Identifying master production scheduling criteria, job sequencing, material requisition planning & bill of material

1. MANAGING PRODUCTION ACROSS
THE SUPPLY CHAIN
&
LOGISTICS NUMERICAL

2. THE BIG PICTURE

3. MASTER SCHEDULING
 Controls the timing and quantity of production for
products or product families
 Primary interface point for actual customer orders
 Coordinates forecasted demand and actual orders
with production activity
 Serves as tool for agreement between marketing
and operations (but at a different level than S&OP)
 Feeds data to more detailed material planning
 Indicates the quantity and timing (i.e., delivery
times) for a product or group of products

4. MASTER SCHEDULING CRITERIA
The Master Production Schedule must:
 Satisfy the needs of sales/marketing
Be feasible for operations
Match with supply chain capability

5. DETAILED MPS FOR A PRODUCT
On-hand inventory at end of October = 100
Month November December
Week 45 46 47 48 49 50 51 52
Forecast Demand 150 150 150 150 125 125 125 125
Orders Booked 170 165 140 120 85 45 20 0
Master Schedule 300 0 300 0 250 0 250 0
Calculate the projected on hand inventory

6. PROJECTED ON-HAND INVENTORY
On-hand inventory at end of October = 100
Month November December
Week 45 46 47 48 49 50 51 52
Forecast Demand 150 150 150 150 125 125 125 125
Orders Booked 170 165 140 120 85 45 20 0
Projected On-Hand
Inventory 230 65 215 65 190 65 190 65
Master Schedule 300 0 300 0 250 0 250 0
e.g., Projected on-hand inventory for week 47: = 65 + 300 – 150 = 215

7. PLANNING HORIZON
How far an MPS looks into the future depends
on
 Variability in demand and market
conditions
 Variability in supplier deliveries and lead
times
 Length of the production process.

8. MRP & JOB SEQUENCING

9. We’ve scheduled 500 chairs to be
ready five weeks from now . . .
. . . Now
what?

10. Back supports (3)
Side rails (2)
Front legs (2)
Cross bars (2)
Seat
MATERIAL NEEDED FOR A CHAIR

11. CHAIR STRUCTURE TREE
(“BILL OF MATERIALS” OR BOM)
Chair
Leg
Assembly
Seat Back Assembly
Legs (2) Cross
bar
Side
rails (2)
Cross
bar
Back
Supports
(3)

12. Chair
Assembly
(1 week)
Week 5Week 4
If final assembly
takes one week,
then we must start
the assembly at the
beginning
of Week 4 . . .
LEAD-TIME I

13. Chair
Assembly
Back
Assembly
Leg
Assembly
(1 week)
(1 week)
(1 week)
Seats (2 weeks)
Week 5Week 4Week 3Week 2
Which
means that
the major
subassemblies
and seats
must be
done by the
beginning
of Week 4 ...
LEAD-TIME II

14. Chair
Assembly
Back
Assembly
Leg
Assembly
(1 week)
(1 week)
(1 week)
Back Support (2 weeks)
Legs (2 weeks)
Side Rails (2 weeks)
Cross Bar (2 weeks)
Cross Bar (2 weeks)
Seats (2 weeks)
Week 5Week 4Week 3Week 2Week 1
LEAD-TIME III

15. LEAD-TIME KEY POINTS
 To have finished chairs at the beginning
of Week 5 with no work in progress, we
must begin production and order
materials in Week 1.
 “Exploding” the bill of materials tells us
when to order things.
 Not much we can do to adjust output of
chairs for the next 4 weeks

16. MATERIAL REQUIREMENTS
PLANNING (MRP)
Requires:
1. Bill-of-Materials (BOM)
2. Inventory record
3. Master schedule
to determine what should be ordered
when, and how much to order.

17. OTHER CONSIDERATIONS I
Planned Orders
Feedback Feedback
Production Suppliers
MRP

18. OTHER CONSIDERATIONS II
 When do we update the system?
 Capacity requirements planning using MRP
output
 MRP ‘nervousness’
 Increasing order chaos, the lower in the BOM
structure of materials
 Lot sizing issues

19. JOB SEQUENCING
 Rules:
 FCFS — first come, first served
 EDD — earliest due date
 Critical ratio — work time remaining divided by
days left before due date
 Performance measure:
 Average lateness — sum of days late for each
job divided by total number of jobs

20. EXAMPLE DATA
Job
Estimated
Time
Days Until
Due
Critical
Ratio
Weldco 8 32 0.250
MetroArt 10 20 0.500
MMCC 9 9 1.000
Jones 6 15 0.400

21. EXAMPLE FCFS
Job
Estimated
Time
Days
Until
Due
Start End
Days
Late
Weldco 8 32 0 8 0
MetroArt 10 20 8 18 0
MMCC 9 9 18 27 18
Jones 6 15 27 33 18
Average lateness = 36/4 = 9 days

22. EXAMPLE EARLIEST DUE DATE
Job
Estimated
Time
Days
Until
Due
Start End
Days
Late
MMCC 9 9 0 9 0
Jones 6 15 9 15 0
MetroArt 10 20 15 25 5
Weldco 8 32 25 33 1
Average lateness = 6/4 = 1.5 days

23. EXAMPLE CRITICAL RATIO
(LARGEST RATIO FIRST)
Job
Estimated
Time
Days
Until
Due
Start End
Days
Late
MMCC 9 9 0 9 0
MetroArt 10 20 9 19 0
Jones 6 15 19 25 10
Weldco 8 32 25 33 1
Average lateness = 11/4 = 2.75 days

