The main purpose of the control charts is to monitor the health of the process and this is done by monitoring both, accuracy and the precision of the process. The control charts is a tool that helps us in doing so by plotting following two control charts simultaneouslyfor accuracy and precision. Control chart for mean (for accuracy of the process) Control chart of variability (for Precision of the process) E.g. X-bar and R chart (also called averages and range chart) and X-bar and s chart
The Accuracy and the precision
We all must be aware of the following diagram that explains the concept of precision and accuracy in that analytical development.
If you are hitting the target all the time at the bull’s eye is called as accuracy and if all your shots are concentrated at the same point then it is called as Precision.
Figure-1: Accuracy and precision
You are off the target (inaccurate) all the time but your shots are concentrated at the same point i.e. there is not much variation (Precision)
It is an interesting case. Your shots are scattered around the bull’s eye but, on an average your shots are on the target (Accuracy), this is because of the average effect. But your shots are wide spread around the center (Imprecision).
In this case all your shots are off target and precision is also lost.
Before we could correlate the above concept with the manufacturing process, we must have a look at the following diagram that explains the characteristics of a given manufacturing process.
Figure-2: Precision and Accuracy of a manufacturing process
The distance between the average of the process control limits and the target value (average of the specification limits) represents the accuracy of the process or how much the process mean is deviating from the target value.
Whereas the spread of the process i.e. the difference between LCL and UCL of the process represents the precision of the process or how much variation is there in the process.
Having understood the above two diagrams, it would be interesting to visualize the control chart patterns in all of the four cases discussed above. But, before that let’s have a look at the effect of time on a given process i.e. what happens to the process with respect to the time?
As the process continue to run, there will be wear and tear of machines, change of operators etc. and because of that there will be shift and drift in the process as represented by four scenarios described in the following diagram.
Figure-3: Process behavior in a long run
A shift in the process mean from the target value is the loss of accuracy and change in the process control limits is the loss of precision. A process shift of ±1.5σ is acceptable in the long run.
If we combine figure-1 and figure-3, we get the figure-4, which enable us to comprehend the control charts in a much better way. This gives picture of the manufacturing process in the form of control charts in four scenarios discussed above.
Figure-4: Control chart pattern in case of precision and accuracy issue
Above discussion is useful in understanding the reasons behind the importance of the control charts.
Most processes don’t run under statistical control for long time. There are drifts and shift in the process with respect to the time, hence process needs adjustment at regular interval.
Process deviation is caused by assignable and common factors/causes. Hence a monitoring tool is required to identify the assignable causes. This tool is called as control charts
These control charts helps in determining whether the abnormality in the process is due to assignable causes or due to common causes
It enables timely detection of abnormality prompt us to take timely corrective action
It provides an online test of hypothesis that the process is under control
Helps in taking decision whether to interfere with process or not.
H0: Process is under control (common causes)
Ha: Process is out of control (assignable causes)
6. Helps in continuous improvement:
Figure-5: Control Charts provide an opportunity for continuous improvement
KEYWORDS: QbD, 4A’s, DoE, FMEA, Design space, control strategy
QbD is of paramount importance for the patient safety but there is another side of the coin. QbD is also required for timely and uninterrupted supply of medicines into the market. This timely uninterrupted supply is required to fulfill the 4A’s requirement of any Regulatory body as it is their main KRA. But the manufacturers are given an impression that the patients are their main customer, which is not true. Due to which QbD implementation by generic API manufacturers has not picked up. This article tries to tell that the real customer is not patients but the Regulatory bodies who on the behalf of patients are dealing with manufacturer. Hence Regulators need to tell the manufacturer that QbD is required not only for the patient safety but also for meeting the 4A’s requirement, which is equally important. This article tries to correlate the effect of inconsistent manufacturing process on the KRA of the Regulatory bodies and makes a business case out of it. It will help in developing a strong customer-supplier relationship between the two parties and can trigger the smooth acceptance of QbD by generic players. This article also presents the detail sequence of steps involved in QbD using by a process flow diagram.
Nowadays, Quality by design (QbD) is an essential and interesting topic in the pharmaceutical development, be it for drug substance or drug product. Various guidelines have been published by different Regulatory agencies.[i] There is a plethora of literature available on the QbD approach for the process development[ii],[iii] of drug substance, drug product and analytical method development[iv]. Most of the available literature mainly focus on patient safety (QTPP) but if QbD has to sails through, then the generic manufacturer must know why and for whom it is required (apart from patients) and what is there in for them? They should not be taking regulators as an obstacle to their business but as a part of their business itself. There has to be business perspective behind QbD, as everything in this world is driven by economics. It has to be win-win situation for Regulators and the manufacturers. This means that, there has to be synchronization of each other’s expectation. This synchronization will be most effective if API manufacturer’s (i.e. supplier’s) consider Regulators as their customer and try to understand their requirement. In this context it is very important to understand the Regulator’s expectation and their responsibility towards their fellow countrymen.
Sole responsibility of any Regulator towards its country is to ensure not only acceptable (quality, safety and efficacy) and affordable medicines but also they need to ensure its availability (no shortage) in their country all the time. Even that is not enough for them; those medicines must be easily accessible to patients at their local pharmacies. These may be called as 4A’s and are the KRA of any Regulatory body. If they miss any one of the above ‘4As’, they will be held accountable by their Government for endangering the life of the patients.
