I‑MR Control Chart: A Tool for Judging the Health of the CurrentManufacturing Process of an API and for Setting the Trial ControlLimits in Phase I of the Process Improvement†

K. Mukundam,§ Deepak R. N. Varma,§ Girish R. Deshpande,‡ Vilas Dahanukar,‡

and Amrendra Kumar Roy*,‡

‡Custom Pharmaceutical Services, Dr. Reddy’s Laboratories Ltd., Bollaram Road, Miyapur, Hyderabad, Andhra Pradesh,PIN 500 - 049, India§Chemical Technical Operations, Dr. Reddy’s Laboratories Ltd., Unit-II, Plot Nos. 110 and 111, S.V. Co-operative, Industrial Estate,Bollaram, Jinnaram Mandal, Medak District, PIN 502 - 325, India

*S Supporting Information

ABSTRACT: It has been observed that the main focus during the process development and manufacturing of an API is to meetthe customer’s specifications (LSL and USL) rather than estimating and improving the natural control limits (LCL and UCL) ofthe process. It results in the overlap of the natural control limit and customer’s specification, which in turn increases the chance offailure with respect to the customer’s specifications. A better approach is to work on decreasing the variability of the process sothat natural control limits become much tighter than customer’s specification. The statistical control charts not only help inestimating these internal/natural control limits but also raises an alert when the process goes out of control. These alerts triggerthe investigation through root cause analysis leading to the process improvements which in turn lead to the decrease in variabilityof the process. This process continues till inherent variability of the process is due to common causes only and cannot beattributed to assignable causes. At this point, the natural control limits of the process can be taken as internal specification for anoutput quality parameter.

1. INTRODUCTION

The research-driven pharmaceutical company aims to producean API which either meets or exceeds the customer specificationon the quality parameter1 although the real challenge is tomaintain that consistency from batch to batch.2 Due to certainunavoidable reasons such as wear and tear of machines, a newoperator, a new supplier, fluctuation in atmospheric conditions,etc. could disturb the process resulting in a variation in the outputattribute of an API (Figure 1).

Any chemical process is affected by two types of input variablesor factors. Input variables which can be controlled are calledassignable or special causes (e.g., person, material, unit operation,and machine), and factors which are uncontrollable are callednoise factors or common causes (e.g., fluctuation in environ-mental factors such as temperature and humidity during theyear). Usually, when a process is developed, e.g. using QbD orDoE principles,3 it takes care of the assignable causes, and the

only variation left in the process is because of common causes.4

Under this condition the process is said to be under control. Agiven process can only be improved if there are some toolsavailable for timely detection of an abnormality due to assignablecauses. This timely and online signal of an abnormality (or anoutlier) in the process could be achieved by plotting the processdata points on an appropriate statistical control chart. Thesecontrol charts can only tell that there is a problem in the processbut cannot tell anything about its cause. Investigation andidentification of the assignable causes associated with theabnormal signal allows timely corrective action which ultimatelyreduces the variability in the process (Figure 3) and graduallytakes the process to the next level of the improvement. This is aniterative process resulting in continuous improvement untilabnormalities are no longer observed in the process and whatevervariation is observed is due to common causes only. To conclude,the main objective of control charts is to calculate the presentstatus of the process, raise the alerts when the process is going outof control, and finally facilitate the process monitoring (detectingoutliers) and improvement (decrease in variability of theprocess) until the process comes under statistical control.5

The significance of these control charts is evident by the factthat it was discovered in the 1920s by Walter A. Shewhart,6 andsince then it has been used extensively across the manufacturingindustry and became an intrinsic part of SPC7 and DMAIC

Received: April 24, 2013Published: July 8, 2013

Figure 1. Causes of variation for an output attribute, A and B are someassignable or special causes, whereas N1 and N2 are noise factors orcommon causes.

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methodology of the 6σ8 process. Except for a few, there are notmany references available for the API manufacturing.9−13

A control chart also provides an online test of the hypothesis14

if the process is under statistical control.

null hypothesis

=

=

H process under control (observed mean

process mean)o

alternate hypothesis

=

≠

H process is out of control (observed mean

process mean)a

If points on the control charts are between UCL and LCL(natural control limits of the process), it can be concluded thatobserved mean is equal to process mean. It indicates that the nullhypothesis cannot be rejected and the process is said to be understatistical control. On the other hand if points are beyond thecontrol limits, it indicates that the observed mean has driftedfrom the process mean and now twomeans are not equal, leadingto the rejection of the null hypothesis. In the latter case areadjustment of the process15 is required. The control chart alsohelps in minimizing the Type-I error (unnecessary adjustment ofa stable process) and Type-II error (continuation of anuncontrolled process) associated with the hypothesis testing(Table 1).