24. DISTRIBUTION REQUIREMENTS
PLANNING (DRP)
 Anticipates downstream demand
 Uses this information, not predetermined
reorder points or periodic reviews, to
determine when to order
 Computer-based software systems
needed to deal with the added
complexity

25. DRP BENEFITS
 Helps improve customer service
 Provides a better and faster understanding of
the impact of shortages and/or promotions
 Helps reduce costs
 Inventory
 Freight
 Production
 Provides integration between the stages in
the supply chain

26. LOGISTICS NUMERICALS

27. SCORING MODELS
 Scoring models emphasize the factors that are important for
locations, but which cannot easily be costed or quantified.
 The important factors in location decisions
 Mostly use scoring model in facility / warehouse and
transportation decisions

28. SCORING MODELS - FACTORS IN LOCATION DECISIONS
 In the region and country
● availability, skills and productivity of workforce
● local and national government policies, regulations, grants and
attitudes
● political stability
● economic strength and trends
● climate and attractiveness of locations
● quality of life – including health, education, welfare and culture
● location of major suppliers and markets
● infrastructure – particularly transport and communications
● culture and attitudes of people.

29. SCORING MODELS - FACTORS IN LOCATION DECISIONS
 In the city or area
● population and population trends
● availability of sites and development issues
● number, size and location of competitors
● local regulations and restrictions on operations
● community feelings
● local services, including transport and utilities.

30. SCORING MODELS - FACTORS IN LOCATION DECISIONS
 In the site
● amount and type of passing traffic
● ease of access and parking
● access to public transport
● organizations working nearby
● total costs of the site
● potential for expansion or changes.

31. IMPORTANT FACTORS FOR SCORING MODELS
 availability of a workforce with appropriate skills
 labor relations and community attitudes
 environment and quality of life for employees
 closeness of suppliers and services
 quality of infrastructure
 government policies toward industry.
When decision maker want the facility near to raw material

32. IMPORTANT FACTORS FOR SCORING MODELS
Concerning with customers, decisions about location put more
weight on:
 population density
 socio-economic characteristics of the nearby population
 location of competitors and other services
 location of other attractions such as retail shops
 convenience for passing traffic and public transport
 ease of access and convenient parking
 visibility of site.

33. SCORING MODELS THE BASIS OF SCORING MODELS,
Five steps:
Step 1
 decide the relevant factors in a decision
Step 2
 give each factor a maximum possible score that shows its importance
(usually 0-100) and weight for each factor (0.00-1.00)
Step 3
 consider each location in turn and give an actual score for each factor, up to
this maximum
Step 4
 add the total weighted score (= Site Score x Factor Weight) for each
location and find the highest
Step 5
 discuss the result and make a final decision.

34. SCORING MODEL EXAMPLE
 Samson Ltd. is considering three alternative sites for its new
facility.
 After evaluating the firm’s needs, the Managers have narrowed
the list of important Selection Criteria down into three major
factors.
- Availability of skilled labor
- Availability of Raw materials, and
- Proximity to the firm’s markets.

35.  Based on these criteria, the three Alternative sites were scored
between 0 and 100 points:
Scoring model Example (cont.)

36.  Weights of each factor have been assigned as follows:
Scoring model Example (cont.)

37. FACTOR Factor
Weight
(Total=1)
Site A Site B Site C
Score Weighted
Score
Score Weighted
Score
Score Weighted
Score
Skilled labor 0.5 70 35 70 35 50 25
Raw materials 0.3 60 18 40 12 90 27
Market Prox. 0.2 70 14 95 19 60 13
Total Weighted Score 67 66 64
Scoring model Example (cont.)
 Now we will multiply each score by its corresponding factor weight
 Weighted scores are calculated as: (Site Score) x (Factor Weight)
 From these results, the largest total weight is for Site A. It appears to be
the best location.

38. NETWORK MODELS
Electronic maps of road networks allow another
approach to location, which is based on actual road
layouts.

39. SINGLE MEDIAN PROBLEM
 Finds the location of one facility on a network that minimize total cost is
called the single median problem
 The easiest way to find the single median
- Starts with a matrix of the shortest distances between towns.
- To find the shortest average distance, we have to combine these
distances with the loads carried.

40. EXAMPLE
 Ian Bruce delivers goods to eight towns, with locations and
demands as shown in next slide. He wants to find the
location for a logistics centre that minimizes the average
delivery time to these towns. Where should he start looking?

41. HT
AL
FR
10
CP
BE
DI15 GO
EN
15
25
20
10
10
15
20
22
9
8
14
5
6
6
7
Distance between
AL and CP
Facility CP
Demand at CP
Map of Ian Bruce’s problem
Single median problem – Example (cont.)
8

42. HT
AL
FR
10
CP
BE
DI15 GO
EN
15
25
20
10
10
15
20
22
9
8
14
5
6
6
7
Map of Ian Bruce’s problem
Single median problem – Example (cont.)
8
Ways from AL to EN
RED= 15+9+7= 31

43. HT
AL
FR
10
CP
BE
DI15 GO
EN
15
25
20
10
10
15
20
22
9
8
14
5
6
6
7
Map of Ian Bruce’s problem
Single median problem – Example (cont.)
8
Ways from AL to EN
RED= 15+9+7= 31
GREEN= 15+8+6+6= 35

44. HT
AL
FR
10
CP
BE
DI15 GO
EN
15
25
20
10
10
15
20
22
9
8
14
5
6
6
7
Map of Ian Bruce’s problem
Single median problem – Example (cont.)
8
Ways from AL to EN
RED= 15+9+7= 31 (smallest)
GREEN= 15+8+6+6= 35
YELLOW= 22+6+6= 34