Whiteboard2Boardroom presents innovations from regional universities, hospitals, research organizations to encourage commercialization.

1. Novel Resource Distribution
Decision Tool to Optimize Complex
Industrial Projects
LEAD INVENTOR
Haitao Li, Ph.D.
Associate Professor, Supply Chain Management & Analytics
University of Missouri – St. Louis
Supported by funding from the UM System
Interdisciplinary Intercampus Research and
FastTrack programs

2. Such industries include:
o Construction
o Pharmaceutical
o Oil & Gas
o Military
o Supply Chain
o Logistics
Across many industries, optimizing resource management,
supply chain design and logistics is essential in minimizing
costs and keeping projects on time.
Project Management Optimization Needs
What is the Problem?

3.  Cost over-runs are not uncommon because resources are not
optimally allocated to project tasks
 Uncertainty in task duration caused by variations in crew productivity
 Difficulty of scheduling payments to meet complex payment terms
Current tools designed to save time and money by optimizing project
management are lacking – they aren’t dynamic and very few provide
real-time feedback or other useful functionality.
What is the Problem?
Project Management Challenges

4.  Optimizes the time-cost tradeoff (crashing) decisions with
complex payment structure and discounted cash flows
 Sequential and adaptive solution approach to capture the
variation of project operations efficiency and productivity
 A data-driven approach closing the gap between decision
support and the “big data” made available through IT
advancement
How Does This Technology Solve
the Problem?

5. (2) External Disturbance
0 1 2 3
(1) Initial Crashing
Decision
(3) Observe External
Disturbance and Update
Problem Parameters
(4) Optimize the Current
Crashing Decision
…
…
Sequential, Adaptive Solution
Framework

6. Optimization Model
OUTPUTS
• Option chosen (duration & cost) for
each task
• Start time of each task
• Maximum Return on Revenue
(ROR)
• Optimal project makespan
MIXED-INTEGER PROGRAMMING
(MIP) Model
Optimization Model for Maximum ROR
INPUTS
• Total project contract value
• Project makespan due date
• Duration of options for each task
• Cost of options for each activity
• Indirect cost rate
• Discount and interest rate
• Exogenous disturbances

7. Maximized return on revenue (ROR)
 ROR Increased by 16% in a real-world construction
project case study involving only 8 tasks (details provided
in the handout)
Improved efficiency for project operations
Duration improved from 65 days to 59 days in the noted
case study
Improved quality of service delivery
Value Proposition

8.  Optimizes the time-cost tradeoff (crashing) decisions
with complex payment structure and discounted
cash flows
 Leverages the availability of data to provide data-
driven project scheduling and resource
distribution solutions
Market Use

9. Project Management for the U.S. construction
industry represents a significant, growing market
 Estimated at $1.3 billion in 2018
 Expected to reach between $1.62 billion and $2.71 billion by
2024
 Growing at a CAGR of between 7.9% and 8.8% (2019-24)
 Of smaller construction firms:
 1/3 currently use construction software
 1/3 used generic tools such as email/spreadsheets
 28% cite improved workflow efficiency as their top need/priority
Sample Target Industry: Construction

10. Tool Task
Scheduling
Cash-Flow
Prediction
Payment
Scheduling
Dynamic,
Adaptive
Real-Time
Feedback
MS Project Yes No No No No
Primavera Yes Yes No No No
Buildertrend,
Contractor
Foreman
Yes No No No No
CoConstruct,
Raken, RedTeam,
CMiC
Yes No No No Yes
PASKR Yes No No No No
PlanGrid No No No No No
ProCore, eSub Yes No No No Yes
OUR TOOL Yes Yes Yes Yes Yes
Competition

11. The IP includes both copyrights and patent
rights.
 Provisional (62/454,511) filed 02/03/17
 PCT/US2018/16369 filed 02/01/18
 US National Stage application filed 08/03/19
Intellectual Property

12.  A prototype has been developed with:
web-based architecture using ASP technology (eliminates the
need for user to purchase optimization solver);
successful integration of GUI, database and optimization
models; and
controlled user portal and privilege
 Testing with real-world construction company data
completed
Development Stage

13.  Design and development of user interface for enhanced
user experience
 Automation of workflow for users with different roles in a
project
 Cloud-based architecture to support mobile access
 Seamless access and data exchange with project
management applications (e.g., MS Project)
 Additional testing and de-bugging of the tool
Further Development Needed

14.  Complex Projects need tools to effectively and efficiently
allocate resources
 Resource allocation decisions have a huge impact on finish
times and ROR
 Our technology provides dynamic optimization decision
support functionalities not offered in currently available
tools
Summary

15. Thank You!
Contact: Tamara Wilgers
Director, Technology Commercialization
wilgerst@umsl.edu
314-516-6884

16. Case Study
BRIDGE CONSTRUCTION PROJECT

17. Activity
number
Activity description Crew ID
1
Shop drawings, abutment, and deck
steel
FAB
2 Shop drawings, foot steel FAB
3 Move in N/A
4 Deliver piles N/A
… …
35 Painting B78
36 Guardrail N/A
37 Clean up A5
38 Inspection N/A
BRIDGE CONSTRUCTION CASE STUDY:
Activities

18. BRIDGE CONSTRUCTION CASE STUDY:
Project Network

19. • Labor Hours = Total Mhrs / Daily Output
• Daily Output is assumed to be a non-linear function of crew size to
model the diminishing return of crew size
BRIDGE CONSTRUCTION CASE STUDY:
Activity Info for Updating

20. 0
2000
4000
6000
8000
10000
12000
14000
16000
18000
Before Project Start Above Cost & Ahead Schedule Above Cost & Behind Schedule Below Cost & Behind Schedule
All-Normal Optimal Solution
16.0%
16.0%
19.5%
2.8%
BRIDGE CONSTRUCTION CASE STUDY:
Computational Results Summary