In order to take the correct decision for readjusting an out-of-control chemical process it becomes imperative to monitor notonly the mean value of an output parameter (e.g., impurity level,assay, etc.) but also its variability or its spread.16 Monitoring of

the mean value of an output parameter is done with the help of acontrol chart for the mean (X̅, I-charts), and monitoring of theprocess variability is done by preparing the control chart for thestandard deviation ‘s’ or the range ‘R’ or the moving range ‘MR’.The mean value of an output parameter gives a sense of accuracy,while variability gives an indication of the precision of theprocess.17 As both accuracy and precision are important (Figure 2)for monitoring, both X̅- and R-charts are analyzed together fortaking any decision on readjusting the running process althoughthey are prepared separately for a given output parameter. Insummary, a control chart enables an online scrutiny of the process.Any control chart has the following basic components as

shown in Chart 1. Values of USL and LSL are customer defined,

or its values are taken from a pharmacopeia.18 Values of UCL,LCL, σ, and mean are calculated on the basis of the trend data ofan output parameter. UCL and LCL represent the naturalvariability of the process. Any data point (outliers) outside thesecontrol limits (red circle, Chart 1) indicates that some assignablecause has resulted in this drift which needs immediate attentionand investigation. This would lead to the process improvement(i.e., decreasing the σ of the process), and after some iterativecycle it will bring the process into the state of statistical control(Figure 3). Careful observation of the patterns on the controlcharts can reveal a lot about the health of the current process and

Table 1. Type-I and Type-II errors associated with a process

state of process

decision in control out of control

continue theprocess

correct decision Type-II error: continuing an out-of-control process

adjust theprocess

Type-I error: adjusting an in-control process

correct decision

Figure 2. In-control and out-of-control processes.

Chart 1. Typical control chart and the Western Electric Rulefor detecting assignable causes

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gives an indication of an assignable cause when the process goesout of control. These assignable causes can be detected by usingestablished Western Electric Rules as shown19 in Chart 1.It is not necessarily true that all the deviations on control charts

are bad (e.g. the trend of an impurity drifting towards LCL, whichis good for the process). Regardless of the fact that the deviationis ‘good’ or ‘bad’ for the process, the outlier points must beinvestigated. Reasons for good deviation then must beincorporated into the process, and reasons for bad deviationneed to be eliminated from the process.20 This is an iterativeprocess until the process comes under statistical control.Gradually, it would be observed that the natural control limitsbecome much tighter than the customer’s specification, which isthe ultimate aim of the process improvement (Figure 3). Theprocess thus developed would not only be capable of producingan API that always meets customer’s specifications (Figure 3 andeqs 8, 9, and 10) but also would reduce the risk of failure.21

In general UCL and LCL are calculated on the basis of naturalvariability of the process (σ/R/MR) in such a way that they are±3σ away from the process mean (eqs 5, 6, and 7) and containapproximately 99.73% of the data points. Often warning controllimits at ±2σ are also plotted on the control chart to alert anoperator, but this increases the chance of a Type-I error.22

1.1. I-MR Chart: Individual Moving Range Chart. Thereare various control charts that are available for monitoring purposes.Selection of a control chart for a given process depends on twothings, first is the phase23 of the process improvement and secondconsists of the type of data available and the rational sub grouping.24

There are two phases in statistical process control. Phase I iswhere control charts are being prepared for the first time and themain focus is on calculating current natural control limits of aprocess (UCL and LCL). Usually in phase I, the process is notunder statistical control, and the main objective is to monitorforthcoming batches for any outliers and assignable causesassociated with it, followed by investigation and processimprovement by incorporating good deviations and eliminatingbad ones from the process. Once all assignable causes areremoved from the process, process improvement efforts enterphase II (Figure 4).The type of data available and rational subgrouping are other

criteria for the selection of an appropriate control chart.25 In thepresent case, where the batches of API were coming very slowlyfrommanufacturing and only one sample was provided at a giventime, the most appropriate control chart to be used was the‘I-MR’ control chart, where ‘I’ denotes the individual data of anoutput attribute of the API and ‘MR’ is the moving range (eq 1)which is the measure of variability of the given output attribute.The I-MR chart is also the most appropriate chart to be used inphase I of the improvement for individual observations,26 andwhen it is used withWestern Electric Rules (Chart 1), it is really a

powerful tool for detecting large shifts in the process (more than±1.5σ). When a process enters phase II, it is characterized by astable process with inherent variations (due to common causes)in the process. Now the main focus is on monitoring anddetecting the small changes (less than±1.5σ) in the process, andfor this, more sensitive control charts such as CUSUM27 andEWMA28 charts are employed.

