Post on 22-Jan-2018
Lean six sigma project PDI logistics
• Dharanidhar Malladi
• Hsiao Chuang-Jen
• Hitarth Patel
• Rachit Jauhari
DEFINE – Project definition
Project business Case :
• PDI is package pickup and delivery company which OUTSOURCES its ground operations to a third party
• Inefficiency in processes and miscommunication at PDI -> rebates and idle time costs
• Gross margin needs to be improved to sustain operations and for growth in future
Problem Statement :
Identify causes behind current inefficiencies (rebate and idle cost) impacting gross margin.
Goal: to suggest improvements in the system for achieving at least 30% profit before tax with minimum errors and defects
Out of scope
• Market-research data for the purpose of increasing market share.
• Improvement in Customer satisfaction metrics other than rebate.
• Details of an employee reward system for incentivizing employees.
DEFINE- CTQ
Characteristics of product or
service
On time pickup/delivery , no damage to package , convenience of
scheduling
Measures and operational
definitionsRebates due to lateness or damage to package
Target value of measures Zero rebate
Specification limits Time window of 15 mins and undamaged package
Defect rate of measures Reduce rebate occurrence from 33.3% per total pickups to 5% or less.
*Idle time is an error in the process and an inefficiency between PDI and third party *Rebate is a defect
5. Suppliers 4. Inputs 1. Process 2. Outputs 3. Customers
sender Sender order detailsorder received from customer(package
sender)Order details (name, address, contact no.)
PDI
third party dispatcher
Time window from dispatcher
sender informed on estimated time window
Estimated 15 min time window for sender
sender
senderPayment
Confirmationpickup of package
Pickup confirmation from sender
Third-party dispatcher/fieldoperator
Sales operator at PDI
revenue calculationbill generated and package recipient informed on 15 mins time-window
Bill , estimated 15 min time window for recipient
sender and recipient
Recipientaddress details of
recipient package delivered to recipient
Receipt confirmation from recipient
Third-party dispatcher/fieldoperator
DEFINE- SIPOC
DEFINE – VSM and focus area
DEFINE – Assumptions
• Field operators are always available.
• Time window is negotiated between sender and Sales Operator and is an input to
dispatcher.
• Performance of field operator is out of our control.
• Package pickup and delivery are 2 isolated processes and we need to focus only on
package pickup.
• Payment issues from sender are not be considered.
Define – Initial business state
Sigma Measurement
Defects ( rebate occurrences) 706
Opportunities 2096
Defect Opportunities per unit 1
DPMO 336832.0611
Sigma Level 1.9
Financials
Revenues $125,335.40
Fields ops cost ( including Idle time cost) $42,753.90
Rebate cost $38,106.00
Gross margin $44,475.50
Fixed costs $35,000.00
Profit before tax $9,475.50
Profit before tax ( % of revenue) 7.6%
MEASURE – Data Collection requirements: Planning Sheet
Question To Be Answered Name of Data Required Operational Definition
how often are we early ? early frequencyMinutes To Customer (One Way < Trip Time Lower Spec Limit
how often is rebate due to lateness ? late frequency Minutes To Customer (One Way > Trip Time upper Spec Limit
By how much is the dispatcher overestimating the amount of forecast time in case of early errors ?
amount of early error(one-way distance*60)/ forecast speed))+7.5 mins –(minutes to customer )
by how much is the dispatcher underestimating the amount of forecast time in case of late errors ?
amount of late error(minutes to customer ) - ((one way distance*60)/forecast speed) - 7.5 mins
Details about bike and Truck usage conditionsDMB, DMT, DEB, DET, SMB,
SMT, SEB, SETCombined Indicator variables with rebate and idle time
MEASURE - Identify Sampling Bias and Measurement Problems
Sampling Bias
• Unequal weightage of data for bicycle and truck
• Unequal weightage of downtown and suburbia
• Seasonal fluctuation in data
• More/less incidents of damaged package considered than normal
• Higher proportion of data for particular range of package weight (can influence speed)
Measurement problems
• Estimation of route to be travelled by field operator
• Tracking of occurrence of a late pickup/delivery
• Variation in field ops cost with no relation to vehicle used.
