1. Operations Scheduling
35E00100 Service Operations and Strategy
#5 Fall 2015

2. Topics Principles of scheduling Practices in Finnish companies
Objectives and constraints
Problem types
Practices in Finnish companies
Setting lead times and order priorities
Lead time quoting
Basic dispatching rules
Advances scheduling heuristics
Key points
Useful material in the textbook and course package:
Hopp, W. & Spearman, M. (2000), Factory Physics, Chapter 15
Vepsalainen, A. & Morton, T. (1987) “Priority Rules for Job Shops with Weighted Tardiness Costs”, Management Science

3. Production Scheduling
Principle
Concerns the allocation of limited resources to tasks over time (Pinedo, 1999).
Determines in what sequence orders are processed on available resources.
Justification
Allows better matching of customer requirements and service.
e.g. response times and delivery accuracy
Supports coordination of material flows.
Machine a
Machine b
Machine k ai ri Ci …
pi1a
dij
pi2b
di2
pijk
dij di
Operation 1
Operation 2
Operation j

4. The Definition of Scheduling
A scheduling system dynamically makes decisions about matching activities and resources in order to finish jobs and projects needing these activities in a timely and high-quality fashion while simultaneously maximizing throughput and minimizing direct operating costs.
(Morton & Pentico 1993, 10)
Basic scheduling decisions typically include
Routing
Sequencing
Timing / release

5. A Two-Operations Process A Flow Shop1 2
Input
Output
Buffer
(WIP)

6. Most scheduling environments are extremely complex..B E
Raw materials
Finished products C F
Queue
Decision
Work center

7. Types of Scheduling Models (1)
Project Scheduling
Objective is to minimize total time
Unlimited number of machines
Precedence constraints  critical path to be identified
Important in large multi-stage projects
Job Shop Models
Jobs with a number of operations
Machine configuration / job routing
One or more objectives
Both manufacturing and service industries
Production Systems with Automated Handling
Jobs with a number of operations
Material handling and conveyor systems control the movement of jobs and timing of their processing on various machines
Objective is to maximize throughput
Applied for example in FMS and in automotive industry

8. Types of Scheduling Models (2)
Lot Scheduling
Medium- and long-term production planning and control
Objective is usually to minimize total inventory and changeover costs
Continuous production & demand processes
Handle a variety of different products
Process and retail industries
Workforce Scheduling
Different, although often intertwined with machine scheduling
Reservation and Timetabling Models
Reservation systems
Objective is to process as many jobs as possible
Starting and completion times of jobs are fixed
Timetabling models
Objective is to process all jobs and minimize makespan
Starting and completion times are not fixed
Two jobs that require same tool cannot be processed at the same time
Facility scheduling (classrooms, vehicles)

9. Order Management Decisions
Markets / Customers
Material requirements
Orders
Demand forecasts
Order
acceptance
Production planning
Master scheduling
Shop status
Schedule performance
Scheduling constraints
Capacity status
Due date
assignment
Schedule
Shop orders
Release dates
Job loading
Material requirements planning, Capacity planning
Order
release
Detailed scheduling
Scheduling and Rescheduling
Order
sequencing
Dispatching
Job
dispatching
Shop floor management
Modified from
Pinedo and Chao 1999, 7

10. Practices in Finnish Industrial Firms
Illustrative Examples
Practices in Finnish Industrial Firms
Order processing
Via worldwide sales network
Cooperation between mfg and centralized sales support
Chain-wide IT integration in progress
A part of orders electronically
Decision-makers
Order acceptance: sales (market)
LT estimation: sales (mill)
Final schedule: sales and production planning
Dispatching: shop floor
Main criteria in use
Order acceptance: profit, relationship
Lead-times: standard estimates
Schedule: delay penalties
Case 1: Machinery
Order processing
Via sales unit network in Europe
Centralized sales support
IT integration within the firm; there are efforts to share information openly through the supply chain
Orders via fax and
Decision-makers
Order acceptance: sales (market)
LT estimation: sales (market)
Final schedule: production planning
Dispatching: --- (not needed)
Main criteria in use
Order acceptance: capacity
Lead-times: standard estimates
Schedule: no priorities or penalties
Case 2: Metal Products

11. Practices in Finnish Industrial Firms
Illustrative Examples
Practices in Finnish Industrial Firms
Case 3: Electronics
Case 4: Paper
Order processing
Directly to manufacturing units
IT systems integrated partly in the chain: limited information sharing
A part of orders electronically
Decision-makers
Order acceptance: sales (mill)
Lead-time estimation: production planning
Final schedule: production planning
Dispatching: shop floor
Main criteria in use
Order acceptance: capacity
Lead times: order-specific estimates
Schedule: some implicit priorities, penalties occasionally considered
Order processing
Via sales units and agents
Centralized sales support
System integration in-progress; currently order and schedule information is shared only internally
Decision-makers
Order acceptance: sales (mill)
Lead-time estimation: sales (mill)
Final schedule: sales (mill) & production planning
Dispatching: --- (not needed)
Main criteria in use
Order acceptance: capacity
Lead-time: standard estimates
Schedule: no priorities or penalties