In earlier days when the penetration of health services to large section of the society was not there, the main focus of Regulators was on the quality and price of the medicines. During those days margins were quite high and the effect of reprocessing and reworks on manufacturer’s margins were not much. So Regulators were happy as they were getting good quality at best price for their citizens. Gradually the health services gained penetration in to the large section of the society in developed countries and as a result they needed more and more quantities of medicine at affordable price. The KRA of Regulators changed from “high quality and low price” to “quality medicine at affordable price which is available all the time at the doorstep of patients”. Another event that led to the further cost erosion was the arrival of medical insurance and tender based purchasing system in hospitals. Increased demand made manufacturer to increase their batch size but because of insurance and tender based purchasing system, now they don’t have the advantage of high margins and couldn’t afford batch failures/reprocessing anymore. But now, these wastages led to erratic production and irregular supply of medicine in the market, thereby creating a shortage. This affected the KRA (4A’s) of the regulatory bodies; hence they were forced to interfere with the supplier’s system. They realized that in order to ensure their 4A’s, there has to be a robust process at manufacturer’s site and if it is done the medicines would automatically be available in their country (no shortages) and will be accessible to all patients at affordable price. This process robustness is possible with the use of some proven statistical tools like six sigma and QbD during the manufacturing of an API. This path to robust process was shown by the Regulators in the form of Q8/Q9/Q10/Q11 guidelines1 where QbD was made mandatory for formulators but and it is strongly recommended for API manufacturer and soon it would be made mandatory. While making QbD mandatory, they are emphasizing on how QbD is related to patient safety and how it will make the process robust for the manufacturers which in turn would eliminate the fear of audits. Regulators are right but somewhere they missed to communicate the business perspective, that was behind the QbD implementation i.e. manufacturers were not having much clue about the Regulator’s KRA and as a result a customer-supplier relationship never developed.
Figure 1: Regulator’s unsaid expectations
Manufacturer’s point of view
As Regulators were insisting on QbD, manufacturers have their own constraints in plant due to inconsistency of the process (Figure 2). As Regulator’s emphasis was on the patient’s safety rather than 4A’s, manufacturer took patients as their customer instead of Regulators and they make sure that there is no compromise with the quality of the medicines to delight the customer ie, patients. It doesn’t matter to manufacturer, if the quality is achieved by reprocessing/rework as far as the material is of acceptable quality to the customers. Due to this misconception about who the real customer is, 4A’s got neglected by the manufacturer.
Another problem is the definition of quality perceived by two parties. Quality of an API from the customer’s perspective has always been defined with respect to the patient safety (i.e. QTPPs which is indeed very important) but for the manufacturer quality meant only the purity of the product as he enjoyed handsome margin.
Coming to prevailing market scenario, the manufacturers doesn’t have luxury to define the selling price, now the market is very competitive and the price of goods and services are dictated by the market, hence it is called as market price (MP) instead of selling price (SP). This lead to the change in the perception of quality, now quality was defined as producing goods and services meeting customer’s specification at the right price. The manufacturers are now forced to sell their goods and services at the market rate. As a result the profit is now defined as the difference of market rate and cost of goods sold (COGS). If manufacturing process is not robust enough then COPQ will be high resulting in high COGS and either (patient or manufacturer) of the party has to bear the cost. According to Taguchi, it is a loss to the society as a whole as neither of the party is getting benefitted. If these failures are more frequent it leads to production loss and as a result timely availability of the product in the market is not there and manufacturer is not able to fulfill the 4A’s criteria of the customer. This not only leads to loss of market share but also loss of customer’s confidence and customer in turn would look for other suppliers who can fulfill their requirements. This is an intangible loss to the manufacturer.
The COPQ has direct relationship with the way in which process has been developed. There are two ways in which a process could be optimized (Figure 3). It is clear from the Figure 3 that if one focus on the process optimization, it will lead to less COPQ and process would be more robust in terms of quality, quantity and timelines thereby reducing the COGS by elimination COPQ. This raises another question, how process optimization is different from product optimization and how it is going to solve all problems related to inconsistency? This can be understood by understanding the relationship between QTPPs/CQAs and CPPs/CMAs. As a manufacturer we must realize that any CQA (y) is a function of CPPs & CMAs (x) i.e. the value of CQA is dictated by the CPPs/CMAs and not vice versa (Figure 4 & 7). It means that by controlling CPPs/CMAs we can control CQAs but in order to do this we need to study and understand the process very well. This will help in quantifying the effect of CPPs/CMAs on CQAs and once it is done, it is possible to control the CQAs at a desired level just by controlling the CPPS/CMAs. This way of process development is called as process optimization and QbD insists on it. Another important concept associated with process optimization is the way in which in-process monitoring of the reaction is done. Traditionally, a desired CQA is monitored for any abnormality during the reaction whereas process optimization methodology it is required to monitor the CPP/CMA (Figure 4) which is responsible for that CQA. Hence it requires a paradigm shift in which the process is developed and control strategy is formulated by a manufacturer if the focus is on the process optimization.
Figure 3: Two ways of optimization
From the above discussion, it is clear that the real customer for a generic manufacturer is not the patients but the Regulators. This is because patients can’t decide and they don’t have capability to test the quality of the medicines, for them all brands are same. Hence Regulators comes into the pictures, who on the behalf of patients are dealing with manufacturers because they have all means and capability of doing so. Going by the Figure 5, patients are the real customer for the Regulators and who in turn are the customer for the manufacturer. In business sense, patients are just the end user of the manufacturer’s product once the product is approved by Regulators for use.