2. RESULTS AND DISCUSSIONHerein we present our endeavor to deploy statistical controlcharts for the first time (i.e., we were in phase I of the processimprovement) to the manufacturing process of an API.29 Themain aim was to access the current health of the process,calculating its natural control limits, and on that basis identify thelarge shifts in the future batches. As the batches were coming outvery slowly, providing only one sample per batch at a given pointof time, the most simple and appropriate control chart that couldbe used was the I-MR chart. This article describes the use of the I-MR chart for phase I improvement of the process, and UCL andLCL were estimated on the basis of the historical data obtainedfrom the past 53 batches (Table 3) of the API. These limits were

Figure 3. Association of process improvement with process variability.

Figure 4. Flowchart for process improvement and estimation of UCLand LCL.

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then taken as the internal specification (rather than thecustomer’s specification) and would be used as a baseline forthe monitoring of the future batches. The ultimate aim would beto increase the gap between natural control limits and thecustomer’s specification (Figures 3 and 5).

There were many output quality attributes associated with thisAPI as shown in Table 2. As evident from the data, all other

quality attributes30 were well within the specification limitsexcept for the assay. Although the assay values of all 53 batchesproduced in the past were well within the USP specifications(NMT 102% and NLT 98%), some of the observed values weretoo close to the specification limits (Tables 2 and 3) whichincreases the probability of failure in the future. Hence, it wasdesired to monitor the assay using the I-MR control chart so thatthe variation in the assay value could be minimized.As a first step, the historical data of all 53 batches were used to

establish the natural control limit of the existing process whichwould then become the internal benchmark for future batches.

Each and every data point that goes the beyond control limits infuture batches would then be investigated, and subsequently theprocess would be improved using QbD or DoE, leading to thenarrowing of the internal control limits.31 Gradually, these controllimits would become much stringent than USP specifications(Figure 3 and Table 5), thereby increasing the process capability.

2.1. Calculation of UCL, LCL, Mean, MR, σMR Requiredfor Generating the I-Chart.32 2.1.1. Normality Test of theData.33 I-MR charts are very sensitive to the normality of thedata, and there are many statistical methods available for testingthe normality.34 Subjecting the data set from Table 3 to theAnderson−Darling test35 gave a ‘p value’ of 0.02, indicating thatdata set was not normally distributed. This was because of the fewdata that were near the lower and upper ends of the data setwhich resulted in tailing. As the reason for non-normality wasobvious, these outliers were ignored later on (see Table 4 for

revised calculations) for obtaining the more realistic controllimits. As a first step, the I-MR chart was prepared deliberately byincluding all data points from Table 3 as discussed below.

2.1.2. Moving Range (MR). MR is the absolute value of thedifference between two consecutive data points as shown by eq 1.MR values are used as a measure of the variability or the spread ofthe data points. If there is not much variation between thebatches, then MR values would remain almost constant, and ifthere is variation in the process, then there will be a suddenincrease in MR values on the MR-chart.

Figure 5. Interpretation of 6σ for the assay data.

Table 2. Specification of the API

USP specifications observed trend %

impurity-1 USL = NMT 0.2% maximum 0.0405LSL = 0% minimum 0

individual impurity USL = NMT 0.1% maximum 0.06LSL = 0% minimum 0.03

total impurities USL = NMT 0.5% maximum 0.12LSL = 0% minimum 0.07

assay USL = NMT 102% maximum 101.5LSL = NLT 98% minimum 98.9

Table 3. Assay data of 53 batches of the API

batch

1 2 3 4 5 6 7 8 9 10 11 12 13 14

assay 100.8 100.8 101.5 101.5 101.5 101.3 100.9 100.6 100.6 100.4 100.8 100.1 100.7 100.4MRi 0.0 0.7 0.0 0.0 0.2 0.4 0.3 0.0 0.2 0.4 0.7 0.6 0.3

batch

15 16 17 18 19 20 21 22 23 24 25 26 27 28

assay 100.4 100.3 98.9 100.3 100.3 100.3 100.3 100.2 100.8 100.8 99.5 100.7 100.4 100.4MRi 0.0 0.1 1.4 1.4 0.0 0.0 0.0 0.1 0.6 0.0 1.3 1.2 0.3 0.0

