Data collection

Data collection is crucial for all the process improvement activities. First, it creates a baseline so that improvement activities can be monitored and tracked to ensure that it doesn’t create new problems. Second, the data collected is reviewed and analysed to determine if the process is stable and reliable enough to support future decisions.

Even when some of the required data is already available, emphasis should be put on new data to ensure that the data is applicable to the current situation. However, new data can sometimes be difficult to collect; the team should therefore create a data collection plan with operational definitions. They may also need to create data collection forms to standardised methods that support continuous data collection. [29]

2.6.1 Operational definitions

An operational definition is a clear and concise definition of a measure. The definition is used to ensure that everyone in the system understands and collects data in the same manner. This also supports clear communication to avoid misinterpretations that can add variation to measurements. Therefore, it is important to develop operational definitions at the start of improvement activities to ensure consistent data collection.

An effective operational definition has three elements: (1) a criterion that provides a standard against which to evaluate the measure, (2) a procedure to test the measure, and (3) a decision as to whether test results show the measure meets the criterion. [15]

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2.6.2 Data collection plan

Data collection is a common weakness of process improvement teams [12]. A data collection plan is therefore needed to ensure meaningful and valid data is collected right the first time. The steps below are used to develop a plan that communicates the requirements for appropriate data collection. However, the level of thoroughness and plan formalisation is determined by the complexity of the process measures. .

Step 1: Determine the data to be collected with specified operational definitions. Data can be categorised into two groups: Attribute data and variable data. Attribute data is based on the occurrence of discrete events such as number of defects; and variable data are based on continuous characteristics such as time, length, temperature, and weight. The team should always try to collect variable data because it requires fewer data points for statistical analysis and also enables the quantification of variation. [15]

Step 2: Determine the data source and location where the data will be collected. The team must focus on the location of the activities that produce the significant process characteristics. These characteristics can be derived from the problem and objectives.

Step 3: Determine who will collect the data. Process workers have the best chance to collect data because they are closest to the required data. They also know the process best and can easily detect when problems occur. However, data collectors must be trained in data collection procedures. The team should also be involved to ensure efficient procedures and that anomalies or problems are recorded and corrected. [12]

Step 4: Determine when the data will be collected and the number of data points required. Enough data must be collected to make a statistical determination, but the team must also consider what amount is practical. They should therefore attempt to collect enough data in a specific time when the data is available. It is preferred that each characteristic is measured on a continuous basis, but this isn’t always practical.

Step 5: Determine how the data will be recorded. First think how the data will be analysed and displayed. This helps the team to plan data collection in a format that supports analysis and display. The team can then design and test data collection forms to create standardised methods for defect free and efficient collection methods.

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2.6.3 Data collection forms

Manual data collection is often required by teams; even automated processes do not always have automated data collection procedures. So the team needs to develop forms to meet manual data collection requirements. This should include the people that are assigned to collect the data to ensure clear and simple forms are developed.

Data collection forms have three important uses. First, it is used to record data on the process characteristics identified for analysis. Second, it provides a historical record of a process over time. Third, it simplifies the data collection for people who are not familiar or comfortable with collection procedures as a regular part of their job. This section discusses two types of data collection forms: check sheets and rating scales.

Check sheets

Check sheets are structured forms that enable the collection and organisation of data with minimum effort. These forms are used to capture quantitative or qualitative data in real time at the location where the data is generated. Then the collected data can be analysed and converted into useful information for detailed process assessment.

Three types of check sheets, subjected to the process, are used in practice: tabular, location, and graphic. Tabular check sheets are easy to use because the collector simply makes a checkmark in a column to indicate the presence of a characteristic or records a measurement value. Location check sheets visualise the exact physical location of a specific characteristic. Graphic check sheets are designed to collect and display data together, or in time, because data is recorded onto a graph-like chart. [30]

Chapter 2 Basic process improvement Industrial Engineering Page 29 Note that the examples in Figure 2.11 are for illustrative purpose only and that they still lack a lot of detail. An effective check sheet should provide information about the circumstances that can influence the collected data. Furthermore, it should include the name of the data collector, the date and time, and space to write comments in case of unusual events. It can also include brief instructions on the back of the form.

Rating scales

Rating scales are useful when quantitative data is not available or when it is difficult to acquire; for example, it can be used for surveys where attitudes or satisfaction are measured. However, rating scales treat qualitative data as quantitative data to enable statistical analysis. Various types of rating scales can be used for data collection, but this report focuses on qualitative description rating scales for process improvement.

Qualitative description rating scales are particularly useful to analyse a process when numerical data are not available or sufficient. The typical format consists out of five or ten levels that are arranged in hierarchical order with operations definitions. A scale is used by an observer to rate a process with reference to one of the available levels.

Table 2.2: Rating scale A clean and

organised workstation

Description

Level 1  Cigarette butts, scraps of paper, and tools are scattered around the floor

Level 2  Clutter is found by the walls  The passageways are not clear

Level 3 _{ Tools are disorganised in the storage areas}

Level 4

 Machinery and equipment are clean

 Items are clearly labelled

 Necessary items are aligned to a grid

Level 5  There is constant and continual cleaning from wall to wall  The area is clean, and the tools are laid out separately

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