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90-109 - The legal system of Ghana is a unique and dynamic framework that has evolved
Ghana Legal System (FLAW 103)
University of Ghana
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9 Processes
9 Introduction
####### 9.1 Definition of a Process
A business process, simply defined, is any activity, or set of activities designed to change one or more inputs – which may be physical or information- into one or more outputs. It is desirable, although not universally true, that a process should in some way add value to the inputs so that the output is worth more than the combined value of the inputs and the processing. Figure 9.1 show this in diagrammatic form.
Figure 9. A process
Based on this definition, a process can refer to a physical manufacturing process or to a virtual or service operation where the output is not a physical product – a doctor’s advice, or the transfer of funds between bank accounts for example.
####### 9.1 Production as a System
In chapter 2 we introduced Deming’s model as shown in figure 9.
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Suppliers of materials & equipment Rec eipt & test of materials
Design & redesign Consumerresearch
Consumers Distribution Produc tion, assembly, inspec tion
Tests of processes, mac hines, methods, c osts
A B C D
Figure 9. Production as a system (Deming, 1990)
The model looks initially chaotic, but simply reflects the myriad of activities that go on within a production environment. The flow is as follows:
- Consumer research drives an initial design.
- This is flowed down to suppliers who pass material into the organisation.
- The material is verified to design and passed into production.
- Processes, machines, methods etc. are monitored as the material flows through the production process.
- On successful completion goods flow into the distribution chain to consumers, whose feedback is sought to drive design changes as appropriate, and the cycle begins again.
This concept is hardly revolutionary now and, indeed, the wording of the model may look rather dated. However, the recognition that outputs of a process are clearly driven by inputs was the vital first step on the road to managing processes rather than outcomes. It may also be worthy of note that, even today, many management approaches spend more time focusing on the outcome than the means to achieve them (MBO and performance appraisal are perhaps chief amongst these).
Deming made some supplementary points on viewing production as a system. He noted that ‘the system must have an aim’ (defined by the customer of the process). An obvious comment, but it is amazing how often we lose sight of the end goal of the process in the endless debates over precedent and practicality which attend most manufacturing processes. Deming also noted that in the increasingly competitive production environment of recent years it is necessary to improve the system ‘constantly and forever’.
Perhaps the most insightful of his comments is that:
“Every organisation is perfectly designed to achieve the results that they do” (Deming, 1990)
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Figure 9 shows schematically how this might look. The typical ‘owner’ departments are shown in each coloured segment and indications of key measurements that might be applied are shown in bold. It is interesting to note that the interfaces on this diagram require careful management if conflict is to be avoided. This, in effect, is where the continuous process model is most likely to break down with sub-process optimisation and local goals taking precedence over the broader picture. This picture is why the cry “I can’t believe they work for the same company as me!” is so common, everyone is being driven by different goals so that the commonality of purpose one might reasonably expect breaks down. No doubt this gave rise to Deming’s point 9 in his 14 points (Deming, 1990) ‘Break Down Barriers Between Departments’ where he expounds the virtues of the systems vision which optimises the whole rather than individual parts of the system.
Until departments can look beyond their own boundaries conflict will always exist. It can be argued that this integrating function is, perhaps the key function of management. Developing the vision and buy-in required to make this a reality can be supported by the application of Hoshin Kanri planning systems, (see Strategic Quality Management notes). Deming in his System of Profound Knowledge provides a holistic theory of process management.
9 Process Planning
Processes need to be planned in order to be successful, in previous sections we have discussed how corporate goals and visions can be deployed to departmental/process levels. This section looks at a methodology for creating a process focused on the needs of the customers of this process. The approach suggested is based on the QFD process-planning matrix as shown in figure 9.
Figure 9. The Process Planning Matrix
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The process-planning matrix links the customer requirements (prioritised by importance on the left hand side of the diagram) to the process parameters (across the top) via the relationships matrix in the centre of the chart. The process owners in consultation with customers and process fact holders will fill in this chart. Where difficulty is experienced in filling in the chart it will indicate a need to develop process understanding further via discussion or experimentation. An example of a completed chart for bulk drug manufacture process, which is then dispensed into phials by a subsequent process, is shown in figure 9.
