It is argued that it is through historical knowledge that we can understand the here and now and the future. History is useful to the extent that history can provide an improved understanding of the transformation process. History is not useful when historical data numbers are used in forward looking projections. Historical analysis is a poor surrogate for process knowledge.

 Historical analysis, the focus of this article, incorporates past historical data into an analysis to project future results. Why do we want to look back when the here and now is the result of the vortex of the multitude of transformation process in perpetual cyclical execution? Past process results are a vapor trail of how the transformation process interacted with its environment in the past. Historical analysis searches for past patterns in the vapor trail to project the future. You can look at the sky to see the vapor trails of airplanes. You can mentally follow those vapor trails to project where the airplane is headed. At times you will be correct. But such projections can never be as insightful as direct process knowledge that shapes all outcomes.

 The objective of process knowledge is to provide a relevant projection of the likelihood of the next stage of transformation and what changes should be initiated to increase the probability of achieving a better outcome. Every transitional state is, by definition, point in time specific. The starting conditions depend on how the transformation interacted with the environmental forces in previous transformation stages. We must accept the current state for what it is there is absolutely no action that can be taken to change the past. If we want a different outcome, then we must make necessary change to control the environment or change the transformation process.

 We have two options. One option is to use historical knowledge to issue an exhumation order, perform a post-mortem examination, hold an inquest, seek evidence of the experts, issue a verdict of the coroners jury, hold a trial, and condemn the guilty parties. In the meantime, time is passing and the process is continuing on producing similar results.

 A second option is to employ process knowledge to guide a process to better results. Knowledge of today's process state and environmental factors is used to project the probable next transitional results. If the probable outcome is not acceptable, then initiate corrective action to control the environment or change the transformation process.

 Progress occurs when, as we transverse each stage of transformation, the results bring us closer to the desired outcome than it would otherwise. Progress can only occur when action is taken to control the environment or shape the transformation process to create better results. Progress is about shaping tomorrows results through proactive actions. Proper process cultivation will result in future performance being superior then if the actions were not undertaken. Processes explain change over time.

 The process approach is summarized as:

             Outcome _(tomorrow) = f (Current Conditions _(today), Transformation _{(process knowledge)} )

 (1)   Theorem of process history the current transitional state (Current Conditions _(today)) encapsulate all the relevant impact of past transition states. There is nothing from the past that has not already impacted the current transitional state. The past is unchangeable so either a previous event impacted or did not impact the current transitional state. All past factors are enfolded to the current state of being.

(2)   Theorem of a future process state the next future transitional state (Outcome _(tomorrow)) is a function of the transformation (Transformation _{(process knowledge)}) process logic, the current transitional state and how it is impacted by unfolding environmental factors.

(3)   Theorem of a process outcome the degree that the future transitional state will approximate the desired process outcome is dependent on how well the environmental conditions are controlled and the transformation process approximates ideal conditions.

 Note: there is no history; there is only today, process knowledge and tomorrow.

 An important implication of the theorem of process history is that the impact of history is completely enfolded in the current transitional state. At any point in time, the current transitional state is dependent on past transitional states. Transition implies a changing from one state to another. While the process transformation (Transformation _{(process knowledge)}) is independent of the initial conditions (although it can be adapted), the transformation outcome are highly dependent on the initial conditions.

 It is critical to determine the relevant information regarding the current transitional state (Current Conditions _(today))_. I refer to this assessment as process scanning. Process scanning must be robust enough to cope with two distinct circumstances. The first condition is where the current transitional state is evident. One needs only to look at a flower to determine whether it needs water.  Or, a spare part can be tested to determine whether it is within specifications.

 A second condition exists when current conditions are hidden. Lea Patterson of Pilbara Group presented an excellent example of hidden transitional knowledge playing black jack with a fixed number of cards. As you transition from one hand (state) to another the cards you draw come from a subset of cards that were not previously played. The ability to project future results depends on knowledge of the current state what cards have and what cards have not been played. As each hand progresses, the dealer conceals the cards that were played in previous rounds. Card counting enables players to better comprehend the current transitional state by passing information from one transitional state to another. The available cards and the previously played cards are immutable facts at each transitional stage.