12. HSE Study on Priority Scheduling and Customer Order Management
Research question
Do priority rules provide a protocol for coordinated order management?
Objectives
Identify robust and well-performing priority rules or families of such rules for order mgmt.
Examine different technical specifications and tolerances for information and communication necessary to implement the scheduling rules.
Demonstrate the sensitivity of system performance to different conventions in order handling.
The scheduling problem analyzed
Job shop problem with standard assumptions
Non-delay scheduling
Weighted tardiness problem
Heterogeneous lead time expectations
Different costs for inventory, expediting, and tardiness
35E00100 Service Operations and Strategy HSE/Logistics

13. Order Management and Scheduling Practices in Finnish Manufacturing
Explorative multi-case study
16 manufacturing companies with operations in Finland
Leading players in their field
electrical equipment and components, heavy machinery, metal & paper
Objective
To review current intra- and inter-firm order management and scheduling practices
Data collection process
Personal interviews based on a questionnaire
in total about 160 questions
mostly structured questions
Data includes detailed descriptions on
organization structure and culture
operations within the order-delivery process and supply chain
firm and supply chain performance
Companies interviewed
Contract manufacturing industry
Elcoteq: terminal products and electronics
Flextronics: industrial electronics
Tellabs: network components
Machinery industry
ABB Industry: engines and generators
KCI Konecranes: lifting equipment and cranes
Kone: elevators
Metso Automation (Neles): industrial valves
Metso Paper (Valmet): paper machines
Metal manufacturing industry
Outokumpu Drawn: copper rods and profiles
Outokumpu Rolled: rolled copper products
Outokumpu Tubes: tubular copper products
Rautaruukki Metform: welded steel tubes and tubular products
Paper industry
M-Real: publishing and printing paper
Myllykoski Paper: printing paper
Stora Enso: fine paper
UPM-Kymmene: newsprint paper
35E00100 Service Operations and Strategy HSE/Logistics

14. Descriptions of the Selected 16 Cases
Companies interviewed
Contract manufacturing industry
Elcoteq: terminal products and electronics
Flextronics: industrial electronics
Tellabs: network components
Machinery industry
ABB Industry: engines and generators
KCI Konecranes: lifting equipment and cranes
Kone: elevators
Metso Automation (Neles): industrial valves
Metso Paper (Valmet): paper machines
Metal manufacturing industry
Outokumpu Drawn: copper rods and profiles
Outokumpu Rolled: rolled copper products
Outokumpu Tubes: tubular copper products
Rautaruukki Metform: welded steel tubes and tubular products
Paper industry
M-Real: publishing and printing paper
Myllykoski Paper: printing paper
Stora Enso: fine paper
UPM-Kymmene: newsprint paper
35E00100 Service Operations and Strategy HSE/Logistics

15. Categorization of Production Planning and Control Approaches
Type of Logistics System
Contingent Projects
Standard Deliveries
Routine Shipments
Customer-driven
Planning & Control
Job Shop
Priority Scheduling
Type of
Production System
Flow Balancing in Planning & Control
Assembly/ Batch Line
Capacity-driven
Planning & Control
Continuous Flow Production
35E00100 Service Operations and Strategy HSE/Logistics

16. Positioning of the 16 Cases Framework of Operational Systems
Type of Logistics System
Contingent Projects
Standard Deliveries
Routine Shipments
Case F Case H
Group 1
Case E Case G
Group 2
Job Shop
Case D
Group 3
Type of
Production System
Case A Case B
Case C
Group 4
Case J
Case K
Case L
Group 5
Assembly/ Batch Line
Case I
Case M
Case N
Case O
Case P
Group 6
Continuous Flow Production
35E00100 Service Operations and Strategy HSE/Logistics

17. Categorization of Production Scheduling Methods
Objective of Scheduling
Sales-oriented,
Customer Requirements
Production-oriented, Productivity Requirements 1
Order-based scheduling 3
Production and materials planning
Adjustable Capacity
Capacity Constraint 2
Sales budgeting / Capacity allocation 4
Product sequencing for capacity
Fixed
Capacity
35E00100 Service Operations and Strategy HSE/Logistics

18. A Generic Framework of Order Management Decisions
(modified from Pinedo 1995)
Suppliers
Customers
Orders
Forecasts
Material requirements
Order
acceptance
Production planning, Master scheduling
Due date
assignment
Capacity status
Material requirements planning, Capacity planning
Order
release
Shop orders
Scheduling constraints
Release dates
Scheduling and Rescheduling
Order
sequencing
Detailed scheduling
Schedule performance
Schedule
Dispatching
Job
dispatching
Shop status
Job loading
Shop floor management
35E00100 Service Operations and Strategy HSE/Logistics

