cycle (PDC). In fact, the third proposition often found in the literature posits that the tasks critical to creating knowledge exist in this PDC. Thus, there must be an infrastructure which assures that the results of these interactions are pushing the organization toward some identifiable form or range of competencies.
Brown & Duguid (1991) emphasize the importance of the emergent nature of what is called a community-of-practice (also refer to informal networks: Ibarra, 1992; Nohria, 1992). Relying upon other applicable literature, Brown & Duguid (1991) demonstrate the extensive efforts workers must exert when the organization does not support them with fundamental domain knowledge relevant to their work. Unsupported workers must develop their own routines from scratch and form informal relations to transmit current and relevant knowledge (cf. Kusterer, 1978; Juravich, 1985; Darrah, 1996). It is usually only the formal structure that can provide centralized, advanced repositories of explicit information and knowledge, communications tools, and the sanction for decentralized application of the resources to allow workers to experiment and to collaborate (absorb) with the design sources of knowledge under the TPT.
Core competence is a critical knowledge-oriented organizational design principle, and it is molded by these communities-of-practice. The most cited definition of core competence is Prahalad and Hamel's (1990) where it is defined by management's ability to coordinate and organize learning processes. My definition builds from Wikström and Normann (1994): "competence is the capacity to utilize knowledge for given purposes." Thus, I define core competence as a portfolio of knowledge embedded in the institution through the organizational links amongst agents. A portion of knowledge is a core competence if it directly enables some set of critical elements of the product-creation process. A core competency must be defined broadly enough such that it requires participation of the entire community-of-practice. As a result, it is important to define competence at the institutional level in very narrow terms for competitive focus, but on the micro level it must have a heterogeneous constituency.
Knowledge then steps to the foreground in analyzing the potential of production workers and their organizations, and we may re-conceptualize the production worker around knowledge: knowledge is the potential to do work. This interpretation is a special case of Huber's (1991) generalized definition of learning: "An entity learns if, through its processing of information, the range of potential behaviors is changed." Similarly, Nonaka and Takeuchi (1995) assert that "knowledge is essentially related to human action," while Kim (1993) states that "Learning...[increases] one's capacity to take effective action." Espejo, et al. (1996) define learning as "enhancing the potential for effective action." Finally, from the economic perspective, Miller (1996) defines capital "as an anticipated capacity to produce."
A truly empowered production center is able to transmit its own knowledge to the design function and to internalize some design knowledge. Without this bi-directional flow, production knowledge becomes highly specialized but under-appreciated and the organization loses some heterogeneity in its core competencies. Active questioning and self-exploration can also lead to innovative thinking which creates new processes or new applications of old processes (cf. Darrah, 1996). The total knowledge accumulated through this enhanced product development cycle contributes to an expanding, or evolving, field of core competencies that feeds back into the system to further enhance competitiveness and product potential.
Organizing Production Workers to Manage the Product/Knowledge Conflict
Since knowledge can grow from most any production activity, and since it is a scarce resource, there will always tend to be a core of individuals in a manufacturing community who possess critical production knowledge. This is related, but slightly different from Wikström and Normann's (1994) "professional core" and Upton & Macadam's (1997) "caretakers and craftspeople." Preliminary results from my own field research reveal the existence of this core. These core of workers have established expertise, are active seekers of knowledge, and are usually most capable of leading processes of innovation. Those remaining settle around the core, or the "peri-core", and consists of workers who (Type 1) are novices lacking the knowledge but will eventually join the core, or (Type 2) become active innovators when absolutely required, or (Type 3) are only interested in executing an assigned job. Note that tenure is not necessarily a defining characteristic of these populations. The core and peri-core must engage each other through communities-of-practice because the most innovative workforces will be those in which workers graduate towards the core through demonstrated competence in the TPT.
However, no system that needs knowledge will survive if only an elite steadfastly control the critical assets. Such control tends to create a lack of variety in the viewpoints and methods of the elite through the homogenization of enduring associations and experience (cf. March, 1991). One good method for system promotion is to diffuse knowledge throughout communities-of-practice that each work together in an integrated form of knowledge-intensive relationships where assumptions are always checked and rechecked against multiple viewpoints (Argyris, 1993). There also exists an ideal promotion rate: March (1991) theorizes that one must moderate the rate of learning to avoid ossifying bad lessons as well as to ensure high exposure rates to a variety of knowledge sources before patterns of behavior become firmly established. Many organizational learning theorists advocate variety and redundancy in types of expertise (Senge, 1990; Nonaka & Takeuchi, 1995), but do not address the needed variation in levels of expertise. Different levels of expertise give you the different rates of failure that will remind the rest of the organization of lessons that still need learning or suggest new problems to solve while still maintaining a brake on the repetition of old mistakes. Additionally, since novices refer to elemental concepts to execute work, they may invent new ways to utilize this knowledge to bear on problems that the core has internalized and long taken for granted.
