Understand what value can be gained by using simulation-based predictive analytics for supply chain, distribution center, logistics and warehouse design, operations, and improvement

1. 1
Supply Chain Analytics
Michael J Rice
Director | Manufacturing & Logistics
Eastern US & Canada
mrice@promodel.com
O: 207.406.4993
M: 845.781.3514
Andy Schild
Director | Manufacturing & Logistics
Central US & Canada
aschild@promodel.com
O: 585.398.8178
M: 585.507.8499
Mike Townsend
Director | Manufacturing & Logistics
Western US & Canada
mtownsend@promodel.com
405.850.7610

2. 2
 ProModel Story
◦ Founded in 1988 by Dr. Charles Harrell.
◦ Developed and distributed easy to use simulation products
for use of standard PC’s with focus on manufacturing/logistics
◦ Expanded Simulation to the Planning Domain in the late 90’s
◦ Expanded Simulation to Custom Decision Support Applications in early 2000’s
 3 Major US Offices
◦ Allentown, PA – Admin, Marketing & Sales
◦ Orem, UT - Development, PM, Sales & Support
◦ Ann Arbor, MI – Development, PM & Sales
 90 Direct employees
 24+ VARs with offices world wide.
 Over 7000 users world wide, 50%+ Fortune 500
Main Offices Remote Employees
24 VARS

3. 3
ProModel Provides Simulation-Based Decision Support Solutions that
allow organizations to understand system performance in a low-risk
environment
ProModel combines a Powerful Suite of Simulation Technology with 28
years of delivering training and consulting solutions enabling our
clients to:
• Maximize Throughput
• Control Operational Costs
• Increase labor productivity
• Understand effects of budget system constraints (bottlenecks)
Understand the Impact of Critical Business Decisions…
Prior to Implementation

4. 4
Definition of analytics
“Data analytics is the science of examining raw data to
help draw conclusions about information. It is used in
many industries to allow companies and organization
to make better business decisions and in the sciences
to verify (or disprove) existing models or theories.”
Definition of supply chain
” the sequence of processes involved in the production
and distribution of a commodity.”

5. 5
• Seasonal Fluctuations
• Oscillations in demand / “Bullwhip” effect
• Ineffective forecasting methods/data
• Cost of holding inventory vs. cost of stock outs
• Logistics Systems
• Limited visibility to suppliers
• Customer satisfaction
• Responsiveness to market changes
• Synchronizing ordering to inventory levels

6. 6
Forecast Volumes
Design Specs
Shift Setups
Productivity Rates
Volume Splits
Conversion Factors
Process Throughput
% Utilization
Productivity Impact
Kanban Requirements
Headcount Planning
Equipment Requirements
Yard Capacity
“In other words, we convert
information we do have into
the answers we want to
have…”

7. 7
 Dynamic platform that predicts/explains current and/or future
behavior
 Accounts for
 Variability
 Real World Complexities and Constraints
 Reports on Key Metrics
 Throughput, inventory, lead times and resource utilization
 Reduce Risks
 Test changes prior to implantation
 Quantify costs and changes to flow
 Quantify the impact of change

8. 8
Variation: “Averages” are Dangerous
How much
Risk can
you afford
to take?

9. 9
Trying to optimize a
complex supply
chain/logistic systems is
like trying to solve the
Rubik's cube.
Simulation can
help understand
the trade-offs!!
 Maximize Resilience
 Reduce Fulfillment Times
 Right Size Inventory
 Minimize CapEx
Interdependencies: The Domino Effect

10. 10
 Supplier Disruptions
 Equipment Failures
 Resource breaks / days off
 Staffing
 Transportation Delays

11. 11
Facility
Design
Slotting
Analysis
Material
Handling
Analysis
Labor
Analysis
Process
Improvement
Support
Throughput
/ Capacity
CapEx
Validation
Pick & Put-away
Strategies
Effects of
Seasonality

12. 12
System
Logistics
Transportation
Network Design
Supply Chain
“Optimization”
Optimize
Location of
Facilities
Process
Improvement
Support
Material
Replenishment
Strategies
Determine
Quantity of
Facilities
Service Level

13. 13
• How Robust is the Supply Chain
• What are the dynamics of the supply chain?
• What strategies will best mitigate against undesirable effects?
• How will a supply chain react to fluctuations in demand?
• How will customer behavior affect supply?
• Financial Performance
• How can we improve system performance and cut costs?
• Supply Chain Design
• Placement factories, distribution centers. wholesalers, & retailers
• What inventory rules should be engaged at various places in the network?
• How Do we Minimize Risk
• Can Risks be mitigated?
• Can changes to the system be made without affecting supply?

14. 14
“Profits for your company can
rocket upward if you achieve
sufficient savings in supply chain
costs. It's not uncommon for a
concerted effort to yield annual
savings of between US $2 million
and $10 million, depending on the
size of the company.” (Supply Chain
Quaterly)

15. 15
Optimize
Scenarios & KPI
Comparisons
Analyze
the Results
Visualize
the Process
• Demand & Mix
• Process times
• Qty Personnel
• Qty Equipment
• Yields
• Batch Sizes
• Utilizations
• Inventory Levels
• Throughput
• Lead Times
• Costs
• Manufacturing Facility
• Value Stream Map
• Warehouse
• Supply Chain
• Business Process

16. 16

17. 17
 Large Big Box Retailer Distribution Center
 Physical PILOT was underway testing a Flat Flow concept favored
by management
 Additional VIRTUAL Simulation PILOT was run to validate results
Flat Flow Layout Original Layout

18. 18
Physical Pilot Results
 Physical Pilot being tested in an actual facility under intense MANAGEMENT
scrutiny yielded 17% productivity Improvement
 Validated a spend of $1,000,000 x 6 facilities to recreate in all of their facilities
Simulation Pilot Results
 ProModel modeled current (to validate model) and proposed designs
 Surprisingly, model showed the OPPOSITE of the observed improvement
 Model showed A LOSS in productivity (-2.4%) by implementing the proposed
flat flow
 After further study, it was realized that the initial gain in productivity was due to
the “Hawthorne Effect”, i.e the intense scrutiny drove higher than normal
levels of productivity among the worker.
 Client was able to cancel the proposed $6M investment and led to the future
mandate that all projects above specific $ values would be modeled and to
validate the spend

19. 19
 Multi-phased project to support new production facility
 The focus of the study was to look at Line-Side & Market
Place Inventory Levels, Reorder Points and Reorder
Quantities while taking into account variable lead times from
their supplier to minimize the WIP held onsite
 The material handling types and quantities were analyzed to
right-size the labor and equipment for the specified
frequency of deliveries

20. 20

21. 21
 The Company was able
to assess multiple
scenarios around
material replenishment
strategies and assess
their viability with
regards to meeting
production demands

22. 22
 Company was able to
evaluate the scenarios and
understand how the
decisions they made
regarding replenishment
frequencies, and amounts
affected the subassembly
availability Line-side, over
time

23. 23
 Simulation Analysis was able to
show the utilization levels of the
various material handling
equipment (Tuggers & Fork
trucks
 Model showed that the
replenishment strategy that was
most cost effective and
manageable from a supplier
standpoint would require an
additional resource

24. 24
 Takeaways:
◦ Established minimum SAFE level of inventory the client could
keep Lineside and in their Marketplace to support assembly
 50% reduction in inventory holding costs for the client based on the
proposed design
◦ Space for the subassemblies lineside was reduced making
room for future growth (and was used for future expansion)
◦ Planned MHE level could NOT support the level of deliveries
 Client avoided potential bottlenecks by identifying in the model the
specific number of MHE needed to support the replenishment
strategies

25. 25
• Supply Chain Modeling and Simulation can
provide organizations with ability to design, test
and deploy robust networks that create value.
“Organizations can gain competitive advantage by running supply chain
network scenarios, evaluating and proactively implementing changes in
response to dynamic business scenarios like new product introduction,
changes in demand pattern, addition of new supply sources, and
changes in tax laws.”
(Industry Week, 2013; L.N. Balaji)