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Much of the material listed on these archived web pages has been superseded, or served a particular purpose at a particular time.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|
|Introduction to Detailed Study|
|Data Collection - Introduction|
|Data Collection - Quantitative Usage Maps|
The detailed study is a systematic compilation, validation and assessment of process data to identify specific opportunities and options for reducing or eliminating waste and is likely to increase business profitability.
The detailed study builds on the data and information in specific areas of the preliminary assessment. It seeks to develop a more detailed picture of the selected process inputs and outputs, and examines process variation (per unit of production) over time. This will lead to identification of opportunities for improvement, and options for achieving that improvement. The outcome of the detailed study is a business improvement plan.
A detailed study comprises five elements:
An overview of each of these elements is given below, and they will be discussed in detail in this and the following sessions.
Data is collected to complete the quantitative usage maps and to get a fuller understanding of mass and energy flows for the chosen inputs and wastes. This additional data will often give new insights into business inefficiencies and identify new waste streams.
Data validation is the process of gathering evidence that the data collected is accurate and correcting inaccurate data. A number of techniques are used to accomplish this.
Data is assessed to obtain useful information on levels and sources of waste. This information is used to identify specific improvement opportunities.
Improvement options are developed and evaluated. Improvement options are potential courses of action that will realise the improvement opportunities identified in the previous element.
Improvement options are prioritised in terms of criteria such as cost, benefits, practicality and probability of success.
Having decided which improvement options to implement, improvement targets are set, and one or more performance indicators are chosen to measure success in meeting these targets.
The final outcome of the detailed study is a business improvement plan. This plan outlines the result of each stage of the detailed study and presents a plan of action for achieving the identified business improvements.
While the preliminary assessment was completed in one or two days, the time required for the detailed study will be significantly greater. Because it is an iterative process, the time needed cannot be easily estimated. Rest assured though that, like any investigation, it could easily soak up many, many hours of work.
Even though the preliminary assessment has narrowed the areas to be investigated, efficient time planning and utilisation is essential.
Collecting accurate data may not be easy. It is common in business, especially small business, to have poor records for purchases of materials and energy, no records for actual usage in production, and even poor records of quantities of product sold. It may be a sizeable challenge to find any accurate data.
The reason for this is simple. The records maintained by a business are generally directed toward monitoring turnover and managing cost levels. Business traditionally does not monitor efficiency or wastage levels, and so there may be little easily available data of this type.
If the student team simply accepts the data and information given to them by business employees, then they may conclude on the evidence available, that there is little opportunity for improvement. However, as the data validation process sorts out the accurate data from the broad estimates, and fills in the gaps, opportunities will become apparent.
Based on the data available to them, most business managers will believe their business is efficient and wastage is as low as practical. The student team will need to discover the inaccuracies in this data and dig out the data that isn't known to them.
When your team finds data collection difficult, you should be heartened in the knowledge that wastage (and opportunities for improvement) is likely to be greatest in businesses with poor records. If waste levels are not monitored, they are unlikely to be controlled.
For several reasons, tables of data are not always complete. It only needs the employee entering the data to be absent for a period and regular data entries can be missed. From time to time, a person might intentionally leave out a data entry to hide the fact that the process for which they were responsible was not operating correctly.
It is possible that the data available is inaccurate, usually because of inadvertent mistakes and poor estimation. For example:
Estimates made "off the top of the head" are notoriously unreliable. Also, the only information on rework rates, scrap levels and production information may be a business manager's "off the top of the head" estimate. While such estimates are adequate for the preliminary assessment, they should be thoroughly investigated in the detailed study.
It will often be necessary for the student team to make up for missing data by collecting their own (eg. by reading meters) and making their own measurements (eg. by measuring tank dimensions, measuring amount of scrap in bins or observing the process and taking notes).
The aim of data validation is to discover the inaccuracies and omissions in the data collected in the preliminary assessment, and to add further detail to this data so as to gain a more thorough understanding of unit processes within the business.
Detailed study elements:
Data collection in the preliminary assessment employed a broad-brush approach to identifying the main inputs and wastes with the aim of identifying likely targets for detailed study. In the detailed study, data collection is more focussed and more thorough. There is also a need to ensure that the data is accurate - this is data validation.
For the materials, energy sources and waste streams prioritised in the preliminary assessment, usage maps must contain quantitative data for every significant component. (A significant component might be defined as any component believed to contribute more than, say, 5% of the whole). This data is needed later to complete mass balances.
Assign both cost and quantity data to the usage maps - the cost data will be used in prioritisation to further refine opportunities for improvement, and the quantity data will be used in setting improvement targets.
As additional data and information are found, the flow diagrams, input/output diagrams and usage maps can be further developed. For example, it may be realised that a single unit process is better represented as two processes, additional inputs and waste streams will be discovered, and waste streams can be further sub-divided.
Study any activities that occur between unit processes (eg. washing product, inspection, transport). If these activities have the potential to produce waste, then include them as separate unit processes.
For the inputs and wastes selected for the usage maps, obtain time-based data (say, monthly data over 12 months) where available. Obtain the corresponding "units of production" data over the same period of time. For example, monthly electricity usage data and corresponding monthly production data might be collected.
This data will be used to calculate and examine process variation later in the detailed study.
Variation in results of the business' process performance indicators should also be examined, because these are indicative of some loss of control of the process and possibly increased wastage.
Obtaining reliable data to complete usage maps is often not easy and will require considerable resourcefulness. The following sources may be useful in finding this additional data:
All estimates (and especially guesstimates) used in usage maps should be confirmed. These estimates will often be grossly inaccurate. If estimates must be used, ask for estimates on the same quantity from a variety of people in a position to make reasonable estimates (include both management and operators).
Quantitative usage maps are constructed by assigning costs to the qualitative usage maps developed earlier. It is usually easier to assign quantities first, then convert these to costs. Examples of quantitative usage maps are shown below.
When assigning waste costs, use the "total waste cost" calculated in the Annual Usage and Wastage stage.
The following example shows an electricity quantitative usage map for the process of making and distributing bread in a wholesale bakery, (Figure 18.1) and for waste dough & bread (Figure 18.2).
Note that, although costs are expressed in these maps as monthly costs, any convenient time-base can be used.
|Electricity $345/ month||à||Refrigeration||$40|
Figure 18.1: Usage Map (quantitative) for Electricity
|<$10||Spoilage from storage||à||Waste dough/bread $840 (+)/month|
|$20||Damaged bread not packaged||à|
Figure 18.2: Usage Map (quantitative) for Waste Dough and Waste Bread
The total quantity of inputs and wastes can be calculated from the annual usage and wastage already collected. However, the more detailed breakdown is harder to establish. This data may be obtained from analysis of purchasing records, taking relevant measurements or using production data.
For the projects (as in any cleaner production assessment) the team will need to be resourceful in finding ways of collecting the data needed for usage maps.
Quantity data is needed later to complete mass balances.
With quantitative information, an objective prioritisation is possible which will likely lead to identification of the best targets for achieving improved business efficiency.
The cost data will be used in prioritisation to further refine opportunities for improvement, and the quantity data will be used in setting improvement targets.