Figure 4: Relationship between CQAs and CPPs/CMAs
As it is clear that the Regulators are the real customers for the manufacturer and with the current inefficient process, manufacturer is not helping his customer in meeting their goal (4A’s). They can now understand the relationship between his inefficient manufacturing process and the customer’s KRA (Figure 6). In addition, they can clearly visualize the advantage of the process optimization over product optimization and how QbD can act as an enabler in developing a robust process thereby fulfilling the requirement of 4A’s . This will encourage manufacturer to adopt QbD because now it makes a strong business case for them for retaining the existing market and also as a strategy for entering the new market. This is a win-win situation for both the parties. Therefore, QbD should be pursued by manufacturer not because of the regulatory fear but as a tool for fulfilling the customer’s KRA which in-turn would benefit manufacturer by minimizing COPQ. In addition, it helps in building customer’s trust which is an intangible asset for any manufacturer. This will enable the manufacturers to accept Regulators as their customer rather than as an obstacle. This would result in better commitment from manufacturers about implementing QbD because the definition of customer as defined by Mahatma Gandhi is very relevant even today.
“A customer is the most important visitor on our premises. He is not dependent on us. We are dependent on him. He is not an interruption in our work. He is the purpose of it. He is not an outsider in our business. He is part of it. We are not doing him a favor by serving him. He is doing us a favor by giving us an opportunity to do so.”
― Mahatma Gandhi
Figure 5: Dynamic Customer-Suppliers relationship throughout the supply chain
Figure 6: Manufacturer perception after understanding customer-supplier relationship
Manufacturer in customer’s shoes:
Another reason provided by the manufacturer for inconsistency is the quality of KSM supplied by their vendors and any quality issue with KSM will affect the quality of the API as shown by Figure 7 and equation 2. Till now manufacturer was acting as a supplier to Regulators but now manufacturer is in the shoes of a customer and can understand the problem faced by him because of the inconsistent quality of KSM from his supplier (Figure 5, Table 1). Now manufacturer can empathize with Regulatory bodies and is in a position to understand the effect of their process on his customer’s KRA(Figure 6). Table 1 is equally applicable to the relationship between manufacturer and the Regulatory bodies.
Table 1: Effect of process inconsistency from supplier/manufacturer on API quality
Consider Case-1 (Table 1) which represents the ideal condition, where process is robust at both sides. Whereas Case-2 and Case-3 represents an inconsistent process at either of the party and this inconsistency would reflect as an inconsistency in the quality of the API at manufacturer’s site. This would result in an unsatisfied customer (Regulator) and loss of market to someone else. Lastly, an inconsistent process from both the side (Case-4) would result in a disaster situation where it would be difficult for a manufacturer to control the quality of the API because the variance from both the sides would just add up (equation 2). In this case customer can’t even think of getting material from manufacturer as it would pose a threat to the patient’s life and no regulatory body would allow that.
Someone can argue that if consistency is an issue from supplier (Case 3) then they would negotiate with them for cherry-picking the good batches, but no supplier would do the cherry-picking without any extra cost, which in turn would increase the cost of the API. Another consequence of this handpicking is the interruption in the timely supply of KSM which will result in delay in the production at manufacturer’s site. This would result in increased idle time of resources thereby increased overheads which ultimately would reflect in increased API cost. Apart from increased cost it would also result in sporadic supply to the customer. Another viable option for circumventing the inconsistency at supplier’s end is to do a reprocessing of KSM at the manufacturer’s site. Obviously this is not the viable solution as it would escalate the COGS. Hence there is no choice but to take your supplier in confidence and make him understand the implication of his product quality on your business and how his business in-turn would get affected by it. Best solution is to discuss with the supplier and ask him for improving his process (if supplier has the capability) or help them in improving his KSM process (if manufacturer has the capability).
Note: Apart from robust process, Regulators are also auditing the manufacturer’s site for the safety and the ETP facility. It is being done again for the same reason of ensuring the continuous supply of medicines to their country.
How inconsistency of the process affects the quality? And How QbD will help in getting rid of this inconsistency?
Realizing that we need to have a consistent quality and uninterrupted production is not enough, as a manufacturer we must understand the various sources of inconsistency and how it can affects the quality of the API.
Any chemical reaction that is happening in a reactor is a black box (Figure 7) for us and there are three kinds of inputs that go into the reactor. The first input known as MAs are chemical entities that go into the reactor (KSM, reagents and other chemicals). The second input known as PPs are the reaction/process parameters that can be controlled by the manufacturer and third being the environmental/external factors like room temperature, age of the equipment, operators etc. that cannot be controlled. As variance (σ2) has an additive property, hence inconsistency from all the three types of factors amplifies the inconsistency of the product quality. The variation caused by the third type i.e. by external factors is called as inherent variation and we have to live with it. At most the effect of these nuisance factors could be nullified by blocking and randomization during DoE studies. Because of this inherent variation, yield or any other quality parameters are reported as a range instead of a single number. But the variation due to other two types of factors (MAs and PPs) could be controlled by studying its effect on product attributes (QAs) by using a combination of some risk analysis tools and some statistical tools for optimization. The combination of risk based assessment of MAs and PPs and use of statistical tools as DoE/MVA for optimizing the effect of MAs and PPs on QAs is called as QbD. Hence QbD is the tool that manufacturers are looking for, to eliminate the inconsistency in their product thereby fulfilling the customer’s expectations.