19. Operations Control Framework Typical Order Management Procedures with Different Production Scheduling Methods
Objective of Scheduling
Sales-oriented,
Customer Requirements
Production-oriented, Productivity Requirements
Customer orders
Material requirements planning, Capacity planning
Production planning,
Master scheduling
Shop floor management
Dispatching
Scheduling, Rescheduling
Due date
assignment
Order
release
acceptance
sequencing
Job
dispatching
Customer orders
Shop floor management
Material requirements planning, Capacity planning
Production planning,
Master scheduling
Dispatching
Scheduling, Rescheduling
Due date
assignment
Order
acceptance
Job
dispatching
sequencing
release 1
Order-based scheduling 3
Production and materials planning
Adjustable Capacity
Capacity Constraint
Customer orders
Material requirements planning, Capacity planning
Production planning,
Master scheduling
Shop floor management
Dispatching
Scheduling, Rescheduling
Job
dispatching
Order
release
assignment
Due date
acceptance
sequencing
Customer orders
Material requirements planning, Capacity planning
Scheduling, Rescheduling
Dispatching
Shop floor management
Production planning,
Master scheduling
Order
release
Due date
assignment
sequencing
Job
dispatching
acceptance 2
Sales budgeting / Capacity allocation 4
Product sequencing for capacity
Fixed
Capacity
35E00100 Service Operations and Strategy HSE/Logistics

20. Positioning of the 16 Cases Framework of Scheduling Methods
Objective of Scheduling
Sales-oriented,
Customer Requirements
Production-oriented, Productivity Requirements
1 Order-based scheduling
Case companies
E, G, H, J, K, L
3 Production and material planning
Case companies
B, C, D, F
Adjustable Capacity
Capacity Constraint
2 Sales budgeting / Capacity allocation
Case companies
I, M, P
4 Product sequencing for capacity
Case companies
A, N, O
Fixed
Capacity
35E00100 Service Operations and Strategy HSE/Logistics

21. Positioning of the 16 Cases Decision-makers in Order Management
Order Management Decision
Order acceptance
Lead-time estimation
Final production schedule H
Sales organization E I F D O G
Sales at mill P A K M
Decision-making Responsibility J L L G L G
Production planning at mill B B B C C C N N N
Order-based scheduling
Production and materials planning
Sales budgeting / Capacity allocation
Product sequencing for capacity
Shop floor management
35E00100 Service Operations and Strategy HSE/Logistics

22. Characteristics of Scheduling Models Machine Configurations
Single-Machine Models
Parallel-Machine Models
Single bottleneck in a multiple-machine environment
Bottleneck determines the schedule
Up- and downstream operations scheduled after the bottleneck
Reduction of the problem or a decomposition approach
Optimal solutions can be found e.g. by applying dispatching rules
EDD, SPT
Single stage with a number of machines in parallel
Jobs can be processed on any one of available machines
Important for the same reason as the single-machine model: one workcenter may determine the performance of an entire system
Machines are not necessarily identical
e.g. skills of operators, machine age
Specific subsets

23. Characteristics of Scheduling Models Typical Production Environments
Flow Shop Models
Job Shop Models
Routes of all jobs are identical
Machines set up in a series
Whenever completed a job joins the queue of the next machine
Job sequence may vary due to resequencing between machines
Some flow shops allow bypassing of machines
Generalization: flexible flow shop
Different routings are possible
Jobs can visit a machine either once or several times
Jobs do not need to be processed on every machine
Models can be extremely complex!

24. Illustrations of Flow Shop and Job Shop
Flow Shop Manufacturing
Machine a
Machine b
Machine k ai ri Ci …
pi1a
dij
pi2b
di2
pijk
dij di i
Operation 1
Operation 2
Operation j
Job Shop Manufacturing
Machine c
pijc
Machine f
pijf ai ri
Machine a
pija Ci di
Machine d
pijd
Machine b
pijb
35E00100 Service Operations and Strategy HSE/Logistics

25. Service-Related Performance Measures
Processing time related objectives
Throughput is determined by the output rate of the bottleneck machine.
Ensure that the bottleneck machine is never idle (jobs in queue)
Sequence jobs so that the sum of setup times or average setup time is minimized
Makespan is the total processing time of a set of jobs.
Important, if the number of jobs is finite
Due date related objectives
Lateness
Number of tardy jobs
Average tardiness
 Measure the level of customer service and external efficiency
 Weighted versions of indicators can be used.
e.g. Hopp and Spearman 2000, 489

26. Measures on the Fulfillment of Confirmed Delivery Dates
Service level
MTO systems
The part of orders, which is produced/delivered by given due dates.
Fill rate ( service level)
MTS systems
The part of demand, which is fulfilled from the stock without shortage
Lateness
Shop floor control
Difference between due date and completion date
Variance of lateness
Tardiness
If job is late same as lateness, otherwise 0
Important measure for the average delay
e.g. Hopp and Spearman 2000, 489

