Dashboard  Graphic  User  Interface

Invoice Data Management & Container Repair SolutionDepartment  of  Industrial  Systems  Engineering  and  Management  |  IE3100R  System  Design  Project

Team  Members:  Peng  Danni  |  Ng  Xun  Jie  Darren  |   Chia  Zhe  Min  |  Clara  Tan  Wen  QiSupervising  Professor:  Andrew  Lim  

PIL  Supervisor:  Tan  Chew  Eng

PROBLEM  OVERVIEW

SOLUTION  DESIGN  &  ACHIEVEMENTS

PROPOSED  SOLUTION  1:  INVOICE  DATA  MANAGEMENT PROPOSED  SOLUTION  2:  CONTAINER  DEMAGE  RECOGNITION

Pacific  International  Lines   (PIL)  Logistics department  deals  with  numerous  container  operations  daily through  their  Liner  Management  System  (LMS).  However,  inefficiency   that  exists  amongst  various   processes   in  the  system  is  causing  great  manpower  wastage  and  extra  spending.

This  project  mainly  targets  three  aspects  in  the  system  and  aims  to  develop   automated  solutions   to  reduce  manpower  usage,  improve  efficiency  and  facilitate  informed  decision  making  to  minimize  operational  costs.

STEP  2Select  regions  

containing required  data

STEP  3  Apply  OCR  to  

perform  text  recognition

STEP  4  Check  output  data  and  organize  into  required  format.  Save  as Excel

STEP  5Upload  Excel  fileto  LMS  system  for  container  tracking

§ PROJECT  OBJECTIVE § PROBLEM  DESCRIPTION

STEP  1Import  scanned  

invoice  in  PDF  format

ContainerArrival

ContainerInspection

ContainerRepairLiner  Management  System  

(LMS)1

2

3

• Require  container  entering  and  exit  data from  invoices   for  container  tracking in  LMS

• Agents  manually   key  in  data  from  scanned  invoices   into  Excel  sheets   – 30  minutes  per  invoice,   740  invoices   per  month

• Human  error  is  a  common  during  data  entry

• Need  to  identify  the  damage  type  from  photos  of  containers  taken  by  surveyors   at  the  depot

• Currently  the  inspection   is  done  manually

PROBLEM  1 PROBLEM  2 PROBLEM  3• Where  to  repair  the  damaged  container  is  an  

important  decision   to  be  made• Currently,   the  decision   is  made  based  on  

experience,  which  may  not  be  optimal• Incur  costs  that  can  be  saved  alternatively

Require  3  kinds   of  data:1) Container  Number2) Entering  Date3) Exit  Date

Select  regions  by  entering  the  region’s  coordinates  on  image  or  by  cropping.

Improvement:Invoice  layout  is  usually   the  same  for  the  same  vendor.  To  avoid  repetitive  cropping,  we  incorporate  a  function   to  create  and  save  templates  for  each  vendor,  which  can  be  reused  directly  on  invoices  with  the  same  layout  to  crop  out  the  regions.

OCR  (Optical  Character  Recognition)  is  a  technology  that  converts  images  of  text  into  digital  form.

After  comparing  several OCR  engines,  Baidu  OCR  is  chosen   as  it  produces   the  best  quality  output.

Sign  up  Baidu  account  to  access  its  OCR services.

500  free  usage  per  day  per  user  account.  If  exceeded,  $0.002  -­‐ $0.006  per  usage.

Perform  auto  checking  on  OCR  output  data  to  ensure  that  standard  format  is  followed.  Highlight  any  wrong  output.

Organize  data  into  certain  format  to  allow  direct  transfer  to  the  LMS

1) Match  container  entering  and  exit  date  for  invoices   that  have  layout  with  the  two  information  separated

2) Include  invoice-­‐specific  information

TECHNICAL  SKILLSETS

Integrate  steps  Into  a  standalone  applicationInvoice  

Converter

• Clear  and  concise• Intuitive• Attractive  design

• Error  proof• Diverse  features• User-­‐friendly

PROPOSED  SOLUTION  3:  CONTAINER  REPAIR  OPTIMIZATION

Auto-­‐checking  highlights  wrong  output   in  Excel  that  does  not  follow  standard  format

Upload  excel  file  or  key  in  the  values.  Input   follows  LMS  format

Hit  the  button  to  calculate!