The variance that is being shown by Figure 7 represents the variation only at a single stage. Consider a multi-step synthesis (most common scenario) and in such scenarios the total variance at the API stage would be the culmination of variance from all the stages, resulting in a total out of control process as shown below by equation 3.
Figure 7: Cumulative effect of variance from various sources on the variance of API quality
At what stage of product development QbD required to be applied?
The traditional approach of process development of any API is focused more on filing the DMF at earliest. As a result of this improper process development there are failures at commercial scale and process comes back to R&D for fine tuning. But if the process is developed with QbD approach at R&D stage itself, certainly it would take more time initially, but its worth investing the time as there will be less failures or no failures at commercial scale and process could be scaled up in very less time. This will reduce the reprocessing and rework at commercial scale thereby minimizing the COPQ, a win-win situation for all as depicted in Figure 8.
Figure 8: Risk and reward associated with QbD and traditional approach
[i]. (a) ICH Q8 Pharmaceutical Development, (R2); U.S. Department of Health and Human Services, Food and Drug Administration, Center for Drug Evaluation and Research (CDER): Rockville, MD, Aug 2009. (b) ICH Q9 Quality Risk Management; U.S. Department of Health and Human Services, Food and Drug Administration, Center for Drug Evaluation and Research (CDER): Rockville, MD, June 2006. (c) ICH Q10 Pharmaceutical Quality System; U.S. Department of Health and Human Services, Food and Drug Administration, Center for Drug Evaluation and Research (CDER): Rockville, MD, April 2009. Understanding Challenges to Quality by Design, Final deliverable for FDA Understanding Challenges to QbD Project, December 18, 2009.
[ii]. (a) Jacky Musters, Leendert van den Bos, Edwin Kellenbach, Org. Process Res. Dev., 2013, 17, 87. (b) Zadeo Cimarosti, Fernando Bravo, Damiano Castoldi, Francesco Tinazzi, Stefano Provera, Alcide Perboni, Damiano Papini, Pieter Westerduin, Org. Process Res. Dev., 2010, 14, 805. (c) Fernando Bravo, Zadeo Cimarosti, Francesco Tinazzi, Gillian E. Smith, Damiano Castoldi, Stefano Provera, Pieter Westerduin, Org. Process Res. Dev., 2010, 14, 1162.
[iii]. (a) Sandeep Mohanty, Amrendra Kumar Roy, Vinay K. P. Kumar, Sandeep G. Reddy, Arun Chandra Karmakar, Tetrahedron Letters, 2014, 55, 4585. (b) Sandeep Mohanty, Amrendra Kumar Roy, S. Phani Kiran, G. Eduardo Rafael, K. P. Vinay Kumar, A. Chandra Karmakar, Org. Process Res. Dev., 2014, 18, 875.
[iv]. Girish R. Deshpande, Amrendra K. Roy, N. Someswara Rao, B. Mallikarjuna Rao, J. Rudraprasad Reddy, Chromatographia, 2011,73, 639.
Before we try to understand the 6sigma concept, we need to define the term “quality”.
What is Quality?
The term “quality” has many interpretations, but this by the ISO definition, quality is defined as: “The totality of features and characteristics of a product or service that bear on its ability to satisfy stated or implied needs”.
If we read between the lines, then the definition varies with the reference frame we use to define the “quality”. The reference frame that we are using here are the manufacturers (who is supplying the product) and the customer (who is using the product). Hence the definition of quality with respect to above two reference frame can be defined as
This “goal post” approach to quality is graphically presented below, where a product is deemed pass or fail. It didn’t matter even if the quality is on the borderline (football just missed the goalpost and luckily a goal was scored).
This definition was applicable till the time there was a monopoly for the manufacturers or having a limited competition in the market. The manufacturers were not worried about the failures as they can easily pass on the cost to the customer. Having no choice, customer has to bear the cost. This is because of the traditional definition of profit shown below.
Coming to current business scenario, the manufacturers doesn’t have luxury to define the selling price, now the market is very competitive and the price of goods and services are dictated by the market, hence it is called as market price instead of selling price. This lead to the change in the perception of quality, now quality was defined as producing goods and services meeting customer’s specification at the right price. The manufacturers are now forced to sell their goods and services at the market rate. As a result the profit is now defined as the difference of market rate and cost of goods sold (COGS).
In current scenario if a manufacturer wants to make a profit, the only option he has is to reduce COGS. In order to do so, one has to understand the components that makes up COGS. The COGS in has many components as shown below. The COGS consist of genuine cost of COGS and the cost of quality. The genuine COGS will always be same (nearly) for all manufacturers, but the real differentiator would be the cost of quality. The manufacturer with lowest cost of quality would enjoy highest profit and can influence the market price to keep the competition at bay. But in order to keep cost of quality at its lowest possible level, the manufacturer has to hit the football, right at the center of the goalpost every time!
The cost of quality involves the cost incurred to monitor and ensure the quality (cost of conformance) and the cost of non-conformance or cost of poor quality (COPQ). The cost of conformance is a necessary evil whereas the COPQ is a waste or opportunity lost.
