Seasonal variation in demand
E.g. Ice Cream Factories
Agora on employment and
departmental space allocation
Adeyl Khan, Faculty, BBA, NSU
Planning Horizon/Levels
Short-range plans
(Detailed plans)
• Machine loading
• Job assignments
Intermediate plans
(General levels)
• Employment
• Output
Long-range plans
• Long term
capacity
• Location / layout
Long range
Short
range
Now
Intermediate
range
2-3 months
Adeyl Khan, Faculty, BBA, NSU
Aggregate planning: Intermediate-range
capacity planning, usually covering 2 to 12
months. (Also called Macro planning)
18 Months
13-2
Aggregation- what is?
Aggregate Plan
Month
Jan
# of Motors
Feb
40
Mar
25
Apr
55
May
30
Jun
30
Jul
50
Aug
30
Sep
60
40
Master Schedule
Month
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
5 hp
15
-
30
-
15
30
-
-
10
25 hp
20
25
-
15
-
15
20
30
20
20 hp
-
-
25
-
15
-
10
10
-
50 hp
5
-
-
15
-
5
-
20
10
Adeyl Khan, Faculty, BBA, NSU
3
Why do it?
 To develop a feasible production plan on an
aggregate level that achieves a balance of
expected demand and supply
 usually demand and supply are converted to
aggregate units such as labour-hours, working
days, general product units, etc.
Adeyl Khan, Faculty, BBA, NSU
Objective of Aggregate Planning
Corporate
strategies
and policies
Economic,
competitive,
and political
conditions
Planning Sequence
Aggregate
demand
forecasts
Business Plan
• Establishes production and capacity strategies
Production plan
• Establishes production capacity
Master schedule
• Establishes schedules for specific products
Adeyl Khan, Faculty, BBA, NSU
Aggregate Planning Approaches
 Maintain a level workforce
 Maintain a steady output rate
 Match demand period by
period
 Use a combination
of decision variables
Basic approaches
Level capacity
Maintaining a steady rate
of regular-time output
while meeting variations
in demand by a
combination of options.
Adeyl Khan, Faculty, BBA, NSU
Chase demand
Matching capacity to
demand; the planned
output for a period is the
expected demand for
that period.
Level and Chase Strategies
Quantity
Output less
than demand
Demand
Normal
capacity
Demand
Output
level
Output exceeds
demand
Output above
normal
Output below
normal
Adeyl Khan, Faculty, BBA, NSU
Level Output Strategy
Demand
Chase Demand Strategy
Normal
capacity
Cumulative output/demand
Cumulative Graph
1
Cumulative
production
Cumulative
demand
2
Adeyl Khan, Faculty, BBA, NSU
3
4
5
6
7
8
9
10
Example - a personal plan
 An NSU UG student
 One year expenses





tuition 40,000
transportation 1000 x 12 = 12000
food and meal 2000 x 10 = 20000
summer
5000 x 2 = 10000
others
500 x 12 = 6000
 Total
Adeyl Khan, Faculty, BBA, NSU
88,000
Example - a personal plan
 A NSU UG student
 plan income





Bank loan, etc 40000
private tutoring
part time job
summer job
family money
 Total

saving
2000 x 12 = 24000
2000 x 10 = 20000
6500 x 2 = 13000
1000 x 10 = 10000
107000
107000 - 88000 = 19000
 Objective: income meets expenses; maximize
saving; etc. (What do you call this?)
Adeyl Khan, Faculty, BBA, NSU
General steps in AP
 1.
Forecast demand in the period
 2. Develop plan(s) to meet the demand by
setting levels on output, employment, inventory,
etc.
 3. The plans are refined or reworked until a
feasible and satisfactory plan is uncovered.
Adeyl Khan, Faculty, BBA, NSU
Options to affect demand level
 Pricing
 e.g., shift demands from peak periods to off-peak
periods. The more the elasticity, the more
effective pricing will be on the demand pattern.