The chart allows for emphasis to be put on the areas where performance lags expectation by the greatest amount. For example, in the figure above ‘moisture < 0%’ is a key focus since it is a high priority. Following this example through, we can see that the process parameter that most affects the moisture content requirement is ‘temperature at the end’. This would be a key focus for improvement to move the process forward.
Figure 9. Example of a Process Planning Matrix for a Chemical Process
By using this approach there is a formal recognition of the need to link goals to means and to robustly define the priorities for improvement within the process. This is a necessary pre-cursor to establishing process stability and capability on key parameters, rather than wasting improvement effort on less relevant aspects of the process.
9 Process Control
Having established what aspects of a process are important to deliver customer satisfaction, it is necessary to ensure that these aspects are properly controlled, in order to deliver the required outcomes.
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- It is too late: Products have already been made before diagnosis, and often there is sufficient lag between production and inspection that any feedback would be meaningless.
- It misplaces responsibility: Responsibility for quality devolves from the person making the item to the inspector of the item whilst the control of quality remains where it always will remain, with the person in control of the production process. Thus, the only one with the ability to affect the final quality of the finished product has no incentive to pursue such improvements.
The logical way to overcome the problems associated with this type of system is to apply preventative techniques at the operation stage to ensure that the product is produced to the required quality. Such a system is shown in schematic form in figure 9, the approach is based on Statistical Process Control (SPC) which is a statistical method of data collection and analysis that works in such a way as to monitor the operation and control it to its maximum potential. This enables the operation to be carried out in confidence that the final product will be good.
Figure 9. The application of Statistical Process Control.
The origins of SPC date back to the inter-war period, and are based on the work of Walter Shewhart (1980) who, in 1927 identified the use of control charts to detect process variation. The man who is seen to have most influenced the development of SPC as a technique, and popularised its use is W. Edwards Deming. Deming was a disciple of Shewhart and was sent to Japan at the end of World War Two to help redevelop Japanese industry. Amongst other philosophies he propounded the principles and practices of SPC, the Japanese listened, took up his teachings with enthusiasm and the rest is, as they say, history.
The core principle of SPC is the belief in the need to understand the variation in a process and manage it on that basis. The long-term aim of SPC is to minimise variation in processes so that customer requirements are more closely met than before. There are three key elements in achieving this aim:
- Providing control systems
- Evaluating capability
- Providing guidance towards continuous improvement
####### 9.4 Special and Common Causes of Variation
Variation is part of our everyday lives. Both at work and in our private lives we make allowances for its effects from the process of getting to work in the morning to the output of a complex manufacturing system. However, whilst a seat- of-the-pants approach to deciding how long we allow ourselves to get to work may be perfectly adequate, a similarly haphazard approach to managing processes at work is not desirable. We need to get a quantitative feel for the variation in our processes. There are two basic elements to this variation: the central tendency and the spread. We need to have a handle on both these since they are vital to a successful process. It’s no good being the right temperature on average if, to achieve this, you’ve got one foot in the fire and one in the fridge!
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At this stage it is important to note the two potential causes of variation that can affect a process, these will be illustrated by means of a simple example of driving to work in the morning: even when we set off at exactly the same time, following the same route, in the same car it is apparent that arrival time will vary.
Common Cause (Unassignable) Variation: This is variation that is inherent in the process; it is always there. In the process of getting to work this will mean things like waiting time at fixed traffic lights, or the driver’s mood and condition, or weather conditions. Only fundamental action on the process can change common causes. For example, changing route to avoid the traffic lights will remove that cause of variation.
Special Cause (Assignable) variation: This is variation due to transient causes outside the process norms and can usually be traced back to the specific cause. In the journey to work example this would include road works, breakdowns etc. In many cases action can be taken to achieve a reduction in the future effect of these ‘transient problems’. For example, better maintenance to avoid breakdowns, which does not fundamentally change the process.
The difference between the two types of variation is crucially in their effect on the process. Common cause variation affects the overall spread of the process (so, for example, a journey with a lot of traffic lights would tend to have a wide variation as the variation caused by red or green at each light would add up), it would not affect predictability. A process which is subject to only common causes will be predictable (within limits), so we know that our journey to work might take between 20 and 30 minutes provided that nothing odd happens. We cannot, of course, predict the exact time it will take tomorrow, but we can make sensible decisions with regards to process management.