Coming to the present scenario, with increasing demand of goods and services, manufacturers required to fulfill their delivery commitment on time otherwise their customers would lose market share to the competitors. The manufacturers has realized that their business depends on the business prospects of their customers hence, timely supply of products and services is very important. This can be understood in a much better way using pharmaceutical industry
Sole responsibility of any Regulator (say FDA) towards its country is to ensure not only the acceptable (quality, safety and efficacy) and affordable medicines but they also need to ensure its availability (no shortage) in their country all the time. Even that is not enough for them; those medicines must be easily accessible to patients at their local pharmacies. These may be called as 4A’s and are the KRA of any Regulatory body. If they miss any one of the above ‘4As’, they will be held accountable by their Government for endangering the life of the patients. The point that need to be emphasized here is the importance of TIMELY SUPPLY of the medicines besides other parameters like quality and price.
Hence, the definition of quality again got modified as “producing goods and services in desired quantity which is delivered on time meeting all customer’s specification of quality and price.” A term used in operational excellence called as OTIF is acronym for “on time in full” meaning delivering goods and services meeting customer’s specification on time and in full quantity.
Coming once again to the definition of profit in present day scenario
We have seen that the selling price is driven by the market and hence manufacturer can’t control it beyond an extent. So what he can do to increase his margin or profit? The only option he has is to reduce his COGS. We have seen that COGS has two components, genuine GOGS and COPQ. The manufacturers have little scope to reduce the genuine COGS as it is a necessary evil to produce goods and services. We will see latter in LEAN manufacturing how this genuine COGS can be reduced to some extent (wait till then!) e.g. if we can increase the throughput, we can bring down genuine COGS (if throughput or the yield of the process is improved, which results in less scrap would decrease the RM cost per unit of the goods produced).
But the real culprit for the high COGS is the unwarranted high COPQ.
The main reasons for high COPQ are
Low throughput or yield
More out of specifications (OOS) products which required to be either
Has to be scraped
Inconsistent quality leading to more after sales& service and warranty costs
Biggest of all loses would be the customer’s confidence in you, which is intangible.
If we look at the outcomes of COPQ (discussed above), we can conclude one thing and that is “the process is not robust enough to meet customer’s specifications” and because of this manufacturers faces the problem of COPQ. All these wastages are called as “mudas” in Lean terminology hence, would be dealt in detail latter. But the important
What causes COPQ?
Before we can answer this important question, we need to understand the concept of variance. Let’s take a simple example, say you start from the home for office on exactly the same time every day, do you reach the office daily on exactly same time? Answer will be a big no or a better answer would be, it will take anywhere between 40-45 minutes to react the office if I start exactly at 7:30 AM. This variation in office arrival time can be attributed to many reasons like variation in starting time itself (I just can start exactly at 7:30 every day), variation in traffic conditions etc. There will always be a variation in any process and we need to control that variation. Even in the manufacturing atmosphere there are sources of variation like wear and tear of machine, change of operators etc. Because of this variation, there will always be a variation in the output (goods and services produced by the process). Hence, we will not get a product with a fixed quality attributes, but that quality attribute will have a range (called as process control limits) which need to be compared with the customer’s specification limits (goal post).
If my process control limits are towards the goal post (boundaries of the customer’s specification limits) represented by the goal post, then my failure rate would be quite high resulting in more failures, scrap, rework, warranty cost. This is nothing but COPQ.
Alternatively if my aim (process limits) are well within the goal posts (case-2), my success rate are much higher and I would be have less, scrap and rework thereby decreasing my COPQ.
Taguchi Loss Function
A paradigm shift in the definition of quality was given by Taguchi, where he gave the concept of producing products with quality targeted at the center of the customer’s specifications (a mutually agreed target). He stated that as we move away from the center of the specification, we incur cost either at the producer’s end or at the consumer’s end in the form of re-work and re-processing. Holistically, it’s a loss to the society. It states that even producing goods and services beyond customer’s specification is a loss to the society as customer will not be willing to pay for it. There is a sharp increase in the COGS as we try to improve the quality of goods and services beyond the specification.
The purity of medicine I am producing is > 99.5 (say specification) and if I try to improve it to 99.8, it will decrease my throughput as we need to perform one extra purification that will result in yield loss and increased COGS.
Buying a readymade suit, it is very difficult to find a suit that perfectly matches your body’s contour, hence you end up going for alterations. This incurs cost. Whereas, if you get a suit stitched by a tailor that fits your body contour (specification), it would not incur any extra cost in rework.
Six Sigma and COPQ
It is apparent from the above discussion that “variability in the process” is the single most culprit for the failures resulting in high cost of goods produced. This variability is the single most important concept in six sigma that required to be comprehended very well. We will encounter this monster (variability) everywhere when we will be dealing with six sigma tools like histogram, normal distribution, sampling distribution of mean, ANOVA, DoE, Regression analysis and most importantly the statistical process control (SPC).
Hence, a tool was required by the industry to study the variability and to find the ways to reduce it. The six sigma methodology was developed to fulfill this requirement. We will look into the detail why it is called as six sigma and not five or seven sigma latter on.
Before we go any further, we must understand one very important thing and must always remember this “any goods and services produced is an outcome of a process” also “there are many input that goes into the process, like raw materials, technical procedures, men etc”.
Hence, any variation in the input (x) to a given process will cause a variation in the output (y) quality.
Another important aspect is that the variance has an additive property i.e. the variance from all input is added to give the variance in the output.
How Six Sigma works?
Six sigma works by decreasing the variation coming from the different sources to reduce the overall variance in the system as shown below. It is a continuous improvement journey.
Definition of Quality has changed drastically over the time, it’s no more “fit for purpose” but also include on time and in full (OTIF).
In this world of globalization, market place determines the selling price and manufacturers either have to reduce their COPQ or perish.
There is a customer specification and a process capability. The aim is to bring the process capability well within the customer’s specifications.
