NCR Advanced Analytics Demo
Navin Manaswi
August 10, 2015
## 'data.frame': 480 obs. of 5 variables:
## $ Month : Factor w/ 31 levels "1","10","11",..: 1 12 23 25 26 27 28 29 30 2 ...
## $ Date : Factor w/ 30 levels "1/1/2013","1/1/2014",..: 1 10 13 16 19 22 25 27 29 4 ...
## $ Client: int 1 1 1 1 1 1 1 1 1 1 ...
## $ Type : Factor w/ 4 levels "Original","Remainder",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ Value : num 0.98 0.981 0.978 0.978 0.977 ...## 'data.frame': 480 obs. of 5 variables:
## $ Month : Factor w/ 30 levels "1","2","3","4",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ Date : Factor w/ 30 levels "1/1/2013","1/1/2014",..: 1 10 13 16 19 22 25 27 29 4 ...
## $ Client: Factor w/ 4 levels "1","2","3","4": 1 1 1 1 1 1 1 1 1 1 ...
## $ Type : Factor w/ 4 levels "Original","Remainder",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ Value : Factor w/ 362 levels "-0.03885","-0.03311",..: 220 222 217 218 215 289 293 284 259 223 ...## [1] "Nw-Availability versus Months for Clients 1,2,3,4 respectively"
## [1] "Time Series Decomposition of Nw-Availability"
## [1] "Trend Component"## [1] "Seasonal Component"## Warning in HoltWinters(Fsts): optimization difficulties: ERROR:
## ABNORMAL_TERMINATION_IN_LNSRCH
## [1] "Remainder Component"
## [1] "Trend Component"## [1] "Seasonal Component"## Warning in HoltWinters(Fsts): optimization difficulties: ERROR:
## ABNORMAL_TERMINATION_IN_LNSRCH
## [1] "Remainder Component"
## [1] "Trend Component"## [1] "Seasonal Component"## Warning in HoltWinters(Fsts): optimization difficulties: ERROR:
## ABNORMAL_TERMINATION_IN_LNSRCH
## [1] "Remainder Component"
## [1] "Trend Component"## [1] "Seasonal Component"## Warning in HoltWinters(Fsts): optimization difficulties: ERROR:
## ABNORMAL_TERMINATION_IN_LNSRCH
## [1] "Remainder Component"
## [1] "Distribution of values expressed by Boxplots"
## [1] "Distribution of values expressed by Density plots"
## Package 'qcc', version 2.6
## Type 'citation("qcc")' for citing this R package in publications.## [1] "Quality Control Charts for Client"## [1] "Control Charts for Client 1"
## [1] "process Capability Analysis for each Client"
##
## Process Capability Analysis
##
## Call:
## process.capability(object = obj, spec.limits = c(0.95, 0.999), print = TRUE, digits = 3)
##
## Number of obs = 30 Target = 0.974
## Center = 0.985 LSL = 0.95
## StdDev = 0.00196 USL = 0.999
##
## Capability indices:
##
## Value 2.5% 97.5%
## Cp 4.1612 3.0954 5.225
## Cp_l 5.9366 4.6505 7.223
## Cp_u 2.3857 1.8608 2.911
## Cp_k 2.3857 1.7603 3.011
## Cpm 0.7678 0.4984 1.037
##
## Exp<LSL 0% Obs<LSL 0%
## Exp>USL 0% Obs>USL 0%## [1] "Control Charts for Client 2"
## [1] "process Capability Analysis for each Client"
##
## Process Capability Analysis
##
## Call:
## process.capability(object = obj, spec.limits = c(0.95, 0.999), print = TRUE, digits = 3)
##
## Number of obs = 30 Target = 0.974
## Center = 0.991 LSL = 0.95
## StdDev = 0.00176 USL = 0.999
##
## Capability indices:
##
## Value 2.5% 97.5%