 Promotion
 Backorders (depend on customers’ willingness)
 Develop new demand (market) during off-peak
period
Adeyl Khan, Faculty, BBA, NSU
Options to affect capacity
 Hire and fire workers - depends on the intensity
of labour used, the strength of the union,
corporate culture, labour laws, etc.
 Overtime/slack time - to keep a skilled
workforce and allows employee to increase
earnings
 Partime workers - depend on nature of work
 Inventories - smooth production and buffer
against demand surge; could be costly
 Subcontracting - capacity increase in a short
time without heavy investment; less control
Adeyl Khan, Faculty, BBA, NSU
Mathematical Techniques
 Linear programming: Methods for obtaining
optimal solutions to problems involving
allocation of scarce resources in terms of cost
minimization.
 Linear decision rule: Optimizing technique that
seeks to minimize combined costs, using a set of
cost-approximating functions to obtain a single
quadratic equation.
Adeyl Khan, Faculty, BBA, NSU
Summary of Planning
Techniques
T e c h n iq u e
S o lu tio n
C h a r a c te r is tic s
G r a p h ic a l/
c h a r tin g
T r ia l a n d
e rro r
L in e a r
p r o g r a m m in g
L in e a r
d e c is io n r u le
O p tim iz in g
S im u la tio n
T r ia l a n d
e rro r
In tu itiv e ly a p p e a lin g , e a s y to
u n d e r s ta n d ; s o lu tio n n o t n e c e s s a r ily
o p tim a l.
C o m p u te r iz e d ; lin e a r a s s u m p tio n s
n o t a lw a y s v a lid .
C o m p le x , r e q u ir e s c o n s id e r a b le
e f f o r t to o b ta in p e r tin e n t c o s t
in f o r m a tio n a n d to c o n s tr u c t m o d e l;
c o s t a s s u m p tio n s n o t a lw a y s v a lid .
C o m p u te r iz e d m o d e ls c a n b e
e x a m in e d u n d e r a v a r ie ty o f
c o n d itio n s .
Adeyl Khan, Faculty, BBA, NSU
O p tim iz in g
Example
The VP of Operations is about to prepare the aggregate plan that will cover six
periods in the horizon. The company has forecasted the following demand:
Period
1
Forecast demand 200
2
3
4
5
6
Total
200
300
400
500
200
1800
The output cost is $2 per unit at regular time; $3 per unit at overtime; $6 per
unit if subcontracted. Average inventory cost is $1 per unit per period. Back
orders are possible, however, the Company estimated the cost to be $5 per
unit per period. The initial inventory is zero. There are 15 workers and each
worker is able to produce 20 units of the product per period. Can you help the
VP to develop an aggregate plan?
Adeyl Khan, Faculty, BBA, NSU
Example - solution
 Suppose the VP wants to use a leveling
(capacity) approach, I.e., maintaining a steady
rate of output. The total output by the workers at
the regular time is 20 x 15 x 6 = 1800 which
equals to the forecast demand.
Average
inventory
= Beginning Inventory + Ending Inventory
2
Adeyl Khan, Faculty, BBA, NSU
Period
Forecast demand
Output-regular
Output-over time
Output-subcontract
Output-excess
Inventory
Inven.-beginning
Inven.-ending
Inven.-average
Backlog
Cost
Regular
Overtime
Subcontract
Hire/Fire
Inventory
Backorders
1
200
300
2
200
300
3
300
300
4
400
300
5
500
300
6
200
300
Total
1800
1800
100
100
0
-100
-200
100
0
0
100
50
100
200
150
200
200
200
200
100
150
100
0
50
100
0
0
0
600
600
600
600
600
600
3600
50
150
200
150
50
500
0
600
500
Total cost
650
750
800
750
1150
600
4700
Adeyl Khan, Faculty, BBA, NSU
Example- Chase demand
 The VP learned that a regular worker is retiring.