batch

29 30 31 32 33 34 35 36 37 38 39 40 41 42

assay 100.8 100.7 100.7 100.4 100.0 100.3 100.7 100.7 100.7 100.6 100.0 100.7 100.8 100.3MRi 0.4 0.1 0.0 0.3 0.4 0.3 0.4 0.0 0.0 0.1 0.6 0.9 0.1 0.5

batch

43 44 45 46 47 48 49 50 51 52 53 mean

assay 100.3 100.9 100.5 100.5 101.2 100.6 100.7 100.6 101.1 101.0 101.1 X̅ = 100.59MRi 0.0 0.6 0.4 0.0 0.7 0.6 0.1 0.1 0.5 0.1 0.1 MR = 0.333

Table 4. Recalculations of various components of I-MR chart

mean assay X̅ 100.59std. deviation σ 0.45mean MR MR 0.249std. deviation σMR 0.221

Control Limit for Revised I-ChartUCL 101.248centre line 100.58LCL 99.91

Control Limit for Revised R-ChartUCL 0.753centre line 0.249LCL 0

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= | − | =−X X i Nmoving range MR for 2, ...,i i i 1 (1)

=N number of individual data

=X measurement of the datai

MRi data are captured in row 3, Table 32.1.3. MeanMoving Range MR. Average of all range values as

shown by eqs 2a and 2b. This value is the basis for calculating thestandard deviation of the process.

∑=− =N

MR1

1MR centre of the MR chart

i

N

i2 (2a)

=MR 0.333 (last column of Table 3) (2b)

2.1.4. Standard Deviation (σMR). Standard deviation of theprocess is calculated from the average moving range as shown byeq 3 and represents the average variation of the process. Mainaim of the control charts is to help in minimizing this variation.σMR would reach its minimum value when all assignable causesfrom the process are being eliminated and whatever variation leftafter that would be the inherent variation of the process due tocommon cause only. This is the fundamental basis of any processimprovement strategy.

σ = = = =d

MR MR1.128

0.3341.128

0.295MR2 (3)

d2 is a control chart constant36 and depends on individual data

used for calculating MR. In the current case as two subsequentdata points were used, hence n = 2 and corresponding d2 value is1.128.2.1.5. Mean Assay (X̅). Average value of the assay of all the

batches as shown by eq 4. This becomes the centre line of the I-chart.37

∑̅ = ‐=

XN

X1

centre of the I charti

N

i1 (4)

=mean assay 100.59 (last column of Table 3)

2.1.6. Control Limit for I-Chart.As described earlier, this is thenatural control limit of a process. Both UCL and LCL arecalculated on the basis of the mean and σMR values obtained fromindividual data set. Control limits are calculated in such a waythat they are ±3σMR away from the process mean X̅ as shown byeq 5.

σ= ̅ ± = ± ×Xcontrol limits 3 100.58 3 0.296MR (5)

= + =UCL 100.58 0.888 101.477 (5a)

= − =LCL 100.58 0.888 99.71 (5b)

2.1.7. Generating I-Chart. The I-chart is then prepared byplotting the mean, UCL, LCL, and individual values of the assayas shown in Chart 2.2.2. MR-Chart for the Assay.MR chart has its own control

limits and centre line as calculated below and are plotted asshown in Chart 2b.2.2.1. Control Limits for MR-Chart.

= =D MRLCL 0MR 3 (6)

= =D MRUCL 1.007MR 4 (7)

D3 and D4 are the control chart constants38 and depend on the

number of individual data used for calculatingMR. In the present

case 2 (hence n = 2) as two subsequent data points is used andcorresponding D3 and D4 values are 0 and 3.267, respectively.

2.2.2. MRi Trend (row 3, Table 2).MR (= 0.333) is the centreline given by eq 2a.

2.3. Interpretation. I-chart of the assay (Chart 2a) showsthat the third, fourth and fifth data points lie outside the UCL,indicating the rejection of null hypothesis (alternatively we cansay that process has gone out of control, Table 1), which in turnindicates a possible assignable cause associated with this shift.This required investigation and corrective action in the process.Similarly, assay of the 17th and 25th were out of LCL indicating ashift in the mean. These sudden drops in the mean value of theassay (Chart 2a) resulted inMR values going beyond theUCL onMR-chart (Chart 2b). This does not mean that both mean andthe variability were out of control. In the present case, suddendrops in assay values lead to the large change in the value of theMR.39 It only indicates that the mean was out of control and notan indication that both mean andMR were out of control. This isa very typical behavior of I-MR chart.40