Feedback  is  provided   to  ensure  

calculations  are  done   for  the  correct  set  

of  input

Most  crucial  results  are  

provided   first

Cost  breakdown  are  provided  for  further  investigation  

*note  that  these  results  are  not  the  actual  figure  

Process  Mapping

Criteria Description Remarks

Practicality The  dashboard  provides   quick  consolidation   and  processing   of  data

Usability The  dashboard   is  user-­‐friendly   and  provides   results  clearly

ReliabilityThe  dashboard  prevents  human  error  by  giving  a  feedback  on  the  input  and  

a  breakdown  of  the  cost    

Labour  Cost Material  CostLift-­‐on-­‐lift-­‐off   Cost

Trucking  Cost Liner  ScheduleStevedorage  Cost

Database

Automatic  detection  of  the  following  information  from  images:

1) Container  number

2) Types   of  container  damage

Integration  of  automatic  detection  of  information  from  images  using  A.I.  into  the  LMS  system  to  allow  for  automatic  verification  of  repair  recommendations   received  from  vendors.

The  container  number  and  the  types  of  damages  detected  in  the  images  are  cross-­‐checked   against  all  the  stated  information  in  the  repair  recommendation.

Repair  recommendations   for  damaged  containers  contain:1) Container  number2) Damages  sustained  by  container3) Recommended  repairs  to  be  

conducted  on  container4) Images  of  the  container  taken  by  

port  employees  (~10)

Current  workflow:PIL  employees  manually  inspect  the  images  uploaded   and  verify  that:1) the  container  number  is  correct2) the  damages  reported  are  

accurate and3) the  recommended   repairs  are  

relevant

STEP  1Receipt  of  repair  

recommendation  for  damaged  containers

STEP  2Detection  of  

relevant  information  in  images  using  A.I.

STEP  3  Auto  verification  of  information  in  repair  recommendation

Import  invoice

Select  page

Panel  to  create,  save  and  delete  template

Adjust  orientation

Panel  to    convert  invoice

Panel  to  display  invoice

Right  click  to  adjust  image  size  or  use  mouse-­‐wheel  to  zoom  in  and  out

Select  regions  by  entering  coordinates  or  cropping   from  the  image

Selected  regions      

Choose  a  template  to  use

Enter  invoiceinformation

Allow  multiple  pages  bulk  conversion

Hit  the  button    to  convert!

Python  Programming• Data  Manipulation and  Structure  Design:  Pandas,  Numpy• GUI  Programming  and  Algorithms  Design:  Tkinter• Machine  Learning  Model:  TensorFlow,  Scikit-­‐learn,  Keras• Image  Processing   and  Analysis: OpenCV,  Pillow• Optical  Character  Recognition:  Baidu  OCR  Python  SDK• Natural  Language  Parsing:   Stdnum,Dateutil• Live  Code  Demo  and  Visualization:  Jupyter  Notebook• Other  Libraries  Used:  StyleFrame,  Matplotlib,  cx_Freeze

Other  ISE-­‐related  Skills• Human  Factors  Engineering  – HMI  Design• System  Thinking and  Project  Management                          

R  Programming

• Data  Manipulation:  Tidyr,  Stringr• Currency  Conversion:  quantmod• Dashboard  Interface:  Shiny,  

Shinydashboard,  Rhandsontable• Packaging  into  standalone  app:  

Chrome  Portable

Various  Integrated  Development  Environment  (IDE)  Used

§ SOLUTION  BENEFITS

§ SOLUTION  BENEFITSCriteria Description Remarks

Practicality The  application   integratesall  the  steps  to  enable  automation  of  invoice  recognition  task

Usability The  development   of  the  application  incorporates  various functions to  make  it  intuitive and  easy  to  use

Reliability The  recognition output  is  proven  to  attain  90%  accuracy,  auto-­‐checking   further  helps   identify  wrong  output  

Save  template  for  selected  pages  before  conversion

§ SOLUTION  BENEFITSCriteria Description Remarks

Practicality Automatic  verification  save  hundreds   of  man-­‐hours  monthly  previously   required  to  do  manual  verification

Usability A.I  automatic  detection  is  integrated  into  the  LMS  such that  it  is  readily  accessible  for  daily  usage

detect  using  Baidu  OCR

dirty  floorboardoily  floorboard

Image  Classification  Models• Convoluted  Neural  Networks• AlexNet, ResNet• Support   Vector  Machines  with  

Histogram  of  Oriented  Gradients