On the other hand, a special cause will tend to not only increase variation but also to destroy predictability. For example, if you were involved in a road traffic accident you would expect the journey to take longer. It would not, however, be possible to estimate the effect; it might be 10 minutes to exchange insurance details with anyone else involved, or if the car was no longer fit to drive you might miss the whole day at work. If a process is unpredictable it is not possible to make any sensible management decisions; you could not, for example, allow an extra 30 minutes for your journey time if you knew you were going to have an accident.
Accordingly, a process which is subject only to common cause variation is described as being “In Statistical Control”. This is sometimes reduced to “In Control” or described as “Stable”. This essentially means it is predictable, and we know what is coming (within limits). When a process is under the influence of special causes it is described as being “Out of Statistical Control”, “Out of Control” or “Unstable”.
To effectively manage a process we need to be able to distinguish between In Control and Out of Control conditions. To do this we need to establish what the natural limits of the common cause variation are. To begin this process we need to put the data into context.
####### 9.4 Run Charts
The first step in putting data into context is to see it as part of the history of the process. This is best achieved by the use of run charts. Such diagrams (see figure 9) allow judgements to be made about process trends or shifts. They often also compare the current status of the process to the target or budget associated with that process.
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####### 9.4 Shewhart Charts: Application of Economic and Scientific Principles
The lack of convincing answers to these questions shows the vulnerability of this approach. Shewhart uses the empirical rule for homogenous data (Wheeler, 1995) which suggests that 3 standard deviations is an appropriate level to set up rules by which we can make consistent judgements about changes in the process – these are called the ‘natural’ or ‘control limits’.
Figure 9. A control chart
The concept of natural limits for a process means that we can distinguish significant changes from insignificant ones: Special Causes from Common Causes of variation. Since the decision rules are based upon characteristics of all homogenous data sets rather than the specific attributes of one particular distribution this is a very robust model.
Shewhart’s general approach to process control is to take a subgroup of the data and extrapolate from the results of this subgroup to make predictions for the population. The two elements of the subgroup to which control are applied are the average and the range. It is appropriate at this point to discuss the relative roles of these two elements. Both are necessary for proper control.
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The average chart is concerned with variation between subgroups. The control limits are based upon 3 sigma for the subgroup average distribution. They are essentially testing if individual subgroup averages vary more than could be expected given the variability within individual subgroups. To this end the control limits are calculated using the average range of subgroup data as an estimate of this short-term variability.
For each subgroup calculate the average of the data and plot on the chart.
The range chart is concerned with variation within subgroups. The control limits are based upon 3 sigma for the subgroup range distribution. They are essentially testing if the variation within each subgroup is similar to the variation within the other subgroups. To this end the control limits are calculated using the average range of subgroup data as an estimate of this within subgroup variability.
For each subgroup calculate the range of the data and plot on the chart.
Shewhart has set down methods of calculation for the control limits for each of the charts. These are based on the assumption of 3 sigma limits for both average and range charts. These will not be discussed in detail here, but are covered in “Six Sigma: Principles and Practice”.
It is worth noting that the choice of 3 sigma is an economic rather than a statistical one. Shewhart (1980) states this in his seminal work on the topic. At this level he considers that it would be economic to find and fix the causes of any point outside the limits but uneconomic to do the same for points inside the limits.
####### 9.4 Out of Control Conditions
The purpose of calculating the control limits is to support the identification of out of control conditions and subsequent process learning. There are a number of rules for detecting out of control conditions (Wheeler, 1995), but for the moment we shall only use rule 1; where a data point falls outside the control limits a special cause is said to have occurred.
Figure 9. Rule 1
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These activities will need to involve all process related local personnel and possibly technical experts. Please note that in the best organisations such activities are not merely reserved for the resolution of special causes but learning from and responding to the chart will be shared between the local team in regular informal meetings around the chart. In this way reduction of common as well as special causes can be undertaken even at the local level.
Do not content yourself with tweaking the process when an out of control condition occurs. The point of SPC is to improve not adjust. There are, of course, occasions when adjustment is the correct short-term response, but consideration should be given to how to make the adjustment unnecessary -or less frequent- in the future.
####### 9.4 SPC Practicalities
SPC can be applied to any process where the output can be measured. However, it makes sense to concentrate on areas of most immediate benefit taking into account things like customer complaints, build problems, high scrap/rework, high quality costs etc. The characteristics to control using SPC are the same ones to which priority is given for any form of control. Those that are important to the customer and those with which we are presently experiencing difficulties.