It is important to state once again that, when these controlcharts are prepared for the first time to estimate the naturalcontrol limit of the process (called as estimated or trial controllimits), it is done on the basis of the historical data. Hence, it isgenerally assumed that assignable causes for the outliers wereidentified and the process was rectified at that point of time.41 Onthe basis of this assumption control limits were recalculated byignoring out-of-control points (in the present case, batches 3rd,4th, 5th, 17th, and 25th) to avoid Type-II error (continuing theout-of-control process, Table 2) during the monitoring of thefuture batches. Another reason for ignoring these points is toavoid the inflated values42 of the control limits (σMR would belarger, eqs 5a and b) and to have fairly realistic estimate of thetrial control limits. It is evident from Chart 2 and Chart 3 that thetrial control limits of the process become more stringent afterrecalculation (eqs 5a and b and Table 4). These trial limits are tobe used as a baseline for the monitoring of future batches.

2.4. Recalculation of Trial Control Limits for Assay(ignoring 3rd, 4th, 5th, 17th, and 25th batches). AnAnderson−Darling test for the new data set obtained afterignoring outliers gave a ‘p value’ of 0.10,43 indicating that the datais normally distributed and could be used for I-MR chart.

Chart 2. (a) I-chart for assay (USL = 102 and LSL = 98 are notshown); (b) MR-chart for assay

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Applying Western Electric Rules to Chart 3, it is evident thatthere was one data point on the I-chart (batch 6, Chart 3a) thatwas beyond UCL, but this time it was not ignored once again as itwould not inflate the control limit values significantly onrecalculation. Ignoring this outlier may result in furthernarrowing of the control limits which increase the Type-Irisk.44 Also these control limits are just a baseline for the futurebatches for the identification of new outliers and hence assignablecauses associated with it. The reasons for new assignable causeswould then be identified and rectified till there are no moreassignable causes and the process is said to be under statisticalcontrol. This marks the termination of phase I of the processcontrol. Further monitoring of the process in phase II wouldthen be taken up by more sensitive CUSUM or EWMA controlcharts.The main purpose of the present exercise was to obtain

working or trial control limits for the existing process so that wecould start phase I of the process improvement by using thesecontrol limits for monitoring of forthcoming batches.Process Performance Indices:45 Pp and Ppk. Process

performance index is a dimensionless number that is used torepresent the ability of the process to meet the customerspecification for a given quality attribute. Process performanceindices are being calculated out of curiosity to assess the overall ‘σlevel’ of the process for the assay. This will make more sense whenthe process comes under statistical control. Two most commonlyused process capability index are Pp and Ppk. The processperformance index was preferred over process capability ratio (Cpand Cpk) in the present case because of following reasons:-(1) Since no rational subgrouping was possible hence

compulsion of using overall ‘σ’ (standard deviation) forcalculating indices(2) The current process was not under statistical control as the

process was in phase 1 improvement.(3) Pp and Ppk gives more conservative estimate than Cp and

Cpk.(4) Pp index describes the process performance wrt customer’s

specification or tolerance limit (eq 8).

σσ= −

×= =P

USL LSL6

2.24, 0.45 (Table 4)p (8)

Pp value≥ 1.33 is desirable which is equivalent to the 4σ process,whereas Pp = 2 represents a 6σ performance.46

As Pp calculated above does not reveal anything about thedeviation of the process mean with respect to the centre line

(historical mean), a better way of measuring process capability isby using Ppk (eq 9) which includes themean (X̅) in calculation. Inthis procedure process performance is calculated with respect toboth USL and LSL, and the minimum of the two is taken as theprocess performance index for a given quality attribute of an API.

σ σ= − ̅

×̅ −

×

=

⎡⎣⎢

⎤⎦⎥P

X X

P P

minimumUSL3

,LSL

3

[ , ]

MRpk

pk upper pk lower (9)

It is important to note that in some cases both Ppk upper andPpk lower are important because the process would be out ofcontrol if the data points cross either of the limits, for example‘assay’ and ‘purity’ of an API. However, in certain conditions onlyone of them is important; for example Ppk lower does not have anysignificance for ‘impurities’, because even if it goes below theLSL, it is only good for the process. Hence, in such cases onlyPpk upper should be calculated.Process performance can also be expressed in terms of σ level47

as shown below or by using the conversion table.47

σ = × Plevel of the process 3 pk (10)