27. Due Date Related MeasuresLi
Cost function in practice di Ci
The lateness Li of job i Ti di Ci di Ci
Ci = completion time of job I
di = due date of job i
The tardiness Ti of job i

28. Cost-Based Performance Measures
Setup costs
Measures: total setup time, average setup time, total machine idle time
Minimizing often pays off, if the goal is to maximize throughput and minimize makespan
Not necessarily proportional to setup times
Work-in-process inventory costs
WIP ties capital, increases handling costs
Older WIP easily becomes damaged, etc.
Measure surrogating WIP is average throughput time (~avg # of jobs)
Finished goods inventory costs
In MTO, earliness costs
In MTS, depends on setup and holding costs
Personnel costs
Regular  overtime costs
Other measures: earliness, schedule robustness

29. Best-Known Problem Types
Minimizing average cycle time on a single machine
Total time to complete all jobs does not depend on the ordering
Apply SPT rule
Minimizing average tardiness on a single machine
Total time to complete all jobs does not depend on the ordering
No sequencing rule is guaranteed to minimize this measure (NP hard problem)
EDD is often a good heuristic
Minimizing maximum lateness on a single machine
Total time to complete all jobs does not depend on the ordering
Sequence the jobs according their due dates = Apply EDD rule
Intuitive solution
Minimizing makespan on two machines
Makespan no longer fixed but sequence dependent
Certain schedules might induce idle time
A simple algorithm proposed by Johnson (1954)
e.g. Hopp and Spearman 2000,

30. Classic Scheduling Problems
One-, two- or three-machine problems
Several simplifying assumptions
All jobs are available at the start of the problem (no jobs arrive after processing begins)
Process times are deterministic
Process times do not depend on the schedule (no setups)
Machines never break down
No preemption: once a job starts processing it must finish
Jobs are not cancelled
Why?
To reduce the problem to manageable proportions
To allow restriction of attention to simplified schedules and sequences
e.g. Hopp and Spearman 2000, 451

31. Basic Dispatching Rules
FCFS, FIFO (first-in-first-out)
The order of arrival determines the sequence
SPT (shortest processing time)
The shortest job first
Minimizes average processing time
LPT (longest processing time)
The longest job first
EDD (earliest due date)
Schedule prepared based on due dates
Job with the closest due date first
Minimizes maximum lateness
ERD (earliest release date)
Similar to the EDD rule
In a sense minimizes waiting time
CR (critical ratio)
Ratio calculated by dividing time remaining with work remaining
Process 1st the job with the smallest value
When on-time, prioritizes jobs with long processing time
When delayd, prioritizes short jobs (removes congestion)
Other rules
SIRO = service in random order
WSPT = weighted SPT
MS = minimum slack
LWR = least work remaining
FOR = fewest operations remaining
LNS = largest number of successors
SQNO = shortest operation at next queue
SST = shortest setup time

32. Basic Sequencing Rules Test Setting
Example 2
Basic Sequencing Rules Test Setting
1 machine
Empty at the beginning; No set-ups
5 jobs
Release time: 0 for all jobs
Weight: N/A
4 rules tested
4 performance measures
Mean flow time, Maximum tardiness, Average tardiness, Number of tardy jobs

33. Schedule and Performance First Come First Served (FCFS)
Example 2
Schedule and Performance First Come First Served (FCFS)
Mean flow time 268/5 = 53.6
Average tardiness 121/5 = 24.2
Number of tardy jobs 3

34. Schedule and Performance Shortest Processing Time (SPT)
Example 2
Schedule and Performance Shortest Processing Time (SPT)
Mean flow time 135/5 = 27.0
Average tardiness 43/5 = 8.6
Number of tardy jobs 1

35. Schedule and Performance Earliest Due Date (EDD)
Example 2
Schedule and Performance Earliest Due Date (EDD)
Mean flow time 235/5 = 47.0
Average tardiness 33/5 = 6.6
Number of tardy jobs 4

36. Schedule and Performance Critical Ratio (CR)
Example 2
Schedule and Performance Critical Ratio (CR)
Mean flow time 289/5 = 57.8
Average tardiness 87/5 = 17.4
Number of tardy jobs 4

37. A Comparison of the Schedules
Example 2
A Comparison of the Schedules
Measures
= makespan
= maximum tardiness
= number of tardy jobs
= total flow time
= total tardiness
= total weighted flow time
= total weighted tardiness
Avg flow time
Avg tardiness

38. Different Types of Priority Rules
Dynamic rules (vs. static)
Indices recalculated every time machine is loaded
Examples
CR (slack per remaining processing time pi)
S/RO (slack per number of remaining operations)
MOD (modified due date): Same principle as in EDD but a modified due date d’ = Max (di, t+pi) is used
Composite rules
Additive rules
A ranking expression that combines basic dispatching rules
Each basic rule has own scaling parameter
Heuristics
Combine good characteristics of basic rules
ATC (apparent tardiness cost)
COVERT (cost over time)