Rather than hiring new worker, the VP decides to
use overtime. However, the maximum amount of
overtime output is 40 units per period. Suggest
an aggregate plan for the VP
 Regular worker produce 14 x 20 units = 280
units per period. The total deficiency is 120
units. These 120 units can be satisfied in 3
periods by overtime and can be produced
during the periods of high demand (for cost
consideration. Of course, you can put them in
other periods too.)
Adeyl Khan, Faculty, BBA, NSU
Period
Forecast demand
Output-regular
Output-over time
Output-subcontract
Output-excess
Inventory
Inven.-beginning
Inven.-ending
Inven.-average
Backlog
Cost
Regular
Overtime
Subcontract
Hire/Fire
Inventory
Backorders
1
200
280
2
200
280
3
300
280
40
4
400
280
40
5
500
280
40
6
200
280
Total
1800
1800
80
80
20
-80
-180
80
0
0
80
40
80
160
120
160
180
170
180
100
140
100
0
50
80
0
0
0
560
560
560
120
560
120
560
120
560
3360
40
120
170
140
50
400
0
520
500
Total cost
600
680
850
820
1130
560
4640
Adeyl Khan, Faculty, BBA, NSU
Solved Problems: Problem 1
Period
1
2
3
4
5
6
7
8
9
Total
190
230
260
280
210
170
160
260
180
1,940
22
22
22
22
22
22
22
22
22
220
220
220
220
220
220
220
220
220
1,980
30
-10
-40
-60
10
50
60
-40
40
40
0
30
20
0
0
0
0
40
0
Ending
30
20
0
0
0
0
40
0
40
Average
15
25
10
0
0
0
20
20
20
110
0
0
20
80
70
20
0
0
0
190
5
1,100
1,100
1,100
1,100
1,100
1,100
1,100
1,100
1,100
9,900
10
150
250
100
0
0
0
200
200
200
500
1,100
100
0
0
2000
8000
7000
2000
0
0
0
19,000
1,750
1,350
3,200
9,100
8,100
3,100
1,300
1,300
1,300
30,500
Forecast
# Employees
Output
Regular
Output-Forecast
Inventory
Beginning
Backlog
Costs:
Output
Regular
Hire/Fire
Inventory
Backorders
Total
Adeyl Khan, Faculty, BBA, NSU
13-21
Quantitative approach
 Suppose the VP wants to use a more quantitative approach
that use overtime only and have in mind that the cost be
minimized. Can you help him?
 Let us define the following notation:
 Pt = No. of units produced via regular time at period t, t=1,
…, 6
 Dt = Demand (in No. of units) at period t, t=1, …, 6
 Ot = No. of units produced at period t in overtime, t=1, …, 6
 It = Inventory level (in No. of units) at the end of period t,
t=1, …, 6
Adeyl Khan, Faculty, BBA, NSU
For ease of handling, we introduce the concept of back order Bt at period t. The
following is a kind of “conservation law”
It = It-1 + Pt - Dt , t = 1, …, 6.
The Objective function is given by:
6
 (2P
t 1
t
6
 (P
t 1
t
 O t  Bt  I t )
 3O t  5B t  I t )
subject to
Pt  O t  I t -1  B t - I t - B t -1  D t , t  1,...,6
0  Pt  280, t  1,...,6
0  O t  40, t  1,...,6
B t , I t  0, t  1,...,6
Notice that I0 = 0 and B0 = 0, (D1, …, D6) = (200,200,300,400,500,200)
Adeyl Khan, Faculty, BBA, NSU
Disaggregating the aggregate plan
 The aggregate plan gives the
 level of demand and supply
 in aggregate units
at the macro level
 In order for the company to execute the plan, it
needs to disaggregate the plan into appropriate
units for implementation and monitoring. The
output of this process is a master schedule and a
master production schedule.
Adeyl Khan, Faculty, BBA, NSU
Master Scheduling
Process
Beginning
inventory
Forecast
Inputs
Customer
orders
A master schedule is a schedule
(usually in the form of a table)
indicating the quantity and timing
(I.e., delivery times) for individual
products or a group of individual
products.
