Production Planning and Control

Dept. of MBA, Sanjivani COE, Kopargaon
204 OPERATIONS MANAGEMENT
Unit-III - Production Planning &
Control (PPC)
Presented By:
Dr. Sanjit Singh
Sanjivani College of Engineering, Kopargaon
Department of MBA

Demand Forecasting

Forecasting
Some examples & context
Manufacturing
• A manufacturer of household appliances wants to add to add another
product line for manufacturing microwave ovens The decision requires
a good understanding of the nature of demand for the range of
microwave ovens proposed to be manufactured
Services
• A hospital chooses to add one more specialty health care wing, it needs
to make some assumptions about the demand for the facility
Public Policy
• Government of India needs to have a reasonable estimate of the
population growth over the next 10 – 20 years while it formulates long
term plans for creating infrastructure for transport

Forecasting
• Forecasts are estimates of
– magnitude and
– timing
of uncertain events that happen in every business setting
• An estimation tool
• A way of addressing complex and uncertain environment surrounding
business decision-making
• A tool for predicting events related to operations planning & control
• A vital pre-requisite for the planning process in organizations

Need for Forecasting
• The key applications of forecasting are:
– Understanding Dynamic & Complex environment
– Managing Short-term fluctuations in production
– Better Materials Management
– Rationalized man-power decisions
– Providing a basis for
• Strategic decisions
• Planning & scheduling

Forecasting
Time Horizon
Criterion Short-term Medium-term Long-term
Typical Duration 1 – 3 months 12 – 18 months 5 – 10 Years
Nature of decisions Purely Tactical Tactical as well as
Strategic
Purely Strategic
Key considerations Random (short-term)
effects
Seasonal and Cyclical
effects
Long-term trends
Business Cycles
Nature of data Mostly quantitative Subjective &
Quantitative
Largely subjective
Degree of
uncertainty
Low Significant High
Some examples Revising quarterly
production plans
Rescheduling supply
of raw material
Annual Production
Planning
Capacity
Augmentation
New Product
Introduction
Facilities Location
decisions
New business
development

Develop a forecasting logic by identifying the
purpose, data and models to be used
Establish control mechanisms to obtain reliable
forecasts
Incorporate managerial considerations in using
the forecasting system
Stage 1
Stage 2
Stage 3
Design of a forecasting system
Three stage process

Start
Identify purpose
• Purpose of forecast
• Time horizon
• Type of data needed
Identify a suitable technique
• Collect/analyze past data
• Select an appropriate model
Develop a forecasting logic
• Establish model parameters
• Build the model
Test model adequacy
• Test using historical data
Satisfactory
No
Yes
Stop
Developing a forecasting logic
Steps

Sources of Data
• Field Data
– Sales force estimates
– Point of Sale (POS) Data systems
– Forecasts from supply chain partners
• Secondary data
– Trade/Industry Association Journals
– B2B Portals/Market Places
– Economic Surveys and Indicators
• Subjective Knowledge

Source: http://www.steelexchangeindia.com
B2B Portal as a source of data
An illustration

Models for forecasting
• Extrapolative Models
– Make use of past data and essentially
prepare the future estimate by some
method of extrapolating the past data
– Examples
• Moving Averages – Weighted, Simple
• Exponential Smoothening
• Time Series Methods
 Causal or Explanatory Models
 Analyse the data from a point of
cause – effect relationship
 Examples
 Multiple Regression Models
 Econometric Models
 Technological Forecasting
 Subjective Judgement Methods
 Draw substantially from the expertise of a
group of senior managers using some
collective decision making framework

Moving Average Model
The generalised formula for forecasting using MA method
is given by:
n
t
t
t
t
n
t
n
t
t
t
t
t
t
t
t
W
W
W
W
W
D
W
D
W
D
W
D
F





















.
.
.
.
.
.
3
2
1
3
3
2
2
1
1
Ft = The moving average forecast for period t
n = The number of periods for moving average
Di = Actual demand during period i
Wi = Weight for the ith period demand data
If different periods do not have different weights, the
forecast obtained will be based on a simple moving
average model, given by:
n
D
D
D
D
F n
t
t
t
t
t



 




.
.
.
3
2
1

Weighted & Simple Moving Averages: An illustration
Simple Moving Averages Weighted Moving Averages
Model parameter
Number of periods for moving average 3 months
Month Actual Sales Forecast*
January 24,500
February 27,000
March 25,500
April 26,000 25,667
May 21,200 26,167
June 18,900 24,233
July 17,500 22,033
August 19,000 19,200
September 18,467
Model parameter
Number of periods for moving average 3 months
Weights for three periods
Immediate past 0.45
Two periods before 0.30
Three periods before 0.25
Month Actual Sales Forecast*
January 24,500
February 27,000
March 25,500
April 26,000 25,700
May 21,200 26,100
June 18,900 23,715
July 17,500 21,365
August 19,000 18,845
September 18,525
* Forecasts in this illustration are rounded to units