 The challenge to the process scanning model is how to store hidden information. The answer is that the process scanning model must incorporate relevant information from each transitional state to be passed to the next transitional state.

 The theorem of a future process state replaces the need to look at the troublesome shadow of historical results with the analysis of processes, and their compound interaction with environmental forces. Process knowledge provides an understanding of how a process transforms an input under a given set of conditions, including environmental factors, into an output. 

 Process knowledge is timeless. Process knowledge can be used to explain past results; Process knowledge can be used to explain today's results; Process knowledge can be used to project future results within a range of probability. Process knowledge is the best source of understanding the future since it is the process transformation process that creates the future.

 Historical analysis that incorporates historical data (what was) into a projection of what will be creates a bias to past conditions. There is absolutely no need to introduce a bias into a future projection because the impact of previous states, history, has already been completely embedded in current state (theorem of process history). Process knowledge is sufficient.

 The theorem of a process outcome encapsulates why the adaptive management systems of the future must be based on the best possible foundation. Decisions matter! No organization in the world fulfills its full potential. There is an untold amount of value lost to society every year. The world would be a much better place if we could convert some of the value lost into value creation. Enter our decision making tools. If our results are less than preeminent, so must the decision making tools we embrace.

 Decision making must be perpetual. Decision making must look to the future rather than the past. Decision making must consider the most current set of conditions. Organizations must be proactive rather than reactive. It is today's weather and a 10 day projection of future weather conditions that is important rather than the past ten days. Why? It is meaningless that the past ten days were cold. Tomorrows it might be warm due to a new set of atmospheric conditions. No action can be taken on past information the past is unchangeable. A person can influence their actions only for what is coming. For example, a high probability of projected thunderstorms would result in me turning off the sprinkler system to lower my utility costs.

 The theorem of a process outcome provides hope. The only important consideration is a relevant projection of the likelihood of the next stage of transformation and what changes should be initiated to increase the probability of achieving a desired outcome. If we want a different outcome, then we must make necessary change to control the environment or change the transformation process.

 Process Foundation

 Processes describe the transformation cycle. An input is transformed into an output. The process outcome is bounded by its input, the transformation stages, the resources employed in the process, the environment in which a process occurs and its output. Let us start with the process output. Every process has an intended outcome that similar in nature regardless of where the process is located. Consider a rose bush. There are many varieties of rose bushes. Yet rose bushes have similar characteristics that differentiate it from other types of bushes. When you plant a rose bush, you will not get a hydrangea!

 Process transformation is constantly adapting to a changing environment. Darwin's theory of natural selection articulated this observation. As the process transformation evolves, so also must process knowledge constantly evolve. Also, process knowledge must be constantly updated each time it does not adequately explain transitional results.

 Process knowledge should lead to process improvement. The root cause of performance problems must be isolated and the transformation process changed or new process controls adopted to improve performance reliability. Process improvement must be based on reasoned process knowledge.

 For example, roses have been cultivated for many eons. Rose bushes were grown during Roman times. Throughout the ages, process knowledge of plant breeding practices has led to improving the transformation process by adding a great deal of disease tolerance and winter hardiness to the ancient rose. Hybridization has created some wonderful colors and bush sizes. We also regularly water and fertilize our rose bushes to ensure healthy growth. We study the rose bush process and control the factors that lead to aesthetically better and healthier roses. Learning about processes is important because it leads to universal knowledge.

 The same bounds apply to manmade processes. A bank audit process will never produce an iPad. Every manmade process has its unique intended outcome, technology constraints, employee experience differences and input requirements. And, just like natural processes, are universal in nature. Auditing a bank is similar in China, Australia, Europe and the United States. As with rose bushes, we apply technology and best practices to improve the bank audit process.

 Every process is subject to natural forces (See process laws paper). A rose bush is subject to the natural weather forces temperature, water amount, diseases and sunlight. Manmade processes are subject to varying input features and quality, resource stability and external environmental conditions. Different conditions result in differential results. The closer the actual process outcomes will approach the intended outcome as process conditions approximate ideal conditions.

