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Monitoring Processes Monitoring Processes Presentation Transcript

  • Monitoring Processes
    Andrew Hingston
    switchsolutions.com.au
    PREPARED?
  • Business as usual?
    2
    Percentage of Invoices Unpaid After One Month
  • Today
    1. Process control charts
    2. I and MR charts
    3. X-bar and R charts
    4. p charts
    5. c charts
    6. Monitoring processes
    7. Process capability
    3
    Course
    1. Understanding data
    2. Monitoring processes
    3. Exploring relationships
    View slide
  • 4
    Why
    monitorprocesses
    ?
    View slide
  • 5
    1
    ProcessControl
  • Statistical Process Control Chart
    6
  • Control limits are NOT spec limits!
    Control Limits
    Defined by 3 sigma
    Normal Distribution
    Control Charts
    Recalculated when process changes
    7
    Customer Spec. Limits
    Defined by customer
    Identifies defects
    Histograms and boxplots
    Change when customers requirements do
  • Type of data?
    8
    DiscreteCounting(defects)
    ContinuousMeasuring(time, $, length)
    IndividualObservations(X)
    Occurrence
    Count(c)
    Categorical
    Proportions(p)
    SubgroupAverages( )
    I chartsMR charts
    X-bar chartsR charts
    p charts
    c charts
  • Western electric rules
    Three sigma1
    Two sigma 2 of 3
    One sigma 4 of 5
    Shifts8
    9
    Trends 8
    Too quiet 15
    Too noisy14 zig-zag
    Too variable 8
  • 10
    2
    I chartMR chart
  • I chart
    I is for Individuals
    Tracks individual data
    Continuous measurement data
    11
  • 12
    Revenue
    ($ millions)
    revenue.csv
  • I chart for revenue
    • mydata = read.csv("revenue.csv"); attach(mydata)
    • qcc(Revenue, type = "xbar.one")
    13
  • MR chart
    MR is for Moving Range
    Tracks absolute difference
    Use before I chart
    14
  • 15
    RevenueandPreviousRevenue
    revenue_adj.csv
  • MR chart for revenue
    • adjdata = read.csv("revenue_adj.csv"); attach(adjdata)
    • lab=seq(2,30,length=29)
    • qcc(adjdata, type = "R", labels=lab)
    16
  • Continuous data
    Data normally distributed
    • shapiro.test(X) # Normal if p-value > 0.05
    20+ data points
    Assumptions
    17
  • Steps …
    Collect 20+ data
    Stabilise MR chart
    Stabilise I chart
    Check normal
    Extend control limits
    Plot live data
    Make decisions
    18
  • Extending limits to new data
    • qcc(Revenue[1:20], type = "xbar.one", newdata=Revenue[21:30])
    19
  • 20
    3
    X-bar chartR chart
  • X-bar chart
    X-bar is for subgroup mean
    Tracks samples or subgroup means
    Continuous measurement data
    21
  • 22
    Hotel room
    cleaning times
    (minutes)
    hotel.csv
  • 23
    X-bar chart for room cleaning times
    • mydata = read.csv("hotel.csv"); attach(mydata)
    • qcc(mydata, type = "xbar")
  • 24
    R chart
    R is for subgroup range (max  min)
    Tracks max  min for subgroups
    Use before X-bar chart
  • R chart for room cleaning times
    • mydata = read.csv("hotel.csv"); attach(mydata)
    • qcc(mydata, type = "R")
    25
  • Continuous data
    Means normally distribution
    • shapiro.test(X) # Normal if p-value > 0.05
    20+ subgroups
    Assumptions
    26
  • Steps …
    Collect 20+ subgroups
    Stabilise R chart
    Stabilise X-bar chart
    Check means normal
    Extend control limits
    Plot live data
    Make decisions
    27
  • 28
    4
    p chart
  • 29
    p chart
    p is for proportions
    Tracks % fails in subgroups
    Discrete counting data
  • 30
    Call centre
    Unsatisfactory
    Callscalls.csv
  • 31
    p chart for unsatisfactory calls
    • mydata = read.csv("calls.csv"); attach(mydata)
    • qcc(Unsatisfactory, sizes=Total, type = "p")
  • Fail or pass only
    Probabilities constant
    Fails don’t cause fails
    Average pass and fail > 5
    20+ subgroups
    Assumptions
    32
  • Subgroup sizes can vary
    Results in wobbly control limits
    33
  • Steps …
    Collect 20+ subgroups
    Stabilise p chart
    Extend control limits
    Plot live data
    Make decisions
    34
  • 35
    5
    c chart
  • 36
    c chart
    1 2 3 4 5 6 7
    c is for count
    Tracks # fails
    Unknown and constant opportunities
  • 37
    Biscuit
    Complaints
    complaints.csv
  • c chart for biscuit complaints
    • mydata = read.csv("complaints.csv"); attach(mydata);
    • qcc(Complaints, type="c")
    38
  • Fail or pass only
    Passes unknown
    Constant opportunity
    Fails don’t cause fails
    Average fail > 5
    20+ subgroups
    Assumptions
    39
  • Steps …
    Collect 20+ subgroups
    Stabilise c chart
    Extend control limits
    Plot live data
    Make decisions
    40
  • Why?I and MR charts
    X-bar and R charts
    p charts
    c charts
    Recap
    41
  • 42
    6
    Monitoring
  • 43
    INTERPRETATION
    Special
    Common
    REALITY
    Common
    Special
  • Monitoring live data
    44
  • 45
    7
    Capability
  • Control limits are NOT spec limits!
    Control Limits
    Defined by 3 sigma
    Normal Distribution
    Control Charts
    Recalculated when process changes
    46
    Customer Spec. Limits
    Defined by customer
    Identifies defects
    Histograms and boxplots
    Change when customers requirements do
  • 47
    Process vs Service
    Upper Service Level
    Lower Service Level
    Process Capability Index
    Minimum of ,
    How many 3 stddevs from nearest service level?
    6 SIGMA if > 2
  • Why?I and MR charts
    X-bar and R charts
    p charts
    c charts
    Monitoring live data
    Process capability
    Recap
    48
  • 49
    8
    Exercises
  • Exercises in R
    Exercise 2 R&D
    Exercise 5 Vial weights
    Exercise 8 Sound chips
    Exercise 11 DVD rentals
    50