1. Intelligent Supply Chain Management Production Scheduling
i2 U
Intelligent Supply Chain Management
Course
Module Fourteen:
Production Scheduling

2. Supply Chain Management Key Processes
Fully Integrated top-down directions
Fully Integrated bottom-up feed back _ +
Strategic Supply Chain Planning
Sales & Operations Planning
Number of decisions
Specificities by industries
Impact of decisions
Length of Planning horizon
Master Supply Planning
Inventory Planning
Demand Planning
Production
Distribution
Procurement
Transportation
This module addresses the production scheduling process.
Supplier Scheduling
Production Scheduling
Transportation Scheduling
Inventory Deployment
Demand Fulfillment _ +
Reaction to changing supply conditions
Supply Chain Execution Monitoring

3. After Completing This Module, You are Expected to:
Explain the difference between production planning and production scheduling
Identify the key objectives of the Production Scheduling process
Understand in which production environments the Production Scheduling is critical, and in which it is not
Understand the logic of genetic algorithms and their value for the Production Scheduling process
Identify other key steps that need to be taken in addition to optimizing the Production Scheduling process to reach manufacturing excellence
Identify Production Scheduling key enablers and their resulting business value
Identify Production Scheduling excellence criteria

4. Production Scheduling Process Positioning
operational
tactical
strategic
scheduling
buy
make
move
store
The scope of this process is limited to manufacturing operations and deals with the short to mid-term horizon (where the actual horizon is determined based on the total manufacturing lead time).
sell
hours
days
weeks
months
year +

5. The Difference Between Production Planning and Production Scheduling
Resource Time Buckets
Finds available material and capacity, then fills up “time buckets” with jobs until the bucket is full, without considering the specific sequence.
Modeling:
- Resources may be aggregated
- Average production capacity
- Detailed material requirements 7 6 5 4 3 2 1 7 6 5 4 3 2 1 1 2 3 4 5 6 7
Planning
Day 1
Day 2
Day 3
Scheduling 7 6 5 4 3 2 1
Resource Time Buckets
Looks at the contents of each “time bucket” obtained from the planning tool, then schedules the best sequence according to customer- defined constraints.
Modeling:
- Resources are assigned
- Detailed production capacity
- Critical material dependencies
Day 1
Day 2
Day 3
Minutes
Let us first differentiate production scheduling from production planning, which we have addressed so far in the course. The objective of production planning is to verify that, in each time bucket, the total load generated by the planned production orders does not exceed the total capacity available in the time bucket. Note that these time buckets can be either days, weeks or months. Therefore, the objective is to validate the plan globally, but it is NOT to generate the exact sequence of the jobs at each work center. This means that the production plan resulting from a master supply planning process is a rough-cut plan, in the sense that:
The exact start time and end time of each order is only theoretically feasible.
Because the exact sequence of the jobs has not been defined, it is necessary to use an estimated set up time to adjust the available capacity against which the production plan was evaluated.
The production scheduling process has a different focus: its core activity is to generate a detailed sequence of jobs at each work center, with the ultimate objective of minimizing the production costs while maximizing customer service.

6. Production Scheduling Objectives
Ensure 100% production planning feasibility by not incorporating assumptions from plans (estimated set up time, aggregated resources, …) and respecting detailed constraints
Minimize set up time (labor costs) and maximize throughput
There are, in fact, two major objectives in the production scheduling process:
Ensure 100% feasibility of the production plan by respecting detailed constraints and removing all the assumptions that have been used to generate the master supply plans.
Minimize the overall set-up time and the associated labor costs and, as a result, maximize the production throughput.

7. Cost/Complexity of Changeovers Frequency of changeovers
Criticality of Production Scheduling Depends on Changeover Cost and Frequency
Detailed Scheduling
Critical
High
Cost/Complexity of Changeovers
Low
Like most other supply chain management operational processes, the criticality of the production scheduling process varies significantly from one industry to the other. This criticality is actually dependent on two major factors:
The frequency of the changeovers. Obviously the higher the frequency of changeover, the more critical the process of optimization.
The cost and complexity of changeovers. In some industries the number of changeovers might be extremely high, but the complexity of those changeovers is actually limited.
Repetitive
Mixed Model
Mass
Customization
Make-
to-Order
Frequency of changeovers

