| Self-Organizing Quality Structures |  |
| Complex Systems and Emergent Quality Structures |
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| Every organization struggles to balance freedom with control when implementing process improvement programs. In a perfect world, shop floor teams would spontaneously form, recognize waste, and effectively execute countermeasures without intervention from higher management levels. However, the drive to implement process improvement methodologies often requires a heavier hand, which can stifle creativity, and even create resentment. Complexity Theory and the science of Complex Adaptive Systems may offer some guidelines for the construction of more dynamic, bottom-up process improvement systems - emergent systems that respond and adapt without a central prescription for every activity. |
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| Complex Behavior From Simple Rules | ||||||
| In 1987, a computer scientist named Craig Reynolds was researching the flocking abilities of wild birds. He wrote a computer program to simulate wild birds by creating a collection of autonomous bird-like creatures, "boids", that attempted to move around a group of walls and obstacles on the computer screen. The following description of Reynolds' work is from M. Mitchell Waldrop's book COMPLEXITY - The Emerging Science At The Edge Of Order And Chaos. "Each boid followed three simple rules of behavior:
2) It tried to match velocities with boids in its neighborhood. 3) It tried to move toward the perceived center of mass of boids in its neighborhood. Waldrop continues: "Instead of writing global, top-down specifications for how the flock should behave, or telling his creatures to follow the lead of one Boss Boid, Reynolds had used only three simple rules of local, boid-to-boid interaction. And it was precisely that locality that allowed his flock to adapt to changing conditions so organically. The rules always tended to pull the boids together, in somewhat the same way that Adam Smith's famous Invisible Hand tends to pull supply into balance with demand. But just as in the economy, the tendency to converge was only a tendency, the result of each boid reacting to what the other boids were doing in its immediate neighborhood. So when a flock encountered an obstacle such as a pillar, it had no trouble splitting apart and flowing to either side as each boid did its own thing. Try doing that with a single set of top-level rules … the system would be impossibly cumbersome and complicated, with the rules telling each boid precisely what to do in every conceivable situation. In fact … simulations like that usually ended up looking jerky and unnatural, more like an animated cartoon than like animated life. Since it's effectively impossible to cover every conceivable situation, top-down systems are forever running into combinations of events they don't know how to handle. They tend to be touchy and fragile, and they all too often grind to a halt in a dither of indecision." |
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| Complex Adaptive Systems | ||||||
| A Complex Adaptive System (CAS) is defined as a collection of many individual "agents" that interact dynamically - examples include economic systems, where the agents are people (or even firms at a higher level), or a flock of geese, where the agents are individual birds. The term "dynamic" means that the system is non-linear, and therefore the output is not proportional to the input. In operations terms, the CAS could be defined as a single manufacturing plant, subset of that plant, or service operation, with individual employees as "agents". As illustrated by the "boids" example, studies of complex systems (complexity theory) tell us that very complex structures can emerge in such systems from the bottom up - without orchestration from above - as a result of the agents following simple local rules of behavior. An example of such a structure is a communal organism like coral - if you observe a piece of brain corral, you can see a very intricate structure, a physical pattern formed by a collection of individual organisms - without any central control. Honeycomb is another example from nature of a complex structure that arises without a central plan. |
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| Manufacturing As A Complex Adaptive System | ||||||
A simple manufacturing CAS example is the communication between two operators on an assembly line where a piecework incentive plan is in place - structures of communication develop to maximize total output. A more complex example is the Toyota Production System, which depends upon an incredible network of detailed and timely communication based on a relatively small set of simple rules - not orders from a centralized control structure. In their article "Decoding the DNA of the Toyota Production System" (Harvard Business Review, Sept.-Oct. 1999), Steven Spear and H. Kent Brown identify four simple rules that are the foundation of the Toyota Production System. "These rules guide the design, operation, and improvement of every activity, connection, and pathway for every product and service. The rules are as follows:
Rule 2: Every customer-supplier connection must be direct, and there must be an unambiguous yes-or-no way to send requests and receive responses. Rule 3: The pathway for every product and service must be simple and direct. Rule 4: Any improvement must be made in accordance with the scientific method, under the guidance of a teacher, at the lowest possible level in the organization. The strategic implication of positive self-organized behavior is operational effectiveness that is difficult to duplicate. A self-organized system requires, by definition, less "management", solves problems more quickly, and is more responsive to change. In order for such a system to work, the people involved (agents) must demonstrate three attributes:  Knowledge of the rules, Capability to follow the rules, Willingness to follow the rules. |
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| Following the Rules | ||||||
| According to Spear and Bowen, the Toyota Production System "grew naturally out of the workings of the company over five decades. As a result, it has never been written down, and Toyota's workers often are not able to articulate it. That's why it's so hard for outsiders to grasp. … Toyota teaches the scientific method to workers at every level of the organization. It is these rules - and not the specific practices and tools that people observe during their plant visits - that in our opinion form the essence of Toyota's system." Returning to Reynolds' flock simulation, the application of complexity theory to many existing operations is greatly confounded by the fact that many "boids" don't fly well, don't follow even a few simple rules, and sometimes actively don't want to be part of a flock! Furthermore, many flocks are flying through a fog created by byzantine process flows and incapable processes that obscure or obliterate the information feedback loop necessary to respond to change. Spear and Bowen note that Toyota uses a "teaching and learning approach that allows their workers to discover the rules as a consequence of solving problems. For example, the supervisor teaching a person the principles of the first rule will come to the work site and, while the person is doing his or her job, ask a series of questions: How do you do this work? How do you know you are doing this work correctly? How do you know that the outcome is free of defects? What do you do if you have a problem? |
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| Flight Path | ||||||
| The FAST Company article "Engines of Democracy" highlights a GE aircraft engine plant in Durham, North Carolina as a remarkable example of a lean plant that successfully operates with self-directed teams and virtually no management. The GE plant shares several characteristics with the Toyota Production System in its successful effort to place real responsibility and authority on the shop floor:
These three activities enable the system with people who know the rules and are who are capable and willing to follow them. |
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| Group Incentives | ||||||
| For the majority of operations that don't have the option of hiring a new workforce, the emphasis must be on training and development. Incentive plans can add impetus to those efforts. Beyond the creation of an appropriate set of "local rules" (work practices and procedures), and raising employees' capability through training, incentives can play a positive role to reinforce compliance with those rules and create positive improvement structures over time. Indeed, using group incentives (e.g. goal sharing) to create a sense of shared purpose can reinforce more rapid cultural change. At many organizations, group incentive plans have been the catalyst to propel the emergence of positive structures, such as informal teams working on quality or production problems. Please see the MoreSteam.com discussion of group incentives for further information on this subject. |
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| Other Resources: | ||||||
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For additional references, see the Other Resources section of the MoreSteam.com Toolbox , or consult the Article Archive. |
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