Exponential Smoothening Method
)
(
1 t
t
t
t F
D
F
F 


 
The forecast for the next period is computed on the basis
of the forecast for the current period and the actual
demand during the current period, the difference between
forecast and actual demand is incorporated in the next
period’s forecast
Ft+1 = Exponentially smoothened forecast for period t+1
Ft = Exponentially smoothened forecast for period t
Dt = Actual demand during period t
 = Smoothening coefficient

Exponential Smoothening Method An illustration
0.20
Period Forecast
January 100 90
February 98 95
March 97 105
April 99 110
May 101 100
June 101 130
July 107 90
August 103 110
September 105 100
October 104 140
November 111
Smoothening Constant ()
Model Parameter
Actual Demand
0.80
Period Forecast
January 100 90
February 92 95
March 94 105
April 103 110
May 109 100
June 102 130
July 124 90
August 97 110
September 107 100
October 101 140
November 132
Smoothening Constant ()
Model Parameter
Actual Demand
An example with  = 0.20 An example with  = 0.80
A lower value of  indicates that the forecast is not responsive to the demand

Exponential Smoothening Method
Impact of model parameter (alpha)
80
90
100
110
120
130
140
150
J
a
n
u
a
r
y
F
e
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a
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A
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N
o
v
e
m
b
e
r
Forecast period
Demand/Forecast
(units) alpha = 0.2 alpha = 0.9 Actual Demand alpha = 0.5

Time Series Methods
Components
• Trend (T)
– Long term secular movement in the pattern
• Seasonality (S)
– Fixed cycles in which the time series data often move from period to
period
• Cyclical (C)
– Business cycles that repeat over a much longer period of say 10 – 20
years
• Random (R)
– Uncontrollable events happening in the short term that could influence
the demand

Components of Time Series
Trend: An illustration
0
100
200
300
400
500
600
700
800
900
1
3
5
7
9
11
13
15
17
19
21
23
25
27
29
31
33
35
37
39
41
43
45
47
Month
Trend Actual Demand

Components of Time Series
Seasonality, Cyclical, Random: An illustration
1
3
5
7
9
11
13
15
17
19
21
23
25
27
29
31
33
35
37
39
41
43
45
47
Month
Seasonality Cyclical Random

Accuracy of Forecasts
Alternative Measures
• Forecast Error (FE):
• Sum of Forecast Errors (SFE):
• Mean Absolute Deviation (MAD):
• Mean Absolute Percentage Error (MAPE):
• Mean Squared Error (MSE):
• Tracking Signal (TS):
t
t
t F
D 




n
i
i
1




n
i
i
n
MAD
1
*
1




n
i i
i
D
n
MAPE
1
100
*
*
1 



n
i
i
n
MSE
1
2
*
1

MAD
SFE
TS 
Dt = Demand during period ‘t’, Ft = Forecast during period ‘t’, n = No. of periods

Accuracy of Forecasts
An illustration: Example 13.4
Period Demand Forecast
Forecast
Error SFE
Absolute
Deviation
Cum. Abs.
Deviation MAD
Absolute
Error (%) MAPE
Squared
Error MSE
Tracking
Signal
1 120 109 11 11 11 11 11.00 9.2% 9.2% 121 121.00 1.00
2 114 118 -4 7 4 15 7.50 3.5% 6.3% 16 68.50 0.93
3 130 132 -2 5 2 17 5.67 1.5% 4.7% 4 47.00 0.88
4 124 110 14 19 14 31 7.75 11.3% 6.4% 196 84.25 2.45
5 97 110 -13 6 13 44 8.80 13.4% 7.8% 169 101.20 0.68
6 95 105 -10 -4 10 54 9.00 10.5% 8.2% 100 101.00 -0.44
7 100 98 2 -2 2 56 8.00 2.0% 7.3% 4 87.14 -0.25
8 110 95 15 13 15 71 8.88 13.6% 8.1% 225 104.38 1.46
9 109 104 5 18 5 76 8.44 4.6% 7.7% 25 95.56 2.13
10 123 110 13 31 13 89 8.90 10.6% 8.0% 169 102.90 3.48
11 127 112 15 46 15 104 9.45 11.8% 8.4% 225 114.00 4.87
12 119 119 0 46 0 104 8.67 0.0% 7.7% 0 104.50 5.31
13 130 124 6 52 6 110 8.46 4.6% 7.4% 36 99.23 6.15
14 125 110 15 67 15 125 8.93 12.0% 7.8% 225 108.21 7.50
15 119 90 29 96 29 154 10.27 24.4% 8.9% 841 157.07 9.35
16 120 95 25 121 25 179 11.19 20.8% 9.6% 625 186.31 10.82
17 90 75 15 136 15 194 11.41 16.7% 10.0% 225 188.59 11.92
18 95 65 30 166 30 224 12.44 31.6% 11.2% 900 228.11 13.34
MAD for period 6
Cumulative absolute deviation up to period 6: 54
Number of periods: 6
Therefore, MAD = 54/6 = 9.00
MAPE for period 12
Cum. absolute percent error up to period 12 = 9.2% + 3.5% + … + 0.0% = 92.0 %
Therefore, MAPE = 92/12 = 7.7%
MSE for period 18
Cumulative Squared error up to period 18 = 121 + 16 + … + 900 = 4106
Therefore, MSE = 4106/18 = 228.11
TS for period 15
SFE for period 15 = 96
MAD for period 15 = 10.27
Therefore, TS = 96/10.27 = 9.35