39. The Difficulty of Scheduling Problems
Many problems too “hard” for finding optimal solutions
Class P: a polynomial solution exists
Class NP: no polynomial solution
Sequencing problems grow as n!
Computation times
How many jobs can we sequence optimally if
The computer can examine sequences per second
The response time of the scheduling system should be no longer than one minute?
What if the capacity of the computer is 1000 times faster
Polynomial algorithms can be used to obtain “good” solutions
E.g. Simulated annealing, Tabu search, Genetic algorithms

40. Motivation for Improved Scheduling
Major changes in production
Creeping networking and differentiation of roles
Outsourcing increases the number of supply chain players involved
Distances among decision-makers increase
More uncertainty and variability
Ever-increasing global competition
Higher expectations for response times
Pressure to improve cost efficiency
Less slack in lead times, inventories, and capacities
Advances in information systems
Increasing computing power
More sophisticated IT systems
Use of planning and scheduling techniques
Integration of inter-organizational systems
Lack of real-life applications & proven benefits, yet high expenses
Globalization and networking
Distances among locations increase
Outsourcing typically increases the number of supply chain players involved
Advances in computers and information systems
Significantly higher computing power
More advanced systems
Planning and scheduling techniques
Integration of inter-organizational systems
Increasing global competition
Higher expectations for response times and level of total costs
Less slack in lead times, inventories, and capacities
Individual company
Supply chain level
35E00100 Service Operations and Strategy HSE/Logistics

41. The Fundamental Issue in Order Handling
Which customer order is given a priority when…
Production capacity and/or material are limited?
Disturbances occur due to machinery problems or transport schedules?
Only some customer orders can be delivered fast?
How the value and price of the customer service offered is defined and communicated?
Who takes the responsibility of the operating model before one must?
What are the expected business benefits?
How does the customer gain?
If you don’t measure, you can’t manage…
Can you really manage complicated scheduling tasks without measuring (or estimating) even the most relevant details of success?
35E00100 Service Operations and Strategy HSE/Logistics

42. Research on Scheduling Problems Has Been Fragmented
A classification of research by Jain & Meeran (1999, 393):
35E00100 Service Operations and Strategy HSE/Logistics

43. Researchers have focused on designing yet another dispatching rule
Dynamic pricing & due date mgmt
Number of articles per decade 80 60 40 20
Optimal due date quoting
Parametric analyses
Simple rules
Weighted criteria
Composite rules
Versatile rule testing
Intelligent heuristics
???
Agent-based reasoning
Selective order acceptance
Lead-time estimation
Order release mechanism
Priority classes & pricing
IJIE MS
IJPR OR
NRLQ
Omega
JORS
IIE Transactions
JOM DS
POM
CompsOR
IJPE
IJOPM
PPC
EJOR
Major publication outlets
1960
1970
1980
1990
2000
35E00100 Service Operations and Strategy HSE/Logistics

44. How many rules must a scholar - or a manager - know?
There are over 300 dispatching rules defined in literature, perhaps 120 rules if you exclude situational variants –
but mere 35 rules seem to span the whole scope of dispatching tasks.
35E00100 Service Operations and Strategy HSE/Logistics

45. Key Questions on Tardiness Rules
Why aren’t there business cases that one could learn from?
researchers have developed ever new rules for different types of problems – not much academic glory in proving some rules useful
managers have found most scheduling methods rather complicated to use, even to understand – half-hearted attempts are doomed
some more advanced applications tend to be proprietary and limited to corporate-specific problems
What priority rules are available in commercial planning and scheduling software?
usually only the simple rules (FIFO, EDD, SPT) are readily defined
managerial commitment required for appropriate data & use
What would it take to utilize the best rules in practice?
definition and use of an Order Scheduling Protocol
minor adjustments in the information systems and relationship management
35E00100 Service Operations and Strategy HSE/Logistics

46. Open Questions Which company will be the first one to know?
What does a delayed delivery of an order to a customer really cost?
Which company will be the first one to know?
Who knows how long it takes to fulfill an order in a given business situation?
How much would we gain from being able to predict order fulfillment times of different customer requests?
35E00100 Service Operations and Strategy HSE/Logistics

47. Theoretical Background Literature on Job Shop Scheduling
Research on scheduling problems has been fragmented
Researchers have focused on designing yet another dispatching rule
How many rules of the 300 must a manager know? One family of rules suffices!
Classic scheduling books
Carroll 1965, Conway et al. 1967, Baker 1974, French 1982, Morton and Pentico 1993, Pinedo 1995.
Surveys of dispatching rules
Panwalkar & Iskander 1977, Blackstone et al. 1982, Haupt 1989, Ramasesh 1990.
Key publications on the weighted tardiness problem
Vepsalainen & Morton 1987
Use of Apparent Tardiness Cost (ATC) rule in job shop environment
Normalized performance measures and tardiness penalty as a key job attribute.
Anderson & Nyirenda 1990
Two new combination rules (CR+SPT and S/RPT+SPT).
Kutanoglu & Sabuncuoglu 1999
Comparison of the weighted priority index rules, use of inserted idleness, and resource pricing with the ATC rule.
Jaymohan & Rajendran 2004
Use of both holding cost and tardiness penalty as order-specific weight information.
Introduces a variety of new weighted composite rules.
35E00100 Service Operations and Strategy HSE/Logistics