Main culprit of out of specification product is the unstable process which in turn is because of variability in the process coming from different sources.
Variance has an additive property.
Lean is tool to eliminate the wastages in the system and six sigma is a tool to reduce the defects from the process.
A process was running in a chemical plant. The final stage of the process was the crystallization, which gave the pure product. There were two crystallizer used for the purpose, each operated by a different individual. The SOP says that crystallizer has to be maintained between 30-40°C and for 110 to 140 minutes. The data for a month is captured below
In order to understand the process, I-MR control chart was plotted (for simplicity, R-chart is not captured).
As we have learned from the earlier blog, the alternate points above and below the central line represents some short of stratification (see the short connecting arms and the concentration of data points in zone B and C).
We plotted the histogram of the above data set and kept on increasing the number of classes. What we saw was the emergence of a bimodal distribution as we kept on increasing the number of classes.
So, one thing was sure, there were two processes running in the plant. Now question that was to be answered was “What is causing this stratification?”
We started with crystallizer, as soon as we plotted the simple run chart of the process with groups using Minitab®, we could see the difference. Crystallizer-2 was always giving better yield. This should not happen because both the crystallizer were identical and were connected to same utilities. Then we thought about the different operators might be the reason for this behavior, as this was the only factor that was different for both the crystallizer.
When we plotted the same run chart with grouping, but this time operator was used for the purpose of grouping. We got the same result as was found with the crystallizers, the operator-2 working on the crystallizer-2 was producing more quantity of the product. This run chart is not shown here.
We further grilled down to the operating procedure adopted by the two operators. We studied temperature and the maintenance time using scatter plot. The results are shown below
Finally, it was found that operator-2 was maintaining the crystallizer-2 at the lower end of the prescribed temperature and for longer duration. Hence, specification for temperature and the maintenance time was revised.
Visual Inspection of the Control Charts for Unnatural Patterns
Besides above famous rules, there are patterns on the control charts that needs to be understood by every quality professionals. Let’s understand these patterns using following examples. It would be easier to understand them if we can imagine the type of distribution of the data displayed on the control chart.
Case-1: Non-overlapping distribution
As a production-in-charge, I am using two different grades of raw material with different quality attributes (non-overlapping but at the edge of the specification limits) and I am assuming that the quality attributes of the final product will be normally distributed i.e. I am assuming that most of final product will hit the center of the process control limits.
If the quality of the raw material is detrimental to the quality of the final product then my assumption about the output is wrong. Because the distribution of the final product quality would take a bimodal shape with only few data at the junction of the distribution. Same information would be reflected onto the control chart with high concentration of data points near the control limits and fewer or no points near the center. Here is the control chart of the final product
In this completely non-overlapping distribution, there will be unusual long connecting arms in the control charts. There will be absence of points near the central line.
If we plot the histogram of this data set and go on increasing the number of classes, the two distribution would get separated.
So, whenever we see a control charts with the data points concentrated towards the control limits and no points at the center of the control charts, immediately we should assume that it is a mixture of two non-overlapping distribution. Remember long connecting arms and few data points at the center of the control chart.
Case-2: Partially overlapping distribution
Assume this scenario: A product is being produced in my facility in two shifts by two different operators. Each day I have two batches, one in each shift. There is a well written batch manufacturing record indicating that the temperature of the reactor should be between 50 to 60 °C. The control chart of a quality attribute of the product is represented by following control chart.
We can see that the data points on the control chart are arranged in an alternate fashion around the central line. The first batch (from the 1st shift) is below the central line and next batch (from the 2nd shift) is above the central line. This control chart shows that even we are following the same manufacturing process, there is a slight difference in the process. It was found that the 1st shift in-charge was operating towards 50 °C and the 2nd shift in-charge was operating towards 60 °C. This type of alternate arrangement is indication of stratification (due to operators, machines etc.) and is characterized by short connecting arms.
There are the cases of partially overlapping distribution resulting in a bimodal distribution, which means that there will be few points in the central region of the control charts but, majority of the data points would be distributed in zone C or B. In such cases, it would be appropriate to plot the histogram with groups (like operator, shift etc).
Case-3: Significant Overlapping distribution
If there is significant overlap between the two input distributions then it would be difficult to differentiate them in the final product and the combined distribution would give a picture of a single normal distribution. Suppose the operators in the above case-2 were performing the activity at 55 °C and 60 °C respectively. This would result in an overlapping distribution as shown below
Case-4: Mixture of unequal proportion
As a shift-in-charge, I am running short of the production target. What I did to meet the production target was to mix the current batch with some of the material produced earlier for some other customer with slightly different specification. I hoped that it wouldn’t be caught by the QA!. The final control chart of the process looked like
We can see from the control chart that if two distributions are mixed in an unequal proportions then the combined distribution would be an unsymmetrical distribution. In this case one-half of the control chart (in present case the lower half) would have maximum data points and other half would have less data points.
Case-5: Cyclic trends
If one observe a repetition of the trend on the control chart, then there is a cyclic effect like sales per month of the year. Sales in some of the specific months are higher than the sales in some other months.
Case-6: Gradual shift in the trend
A gradual change in the process is indicated by the change in the location of the data points on the control charts. This chart is most commonly encountered during the continuous improvement programs when we compare the process performance before and after the improvement program.
If it is observed that this shift is gradual on the control charts, then there must be a reason for the same, like wear and tear of machine, problem with the calibration of the gauges etc.