Control limits are calculated using subgroup data and it is conventional to wait until 20 subgroups have been generated before performing the calculation. It is necessary to recalculate limits once a significant positive change in the process has been identified and cemented in by cause analysis or direct action. Do not recalculate limits as a result of negative changes to the process; find out why they happened and remove the cause to restore the process to its original equilibrium position.
9 Process Capability
####### 9.5 Understanding Process Capability
Once a process is stable, it is necessary to determine whether the outcomes of the process can meet customer expectations
- as described by tolerance limits in most product oriented applications and service level agreements in service oriented applications.
The importance of understanding process capability cannot be overstated. If we are to attempt at any level to design for manufacture we need to understand not only the requirements on the process (effectively our specifications) but also what the process is able to achieve (capability). Without both sides of the equation we are not able to make sensible decisions about how to manage our processes at an appropriate stage in the product lifecycle and we doom ourselves to fixing and fire-fighting when we actually try to make the product. A similar argument could be made for supplier selection.
Capability evaluation is the method by which we determine whether a process is up to the job of meeting the specifications set for it. It is important, before attempting to establish the capability of a process to ensure that the process is stable. The key issue is that if a process is not stable the capability will be constantly changing due to the transient effects of special causes and will hence be uncertain.
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Figure 9. Process capability diagrams
Consider the four processes shown above with the specification limits. Clearly, process A is producing many components both above and below tolerance; process B is offset and is, as a result, producing components below the bottom tolerance limit; process C is producing almost all components within tolerance and process D is operating well within the tolerance limits.
Given the information provided in the above diagram we can act upon the process (resetting process B, for example, or attempting to reduce the spread of process A), without such information we would be making such decisions in the dark. Similarly, this information would be of use in selecting suppliers having these capabilities. If we do not understand the capabilities of processes at an early stage in the product lifecycle we give ourselves little chance of making appropriate decisions about which processes to use as they are and which to work on. If we find this out when we reach volume production we incur additional costs. Indices may be calculated to quantify capability, but these are not dealt with here.
####### 9.5 Capability and Customer Satisfaction
Clearly, customers expect all products to (as a minimum) conform to the specifications which relate to their requirements. Capability is useful in determining such conformity. However, the importance goes well beyond this.
The Taguchi Loss Function (Taguchi, 1986) shows how increasing capability (i. reducing product variation in relation to the tolerance band) can improve customer satisfaction even all products already meet specification. The Loss Function as defined by Taguchi is basically a challenge to the traditional ideas on what constitutes acceptable quality for manufactured products. Figure 9 contrasts Taguchi’s Loss Function and the traditional tolerance (also known as specification)-based approach to product quality.
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Taguchi states that to regard the transition from good to bad as a step change is not logical. He contends that, provided the nominal has been specified correctly, any deviation from this target value will have a detrimental effect on the performance of the product and will therefore cause an overall “loss to society”. This concept is probably one of the more esoteric of Taguchi’s ideas. A good example may be to consider the thickness of a polythene sheet used by farmers to protect crops; if the sheet thickness is low (but within tolerance) it may tear more easily and allow the weather to damage the crops. The costs generated by this failure will be outside the company but very real. Firstly, farmers will incur additional replacement costs; secondly, the reduced crop yield will increase the price in the marketplace, a loss borne by all society.
In many cases it is easier to think of the “loss to society” in terms of a long-term loss to the company. The reduced performance of the product caused by non-optimal parts will cause relative dissatisfaction in customers who will, given sufficient stimulus, take their trade elsewhere. The further from optimum performance we deviate the quicker will be their defection.
C
C
B
B
US plant
Figure 9. Sony TV production.
The above diagram is an illustration of the loss function as a long-term loss to the company, and appeared in a Japanese newspaper called “The Asahi” in 1979. The article discussed the preference of American consumers for television sets built by Sony in Japan over those built at an identical plant in the USA. The key performance characteristic is colour density. The ‘A’ band represents excellent colour density; the ‘B’ band good colour density and the ‘C’ band acceptable colour density. Outside of the limits of the ‘C’ bands colour density is deemed unacceptable and the TV is considered a reject.