For example, Ppk for the assay was 1.5 (Table 5), which whenconverted to 6σ gives a value of 4.72σ. This means that the USLof the assay is 4.72σ distance away from the centre (Figure 5)

whereas the internal control limits (UCL) is 3σ away from thecentre. Thus, there is a safety margin of 4.72 − 3 = 1.72σ whichmeans that there is 0.064% probability that the value of the assay willcross the USL. One can guess the significance of the value of 4.72σby the fact that the much talked-about “6σ” is equivalent to 3.4failures per million and in the present case it corresponds to 640 permillion batches; hence, higher values of Ppk are desirable. A Ppk valueof 1.33 (i.e., 4σ) is generally seen as sufficient and corresponds to a99.99% success rate.The above conclusion can also bedrawnby looking at thenumerator

part of the eq 9. The greater the difference between the specificationlimit and the process mean, the greater is the process capability.Hence, it can be concluded that, even if the current process

fails, it is most likely that it will fail towards USL, and thecorresponding failure rate is 0.064% or 640 ppm.48

As APIs are qualified on many output attributes, we proposethat the process performance of an API must be calculated on thebasis of the same rationale that is used for the calculation of Ppk(eq 11). Hence:

=

× − −

P of an API min

[impurity 1, impurity 2, ..., assay, yield, etc.]

pk

(11)

Chart 3. (a) Revised I-chart for assay; (b) revised MR-chartfor MR of assay

Table 5. Calculating process capability ratios for the API

process performanceindicator formula

process performancedata for assay

Pp σ−×

USL LSL6

2.26

Ppk σ σ− ̅

×̅ −

×⎡⎣⎢

⎤⎦⎥

X Xmin

USL3

,LSL

3min[1.57, 2.90]Ppk = 1.50

σ level of process 3 × Ppk 4.72failure rate 0.064% or 640 ppm

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On the basis of eq 11 Ppk values, the quality attribute that is theminimum Ppk should be taken for the improvement program.

3. CONCLUSIONThewhole process of process monitoring and improvement of anAPI starts with the selection of a quality attribute that requiresthe most attention. Data from the manufacturing process arethen collected, and the natural control limits of the process arecalculated, followed by plotting the preliminary control chart. Ifthere are few outliers, then the control limits are calculated onceagain by ignoring those outliers. This final control chart is thenused for the hypothesis testing under which the process is controlledfor the future batches (for detecting any out-of-control points). If thedeviations are good, then the reason for the same is to beincorporated in the process, whereas reasons for bad deviations are tobe eliminated. Thus, the process of monitoring and improvementmust be continued until the process comes under SPC. At that point(end of phase I) the internal or the natural control limits of theprocess become new internal specifications for the API for themanufacturer which are much tighter than the customer’sspecification, and the failure rates would be extremely low. Thisway of setting the internal specification would not only benefit thecustomer and manufacturer but would also avoid many unwantedOOS and OOT investigations and change control requests duringmanufacturing.

■ ASSOCIATED CONTENT*S Supporting InformationCrude data of 53 batches, Excel sheet used for calculating UCL,LCL, mean, Cp, CPk, and σ level of the process. This material isavailable free of charge via the Internet at http://pubs.acs.org.

■ AUTHOR INFORMATIONCorresponding Author*Telephone: +91-40-44658520. Fax: +91-40-44658699. E-mail:amrendrakr@drreddys.com, amrendrakroy@in.com.Notes†DRL Communication Number IPDO-IPM 00366.The authors declare no competing financial interest.

■ ACKNOWLEDGMENTSWe thank the DRLmanagement for supporting this initiative andproviding the required data that are being used for the statisticalanalysis. We are also grateful to Dr. Rakeshwar Bandichhor forhis comments and suggestion while preparing this manuscript.

■ LIST OF ABBREVIATIONSAPI Active pharmaceutical ingredientCp Process capability ratio, does not include meanCpk Process capability ratio that takes mean into accountD3, D4, d2 Statistical constantsDMAIC Define, measure, analyze, improve and control

phases of 6σDoE Design of experimentsEP European PharmacopeiaHPLC High pressure liquid chromatographyI-Chart Individual variable chartLCL Lower control limitLCLMR Lower control limit for MR-chartLSL Lower specification limitsMR Moving rangeMR Moving range mean

MRi Moving range of a data pointN Sample sizen Number of sample taken to calculate control limitsNLT Not less thanNMT Not more thanOOS Out of specificationOOT Out of trendPp Process performance indexPpk Process performance index with respect to meanQbD Quality by designs,σ Standard deviation/variation/spread of a set of data

pointsSPC Statistical process controlUCL Upper control limitUCLMR Upper control limit for MR-chartUSL Upper specification limitUSP United states pharmacopeiaVOC Voice of customerVOP Voice of processXi Individual data pointwrt With respect toσMR Standard deviation of moving rangeμ, X̅ Mean