MEASURE - FMEA
Key Process
Step or Input
Potential Failure
ModePotential Failure Effects
S
E
V
Potential
Causes
O
C
C
Current Controls
D
E
T
R
P
N
Actions
RecommendedResp.
What is the
Process Step
or Input?
In what ways can the
Process Step or
Input fail?
What is the impact on the
Key Output Variables
once it fails (customer or
internal requirements)?
Ho
w S
ev
ere
is th
e
effe
ct to
th
e
cu
sto
me
r? What causes the
Key Process
Step or Input to
go wrong?
Ho
w o
fte
n d
oe
s
ca
use
or
FM
oc
cu
r? What are the
existing controls
and procedures that
prevent either the
Cause or the Failure
Mode? Ho
w w
ell c
an
yo
u
de
tec
t th
e C
au
se
or
the
Fa
ilu
re M
od
e? What are the
actions for
reducing the
occurrence of the
cause, or
improving
detection?
Who is
Responsible for
the
recommended
action?
Dispatch time
calculation
dispatch time
calculation doesn’t
synchronise with
time window
rebate or idle time cost.
10
forecast speed
under/overestima
tion 10
none
10 1000
Estimate dispatch
time with accuracy
dispatcher
selection of
bike or truck
vehicle selected is
wrong as per time
window , location,
distance and time of
day considerations
rebate or idle time cost.
8
error by
dispatcher 4
none
10 320
dispatcher
package pickup package lost or
damaged
rebate 10
Error by Field
operator 2
none 10 200
field operator
ANALYZE – Data set 1-3
• No rebate due to damage • regression reveals that
late rebate mainly due to trucks used in Downtown during morning
Increase reliance on bikes in downtown morning
during morning
ANALYZE – Data set 1 to 3
• Truck performance improved• Overall rebate increased• Gross margin decreased• Current formula for dispatch
mins calculation is: (time to reach – 7.5) mins
Dispatch mins need to be increased to reduce lateness. Lateness is more frequent than idle time occurrence.Improvement 1) (time to reach + 7.5) minsImprovement 2) (time to reach + 15 ) mins
ANALYZE – data set 1 to 3
• Rebates decreased, GM improved• No trucks used in evenings• High rebates during bike usage in
morning
Dispatch time can be further increased to (time to reach + 31 mins)
ANALYZE – Data set 1 to 3
• All lateness occurrences are in morning and mainly due to bike
bike
downtown suburbia downtown & suburbia
AM 25 25 20 - 0.3*(pounds)
PM 15 20 20 - 0.2 * (pounds)
FORECAST SPEEDS
truck
bike
downtown suburbia downtown & suburbia
AM 20 20 20 - 0.3*(pounds)
PM 15 20 20 - 0.2 * (pounds)
FORECAST SPEEDS
truck
ANALYZE – dataset 4 – 6
• Data set 4-6 consisted of 100% bikes as vehicle used
• Over 90% of the pickups had idle time errors
• We focused only on optimizing forecast speed for bike and learnt that we need to monitor both dispatch mins and forecast speed
• In data 6 Idle time error was drastically reduced by increasing dispatch time to (miles/speed)*60 + 31 mins
ANALYZE – dataset 4 – 6
• But this was an impractical condition, so we ignored dataset 4-6 for further analysis
ANALYZE – Data set 7 to 11
bike
downtown suburbia
downtown &
suburbia
AM 25 25 20 - 0.35*(pounds) (miles/forecast speed)*60 + 5
PM 15 20 20 - 0.10 * (pounds) (miles/forecast speed)*60 - 5
forecast speed
truck dispatch mins bike
downtown suburbia
downtown &
suburbia
AM 15 15 20 - 0.40*(pounds) (miles/forecast speed)*60 + 5
PM 15 15 20 - 0.10 * (pounds) (miles/forecast speed)*60 - 5
forecast speed
truck dispatch mins
Rebates are quite low but idle costs are hitting margins now, mainly due to trucks
ANALYZE – Data set 7 to 11
Total profit improved a bit to 26.4%, but idle time cost still remains high
bike
downtown suburbia
downtown &
suburbia
AM 25 25 20 - 0.35*(pounds) (miles/forecast speed)*60 + 11
PM 15 20 20 - 0.10 * (pounds) (miles/forecast speed)*60 - 5
forecast speed
truck dispatch mins
ANALYZE – Data set 7 to 11
bike
downtown suburbia
downtown &
suburbia