Clearly from the figure, Sony Japan was producing defects whilst the American plant was not. However, the key fact is that the chances of having an ‘A’ or ‘B’ grade TV from the Japanese plant was much greater than the American one where the odds of getting any grade were roughly similar. The “in tolerance is OK” attitude was costing a lot of sales for the American plant. The Taguchi belief that variation from the nominal is expensive seems much closer to the truth in this case.
Taguchi (1986) states that the loss function takes the quadratic form shown above for all “nominal the best” type characteristics and the appropriate half of that shape for “bigger the better” and “smaller the better” features. Whether this is in fact strictly the case is debatable. However the principle that deviation from the target is expensive regardless of tolerances and that the rate of deterioration of the situation increases with distance from the target is sensible.
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The principal implications of the loss function for the way we do business is that it gives a financial credibility to the argument for continuous improvement of processes beyond the current specification limits and it firmly indicates the reduction of variation in our processes as the way to increased profitability and success. In fact, as Wheeler (1995) notes, this effectively creates a new definition of world class quality, one with capability at its heart. No longer is in specification sufficient, the new definition is:
“On target with minimum variation”
9 Managing Variation Reduction Using SPC
####### 9.6 Introduction
It is a sad fact that many attempts to implement SPC are unsuccessful (Wood, 2002). These failures are not due to shortcomings in the technique but in the way in which the system is implemented and managed. Typical problems would be lack of commitment from middle and senior management; union opposition; poor response to problems etc. These kinds of issues would be familiar to anyone who has tried to drive through any type of change initiative in an organisation. This is because SPC is a long term change to the way in which an organisation does business, even in the way it thinks. If you fail to recognise that and treat it as another system implementation you will be doomed to making minimal progress with your application.
####### 9.6 The 14 Points
It is significant to understand that SPC is a cornerstone of the 14 points -no successful business transformation along these lines could take place without understanding the principles of variation. It is also true to say that the principles of respect for the individual and the right to take pride in your work are essential for the successful introduction of SPC as a business driver. Similarly the practical aspects of SPC can be seen as an application of the Plan, Do, Study, Act cycle. It is a mistake to see SPC as isolated from the rest of Deming’s philosophy, it will only work to its full potential as a business transformation tool (rather than a trouble-shooting/problem solving technique) if applied in the context of sound management practice and appropriate company culture. This is not to say that you have to have a full Deming transformation cemented in place before beginning to use SPC, it can be a powerful part of the transformation process in itself.
####### 9.6 Juran’s Trilogy
Juran’s Trilogy (Juran, and Gryna, 1998) effectively illustrates the steps in variation reduction.
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This forms an excellent template for managing variation reduction at both a process and an organizational level.
####### 9.6 People Issues
People are the most important element of the implementation of SPC. Don’t forget, this is a way of thinking rather than just a tool. The system will stand or fall on the actions of people rather than just how accurately the charts are developed and interpreted. You are attempting to change behaviour here. Remember these salient points:
- The best people to improve a process are those who are close to it.
- Management need to be actively involved, especially in reduction of common causes.
- Company ‘experts’ can be useful in kick-starting and supporting the process but they should not own it (or be perceived by others to own it).
9 Benefits of SPC
These are, obviously dependent upon the quality of the approach but should include the following:
- A more consistent product or service leading to happier customers (Caulcott, 1996)
- Reduced waste and thus lower costs and better profits (Rungtusanatham et al, 1999).
- Better management decisions at all levels of the business (Wheeler, 1993).
- A more consistent and controlled workplace.
- Pride for all those who are now allowed to be in control of their processes, rather than having the processes run them and living in fear of the next big problem.
9 Summary
Processes are crucial components of how an organization operates and delivers value to its customers. Processes need to be considered holistically as a system, since all business processes will affect each other. The aim of the system needs to be clearly understood, and the impact of the various processes upon this defined in order to establish goals for those processes. Once it is clear what a process should do it is necessary to establish a process which is both in control and capable of delivering its key outputs to the satisfaction of customers (be they internal or external). To paraphrase Deming:
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Process management is a way of thinking with some tools attached: not the other way around.
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90-109 - The legal system of Ghana is a unique and dynamic framework that has evolved
Course: Ghana Legal System (FLAW 103)
University: University of Ghana
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