8. Certain Industries Get Larger Benefits Than Others
Heavy Equipment
Automotive OEMs
High
Metals
Appliances
CPG
Cost/Complexity of Changeovers
Job Shops
Low
Commodities
Electronics/ PC Manufacturers
Examples of industries where the number of changeovers is high but the complexity is low are the electronics industry and the PC manufacturing industry, where the flexibility of the shop floor generally makes those changeovers relatively simple and very cost effective.
At the other end of this cost and complexity matrix there are industries such as heavy equipment, automotive, metals, paper, etc., where one changeover may represent an extremely significant amount of time consumed in order to set up one or several pieces of equipment. In this case a poor job sequence may result in significant losses of production throughput and significant consumption of labor resources. It is in these industries that the production scheduling process is the most critical.
Repetitive
Mixed Model
Mass
Customization
Make-
to-Order
Frequency of Changeovers

9. A Simple Example Problem
6 Orders, 1 Machine
Various due dates and production times
2 different colors, 1 day changeover
6! = 720 permutations
Job
Prod Days
Color
Due A 2
Blue 4 B 3
Green 7 C 1 8 D 9 E 15 F 17
To understand the challenges of the Production Scheduling process and how they can be addressed, let us analyze a simple scheduling problem. In this example, we have 6 production orders to schedule on one machine. Each order has a specific due dates and corresponds to a certain number of production days. There are products of two different colors, and the setup time needed to switch from one product to another is 1 day. The number of schedules that can be generated is a total of 720 permutations!

10. Definition of Constraints
Represent scheduling objectives; example :
Avoid late orders
Reduce number of changeovers
Penalty points for constraint violations
100 points for a late job
50 points for a changeover
Constraints are either hard or soft
Each schedule is graded based on the constraints
2 late, 5 changeover = 450 points
Lowest score is the best
An “optimal” production schedule corresponds to a schedule which best respects scheduling objectives, which can be expressed through the compliance to a set of constraints.
For instance a constraint can be to avoid late customer orders, or to reduce the number of changeovers. Each constraint is then assigned penalty points, which will be computed whenever a generated schedule violates the constraint. In this example, let’s say that a schedule will suffer 100 penalty points for every late order generated and 50 penalty points for any changeover generated. As such, it indicates that the customer service objective takes precedence over the set-up minimization objective.
These two types of constraints are actually soft constraints, in the sense that they can be violated without making the schedule infeasible. There can also be hard constraints, which cannot be violated without the schedule becoming invalid (a typical example is the case of the maximum available capacity).
Once a schedule has been generated, the sum of its penalties defines how good it is, that is how close it is from the scheduling objectives. Of course, the lower the score indicates a better schedule.

11. Traditional Scheduling Methods
JobDue 1 2 3 4 5 6 7 8 9 11 12 13 14 10 15 17 16 18 A4 B7 C8 D9
E15
F17
Forward, by due date
2 late, 5 changeover = 450 pts
Backward, by due date
1 infeasible, 5 changeover = ? pts
Color Campaign
2 late, 1 changeover = 250 pts
Genetic Algorithm ???
Let us now see how traditional scheduling techniques would solve the scheduling problem presented earlier -- in other words what type of sequence are they likely to generate.
The first common form of scheduling logic is the forward scheduling logic, which generally uses the due dates as the main constraint. With such a technique, the probable consequence is a proliferation of changeovers. In this example, the best possible schedule generated by this technique ends up with two late orders and five changeovers - meaning a total penalty of 450 points.
The other popular scheduling technique is backward scheduling, which again focuses on the due date with the objective of minimizing the inventory levels. This type of schedule will not only generate a high number of changeovers but it may also result in an infeasible schedule as some start dates will actually be already past due.
Yet another scheduling technique focuses on minimizing the set-up time. The ultimate effect of this technique is to generate more late orders, which in our example is less desirable than setup time minimization.
All these traditional techniques share the same limitation. They concentrate on one objective and don’t have the ability to consider a combination of scheduling objectives with different levels of criticality.
The scheduling strategy that produces a truly optimal production schedule uses a technology called genetic algorithms, which we will present in the following slides.

