Environment industries archive
Key departmental publications, e.g. annual reports, budget papers and program guidelines are available in our online archive.
Much of the material listed on these archived web pages has been superseded, or served a particular purpose at a particular time. It may contain references to activities or policies that have no current application. Many archived documents may link to web pages that have moved or no longer exist, or may refer to other documents that are no longer available.
|Objective of this Session||To be introduced to the detailed study, discuss issues with data quality, and introduce quantitative usage maps as a tool for data collection|
|The following topics will be covered in this session|
|Data Collection - Process Interruptions|
|Data Collection - Operational Variations|
|Data Collection - Rework|
|Data Validation - Introduction|
|Data Validation - Checking|
Talk to the business manager and to operators to identify all interruptions to business processes and the reasons for those interruptions. Possible interruptions may include:
Establish the frequency of these interruptions. This is best done from production records if possible, because estimates made by employees can be grossly inaccurate.
Establish what additional inputs and waste outputs (including scrap) are generated from:
You should make sure that these quantities are included in the data in the tables on Pages 2 & 3 of the Preliminary Assessment Worksheets.
Operational variations are deliberate changes in operational procedures. These may include:
Only the operators will be able to offer accurate information on operational variations - management will often be unaware of them. Be aware, however, that operators may be cautious about disclosing them and some gentle coaxing may be needed.
It is important to distinguish between operational variations and process variation (discussed later). While operational variations are deliberate changes made for a purpose, process variation is unintended variation in the efficiency of a process over time.
Gather what information there is on the frequency of operational variations. (Keep in mind that information on frequency is likely to be of fairly poor quality).
Make a record of any changes in waste levels that are believed to result from operational variations. (Note that waste levels may increase or decrease).
Rework is re-processing of a product, usually because of a problem with product quality. Talk to employees to establish the types of rework that occur in the business, the reasons for the rework, and the nature of the rework.
Establish the rate (frequency) of the various types of rework. It is very important to try to obtain documented evidence of rework rate or to verify the data offered. It is usually very difficult for employees to accurately estimate rework rate.
Rework can be a very high hidden cost to a business. It can often double production costs for the reworked item. Rework can increase wastage levels and students should try to estimate this additional waste and include it in the waste data.
There are many costs associated with rework, and when they are identified and summed, the resulting data can come as something of a shock to the business manager. Reducing rework rate (and associated costs) can be a fertile area of opportunity in the cleaner production assessment.
Respraying the panels on a car costs the repairer $800. The customer is charged $1,000. If the paint runs and rework is needed, then the cost of doing the repair could be doubled to $1600. The business makes a loss of $600 on the job and it would take the profit on three similar jobs just to break even.
Just because data is written down and is recorded to two decimal places does not mean it is accurate. Data validation is the process of gathering evidence that the data collected is accurate, and correcting inaccurate data.
The aim of data validation is to discover the inaccuracies and omissions in the data collected in the preliminary assessment. During this process, new data will be discovered which will lead to a more thorough understanding of unit processes within the business.
Apply the information given in this and succeeding Data Validation sections to all data already collected. It will also be applied to data collected in the next stage - Identification of specific improvement opportunities.
If you accept the first data you are given, the cleaner production assessment will probably fail because there may be few apparent opportunities. However, when you start questioning, probing and validating data, things probably will not be as they first appeared, and new data and information will reveal hidden waste and opportunities for improvement. Experience has borne this out time and time again.
Data validation can go on forever, so some guidance is needed on how far to go. Your team should find a balance between the time available and the need for accurate data. Data validation should concentrate on data associated with the greatest apparent improvement opportunities, and data that the team judges to be most unreliable.
Techniques used for data validation include:
Some inaccurate and missing data are quite normal in all businesses, and these inaccuracies and omissions usually aren't immediately apparent.
A number of techniques are widely used to validate data and "ferret out" missing data. These are listed below and discussed in the following paragraphs:
Don't accept any data on face value. The first step in validating data is to ask questions about it. For example, ask:
Some personal skills will be required to ask these questions without annoying people and getting noses out-of-joint!
The business' accounts person, when asked for annual electricity usage, may pull out a file of bills, add up the totals and offer the summed data, but this information may be inaccurate. If the bills were examined, it may be found, for example, that:
There is no substitute for examining business records as a means of validating recorded data. Note the cost, quantity, date, time period of charge (for utilities) and other pertinent data from invoices - all this information may be useful later. Business records may also include production reports and other internal documentation.
When the business manager is asked for operational information, the replies he/she gives are often quite different to replies to the same questions when operators are asked. Sometimes it will be necessary to search out company records to resolve the differences.
If information is sourced from only one person, there may be no indication that the information was inaccurate in the first place.
Compare Data from Different Sources
When the same data is obtained from two different sources, the numbers will often not agree. Investigation of these differences will commonly uncover useful information about waste.
For example, if annual purchases of a raw material are compared with usage and inventory records, differences will usually be apparent. Such differences may indicate "hidden" waste, production inefficiencies, poorly calibrated measuring equipment or even fraud.
After the flow diagrams for the business have been compiled, visit the business premises and physically examine the processes. It is highly probable, as you walk around, that you will observe practices and waste that no-one in the business has told you about. This walk-around should not be done quickly - teams should take their time, be inquisitive and ask many questions, and always be on the lookout for waste. A tape measure may be useful.
This will help the team to become acquainted with the business' processes and identify sources of waste. Use the process diagrams and "test" these against actual practice.
Inspect the business site at various times of day (including after close of trading). Look for waste in the form of:
Look for drums of chemicals or raw materials not included in the process input/output process. Look in waste bins - are all the types of waste in the bin accounted for in the input/output diagrams?
When walking around, be very careful not to compromise your own or other people's safety.
Be inquisitive. Inform operators of what you are doing and ask lots of questions. Make notes of all observations and record the date and time of day. The source of information passed on by employees should also be noted. Inquire further into anything that "doesn't feel right" about the process or the data collected.
Attempting to obtain a mass balance (a balance of material inputs and outputs) is a powerful technique for identifying inaccurate or missing data. Discrepancies should be investigated and will often lead to new wastes and improvement opportunities.
Mass balances are explained in detail in the next section.