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Business Sustainability: A cleaner production approach to small business management

Student Manual

Environment Australia
October 2000
ISBN 0642547149


Business Sustainability: Session 21 - Detailed Study - Part 4

OVERVIEW
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 Assessment - Introduction
Data Assessment - Average Performance
Data Assessment - Process Variation
Data Assessment - Process Interruptions, Operational Variations, and Rework
Data Assessment - Prioritisation

Data Assessment - Introduction

Element 2 of Detailed Study

Detailed study elements:

  1. Data collection and validation;
  2. Data assessment;
  3. Improvement options;
  4. Targets for process improvement;
  5. Business improvement plan.

Definitions

Data are numbers. Data are obtained from actual measurements.

Information is "user-friendly" knowledge, usually derived from processed data and observations.

Data assessment (in the context of this course) is the processing of data to obtain information - in this case, information that will assist in finding opportunities for reducing waste and improving business performance. Usage maps, together with data validation, have prepared the way for data assessment. The assessment process is not rigidly structured, but comprises largely intuitive identification of improvement opportunities.

Three Sources of Opportunities

The efficiency of a process can be improved in three ways.

The first is to improve the average performance, for example by making a basic change to a practice or process to reduce waste.

The second is to reduce process variation (variations in efficiency over time) by better process control.

The third is to reduce process interruptions, operational variations and rework that can also give rise to significant waste.

Each of these issues is considered in the following three sections.

Data Assessment - Average Performance

Average Performance

The information and data shown in the completed usage maps should give a comprehensive and quantitative picture of the usage of selected inputs and production of selected wastes averaged over the 12-month period.

Rationale

Assessment of average levels of inputs and outputs can lead to an overall picture of process efficiencies and waste levels, and will assist in identifying areas of greatest waste.

Estimation of Average Wastage

Data on average waste quantities can often be obtained fairly simply. The quantity of each raw material purchased in, say, a 12 month period can be obtained from purchase and inventory records. The total quantities of these raw materials incorporated into saleable product over the same period can be calculated using knowledge of the product along with production or sales records. The difference between these two quantities is waste - that is, raw material that was not sold as product.

Some of this waste may be necessary for the process (process-inherent waste), but much of it is not (incidental waste).

Instructions to Students

From the usage maps already developed, mark (or list) wastage in order of cost to the business. This information will be used a little later in the detailed study.

Data Assessment - Process Variation

Definition

Process variation is unintended variation in the efficiency of a process over time. Process variation is waste - pure and simple. If a process uses 5% more raw materials today than yesterday to produce the same amount of product, then the additional raw material is avoidable waste.

To avoid confusion, you are reminded of the distinction between process variation and operational variation. Operational variations are deliberate changes made to a process by an operator.

Rationale

The efficiency of all processes varies over time. Minimising this variation is the cornerstone of total quality management (TQM). Studying process variation, and the causes of this variation, can also lead to significant new insights into waste and ways of minimising waste, and the formulation of achievable targets for waste reduction.

Process Variation is Waste

Variation in the amounts of energy, water or raw materials, per unit of production is a sign that waste is occurring. Process improvement to reduce waste involves targeting those areas where the performance indicators are quantitatively higher than usual - where more raw materials, water, or energy is used, or where more waste is produced. Often the performance indicators, graphed against production units, vary irregularly. This variation is a sign that the process is not well controlled. A process that is not well controlled wastes materials and energy, and probably produces more defective products (another form of waste).

Identification of Improvement Opportunities

Process variation is one of the best indicators of improvement opportunities. If the business can operate at relatively high efficiencies at one time, then it should be able to do it again and again.

Example

The following graph (Figure 21.1) shows the monthly variation of electricity use (in kWh per tonne of product) over 12 production months in a manufacturing business. The variation from 32 kWh per tonne to 93 kWh per tonne is a strong indication that electricity is being wasted in the process (although from this graph the cause of the waste is not evident).

Electricity use in kWh per tonne of product

Figure 21.1: Electricity consumption indicating process variation

Benchmarking

In the context of cleaner production, benchmarking is the comparison of process efficiencies over time or between operations/companies. Benchmarking is usually associated with "best-practice" performance, ie. the highest efficiency actually attained in a process.

Benchmarking can be done internally or externally by comparing efficiencies between two or more companies or operations. External benchmarking is often co ordinated through industry associations.

Graphing

Time-based input and waste data was collected earlier in the detailed study. Graph this data against time as follows. Assuming monthly data was collected, divide each monthly figure (input or waste) by the production output for the same period. Graph the resulting figures against time as a bar graph.

Set targets for improvement based on what is shown to be achievable by past performance.

Data Assessment - Process Interruptions, Operational Variations & Rework

Rationale

The cost of wastage associated with process interruptions, operational variations and rework are often unknown to small business managers, yet these costs can be very high.

Identify Causes

The costs associated with process interruptions, operational variations and rework were estimated in the previous element of the detailed study. The causes of these interruptions etc. need to be identified. The causes offered by business employees might be somewhat superficial and a closer investigation into the underlying causes could be needed.

For example, a process is stopped regularly because an ingredient is unavailable. Question why this is the case. If told that the ingredient is often found to be spoiled, question this too. If told that the refrigeration temperature varies too much, ask why this variation occurs etc. Eventually, the deep cause (eg. no routine maintenance program) may emerge.

Instructions to Students

In your teams, work through the data collected on process interruptions, process variations and rework identified towards the end of the preliminary assessment of your client business. Validate this data and assess associated:

Data Assessment - Prioritisation

Rationale

A number of opportunities will be identified during the data assessment. Depending on the number, these may need to be prioritised and the less attractive opportunities left for attention at a later stage. The more attractive opportunities proceed to the next stage (development of improvement options).

Simple Method

The simple method of prioritisation used in the preliminary assessment may be used to choose those improvement options with the greatest potential returns from waste reduction.