Analyzing the Process

The following analytical instruments are often used to extract information about a process such as how long the process takes, the quantity of product through the process, the cost of the process, etc. The process analyst team should look for those instruments that will best extract and explain the type of data desired for the process being analyzed.

This is not an exhaustive list but it does contain the more common techniques and will provide a broad spectrum of the types of analytical techniques that could be performed. The analyst or analysis team will rarely use more than a few of these for any one initiative and it is the job of the team to determine which ones are applicable to achieve the desired objective.

Creating Models

Process models are often used to show processes and the various interactions with the process. An entire chapter in the CBOK® is devoted to various techniques that can be used to create process models.

Cost Analysis

Also known as activity based costing; this analysis is a simple list of the cost per activity totaled to comprise the cost of the process. This analytical technique is used frequently by businesses to gain an understanding and appreciation of the true cost associated with a product or service. This type of analysis is often used in conjunction with other analytical tools and techniques discussed in this section.

This analysis is important to the process analyst in order to understand the real dollar cost spent on the process so it can be compared to the dollar value in the new process. The goal being decreased costs, or if increased efficiency, than the value of the increase in production compared against the cost.

Transaction Cost Analysis

A transaction cost analysis (TCA) is also used often in software application design to analyze how much time and computing resources are used for each transaction processed by the application. The TCA is usually accomplished through specialized tools that monitor different aspects of the software within all tiers of the application including client, web server, database server, application servers, etc.

This type of analysis can quickly uncover bottlenecks in the application as well as bottlenecks in business processes as they interact with the system. As most processes are dependent on some sort of automated system, the interaction and cost per transaction of the system is critical to understanding the system.

Cycle-Time Analysis

A cycle-time analysis (also known as a duration analysis) looks at the time each activity takes within the process. Each activity is measured from the time the input begins the activity until the activity creates the desired output including the time any subsequent

activity begins. The total time to complete all activities is the time the process takes to complete.

The purpose of this analysis is to analyze the process in terms of the time the process takes to complete with the goal of reducing that time. It is also very useful to uncover bottlenecks and potential bottlenecks within the process that prevent the process from performing correctly. This analysis assists the analyst in discovering non value added activities that do not contribute to the process output.

Pattern Analysis

A pattern analysis looks for patterns within the process that can be streamlined into a single sub-process to obtain efficiencies. Through the process of discovery the analyst might uncover that the same set of activities happen at one or more stages of the process. By recognizing this pattern the analyst can look for ways to combine these activities (or systems) together to achieve a more efficient process, thus saving resources and time.

Further, systems and activities within organizations tend to mimic themselves within the same organization. By recognizing these patterns in the organization it is possible to find duplications. By combining these patterns together in a single process throughout several organizations it is possible to gain an economy of scale in the organization. An example would be combining the billing process from two separate organizations into a single process.

Decision Analysis

Decision analysis uses a structured method of considering the outcome of a decision. These types of analytical tools include a wide variety of well known practices such as tree diagrams, probability analysis, cause and effect diagrams, etc. The common thread among all of these analytical methods is to examine the relationship between the decision and the outcome. All of these are to aid the process analyst to not only discover why a process has taken shape over time but also to assist in creating a new process.

Distribution Analysis

Although the term “distribution analysis” means different things in different disciplines, the term generally applies to a comparison of attribute-based data. This comparison would be plotted on a chart to show the comparisons of the data points. The shape of the distribution (curve or straight line) can help the process analyst identify the biggest population of data affected by a particular attribute in the data, or assist in predicting the probability of an outcome, or assist in understanding the degree of variation that exists within the data.

An example would be a comparison of the age distribution of a customer base. By plotting the ages of each customer one might find most of the customer base is centered on a particular age group. This might assist the analyst in understanding why a process may or may not work for that customer base.

Root-cause Analysis

A root-cause analysis is a 'post-mortem' technique used to discover what truly caused a given outcome. The intent of the analysis is to prevent the outcome from happening again.

Finding the root cause for an outcome is not always as easy as it may seem as there may be many contributing factors. The process of finding the root cause includes data gathering, investigation, and cause and effect relationship diagramming to eliminate outcomes. This process is much easier when the outcome is isolated and can be easily reproduced.

Sensitivity Analysis

A sensitivity analysis (also known as a “what if” analysis) tries to determine the outcome of changes to the parameters or to the activities in a process. This type of analysis will help the process analyst understand the quality of the process as defined below.

• The responsiveness of the process. This is a measurement of how well the process will handle changes to the various parameters of the process. Such parameters would include an increase or decrease of certain inputs, increasing or decreasing the arrival time of certain inputs. This will enable the analyst to know how quickly the process will flow, how much work the process can handle and where the bottlenecks will occur given any set of parameters.

• The variability in the process. This is a measurement of how the output of the process changes through the varying of parameters in the process. Often, one of the goals in performance improvement is to eliminate variability in the outcome. Knowing how variability in the parameters affects the outcome is an important step to understanding the process.

The sensitivity analysis is instrumental in understanding the optimal performance and scalability of the process and the effects of any variations in the parameters.

Risk Analysis

Similar to the sensitivity analysis, the risk analysis examines the effects of the process under external pressures. Examples of these external pressures include foreign currency fluctuations, civil wars, or natural disasters affecting the supply chain, thereby having an adverse effect upon the process designed. The risk analysis aims to consider what would happen to the process should any of these scenarios happen and ultimately what the outcome would be to the organization.