Master scheduling
Master
production
schedule
Uncommitted
inventory
Adeyl Khan, Faculty, BBA, NSU
Projected
inventory
Outputs
Example
Forecast demand
Initial inventory
is 64
Forecast demand
Committed customer
orders
Projected onhand inventory
MPS
ATP inventory
Adeyl Khan, Faculty, BBA, NSU
1
30
A Master Schedule
June
2
3
4
1
30
30
30
40
July
2
40
June
3
40
4
40
July
1
30
2
30
3
30
4
30
1
40
33
20
10
4
2
2
40
3
40
4
40
Another MPS problem
Initial inventory
is 64
Forecast demand
Committed customer
orders
Projected on-hand
inventory
MPS
ATP inventory
Adeyl Khan, Faculty, BBA, NSU
1
30
June
2
3
30 30
33
20
10
4
30
1
40
4
2
July
2
3
40 40
4
40
Another MPS problem solution
Initial inventory
is 64
Forecast demand
Committed customer
orders
Projected on-hand
inventory
MPS
ATP inventory
Adeyl Khan, Faculty, BBA, NSU
1
30
June
2
3
30 30
33
20
31
1
4
30
1
40
10
4
2
41
70
11
41
70
July
2
3
40 40
1
31
70
4
40
61
70
Another MPS problem solution …
Initial inventory
is 64
Forecast demand
Committed customer
orders
Projected on-hand
inventory
MPS
ATP inventory
Adeyl Khan, Faculty, BBA, NSU
1
30
June
2
3
30 30
33
20
31
1
11
4
30
1
40
10
4
2
41
70
56
11
41
70
68
July
2
3
40 40
1
31
70
70
4
40
61
70
70
Projected On-hand Inventory
Beginning
Inventory
64
Forecast
Customer Orders
(committed)
Projected on-hand
inventory
Customer orders are
larger than forecast in
week 1
1
30
JUNE
2
3
30 30
4
30
5
40
33
20
10
4
2
31
1
-29
JULY
6
7
40 40
8
40
Forecast is larger than
Customer orders in week 3
Forecast is larger than
Customer orders in week 2
Figure 13.8
Adeyl Khan, Faculty, BBA, NSU
13-30
Relevant important ideas
Adeyl Khan, Faculty, BBA, NSU
31
AP and Master scheduling
Aggregate planning
• planning the quantity and timing of output over the
intermediate range by setting/adjusting the
• production rate
• Employment
• Finished goods inventory, and related variables.
• planning activities at this “early” stage are concerned
with homogeneous categories, e.g. gross volumes
Master scheduling
• follows aggregate planning and expresses the overall
plan in terms of the amounts of specific end items to
produce and dates to produce them.
Adeyl Khan, Faculty, BBA, NSU
32
Aggregate Planning Inputs
Resources
(Available)
Demand forecast
Policies
• Workforce
• Facilities
• Subcontracting
• Overtime
• Inventory levels
• Back orders
Costs
• Inventory
carrying
• Back orders
• Hiring/firing
• Overtime
• Inventory
changes
• Subcontracting
AP Outputs
Projected levels of
Total cost of a plan
Adeyl Khan, Faculty, BBA, NSU
•
•
•
•
•
Inventory
Output
Employment
Subcontracting
Backordering
13-33
Aggregate Planning in Services
 Services generally have variable processing
requirements that make it difficult to establish a
suitable measure of capacity.
 Capacity availability can be difficult to predict
 Services occur when they are rendered. Services
cannot be stockpiled or inventoried so they do not
have this option. It is considered "perishable,“
 An empty hotel room cannot be held and sold later
 Demand for service can be difficult to predict
 Labor flexibility can be an advantage in services
Adeyl Khan, Faculty, BBA, NSU
13-34
Time Fences in MPS
 Time Fences – points in time that separate
phases of a master schedule planning horizon.