If one observe that the data points on the control charts are gradually moving up or down, then it is a case of trend. This is usually cause by gradual shift in the operating procedure due to wear and tear of machines, gauges going out of calibration etc.
Summary of unnatural pattern on the control charts
Symptom in control chart
Large shift (strays, freaks)
Sudden and high change
Points near and or beyond control limits
Smaller sustained shift
Sustained smaller change
Series of points on the same side of the central line
A continuous changes in one direction
Steadily increasing or decreasing run of points
Small differences between values in a long run, absence of points near the control limits
A long run of points near the central line on the both sides
Saw-tooth effect, absence of points near the central line
A run of consecutive points on both sides of central line, all far from the central line
Systematic Variation or stratification
Regular alternation of high and low values
A long run of consecutive points alternating up and down
In general, I have seen that people are plotting the control chart of the final critical quality attribute of a product (or simply a CQA). But the information displayed by these control charts is historical in nature i.e. the entire process has already taken place. Hence, even if the control chart is showing a out of control point, I can’t do anything about it except for the reprocessing and rework. We often forget that these CQAs are affected by some critical process parameters (CPPs) and I can’t go back in time to correct that CPPs. The only thing we can do is to start a investigation.
HENCE PLOTTING CONTROL CHARTS IS LIKE DOING A POSTMORTEM OF A DEAD (FAILED) BATCH.
Instead, if we can plot the control chart of CPPs and if these control charts shows any out of control points, IMMEDIATLY WE CAN FORECAST THAT THIS BATCH IS GOING TO FAIL or WE CAN TAKE A CORRECTIVE ACTION THEN AND THERE ITSELF. This is because CPPs and CQA are highly correlated and if CPPs shows an out of control point on its control chart, then we are sure that that batch is going to fail.
Hence, the control charts of CPPs would help us in forecasting about the output quality (CQA) of the batch because, the CPP would fail first before a batch fails. This will also help us in saving the time that goes into the investigation. This is very important for the pharmaceutical industry as everyone in the pharmaceutical industry knows, how much time and resource goes into the investigation!
I feel that we need to plot the control chart of CPPs along with the control chart of CQA, with more focus on the control chart of CPPs. This will help us in taking timely corrective actions (if available) or we can scrap the batch, saving downstream time and resource (in case no corrective action available).
Another advantage of plotting the CPP is for looking for the evidence that a CPP is showing a trend and in near future it will cross the control limits as shown below, this will warrant a timely corrective action of process or machine.
This is the most important topic to be covered in the 7QC tools. But in order to understand it, just remember following point for the moment as right now we can’t go into the details
Two things that we must understand beyond doubt are
There is a customer’s specifications, LSL & USL (upper and lower specification limits)
Similarly there is a process capability, LCL & UCL (upper and lower control limits)
The Process capability and customer’s specifications are two independent things however, it is desired that UCL-LCL < USL-LSL. The only way we can achieve this relationship is by decreasing the variation in the process as we can’t do anything about the customer’s specifications (they are sacrosanct).
If a process is stable, will follow the bell shaped curve called as normal curve. It means that, if we plot all historical data obtained from a stable process – it will give a symmetrical curve as shown below. The σ represents the standard deviation (a measurement of variation)
The main characteristic of the above curve is shown below. Example, the area under ±2σ would contain 95% of the total data
Any process is affected by two types of input variables or factors. Input variables which can be controlled are called as assignable or special causes (e.g., person, material, unit operation, and machine), and factors which are uncontrollable are called noise factors or common causes (e.g., fluctuation in environmental factors such as temperature and humidity during the year).
From the point number 2, we can conclude that, as long as the data is within ±3σ, the process is considered stable and whatever variation is there it is because of the common causes of variation. Any data point beyond ±3σ would represent an outlier indicating that the given process has deviated or there is an assignable or a special cause of variation which, needs immediate attention.
Measurement of mean (μ) and σ used for calculating control limits, depends on the type and the distribution of the data used for preparing control chart.
Having gone through the above points, let’s go back to the point number 2. In this graph, the entire data is plotted after all the data has been collected. But, these data were collected over a time! Now if we add a time-axis in this graph and try to plot all data with respect to time, then it would give a run-chart as shown below.
The run-chart thus obtained is known as the control chart. It represents the data with respect to the time and ±3σ represents the upper and lower control limits of the process. We can also plot the customer’s specification limits (USL & LSL) if desired onto this graph. Now we can apply point number 3 and 4 in order to interpret the control chart or we can use Western Electric Rules if we want to interpret it in more detail.
The Control Charts and the Continuous Improvement
A given process can only be improved, if there are some tools available for timely detection of an abnormality due to any assignable causes. This timely and online signal of an abnormality (or an outlier) in the process could be achieved by plotting the process data points on an appropriate statistical control chart. But, these control charts can only tell that there is a problem in the process but cannot tell anything about its cause. Investigation and identification of the assignable causes associated with the abnormal signal allows timely corrective and preventive actions which, ultimately reduces the variability in the process and gradually takes the process to the next level of the improvement. This is an iterative process resulting in continuous improvement till abnormalities are no longer observed in the process and whatever variation is there, is because of the common causes only.
It is not necessarily true that all the deviations on control charts are bad (e.g. the trend of an impurity drifting towards LCL, reduced waiting time of patients, which is good for the process). Regardless of the fact that the deviation is ‘good’ or ‘bad’ for the process, the outlier points must be investigated. Reasons for good deviation then must be incorporated into the process, and reasons for bad deviation needs to be eliminated from the process. This is an iterative process till the process comes under statistical control. Gradually, it would be observed that the natural control limits become much tighter than the customer’s specification, which is the ultimate aim of any process improvement program like 6sigma.