48. Is it possible to achieve all the goals of scheduling
Is it possible to achieve all the goals of scheduling... All at the same time?
High utilization - low unit costs and
On-time deliveries of important orders - premium service and
Fast throughput of all orders - low inventory levels and
Robust operations that are tolerant to faults
- managerial errors in cost and processing time data
and
- disruptions in ordering and production process ?
Yes – Provided there is a commitment to the Value of On-Time Delivery and a rational look-ahead rule.
35E00100 Service Operations and Strategy HSE/Logistics

49. The most important orders are delivered on-time if an appropriate rule is used
EDD 2x 3x 5x
Look-ahead rules
50%
80%
90%
35E00100 Service Operations and Strategy HSE/Logistics

50. All orders flow through quickly if an appropriate rule is used.
Look-ahead rules
(ATC, COVERT & CR+SPT)
35E00100 Service Operations and Strategy HSE/Logistics

51. Errors in order data can be managed if an appropriate rule is used.
35E00100 Service Operations and Strategy HSE/Logistics

52. Properties of the Look-ahead Rules
ATC has a natural approximation of look-ahead
First day steep, second less, then linear.
Local look-ahead is easier to use.
Decision-makers benefit from the possible interpretation of cost index values.
COVERT misses operation due dates
Not a crucial limitation for one-stage operations.
CR+SPT simple but hard to interpret and apply in practice?
35E00100 Service Operations and Strategy HSE/Logistics

53. The Logic of Apparent Tardiness Cost Rule
ATC:
Priority index value
Look-ahead
Look-ahead t
35E00100 Service Operations and Strategy HSE/Logistics

54. The priority index of Apparent Tardiness Cost (ATC) rule
Priority index value
ATC index:
Two jobs: a (two operations)
b (one operation)
ATC
ATC t
35E00100 Service Operations and Strategy HSE/Logistics

55. The logic of look-ahead rules such as Apparent Tardiness Cost and COVERT and CR+SPT
ATC:
Priority index value
COVERT:
CR+SPT:
CR+SPT
CR+SPT
ATC
ATC
COVERT
ATC
ATC
COVERT t
35E00100 Service Operations and Strategy HSE/Logistics

56. Production scheduling decisions
An Illustrative Example – Final Sequencing Five Orders for Product Y (delay penalty=1)
Confirmed delivery date to the customer (POD)
Post Goods Issue (PGI) date (Scheduled Finish Date is – 1 day)
Order data
Arrival Order Processing Transport Due date OTDV Ex works
# time quantity time (hrs) time (days) (at customer) (delay penalty) due date
Production scheduling decisions
Decisions are made on Oct 11, 7:00 AM.
2 production cells are available for total of 5+5=10 hrs work.
There is material for each of the orders.
If an order is not ready by PM, it may arrive late to the customer.
In what sequence the orders should be made?
‘Due date’–‘transport time’ e.g – 4 days
35E00100 Service Operations and Strategy HSE/Logistics

57. What other criteria (or performance measure) could be used?
An Illustrative Example – Final Sequencing with OTDV=1 Performance in the Example
Order data
Arrival Order Processing Transport Due date OTDV Ex works
# time quantity time (hrs) time (days) (at customer) (delay penalty) due date
Results with different principles/rules
#, %TJ avgTard
Earliest arrival time first: 2, 40% (4-14:00, 5-14:00) 0.8
Smallest order quantity first: 2, 40% (1-13:00, 2-15:00) 0.8
Largest order quantity first: 3, 60% (4-13:00, 3-13:00, 5-15:00) 1.0
Shortest processing time first: 2, 40% (1-13:00, 2-15:00) 0.8
Largest processing time first: 3, 60% (4-13:00, 5-14:00, 3-14:00) 1.0
Using the EDD (=earliest due date) rule may not differentiate orders in the final sequencing.
What other criteria (or performance measure) could be used?
35E00100 Service Operations and Strategy HSE/Logistics

58. Reduction rate per 3 slack hours
An Illustrative Example – Final Sequencing with Different Values for OTD
Reduction rate per 3 slack hours
1.0, 0.7, ½, ⅓, ¼, …
Order data
Arrival Order Processing Transport Due date OTDV Ex works OTD index
# time quantity time (hrs) time (days) (at customer) (delay penalty) due date (TAF*OTDV/PT)
*1/4 = .25
*10/5= 2.0
*2/1= 1.4
*5/2= 1.75
*20/2=7.0
Results with different principles/rules
%TJ avg weighted tardiness
Earliest arrival time first: % tardy 10.0
Smallest order quantity first: 40%
Largest order quantity first: 60%
Shortest processing time first: 40%
Largest processing time first: 60% (or 6.2 if #5 before #4)
Consider also ‘weighted SPT’ & ‘highest penalty first’.
Look-ahead OTD: 20% tardy, average weighted tardiness less than 0.1
- it gets the job (and jobs) done optimally!
35E00100 Service Operations and Strategy HSE/Logistics