AM 30 30 20 - 0.45*(pounds) (miles/forecast speed)*60 + 8
PM 15 20 20 - 0.15 * (pounds) (miles/forecast speed)*60 - 5
forecast speed
truck dispatch mins
profit before tax didn’t change enough. Idle time cost for individual
scenarios needs to be examined. Forecast speed can be increased, dispatch mins can be decreased
ANALYZE – Data set 7 to 11
dispatch mins
bike
downtown suburbia downtown &
AM 27 30 20 - 0.35*(pounds) (miles/forecast speed)*60 + 7
PM 15 20 20 - 0.2 * (pounds) (miles/forecast speed)*60 - 5
FORECAST SPEEDS
truck profit before tax didn’t change enough. Idle time cost for individual
scenarios needs to be examined. Forecast speed can be increased, dispatch mins can be decreased
ANALYZE – Data set 7 to 11
Profit margin is 34 % Sigma level is 3.8
Higher sigma level is desired since target for profit margin has already
been achieved Profit margin is 37% Sigma level is 3.4
ANALYZE – overall progress
ANALYZE – Overall progress
Reduce lateness occurrence and rebates reduce idle time cost to improve profit
ANALYZE – INSIGHTS
Idle Time Lateness
WHEN ? Speed of field operator is underestimated Speed of field operator is overestimated
HOW ?Forecast Speed < Actual Speed
OR Dispatch mins > Mins to customer
Forecast Speed > Actual SpeedOR
Dispatch mins < Mins to customer
SO ? Dispatch mins OR Forecast Speed Dispatch mins OR Forecast Speed
BUT, Reducing dispatch mins more than required can
cause latenessIncreasing dispatch mins beyond certain limit can
cause idle time
15 MINS TIME WINDOW
field operator
dispatcher
early lateMinutes to customer with actual speed
Dispatch minutes calculated using forecast speed
Lower Spec limit
Upper Spec limit
IMPROVE – Pugh Matrix
IMPROVE – Suggested business state
Factors measure improved data
fields ops cost (% of revenue) 34.1% 36.8%
rebate % of revenue 30% 1%
idle time cost % of revenue 0.4% 2.0%
profit before tax % 7.6% 34.0%
% bikes 88% 62%
% trucks 12% 38%
% defects 33.6% 1.0%
% errors 51.7% 50.8%
sigma 1.9 3.8
Updated Forecast Speed and Dispatch Minutes Calculator
Comparison of results
IMPROVE – Lateness Control Chart
CONTROL - Non-Statistics Process Control
• Standardized Operation Procedures –• Dispatcher need to follow our final rule, as below, to calculate bike estimated speed and
dispatch time
• Field Operator must follow suggested route from Dispatcher
• Documentation –• What kind of documentation is available? Improved Process Map, Mainframe process
documentation.
• Where is the documentation located? Change Management folder on shared drive
• Who has access to the information? Change management team
• Who will be responsible for updating the information? Change Management Team
• How is documentation / file change control managed? Change management team updates the version changes in the document.
CONTROL - Non-Statistics Process Control
• Poke Yoke –• Before dispatching field operator, we have to check there is parking spot at
destination for truck delivery.
• Demanded 3rd party regularly implement bike and truck maintenance.
• Routes should be decided by dispatcher and followed by field operator.
• Monitoring –• Conducted a regular monthly meeting to review our gross margin, price rebate
and idle time cost, and perform root-cause analyses.
CONTROL - Statistics Process Control
Gross Margin Control Chart
CONTROL - Idle Time Control Chart
Idle Time Control Chart 5% outliers are above UCL
CONTROL - Idle Time Control Chart
1% outliers are above UCL
Summary
Proposed system should regularly meet these criteria
• Profit before tax should be above 30% of revenue
• Gross Margin should not be lower than LCL ($1392)
• Not more than 5% of idle time outliers should be above UCL (13.76 mins)
• Not more than 1% of lateness occurrences(defects) should be above UCL (0.173 mins)
THANK YOU
MEASURE – DATA SET 1-5