Nearly all organizations have gaps between their aspirations and their actual performance. These gaps typically are the result of the structure of the organization, the environment in which the organization operates, the capabilities of individuals and infrastructure in the organization, and the interactions of these elements. The way we think about these gaps usually affects how successful we will be in addressing the underlying causes of these gaps over time.
A learning organization can incorporate these performance insights into behavioral changes quickly, efficiently, and effectively. For this learning to gain traction, an organization must design affordable interventions, mitigate constraints arising in different situations, and channel information to where it is needed. These practices are more likely to get traction when they emerge from the collective insights of team members rather than appearing as mandated interventions from above.
The first operational goal of any value stream should be the realization of flow. The total elapsed time between the beginning of the first processing step in a value stream and the end of the last processing step of that value stream is the cycle time. Within development processes, productivity improvements are nearly always a function of achieving cycle time improvements in some form. Waterfall-based development processes follow recipes for large-batch processing. They share the same wastefulness, inefficiency, and risk as bureaucracies. Therefor, they tend to accumulate large inventories of work-in-process at each step in production. A strategy of small-batch, continuous flow improves efficiency by eliminating the wasteful burdens of delay, work in process inventory, and communications breakdowns between process steps.
We can highlight these principles by performing a thought experiment within the context of transportation planning. The purpose of our highway system is to enable the efficient transportation of people and goods from one place to another. The total resource consumption and cycle time of such systems are largely a function of traffic congestion in the system, since these pathways are highly subject to the tragedy of the commons. Traffic flow over these pathways is a function of the speed and density of the vehicles, and is.These pathways are highly subject to the tragedy of the commons. As traffic density approaches the capacity of the any particular segment, average traffic speed at that location will begin to decrease. it is easy for bottlenecks to arise in such systems. When traffic density begins to exceed a particular loading factor, local perturbations (such as a driver touching their brakes) can cause a cascading effect over a large distance, and this delay can propogate for a long period of time. While one delay is playing out over time, the probability of a second issue arising is increased, and may have even greater impact.
Predicting the time it takes for a particular vehicle from one location to another is highly dependent upon many factors. Average flow is impacted by many factors - the average capacity of the channels over which it travels, patterns of demand at different points along those pathways, environmental factors, bottlenecks, characteristics of the vehicle, the interactions between vehicles, and
The nature and quality of information available to manage flow changes with time, Further, delays often impact into other parts of the value stream, resulting in further waste and inefficiency. Yet this flow cannot be increased just by encouraging each vehicle's driver to maintain their speed, or . Because of the frequency and impacts of this , infrastructure improvements are designed to enhance flow. For example, incoming lanes are designed to allow entering vehicles to merge at the same speed as the rest of traffic. Metered ramps throttle vehicles on entrance ramps carefully to maintain spacing between traffic. Emergency vehicles are pre-positioned to respond quickly to problems. Drivers hardly require motivation to try to achieve this goal, yet when they try to do something about it, the system they are in just pushes back, in ways that can frustrate everyone.
When resources like gasoline consumption are considered for this transportation system overall, the impacts of relatively minor issues can thus have major systemic effects, and result in far greater impacts than would be immediately apparent. Yet the root cause that caused such effects are often not easily seen from ground level, as the volume of traffic, and the passage of time, introduces significant noise into making these associations. Since new designs for roadways take a long time to change, by the time they are actually implemented, they are often no longer relevant to the new traffic patterns which have emerged since these designs were introduced.
This traffic flow analogy helps to explain situations in which processing time is largely predictable, and there is little variability from item to item. This is the case in most manufacturing environments, and organizations usually strive to drive out all waste which arises during production operations within such settings. To return to our traffic analogy, in such environments, each vehicle would only put as much gasoline in their car as they think will be necessary to get to their destination on average, with a small contingency. When there is little variation item to item, this might be an acceptable strategy. Unfortunately, these conditions rarely exists in other kinds of projects, and would lead to disabled vehicles clogging up the roadway (and additional vehicles servicing those vehicles). In environments where work differs widely in form, context, and structure, the time it takes to process this work will be highly variable.