Following Up to Ensure Success

No matter the objective, strategy, or tactics taken, it is necessary to understand the present position of the company as it compares to the desired result of that organization. Like a pilot measuring the route to the desired destination, they must navigate bad weather, account for wind impact to course, and a myriad of other factors to ensure safe arrival at the destination. It is similar for steering the organization, only more complicated since for an organization to change requires more than one person (pilot) or a small collection of people (the flight deck and air traffic control) to be involved. Unlike the pilot scenario, it is important to keep all relevant staff engaged in the change process. Besides the extra sets of eyes on the effort to ensure we are actually making progress this constant review promotes inclusion and engagement and help effectively navigate risks to success.

Following up, it seems, is is no easy task, experience suggests this to be a common failure mode in projects and organization improvement. It is not as simple as measure things, and follow up on what is witnessed; those things are just the mechanics. The method in which the measurements are acquired matter, as this may tell us something of the validity of the measurements or whether the measurement adequately represents the true situation. This requires measuring the appropriate variable and measuring appropriately as to not bring any bias or error into the measurement. To be able to take effective action requires learning about the situation or process.

In quantum physics, there is a principle known as the observer effect. The observer effect states that the act of observing a phenomenon changes that phenomenon. Attaching measuring instruments such as oscilloscope probes, for example, alters the characteristics of the electrical signal under exploration. There

Figure 5.1 There is a hierarchy of knowledge.

is a similar principle for psychology by the same name and is sometimes referred to as the Hawthorne effect that originated from a study of the Hawthorne plant of Western Electric Company by Elton Mayo and Fritz Roethlisberger in the 1920’s. This study started as an exploration of the effect of work environment (for example lighting) on employee performance. The result of the study was quite interesting.

At the lowest level, clumps of data are throughout the company. Collecting these clumps, and manipulation (not corruption) from raw numbers to something understandable from which we could learn requires careful actions This means data manipulation without bias. The results, of this manipulation is information from which we can take actions to promote greater understanding. For example, projects produce data not matter the method of project management employed. In this way, measurements are fodder for understanding what to change, and provide clues as to how to change, ultimately to see the impact of those changes and ensure the company moving in the desired direction.

Hawthorne Study

There are times when we set out to understand how things work, with some notion of what matters. Our experiments will often reflect this bias. For example, if we believe light to be a particle, then we will set up an experiment to see if we are correct, which will include looking at light from the particle perspective. The perspective or theory selected will influence the parameters of the experiment. However, it is possible (and in this case is also true) that light has wave properties and particle properties. An experiment focused on proving or disproving particle theory will not discover this wave element and will lead us to the conclusion that light is a particle.

It is usually agreed that, historically, the merger of industry and the behavioral sciences in their current form becan with the research conducted by Elton Mayo and his colleagues in the Hawthorn plant of Western Electric Company.^[1]

The original objective of the Hawthorne study was to understand how workplace illumination affect worker productivity.' The study discovered that as illumination goes up, so to did productivity. That is not odd. However, as illumination decreased, the productivity also increased, including as low a level as moonlight. This resulted in the abandonment the illumination studies, changing to areas such as rest periods, work week, incentive plans, free lunch and supervisory styles. Eventually the team brought in Elton Mayo who brough a “psychology of the total situation". He concluded

Figure 5.2 Changes forthe organization of for the product are simiar, check and then change.

that the results were “primarily due to a remarkable change of mental attitude in the group". The simplistic distillation of this over the years has resulted in the moniker the Hawthorne Effect, which suggests the increase in productivity is due to the attention given to the workers under study. Mayo thought this overly simplistic was more attributed to the groups sense of belonging in connection with the work.

Measure Twice, Change Once

It may sound easy but measuring comes with many challenges. Measurements will often provide the rationale as well as the direction for the organization to change. Understanding what to change, and how to go about doing so, will require some understanding of where the company is presently and where it wants to go in the future.

When it comes to measurements, we must consider all the variation that comes with variables and measurements. There is variation in the process (cQ variation due to our sampling routine (o,) and variation due to measuring (o,„).

The measurement variation is determined by identifying the variation due to the scale or physical measurement system and that of the human interpretation of that scale. To understand this, we perform Gauge Repeatability and Reproducibility (Gauge R&R).

The Gauge R&:R is part of the Measurement Systems Analysis (MSA) book published by the Automotive Industry Action Group (AIAG) and is part of the Advanced Product Quality Planning process typically employed in automotive product development and manufacturing. To understand the details of this process, consult that book. However, the objective is to understand the variation in the measurement system due to the tools used in the measurement system along with the impact of the people using those tools on the resulting measurement. Each of these impacts the final reported or recorded result.

Repeatability reflects the variation in measurements made by an operator using one gage to repeatedly measure the same feature of the same part. Reproducibility reveals how closely one operator can duplicate the measurements of a second operator for the identical characteristics of a part using the same gage.*

No matter the measuring system approach, time should be allocated to analyze the system to understand the veracity of the system or the system’s ability to adequately inform the true circumstances.

What Should Be Measured?

In some cases, the organization is likely already taking a variety of measurements that inform performance, cost, and allow for some predictions. Organizations that have a high degree of processes often have data from those processes that can facilitate understanding, provided the appropriate things are measured appropriately. If this information is at hand, then the chore is distilled down this data, that is, turn it into some sort of interpretable information from which decisions can be made. However, it could be prudent to review how the data was collected and question whether this data is representative of the process from which the data theoretically originates. Experience suggests it is possible the measurement is taken from a process although few, if anybody, are following the process. Neglecting to consider this will provide a distorted view of the situation and therefore the experiments performed to learn how to improve will also be impaired.

How Should We Measure?

As good as modern camera equipment and control systems are, it is possible that the sample size could be all products and the measurements and calculations could be automated also, thus removing as much as possible the human factors in the data acquisition, at least beyond the equipment set up.

The Leaders Handbook provides 8 simple steps as hints for getting started. Below is a short synopsis of what should be thought through before we charge off measuring.

• 1. Define the purpose of the data gathering systems.
• 2. Pick a priority measurement target.
• 3- Identify the purpose of the process to be measured.
• 4. Identify the measurement and the purpose of the measurement.
• 5. How will this measure fit into a larger system of measures?
• 6. Develop operational definitions.
• 7- Plan and prepare for the data collection.
• 8. Gather the data.

While these 8 steps are a good start they neglect to address the gaming of data due to misunderstanding. By that we mean that if data is collected by a group, and they assume the data is collected for other than process improvement reasons,

possibly manpower reduction, they might manipulate the data such that they feel safe. According to Burke’s interpretation of Lewin’s model for change the data collected to facilitate change is the individual’s reaction to the collection of that data.* To this end, we say that communication of the purpose of the data collection must be effective and minimize the adverse reaction to the collect of said data.

• [1] Luthans, F. (1972). Contemporary readings in organizational behavior. New York: McGraw-Hill. Hopp, W. J„ & Spearman, M. L. (2012). Factory Physics. Milano: McGraw Hill, page 36