■ REFERENCES(1) (a) International Conference on Harmonization (ICH),. DraftRevised Guidance on Impurities in New Drug Substances. Fed. Regist.Q3A(R) 2000, 65 (140), 45085. (b) International Conference onHarmonization (ICH),. Draft Revised Guidance on Impurities in NewDrug Products. Fed. Regist. Q3B(R) 2000, 65 (139), 44791. (c) Interna-tional Conference on Harmonization (ICH),. Guidelines for ResidualSolvents. Fed. Regist. Q3C 1997, 62 (247), 67377. (d) InternationalConference on Harmonization (ICH),. Test Procedures and Accept-ance Criteria for New Drug Substances and New Drug Products. Fed.Regist. Q6A 1999, 65 (146), 67488. (e) Cimarosti, Z.; Bravo, F.;Stonestreet, P.; Tinazzi, F.; Vecchi, O.; Camurri, G.Org. Process Res. Dev.2010, 14, 993. (f) Castagnoli, C.; Yahyah, M.; Cimarosti, Z.; Peterson, J.J. Org. Process Res. Dev. 2010, 14, 1407. (g) Gavin, P. F.; Olsen, B. A. J.Pharm. Biomed. Anal. 2008, 46, 431.(2) (a) International Conference on Harmonization (ICH),.Pharmaceutical Development. Fed. Regist. Q8 2006, 71 (98), 29344.(b) International Conference on Harmonization (ICH),. Quality RiskManagement. Fed. Regist. Q9 2006, 71 (106), 32105. (c) InternationalConference on Harmonization (ICH),. Pharmaceutical Quality System.Fed. Regist. Q10 2009, 74 (66), 15990. (d) International Conference onHarmonization (ICH),. Development and Manufacture of DrugSubstances. Fed. Regist. Q11 2012, 77 (224), 69634.(3) Lazic, Z. R. Design of Experiments in Chemical Engineering, APractical Guide; Wiley-VCH Verlag GmbH&Co: Weinheim, Germany,2004.(4) Shewhart, W. A. Economic Control of Quality of ManufacturedProduct; Reprinted by American Society for Quality Control, ASQQuality Press: Milwaukee, WI, 1980.(5) Montgomery, D. C. Statistical Quality Control: A ModernIntroduction; 6th ed.; Wiley: India ed., 2009.(6) Shewhart, W. A.; Deming, E. W, Statistical Method from theViewpoint of Quality Control; Dover Publications: Mineola, NY, 1986.(7) Wheeler, D. J. Advanced Topic in Statistical Process Control: Power ofShewhart’s Control Charts; SPC Press: 2004.(8) Kubaik, T. M.; Benbow, D. W. Six Sigma Black Belt Handbook;Pearson Education: 2010.(9) Skibsted, E. T. S.; Boelens, H. F. M.; Westerhuis, J. A.; Witte, D. T.;Smilde, A. K. J. Pharm. Biomed. Anal. 2006, 41, 26.(10) Chen, J.; Liao, C.-M. Ind. Eng. Chem. Res. 2001, 40, 1516.(11) Albazzaz, H.; Wang, X. Z. Ind. Eng. Chem. Res. 2004, 43, 6731.(12) Brownlee, K. A. Ind. Eng. Chem. 1951, 43, 1307.