59. Three Parts of the OTD rule (=ATC rule)
Every order has three factors, which are necessary to establish consistent and rational expectations of the true value of on-time deliveries and reliable lead time estimates, and thereby use them accurately for marketing and internal incentives
Processing Time (PT)
On-time Delivery Value (OTDV) = cost of being tardy
Tardiness Anticipation Factor (TAF)
= f(slack) = f(due date - current date - processing time).
(This factor increases from 0 to 1 as slack is reduced).
The OTD index = (OTDV /PT) x TAF x slack €/time unit
Notice: Waiting time estimates are needed for estimation of slack and evaluation of lead times (also in a one-stage problem)
35E00100 Service Operations and Strategy HSE/Logistics

60. On-time Delivery Value of an Order – What does a delayed delivery of an order to a customer really cost?
Extra costs to the customer caused by lack of product and/or expediting and hustle (increased transaction costs).
5-20% of the value of the order -- € /day + € /day
Late / tardy shipment causes harm and bad-will to the customer.
0-500% of the value of the order -- € /day (probability equivalent)
OTDV depends on the importance and size of the order, and the number of hours or days tardy. What about lead time?
What we mean by that? - Weight is constant w/ linear aggregate value
Severity of tardiness of an order may increase OTDV as time progresses:
Being late from the promised delivery date is a major problem.
Potential cancellation of the customer order – probability increases?
Inventory replenishment may have gradually increasing urgency.
Pushing the standard processing and waiting times, including transfers, extra packaging etc. expediting costs, and the cost of faster transportation.
35E00100 Service Operations and Strategy HSE/Logistics

61. The Good News Due dates - We can set those! Job splitting
We can get smaller jobs by splitting larger ones.
Single-machine SPT results imply small jobs “clear out” more quickly than larger jobs.
Mechanics of Johnson’s algorithm implies we should start with a small job and end with a small job.
Small jobs make for small “move” batches and can be combined to form larger “process” batches.
Feasible schedules
We do not need to find an optimal schedule, only good & feasible
Focus on bottleneck
We can often concentrate on scheduling the bottleneck process, which simplifies problem closer to a single machine case.
Capacity management
Capacity can be adjusted dynamically (overtime, floating workers, use of vendors, etc.) to adapt facility (somewhat) to schedule

62. Challenges of Some Advices Given for Order Management and Scheduling
How do you know when to switch from one rule type to another?
Processing time based rules
Due date based rules
Time
Capacity Utilization
100 %
80 %
How to convince customers that lead times should be determined based on the work content of order?
Lead time estimate
Due date
Job A [1]
Job B [3]
Job C [9]
Constant (value 10)
CON
Total work content (multiplier 3)
TWK
35E00100 Service Operations and Strategy HSE/Logistics

63. Lead Times and Rescheduling
Methods for estimating manufacturing lead times
CON (constant)
No separation between orders
SLK (slack)
Time allowance with equal waiting time or slack
di = ai+ pi + fixed slack
TWK (total work)
A multiple of the processing time
di = ai + bpi
Priority-based
Dynamic
Waiting time proportional to relevant queue length
Iteration-based
Heuristic procedure
Dynamic due date maintenance
Can reduce total inventory level and simultaneously improve customer service
Requires more efforts
Risks?
Filters of rescheduling decision
Ability filters
Only attainable due date adjustments
Magnitude filters
Threshold value = absolute value of the difference between the new and the old due date
Horizon filters
Filter out due date changes too far out in the planning horizon to be of immediate concern
Partly from Vepsalainen & Morton 1988

64. Time when the new job will be filled:
Due Date Quoting
“Emergency”
positions
New job c
Rate Out
rP, s
Backlog b
WIP w
Completed
Time when the new job will be filled:
m = w + b + c
Hopp and Spearman 2000,

65. Characteristics of Scheduling Models Processing Constraints
Routing Constraints
Specify the route a job takes through a system
Machine-Eligibility Constraints
Jobs sometimes have to be processed on a specified subset of parallel machines
Precedence Constraints
A job can be started only after a given set of other jobs has been completed
Precedence constraint graph may have a specific structure
Tooling Constraints and Resource constraints
e.g. training of operator
Material Handling Constraints
Refer to systems that convey jobs from one workcenter to another
Depend on the automization of workcenters: if highly automated (roboticized), processing times are deterministic
Often limit the amount of buffer space  WIP level
Personnel Scheduling Constraints
e.g. consecutive days off, shift types