The significance of these control charts is evident by the fact that it was discovered in the 1920s by Walter A. Shewhart, since then it has been used extensively across the manufacturing industry and became an intrinsic part of the 6σ process.
To conclude, the statistical control charts not only help in estimating these process control limits but also raises an alert when the process goes out of control. These alerts trigger the investigation through root cause analysis leading to the process improvements which in turn leads to the decreased variability in the process leading to a statistical controlled process.
Most of the times continuous improvement programs in an organization gradually cease to exists after consultants leaves. This really disappoint me because, it fails despite the fact that, everyone in the organization knows its benefit. The importance of these initiatives are well known across all industry and this is vetted by the number of vacancies for lean and 6sigma professionals on any job portal. (check it on LinkedIn and other job portals).
The main reasons that I have experienced are following
In order to drive a lean or a 6sigma program, you need to be an external consultant or you need to be at some authorative position within the organization (this will ensure that you get the job done). The main purpose is to have a backing from the higher management.
External consultant will be in direct touch with management hence, people would cooperate
Higher position ensures that your message percolates down the line very well.
If you are at middle management, it is going to be difficult for you to implement these changes even if you have the backing of the higher management (unless they are fully involved.
Above scenario can be well understood by drawing an analogy with the stretching of a spring. As long as consultants are there, spring (employees) remain stretched and as soon as they leave, spring comes back to its original position. Hence, these initiatives should focus on changing the mind-set of the employees and have their buy-in prior to the start of any initiative. So, focus of these initiative should be cultural change rather than focusing on the short term financial gain.
“The quality of an organization can never exceed the quality of the minds that make it up.” Harold McAlindon
It took Toyota 30 years to implement, what is now called as TPS!
Usually, these initiatives are not the part of business strategy but, are usually initiated during the crisis situation and once the crisis is over and consultants leaves, it’s over! Spring regains its original state!
Another reason is the lack of trained man-power in the area of lean and 6sigma. I remember when we were searching for a 6sigma black belt, HR team gave us a list of ~65 candidates claiming to have 6sigma/lean expertise. Believe me, we could find only two persons (requirement was ~10-15) out of 65 having the required skill set.
Out of the curiosity we kept on asking people “from where they have got the certification?” Most of them answer that they have undergone 3-5 days of classroom training followed by the examination to get their black belt! That’s true in most of the cases but, I wonder “how a five day course can qualify a person to be a black belt unless you really sweat at the shop-floor with your team?
There is also a lack of trained people within the organization, who can really interview such candidates. Imagine that I want a black belt for my company to drive the initiative, either I have to believe that a candidate knows the concepts or I have to hire someone who can really interview these people. Latter option is much better! These days QbD has become a buzz word in the pharmaceutical industry, just include that in your CV and you will get an immediate raise.
But the main reason that I experienced was the compartmentalized view of an organization, where right hand doesn’t know what left hand is doing.
Let’s assume that the whole company is excited about the initiative, even then it fails! The major reason being the presence of many compartments/departments within the system and they are habituated to work in silos! They remain committed to their KRAs and their work-flow and doesn’t know much about the processes of the department from where they are receiving the inputs or how their processes affects the processes of the next department (internal customer). These silos are becoming the vertical coffins for the organization. Before we go any further, let’s understand “what is business?” or “How business is being carried out to generate revenue?”
The central planning team, based on the monthly forecast, gives the targets to all vertical coffins for that month. All vertical coffins then perform their duty in silos to complete their target.
Now, if we really look at the business, it is not the departments that makes the product and generates the revenues instead it is the culmination of a process-flow encompassing the entire organization. In order to give a clarity, let’s look at the following example
It is just a flow of the process across the departments that adds value to the raw material for the customers. The most important point is that these processes are being performed by the shop-floor people and not by the management. What I meant to say is that, the material flow happens in horizontal direction at the bottom of the pyramid but processes are being managed vertically and in silos. As a result there is an information gap between the decision point and the execution point. So the shop-floor people are no better than the robots who are busy in meeting their targets. In this scenario we just can’t implement the continuous improvement unless these vertical coffins are dismantled and the gap between information and the material flow diminishes. This can only be made possible through delegation and by empowering the shop-floor people.
Wait a minute! What are you talking about? If we are going to delegate our duty, then what we are going to do? What will be our role? These are the thought that may pop-up in the minds of higher management.
My dear friend, just leave these daily operation to the middle management, do something new, read something new, think something new or make some new strategy for the company. Give some new direction to the company with your vast experience. This is because if you get involved in day-to-day operations, then there is no difference between a shift in-charge and you! If you act like this, ideally your CTC should be added to the overhead of the product! Isn’t it?
Get a right person in the middle management and just get the daily updates from him, interfere when needed. I read somewhere (can’t recall) that as you grow higher in the management, you should distance yourself from the day-to-day operations and focus more on mentoring and drawing future roadmap for the company.
Once this conducive environment is established i.e. delegation and empowering the shop-floor people, it would easier to implement any continuous improvement initiative in the organization, and this is because the real action (process, value addition) happens at the shop-floor. Even if you look at most of the lean and 6sigma tools, you would find that it is being implemented successfully at the shop floor by the shop floor people!