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(13) Carson, P. K.; Yeh, A. B. Ind. Eng. Chem. Res. 2008, 47, 405.(14) Efficient way of learning applied statistics for nonstatisticians is toread statistics books used in business schools. These books can beunderstood easily as it focuses on the application of statistics rather thanon pure statistics. (a) Anderson, D. R.; Sweeney, D. J.; Williams, T. A.Statistics for Business and Economics; South-Western College Publication:2008.(15) Woodall, W. H. J. Qual. Technol. 2000, 20, 515.(16) Reducing the variability is the main focus of 6σ qualityimprovement programme. As decrease in variability leads to theimprovement of the process (Figure 3)(17) (a) JCGM 200 International Vocabulary of Metrology Basic andGeneral Concepts and Associated Terms (VIM); 2008; http://www.bipm.org/utils/common/documents/jcgm/JCGM_200_2008.pdf. (b) Tay-lor, J. R. An Introduction to Error Analysis: The Study of Uncertainties inPhysical Measurements; University Science Books: Mill Valley, CA, 1999.(18) Usually these are not plotted on control charts.(19) Western Electric Company. Statistical Quality Control Handbook;Indianapolis: IN, 1956.(20) See ref 8, p 389.(21) As customer’s specification and process’s natural control limits arefar apart.(22) See ref 5, p 189.(23) See ref 5, p 198.(24) See ref 5, p 193.(25) See ref 5, p 193 and ref 9, p 357.(26) (a) Wheeler, D. J. Understanding Variation: The Key to ManagingChaos; SPC Press, Inc.: Knoxville, TN, 2000. (b) Wise, S. A.; Fair, D. C.Innovative Control Charting: Practical SPC Solutions for Today’sManufacturing Environment; ASQ Quality Press: Milwaukee, WI, 1998.(27) (a) Page, E. S. Biometrika 1954, 41, 100. (b) NIST/SEMATECHe-Handbook of Statistical Methods; http://www.itl.nist.gov/div898/handbook.(28) Roberts, S. V. Technometrics 1959, 37, 83.(29) The name of the API could not be disclosed due to confidentialityreasons.(30) All output quality attributes of an API are to be monitoredseparately. In the present case other quality attributes were notconsidered for an additional reason that the data set was not normallydistributed. I-MR could not be applied to them because these charts arevery sensitive towards the normality assumption and could lead to falsecontrol limits. Hence, CUSUM and EMWA charts which are insensitiveto normality would be used for these, and it will be a topic of discussionin a forthcoming article.(31) This article does not cover the improvement efforts forelimination of the assignable causes. This article is all about estimatingthe current health of the process before starting any improvementprogram.(32) All calculations and control chart preparation were done using aMicrosoft Excel sheet, see Supporting Information.(33) (a) Borror, C. M.; Montgomery, D. C.; Runger, G. C. J. Qual.Technol. 1999, 31, 309. (b) Willemain, T. R.; Runger, G. C. J. Qual.Technol. 1996, 28, 31.(34) For a normality test, the hypothesis is constructed in the followingwayH0: the given distribution is normal.Ha: the given distribution is notnormal; p > 0.05 results in acceptance of a null hypothesis, meaning it is anormal distribution. In the present case, p = 0.02 indicates that the nullhypothesis is false; hence, the data are not normally distributed. Varioustests for normality are the Anderson−Darling test, Kolmogorov−Smirnov test, Shapiro−Wilk test. The template for the Anderson−Darling test is given in an Excel sheet in Supporting Information andcould be calculated by using software such as JMP and minitab(a) Anderson, T. W.; Darling, D. A. Ann. Math. Stat. 1952, 23, 193.(b) Stephens, M. A. J. Am. Stat. Assoc. 1974, 69, 730. (c) Smirnov, N. V.Ann. Math. Stat. 1948, 19, 279. (d) Shapiro, S. S.; Wilk, M. B. Biometrika1965, 52 (3−4), 591−611. (e) The underlying assumption ofnormality is much more critical when there are no subgroups. Burr, I. W.Engineering Statistics and Quality Control; McGraw-HilI: New York,1953.

(35) Template used for calculating the AD test: http://www.kevinotto.com/rss/templates/anderson-darlingnormality test calculator.xls.(36) Value for a given number of sample could be found in anystatistical process control book(37) Sometimes a target value is taken in place of the mean as a centrepoint, e.g. when one wants to target an assay value of 100%.(38) Values for a given number of samples could be found in anystatistical process control book.(39) As absolute value of the difference is considered for drawing theMR chart.(40) See ref 5, p 261.(41) Since the calculation done in this article was based on thehistorical data, it was assumed that those outliers were taken care of inorder to have a realistic value of control limits so that it can be used tomonitor the upcoming batches.(42) Inflated because of the higher value of σ which, in turn, is becauseof the outliers.(43) Reference 34; values greater than 0.05 indicate that the nullhypothesis is true(44) Ignoring another point will lead to tighter control limits, and anypoint near or above this limit could be considered an outlier and wouldresult in investigation; i.e. it might result in a false alarm even though theprocess is running between its natural control limits (Type-I error).(45) (a) Breyfogle, F. W. Implementing Six Sigma: Smarter SolutionsUsing Statistical Methods; Wiley & Sons: New York, 1999. (b) Reference5, p 363; ref 8, p 171.(46) Conversion table:

(47) Calculation of 6σ can be done with Excel sheet templates availableon the Internet or by using software such as minitab.(48) These σ values were obtained after a few outliers were ignoredfrom the calculation. The real value would be obtained once the processcomes under statistical control.

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