66. Characteristics of Scheduling Models Processing Characteristics
Order Quantity Limits and Due Dates
In MTS environment
Jobs do not have tight due dates
Inventory level triggers production of stocked items
Amount produced depends on setup costs and inventory holding costs
In MTO environment
Jobs have specific due dates
Quantities determined by customer
MTO and MTS often combined
Sequence-Dependent Setup Times and Costs
Reconfiguration or cleaning of a machine; known as setup or changeover
The length of a setup depends on the job just completed and on the one to be started
Preemptions
Take place for example due to breakdown or an arrival of high priority order
Different forms
Preemptive resume (work not lost)
Preemptive repeat ( work lost)

67. Trade-off Curves in Job Shop Environments
Idle time
Current system
Improved system
Work-in-process

68. Order Management Protocols
Justification of open protocols
Increasing networking calls for integrative methods within one company and across independent players:
Centralized decision-making – by whom?
Real-time response - how?
Could simple standardized protocol be more efficient than customized ones?
Order management would be less expensive via protocols that are available to all potential users.
 Rationale of open protocols
Coordination of decentralized, postponed and localized decision-making
Ease of use and accessibility
Suggested three layers of protocols
Rules of scheduling behavior
Technical specifications and tolerances
Conventions of usage
35E00100 Service Operations and Strategy HSE/Logistics

69. Layers of an Order Management Protocol
Screening rule – who, when, how
Release rule – who, when, how
Sequencing rule – who, when, how
Conventions of usage
OTD rule helps in estimating lead times, OTDVs and relative importance of orders.
Scheduling rule
Data tables for OTDV, TAF index, and lead time estimates
Technical specifications and tolerances
35E00100 Service Operations and Strategy HSE/Logistics

70. The Order Management Protocol for the On-Time Delivery Rule (OTD Rule)
Rules of scheduling behavior
Prefer look-ahead rules, especially decomposable ATC-rule
Technical specifications and tolerances
Many lead time estimation methods work, esp. w/look-ahead rules
Moderate errors in the cost data and processing times estimates do not impact the ranking of the rules.
Rough-cut priority classes can improve shop performance compared to not using any method but performance is still significantly worse than with the look-ahead rules.
Data aggregation has limited benefits for most of the rules.
Conventions of usage
Priority rules can substitute for order release mechanisms
The look-ahead rules improve tardiness performance of production systems, even if applied at one stage only.
Predictability of tardiness behavior of orders over operations is easier with some given priority rules.
35E00100 Service Operations and Strategy HSE/Logistics

71. Business Potential of the Order Management Protocol and the OTD Rule
Streamlines the process
Focus on the important issues – it is not necessary to maintain all options
The real bottlenecks will be revealed and resources aligned
Helps to learn the value of OTD to customers and fulfill your promises
Develop customer relationships and sales
Define and follow supplier contracts more accurately
Design rational basis for a market-driven incentive system
Enables the differentiation of service for satisfaction and better price
Implementation requires training and minor changes in information system
Running is free!
Savings in personnel and shipping costs
Enables Premium service w/o building new capacity
35E00100 Service Operations and Strategy HSE/Logistics

72. Using Order Priorities within Customer Order Management Process
Special indications from the sales / supply team.
Orders are anonymous.
Sales Office
Manufacturing and Logistics Operations
Supply Team
OTD
Order
acceptance
Order
release
Customer service
Capacity Allocation & Demand-Supply Balancing
Material requirements planning & supply mgmt
Scheduling &
Re-scheduling
Final Sequencing
Dispatching & Shop floor management
Packaging & Shipping
Transport & final delivery
Factories are given the workload.
MTO is a must!
Demand peaks at the beginning of the week and at the end of period/quarter.
Capacity allocations weekly: remaining (frame blocks) is released later.
Expedited deliveries paid by the client
The number of rush
orders increases by the
end of period/ quarter.
Suppliers, Subcontractors, Partners and Logistics Service Providers
35E00100 Service Operations and Strategy HSE/Logistics

73. Main Challenges and Risks Can you accept the following?
Orders are different, situations / business cases are different.
 Different lead times
Some orders should go faster.
 Would you trust simple rules?
Control parameters & performance criteria can (and should) be quantified.
 How else differentiate the service?
 How else communicate the time value of an order?
 How to manage if you can’t measure?!
35E00100 Service Operations and Strategy HSE/Logistics

74. Key Points Scheduling is challenging! Scheduling can be simplified
Flow simplification (sequencing in place of scheduling)
Due date quoting
Priority scheduling
Easy to understand
Flexible and perhaps not as myopic as claimed
Finite capacity scheduling will emerge

75. Notation and Abbreviations Used
ATC = apparent tardiness cost
COVERT = cost over time
EDD = earliest due date
FCFS = first come first served
FMS = flexible mgg system
MTO = make to stock
MTS = make to stock
SPT = shortest processing time
WSPT = weighted SPT