12. Genetic Algorithms: A New Approach to Complex Problems
Very much like bio-genetic technology
Concept invented at U of Michigan, ‘70’s
Based on optimizing “Population Genetics”
“Evolves” good schedules from a “population” of possible schedules
Today, it is well accepted that the best logic for complex, short-term scheduling problems are genetic algorithms. As we will see in the next slides, the genetic algorithm logic is inspired from bio-genetic technology.
Its core concept is use population genetics to evolve a good schedule from a population of possible schedules. Let us illustrate how this works.

13. Genetic Algorithms - How it Works
Entire sequences are encoded like genes in a chromosome
a job is a gene
a sequence of genes (jobs) is a chromosome
a population is a group of chromosomes A B C D E F
The operating logic of a genetic algorithm is to take an entire sequence of jobs (that is, a set of production orders) and encode them like genes in a chromosome. In this analogy, a job is considered as a gene, a sequence of jobs (that is, a schedule) is a chromosome and a ‘population’ is a group of chromosomes, or, a group of schedules.
In this example, two chromosomes/schedules have been generated out of the 6 genes/jobs -- A,B, C, D, E and F. A B C D E F

14. Genetic Algorithms - How it Works
Survival of the fittest
Good sequences and their characteristics are saved and used as the parents for the next generation
Bad sequences and characteristics are discarded
New sequences are generated
Many generations are tested
Best sequence selected
The central logic of the genetic algorithm is to evaluate the quality of several schedules, and apply the “survival of the fittest” rule. This means that the good sequences -- in other words, the sequences that generate the least amount of penalties -- and their characteristics are saved and used as the parents for the next generation that will be created, while on the other hand the bad sequences and characteristics are discarded, and when the new generation of schedules is created, these bad sequences and characteristics will be avoided.
When genetic algorithms are applied, new sequences are constantly generated, many “generations” of schedules are tested, and with every “generation” the best sequence is selected, which means that the resulting schedule is constantly evolving toward an optimal status, guided by the amount of penalties generated.

15. Genetic Algorithms 1 n
Generates a random set of schedules at the first pass and gives penalty points
Then “cross-breeds” the best of breed schedules in the second pass and gives penalty points
Then “cross-breeds” the best of breed schedules in the third pass and gives penalty points
Then……
and comes up with an optimal schedule!! 1 2 3 4 5 6 7 8
This slide further illustrates the genetic algorithm logic :
The algorithm starts by generating a random population of schedules, and then calculates the penalty points generated by each schedule.
The second step consists of “crossbreeding” the best-of-breed schedules, while eliminating the least desirable schedules and then assigning penalty points to the new generation of schedules.
The more this crossbreeding is repeated, the more it will lead to an optimal schedule. 9 10

16. Genetic Algorithms Compared to Traditional Scheduling Methods
Forward, by due date A4 B7 C8 D9 D9
E15
F17
F17
2 late, 5 changeover = 450 pts
Backward, by due date A4 A4 B7 C8 D9
E15
F17
1 infeasible, 5 changeover = ? pts
Color Campaign A4 C8
E15 B7 B7 D9 D9
F17 B7 D9
Genetic Algorithm A4 C8
F17
E15
0 late, 2 changeover = 100 pts
2 late, 1 changeover = 250 pts
If we compare the outcome of the genetic algorithm logic to traditional scheduling methods through our previous example, the schedule generated by this logic will be “smarter” and therefore more “optimal” because it has been able to balance the different objectives of the scheduling function.
In this example, the genetic algorithm logic has been able to minimize the number of late orders while at the same time minimizing the number of changeovers. The schedule is not ideal (because it cannot be) but the total amount of penalties is minimized. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

17. Production Scheduling Process Key Enablers And Related Business Benefits
100% valid schedules ensured by considering ALL constraints
Best of breed logic to generate optimal schedules (genetic algorithm)
High speed schedule generation (easy what if simulations)
Customer service (delivery speed and reliability)
Throughput
Inventory turns
In summary, the key enablers for an optimized production scheduling process are:
The ability to ensure a 100% valid schedule by considering adequately all existing constraints. It is important to remember that the constraints are not only manufacturing constraints but may also correspond to other business objectives like inventory turns or delivery dates.
The use of the best of breed logic to generate optimal schedules (in other words genetic algorithms) .
The ability to generate schedules fast, which is critical to allowing as many simulations as possible, in order to allow schedule evolution to reflect the weighting of each constraint.
The related business benefits are:
First of all, an increase in customer service performance, both in terms of delivery speed (because of the reduction of the total manufacturing cycle time which can be achieved) and the delivery reliability (because the schedules are constantly valid).
Obviously, an increase in the manufacturing throughput by minimizing the idle time and the changeover time.
Finally, an increase in inventory turns because those inventory turns can be set up as an objective of the scheduling function.