## Cp 4.6299 3.444 5.8135
## Cp_l 7.7430 6.068 9.4184
## Cp_u 1.5169 1.174 1.8594
## Cp_k 1.5169 1.109 1.9250
## Cpm 0.4929 0.319 0.6668
##
## Exp<LSL 0% Obs<LSL 0%
## Exp>USL 0% Obs>USL 0%## [1] "Control Charts for Client 3"
## [1] "process Capability Analysis for each Client"
##
## Process Capability Analysis
##
## Call:
## process.capability(object = obj, spec.limits = c(0.95, 0.999), print = TRUE, digits = 3)
##
## Number of obs = 30 Target = 0.974
## Center = 0.954 LSL = 0.95
## StdDev = 0.0134 USL = 0.999
##
## Capability indices:
##
## Value 2.5% 97.5%
## Cp 0.60743 0.45185 0.7627
## Cp_l 0.09016 -0.01181 0.1921
## Cp_u 1.12470 0.86197 1.3874
## Cp_k 0.09016 -0.03135 0.2117
## Cpm 0.32903 0.22126 0.4367
##
## Exp<LSL 39% Obs<LSL 30%
## Exp>USL 0.037% Obs>USL 0%## [1] "Control Charts for Client 4"
## [1] "process Capability Analysis for each Client"
##
## Process Capability Analysis
##
## Call:
## process.capability(object = obj, spec.limits = c(0.95, 0.999), print = TRUE, digits = 3)
##
## Number of obs = 30 Target = 0.974
## Center = 0.985 LSL = 0.95
## StdDev = 0.00264 USL = 0.999
##
## Capability indices:
##
## Value 2.5% 97.5%
## Cp 3.0992 2.3054 3.891
## Cp_l 4.4497 3.4835 5.416
## Cp_u 1.7486 1.3579 2.139
## Cp_k 1.7486 1.2831 2.214
## Cpm 0.7426 0.4835 1.002
##
## Exp<LSL 0% Obs<LSL 0%
## Exp>USL 0% Obs>USL 0%## [1] "Months" "NW...CLIENT1"
## [3] "HW...CLIENT1" "HW.Target...CLIENT1"
## [5] "NW...CLIENT2" "HW...CLIENT2"
## [7] "NW.Target" "NW...CLIENT3"
## [9] "NW.Target.1" "HW...CLIENT3"
## [11] "HW.Target" "NW.CLIENT4"
## [13] "HW.CLIENT4..Hardware." "NW.Target.2"
## [15] "Ans.Rate...CLIENT1" "Aband.Rate...CLIENT1"
## [17] "ASA...CLIENT1" "Calls...CLIENT1"
## [19] "Ans.Rate...CLIENT2" "Aband.Rate...CLIENT2"
## [21] "ASA...CLIENT2" "Calls...CLIENT2"
## [23] "Ans.Rate...CLIENT3..80.." "Ans.Rate...CLIENT3..95.."
## [25] "Aband.Rate...CLIENT3" "ASA...CLIENT3"
## [27] "Calls...CLIENT3" "Ans.Rate...CLIENT4"
## [29] "Aband.Rate...CLIENT4" "ASA...CLIENT4"
## [31] "Calls...CLIENT4"
##
## Pareto chart analysis for pare1
## Frequency Cum.Freq. Percentage Cum.Percent.
## HardFau 44 44 43.564356 43.56436
## CashOut 16 60 15.841584 59.40594
## Comms 12 72 11.881188 71.28713
## DailyBalan 12 84 11.881188 83.16832
## Host 9 93 8.910891 92.07921
## Vandalism 3 96 2.970297 95.04950
## SuppliesOut 3 99 2.970297 98.01980
## PM 2 101 1.980198 100.00000## [1] "Downtime Reasons and its root cause analysis"## [1] "Radarcharts showing which factors are causing the particular downtime reason in major way"## [1] "Downtime Reasons :Top Left: Hard Faults, Top Right: Comms, \vBottom Left : Cash out , bottom right : Daily Balance "
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## NULL## [1] " Downtime Reasons: Top Left: host, Top Right: supplies out, \vBottom Left : vandalism , bottom right : pm"
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## NULLNote that the echo = FALSE parameter was added to the code chunk to prevent printing of the R code that generated the plot.