Period
1
2
“frozen”
(firm or
fixed)
Adeyl Khan, Faculty, BBA, NSU
3
4
5
“slushy”
somewhat
firm
6
7
8
9
“liquid”
(open)
13-35
Calculating ATP
 Calculated only in current week and any week
with MPS>0
 Current period: on-hand plus any current period
MPS, minus all orders in that and subsequent
periods until next MPS
 Later periods: MPS – all orders until next MPS
Adeyl Khan, Faculty, BBA, NSU
level production plan
1
Forecast
Available (End)
MPS
On hand (start)
2
3
4
5
6
7
8
9
10
11
12
10 10 10 10 10
10
10 10
10
10
10
10
20 20 20 20 20
20
20 20
20
20
20
20
10 10 10 10 10
10
10 10
10
10
10
10
20
Forecast
Available
(Ending)
MPS
On hand
(starting)
1
5
2
5
3
5
4
5
5
5
6 7 8 9 10 11 12
5 15 15 15 15 15 15
25 30 35 40 45 50 45 40 35 30 25 20
10 10 10 10 10 10 10 10 10 10 10 10
20
Different sales forecast - Same total: 120 units, starts lower, goes higher
Adeyl Khan, Faculty, BBA, NSU
“Chase” production strategy
Forecast
Available
(Ending)
MPS
On hand
(starting)
1
2
3
4
5
6
7
8
9
10
11
12
5
5
5
5
5
5
15
15
15
15
15
15
20
20
20
20
20
20
20
20
20
20
20
20
5
5
5
5
5
5
15
15
15
15
15
15
20
Production adjusts to meet demand
Adeyl Khan, Faculty, BBA, NSU
38
Lot size of 30 units
Forecast
Available (Ending)
MPS
On hand (starting)
1
2
3
4
5
6
7
8
9
10
11
12
5
5
5
5
5
5
15
15
15
15
15
15
15
10
5
30
25
20
5
20
5
20
5
20
0
0
0
30
0
0
0
30
0
30
0
30
20
Produce if projected balance falls below 5 units
Extra on-hand inventory is “cycle stock”
5 unit “trigger” is safety stock
Adeyl Khan, Faculty, BBA, NSU
Adeyl Khan, Faculty, BBA, NSU
40
BOM formats
 Single-level BOM only
shows one layer down.
 Indented BOM
 Bike
 Frame Assembly


Wheel
Assembly
 Wheel Assembly

Wheel
Tires

Adeyl Khan, Faculty, BBA, NSU
Components
Frame
Wheel
 Hubs & Rims
 Spokes
Tires
Low-Level Code Numbers
 Lowest level in structure item
occurs
 Top level is 0; next level is 1 etc.
 Process 0s first, then 1s
 Know all demand for an item
 Where should blue be?
The low-level code controls the sequence
in which the material
is planned in an MRP run: First the
materials with low-level code 0 are
planned, then the materials with low-level
code 1, and so on. The lower
the low-level code, the higher the number
that is assigned to the
material.
Adeyl Khan, Faculty, BBA, NSU
LLC
0
1
2
3
4
LLC Drawing
 Item only appears in one level of LLC
drawing
 Easier to understand
 Simplifies calculations
LLC
0
1
2
3
4
Adeyl Khan, Faculty, BBA, NSU
Final Assembly Schedule
 Master Production schedule is anticipated build
schedule
 FAS is actual build schedule
 Exact end-item configurations
Adeyl Khan, Faculty, BBA, NSU
Schedule Stability
 Stable schedule means stable component
schedules, more efficient
 No changes means lost sales
 Frozen zone- no changes at all
 Time fences
 >24 wks, all changes allowed (water)
 16-23 wks substitutions, if parts there (slush)
 8-16 minor changes only (slush)
 < 8 no changes (ice)
Adeyl Khan, Faculty, BBA, NSU