18. To Guarantee Valid Schedules, One Must be Able to Model All Types of Constraints
Sequence dependant set up
Precedence rules
Cool down time
Tank scheduling
Incompatible productions
Task synchronization
...
A key prerequisite of the production scheduling process is the ability to adequately model ALL the characteristics of the manufacturing environment. This is a challenge because these manufacturing constraints can be very different from one another, requiring very different modeling logics. Here is a representative sample:
Setup times (also known as changeover times). These indicate how long it takes to set up equipment when one specific product has just been manufactured and another one follows in the sequence. The difficulty comes from the fact that the set up time is not fixed, but rather sequence dependent, and its duration can actually be influenced by a combination of factors; color, width, size, weight, etc. Of course, a sequence in which two products have the same characteristics requires significantly less setup time than switching from one product with a certain color, width or size to another product with different characteristics -- which requires a tooling change, or a modification of the equipment configuration, or cleaning the equipment so that colors are not be mixed. The representation of these sequence-dependant set up times therefore requires a multi-dimensional database.
Precedence rules. These correspond to sequence requirements in the manufacturing process, like not being able to overlap two operations, or having to wait a certain number of hours after the completion of the previous task is completed, because of testing time, cooling time, or whatever. Times can vary significantly from one sequence to another, and thus representing precedence rules requires a multi-dimensional database.
Tank scheduling is another well known complex scheduling issue, where the difficulty is to model continuous incoming and outgoing flows, which don’t necessarily have the same rate.

19. Incompatible productions is yet another constraint, which is frequent in the food and pharmaceutical industries. In certain cases, product A and product B cannot be produced simultaneously, because of the risk of cross contamination between them.
Task synchronization is the last example we will take from a list of possible constraints that is actually much broader. For consumer packaged goods (typically in the food industry), in certain manufacturing processes different feeding lines concurrently feed one packaging line, like, for instance, different types of biscuits which are packaged together. In these cases, the flows need to be fully synchronized.

20. Production Scheduling is Only One Ingredient Necessary to Reach Manufacturing Excellence
TQM techniques must be applied to maximize the manufacturing process predictability -- by minimizing :
Yield variation
Lead time variation
Other TQM techniques must be considered to maximize the flexibility of the manufacturing process :
Reduce the total production cycle time in order to move from a ‘Build to stock’ to a ‘Build to Order’ production process
Changeover time reduction
Design for manufacturability techniques
Accelerated material flow through ‘Just-in-Time’ techniques
As critical as the production scheduling process can be in some industries, it is important to highlight the fact that it represents just one ingredient of the manufacturing excellence toolkit. Other conceptual approaches and techniques, generally regrouped under the banner of Total Quality Management (TQM), are equally critical to enable a fully optimized manufacturing environment, which ultimately have a dramatic impact on the overall supply chain performance. Although these approaches and techniques are not in the scope of this course (which is essentially focused on the decision support activity of the Supply Chain Management process), we will make a brief review of some of the most important ones.
First, some TQM techniques can be applied to maximize manufacturing process predictability by minimizing all the sources of variation in the manufacturing process, like yield variation, lead time variation, quality issues, etc. Total Quality Management uses a very broad set of tools to reach this goal, including statistical process control, root cause problem analysis, fishbone diagram, etc. (to adequately present all these tools, a course of the same duration as this one would be necessary!). It is absolutely critical to apply these techniques. If this is not the case, then the entire Supply Chain Management process is hurt because remaining sources of unnecessary process variability eventually translate into lower customer service, lower inventory turns and lower asset utilization, no matter how optimal the planning and re-planning processes.