The different levels of expertise will have varied impacts on the TPT. During operational tasks, experts in the core and the peri-core will often dominate as consistency and reliability become the imperatives of product creation. Novices in the peri-core will typically learn from the core through imitation and LbD. However, since each expert often has an alternate way of accomplishing the same task (Darrah, 1996), and since the transfer of tacit knowledge is difficult and imperfect, there is ample opportunity for variety in the kinds of developed knowledge. During experimentation, the core and peri-core complement one another: the peri-core, primarily Type 1, is more willing and enthusiastic about ad hoc trial-and-error whereas the core can provide the structure for orderly gathering, recording, and analyzing of the lessons learned. During absorption, both knowledge sources have unique impacts. The peri-core is being exposed, mainly within a community-of-practice, to many new sources of information and knowledge. The core can use design knowledge to justify their own process theories and the more open, and ultimately more successful, workers will adjust processes taken for granted and will heed some of the fermenting in the peri-core. The core can also help the peri-core identify valuable sources of design knowledge. Overall, integrated communities-of-practice are extremely important: most knowledge is developed, maintained, and disseminated through social mechanisms, and a community-of-practice provides the relationships through which knowledge can flow and grow.
The relationship between the community-of-practice and the technological environment is also important. In knowledge-intensive production, the operator contributes to the machine design or the decision to purchase the machine. Start-ups are notoriously problematic and provide the variability that induces more learning and reinforces problem-solving skills. In a stable start-up, most of the learning occurs prior to real-time production, but if production workers do not participate, they will be ill-equipped to contribute innovative thinking to a process which has its learning opportunities designed out of it. At another extreme, a dysfunctional reaction to turbulent start-ups is to rely on diagnostic knowledge which seeks to just get around problems; instead, "interventions in problem situations are opportunities to complete the design and increase learning and knowledge" (Norros, 1995). This again is an excellent opportunity to organize production workers for knowledge development and an advanced step in the process.
The principles for organizing production workers exist at a confluence of the tripartite of production tasks, the communities-of-practice with its cores and peri-cores, the design and operational sources of knowledge, manufacturing management, and the evolving core competencies from repeated cycles of product development, effectively creating a mutually supportive environment of learning, knowledge development, innovation, and product creation. It is certainly a challenging undertaking, but when these various aspects of manufacturing flourish, the conflict between knowledge development and product creation can be effectively managed and directed towards manufacturing excellence.
The organizational contention between knowledge development and product creation consists of several potential sources of sub-conflicts: core and peri-core, design and operational knowledge sources, LbD and innovation, and management and production workers. Negotiating the entire system is indeed challenging. Rather than searching for an absolute resolution, this paper establishes methods for shaping these conflicts into constructive duality, methods which continuously drive the product development cycle to produce an on-going evolution of core competencies.
This paper extends the concept of knowledge development for production workers beyond learning-by-doing and a static dependence upon design sources of knowledge. The role of production workers metamorphoses to encompass the empowering tasks of operations, experimentation, and absorption. Such activity occurs under the auspices of communities-of-practice that constitute the overall nexus of evolving manufacturing competencies and provide the balance between product creation and knowledge development in collaboration with other organizational functions.
Schwartzman (1993) quotes June Nash's proclamation that "The central problem...is to find ways of improving the relationship between human potential and the productive process." Human productive potential is best expressed through knowledge. The development of knowledge involves learning and ultimately innovation. This paper demonstrates that production workers can transcend the product/knowledge conflict and can participate in processes of creation, learning, and innovation. Through this conceptual framework, I encourage others to use knowledge-based principles to craft ever more effective tools and concepts of competitive manufacturing practice and production work that will realize the highest of human productive potential.
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N. Duru Ahanotu is a PhD candidate, Engineering-Economic Systems and Operations Research, Stanford University. He can be contacted by: