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Jack Kanet

Niehaus Chair in Operations Management

University of Dayton

Smart Sequencing—an

Innovative Tool for a

Simpler Process

Applying OR/Analytics to Scheduling

Operations Management at University of Dayton

• Senior capstone student consulting projects

• Student teams/faculty mentor

• Logistics, project management, inventory, scheduling,

layout, supply chain management, process analysis

• Optimization, simulation, statistics, business analytics

• 2 Semesters

• Regional Firms, ≈ 70 projects over the last 9 years

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Applying OR/Analytics to Scheduling

Johnson Electric Profile

• Global manufacturer of solenoids for OEM markets

• Branch located in Vandalia, OH

• Manufacturer of:

• Rotary solenoids

• Tubular solenoids

• Found in multitude of products

• Hair dryers

• Car door locks

• Landing gear

• Vending machines

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Cell Production

Current Scheduling Method

• Methodology:

– “Manual”: Spread sheet based

– Uses experience based knowledge

– Sequence of jobs based on:

• Job due date

• Quantity (run time)

• Size changeover (setup times for 4 different sizes))

• Other (customer priority, order revenue)

• Current metric: Percent on-time orders

– Approximate 2 week horizon (≈20 jobs)

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Sequence-Dependent (quasi)

Setup Times

Setup Time Matrix (hrs)

Sample Schedule

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Current Scheduling Performance

• On-Time delivery percentage

– 2009 & 2010: Average of 76% on-time delivery

Client Expectations

• Initial expectations

– Achieve 100% on-time delivery

– Reduce changeover time

– Increase productivity

– Keep inventory minimal

– Automate the process

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Approach

• Developed an Excel-based scheduling tool

– Create and optimize cell schedules using premium

solver

Approach

• Excel-based premium solver tool

– Job data downloaded from MRP system into Excel

– Run macro to enter selected data into optimization table

– Run premium solver application

• Define objective (e.g., % on time)

• Specify decision variables (job position in schedule)

• Identify constraints ( positions ≤ job count, alldifferent,

integer)

• Choose search engine (evolutionary algorithm)

– Calculate dispatch order-based on selected objective

– Run second macro to provide formatted schedule output

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Premium Solver Dialog

Available Objectives

• Possible objectives:

– Minimize % tardy

– Minimize total tardiness

– Minimize total weighted tardiness

• Weights defined by job revenue

– Minimize total setup time subject to minimized % tardy

– Minimize total tardiness subject to minimized % tardy

– Minimize total weighted tardiness subject to minimized %

tardy

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Comparative Study

• Obtained 12 historical manual schedules

• Entered historical schedules into model

– Observed and recorded metrics

• Ran optimization model for each schedule

– Used all available objectives

• Compared the model’s results to historical

manual approach

Manual vs. Model Analysis

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Manual vs. Model Analysis

Manual vs. Model Analysis

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Manual vs. Model Analysis

Comparative Analysis of 12 Schedules

• Average % tardy– Manual- 24%

– Model- 7%

• Average set-up time (Subject to % tardy)– Manual- 2.85 hrs

– Model- 2.08 hrs

• Average total hours tardy (Subject to % tardy)– Manual- 110.43 hrs

– Model- 77.83 hrs

• Average weighted tardiness (Subject to % tardy)– Manual- 986,526 {∑ (sales revenue x tardy hrs.)}

– Model- 81,738 {∑(sales revenue x tardy hrs.)}

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Benefits and Future Applications

• Improved on-time delivery percentage

• Time savings

– Manual scheduling ≈ 1 hour per week (per cell)

– Model scheduling ≈ less than 5 minutes per week

• Standardized tool and process

• Easy rollout to other product lines

• Allows user to easily amend schedule

Recommendations

• Purchase premium solver

– 010PREM Premium Solver V10.5 (basic optimization)

• License and support total: $895.00

• 5 or more licenses earn 10% discount

• Implement the model into the scheduling

process

• Use best objective for evaluation

– Minimizing total weighted tardiness

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Extensions

• Easy addition of additional constraints– Job X to be in position 2

• Add constraint: Position(X) = 2

– Job A to precede job B • Add constraint: Position (A)<Position(B)

– Job C and D to run consecutively • Add constraint: Position(C) = Position(D)+1

• Feedback to MRP to schedule materials– Extend the scheduling horizon from 2 weeks to 4 weeks

– Feed schedule start dates back to MRP for better priority

planning of components

Caveats• Scheduling performance is not the same as

factory execution performance ! --- But it’s a

good start!

• 2-year average on time execution

performance: 76%

• 12 historical manual schedules projected on

time (scheduling) performance: 79%

• 12 analytics-based projected on time

performance: 94%

•

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UD Students Win APICS Award

Dayton OH, May 6 2011:University of Dayton students Matt Schatzman, Nick Hanneken, and Alex Johnston received the $500 award for best Operations Management Senior Consulting Project from Rich Graff, Dayton Student Chapter Liason.

Applying OR/Analytics to Scheduling

Thanks for your attention !

Join us at the LinkedIn "University of

Dayton Operations Management Group”

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Schedule Optimization Tool