21. Seemingly, another set of TQM techniques must be also considered to maximize the manufacturing process velocity and flexibility.
One of the major objectives of increased velocity and flexibility is to reduce the total manufacturing cycle time, so that it becomes possible to move from a “build-to-stock” to a “build-to-order” production process, which results in an immediate and structural impact on the customer service and inventory turns performance, by being able to react faster to customer requests while at the same time eliminating the need to maintain finished goods inventories.
One of the key enablers to maximum manufacturing flexibility is indeed to look for ways to reduce the time needed to perform a changeover. This not only reduces the manufacturing cycle-time but more importantly, allows more changeover in the schedules, which directly results in smaller batch sizes and, consequently, in higher inventory turns. At the same time, it allows increased responsiveness toward customers, with more flexibility to define the schedules.

22. Changeover Time Reduction
Traditional Changeover Program
Quick Changeover Program
Erratic, unpredictable higher changeover times
Plan changeover times and material schedule availability in advance
Separate internal from external activities to minimize downtime
Specify tasks and time duration for each member of the changeover team; error-proof the process
Monitor and track results during the changeover and analyze the variance with corrective actions
Consistent, predictable lower changeover times resulting in increased flexibility and increased capacity
Changeover time
Changeover time
This illustration highlights the typical result of the application of a changeover time reduction program, such as the “Single Minutes Exchange of Dies” (SMED) methodology, which has proven to reduce changeover times in dramatic proportions, sometimes up to 90%, and also has eliminated the variability in this activity.
Another methodology ensuring maximum flexibility in the manufacturing operations, and consequently in Supply Chain Operations, is the set of “design for manufacturability” techniques, which ensures that products designed by the R&D department are ideally profiled for maximum manufacturing efficiency. In the same vein, just-in-time techniques also enable manufacturers to obtain greater level of flexibility by accelerating and streamlining the material flow through the use of, for example, “kanbans” or “pull-system” techniques.
# of Changeovers
# of Changeovers

23. Production Scheduling is Only One Ingredient Necessary to Reach Manufacturing Excellence
TQM techniques must be applied to maximize the manufacturing process predictability - by minimizing :
Yield variation
Lead time variation
Other TQM techniques must be considered to maximize the flexibility of the manufacturing process :
Reduce the total production cycle time in order to move from ‘Build to stock’ to ‘Build to Order’ production process
Changeover time reduction
Design for manufacturability techniques
Accelerated material flow through ‘Just-in-Time’ techniques
Finally, manufacturing process simplicity must be achieved through techniques such as :
Backflushing
Repetitive manufacturing
Flattened bill of materials
Simplified material flows (cellular manufacturing, focused factory, …)
Finally, TQM techniques can also be applied to simplify the manufacturing process by applying techniques such as:
Backflushing -- the ability to automatically consume the components once a work order has been completed, by reading the bill of material and generating an automatic inventory consumption.
Repetitive manufacturing -- where the same schedules are performed on a regular basis every day, every week, so that the process gets maximum stability and simplicity.
Flattened bill of materials -- which consists of eliminating, as much as possible, the intermediate levels of semi-finished goods.
Cellular manufacturing, focused factory, etc -- which help simplifying the material flow.
In summary, the message of this slide is that in the manufacturing area, companies need to take a global perspective on manufacturing excellence, which includes optimizing the production scheduling process but which is far from being limited to this activity.

24. Production Scheduling Process Excellence Criteria
All production constraints (including elements such as sequence dependent set-up, cool down time, precedence rules, task synchronization, … as well as material availability and delivery date) are considered when the production schedules are generated, thus ensuring that the schedules are 100% accurate and valid.
The right optimization technique is applied to generate the ‘best’ possible schedule in the shortest period of time.
The production scheduling process is tightly integrated with the Master Planning process. Changes in the production schedules are immediately reflected in the master plans.
The scheduling horizon is long enough to accommodate the entire order backlog.
Let us now wrap up this module focused on the production scheduling process by reviewing the list of excellence criteria.
The first excellence criterion is the critical ability to adequately represent or model the characteristics of the manufacturing environment in order to ensure that the schedules are 100% accurate and valid.
The second criterion relates to the use of the right optimization technique (i.e. genetic algorithms) to generate the best possible schedule in the shortest period of time.
The third criterion focuses on the need to integrate the production scheduling process and the master supply planning process. This especially highlights the fact that whenever a production schedule is changed, this has to be immediately reflected in the master supply plan (or at least compared with it), with alerts generated whenever these changes have an impact on the overall balance of the master supply plan.
Finally, the last criterion is to ensure that the scheduling horizon used is able to accommodate the entire order backlog.