 Without an understanding of processes, change appears chaotic, unpredictable, and often out of managements control. Managers see a current crisis as a situation specific event rather than as part of a never ending need to adapt. Adaptation is a process, not an event. Process management identifies the major forces of change and highlights the need to continuously adapt to changing forces.

 Those organizations that accept the importance of process knowledge will create adaptive management systems. Process knowledge will lead to a better understanding of the forces that shape the future and what changes are required to our processes and how to control the environmental forces that shape the future. Better resource allocation will lead to greater value creation.

The Fallacy of Historical Data Analysis

 There are several critical nuances that can blur the distinction between process knowledge and historical data analysis. The first nuance is timing. The current state is very fleeting. No sooner does it appear than it becomes old (historical). But there is a very important distinction between focusing analysis on the current state or unashamedly basing it on past events of yesterday, last week, last month or last year. Current state analysis is termed real time analysis. The goal of real time analysis is to juxtapose decision making with events as they occur, without any jumps in time. Real time analysis shortens the time between the current manifestation of an event and its influence on decision making.

Real time analysis is only theoretically possible if the time lag between manifestation and action is zero. This goal is impractical in practice. However, the intention of real time systems is very different from historical systems. The fundamental difference is the nexus of the analysis period.

A second nuance is whether using history to create process knowledge makes history relevant. Keep in mind the thesis of this article is not whether history is relevant but whether historical analysis is relevant. The importance is not how process knowledge is developed but, once it is developed, whether it is superior to historical analysis in projecting the future.

 Paul Juras, a professor at Wake Forest University, presented an example of plotting a hurricanes path from historical data. We need only to turn on a TV during hurricane season to see this common practice. While plotting a hurricanes path might make good TV, it should not be the backbone of a meteorologists methodology. Natural forces created the hurricane and natural forces dictate its path. The best predictive power of a meteorologists projections will come from an understanding of why the hurricane is where it is and what the forces are in play that will shape where it is going. Where it was is unimportant. Where it will be is critical so preparations to minimize its effects can be undertaken. Where it will be is best understood by process knowledge based on an understanding of natures laws.

 Getting past these nuances, the fundamental flaw of historical analysis is that it looks at the past as an indicator of the future and thus is an indirect measurement. Historical analysis uses charts and other tools to identify past patterns that are used to suggest future results. Process analysis scans the current transitional state and applies process knowledge and environmental knowledge to project the next transitional state. Process projections are direct measures. An indirect measure is less sensitive to a changing environment than direct measures since forces that might have been insignificant in the past can dramatically alter future results. An indirect measure is always inferior to a direct measure.

 History is a chronological record of events. The telling of history involves interpreting the results to explain the context in which the events unfolded. Thus a fallacy of historical knowledge is that its interpretations can be distorted. It is human nature to want to present results that show the best results. History can interpret past events to arrive at a preconceived result. Often the data analysis is selective and, in the most blatant cases, can exclude important but contradictory data. Joseph Freeman once said that Everyone falsifies history even if it is only his own personal history. Sometimes the falsification is deliberate, sometimes unconscious; put always the past is altered to suit the needs of the present. The best we can say of any account is not that it is the real truth at last, but that this is how the story appears now. Historical interpretations are biased by the agenda of the interpreter.

Harnessing Process Knowledge

 History is the shadow of what was. Today is what is. Tomorrow is what will be. The key to understanding the likelihood of tomorrows results is to understand today's conditions, the anticipated upcoming environmental conditions, the transformation process and any relevant actions that are planned. Each transitional state impacts future transitional states. In this sense, the experience of the past will influence the current transitional state which, in turn, affects future results. The entirety of past effects is embedded in the current state. Since all the past is embedded in the current state then the quintessential challenge is to understand the current transitional state.

 Process knowledge studies the transformation cycle to understand the important factors that influence future results. A process model projects the next level of being (results) within a tolerable range of results. Process knowledge is used for decision making when the process model provides predictable results. Process knowledge must be modified when a result is not within tolerable results.

 The fundamental difference between historical and process knowledge is the focus on the process model to understand today and tomorrow rather than yesterday. A process model should always explain the transformation process. Failure to adequately predict results portends a weakness in process knowledge that must be rectified.