Tracking Signal (Example 13.4)
-5.00
-3.00
-1.00
1.00
3.00
5.00
7.00
9.00
11.00
13.00
15.00
0 2 4 6 8 10 12 14 16 18
Time Period
Tracking
Signal
value

How to get started?
• Choice of model
• Estimation of parameters
Cost
Data
Availability
Time
Frame Key inferences
from
research/practice
Issues in using the system
• How to incorporate external information
• Stability Vs Responsiveness
New
Competitor
Sales
Promotions
When to change the system?
• Parameter re-estimation Vs model
change
Forecast
Reliability
Using the Forecasting System

Demand Forecasting
Chapter Highlights
• Forecasting is an important planning tool in organizations
– It helps to estimate the future demand,
– Predict events pertaining to operational planning and control
– Provides useful information for the strategic planning exercise
• Forecasting context and methodology varies with time horizon.
Consequently, the type of data required and the nature of analyses
done also varies with the planning time horizon.

Demand Forecasting
Chapter Highlights…
• Design of a forecasting system involves three steps
– Developing an appropriate forecasting logic
– Establishing control mechanisms to obtain reliable forecasts
– Incorporating managerial considerations in using the system
• Extrapolative and Causal methods are the two generic classes of forecasting models
available.
– Extrapolative methods devise methods of identifying some patterns in the past data and
extend a similar logic into the future
– Causal methods identify cause – effect relationships between dependant and independent
variables
• By changing the model parameters in moving averages and exponential
smoothening methods, it is possible to have a responsive or a stable forecasting
model

Demand Forecasting
• A time series consists of four components;
– Trend, Seasonality, Cyclical and Random.
– Developing time series models for forecasting involves estimating the parameter values for the
first three components
• Several methods are available to assess the accuracy of the forecasts obtained from
a forecasting model.
• Tracking signal detects impending tendencies of a forecasting model to consistently
overestimate or underestimate the demand.
• Despite having a sophisticated forecasting system, managers must use the forecasts
obtained from the system in the context of external information available from
time to time.

Capacity
• Manufacturing and service systems are arrangements of
facilities, equipment, and people to produce goods and
services under controlled conditions.
• Manufacturing systems produce standardized products in
large volumes. This plant and machinery have a finite capacity
and contribute fixed costs that must be borne by the products
produced. Productivity is measurable.
• Service systems present more uncertainty with respect to
both capacity and costs.

DESIGN AND SYSTEMS CAPACITY

CAPACITY PLANNING
Production managers are more concerned about the capacity for
the following reasons:
• Sufficient capacity is required to meet the customers demand
in time.
• Capacity affects the cost efficiency of operations.
• Capacity affects the scheduling system.
• Capacity creation requires an investment.

PROCESS OF CAPACITY PLANNING
• Long-term capacity strategies: (Forecasting for five or
ten years)
• Following parameters will affect long-range capacity
decisions.
– Multiple products:
– Phasing in capacity
– Phasing out capacity

PROCESS OF CAPACITY PLANNING
• Short-term capacity strategies (up to 12 months)
• The short-term capacity strategies are:
– Inventories
– Backlog
– Employment level (hiring or firing)
– Employee training
– Subcontracting
– Process design

Aggregate Production Planning

Business Planning Exercise
• Business plan is strategic in nature and addresses the following
questions:
– Should we meet the projected demand entirely or a portion of the
projected demand?
– What are the implications of this decision on the overall competitive
scenario and the firm’s standing in the market?
– How is this likely to affect the operating system and planning in other
functional areas of the business such as marketing and finance?
– What resources should we commit to meet the chosen demand
during the planning horizon?
• Aggregate production planning seeks to translate business plans to
operational decisions

Business Plan
Marketing Plan Financial Plan
Production Plan
(rough cut capacity)
Master Production Schedule
Materials
Requirement
Plan
Capacity
Requirement
Plan
Detailed Scheduling
Shop Floor Control
Level 1
Level 2
Level 3
Planning Hierarchies in Operations

Decision Variables: An illustration
• The decisions involve
– Amount of resources (productive capacity and labour hours) to be committed
– Rate at which goods and services needs to be produced during a period
– Inventory to be carried forward from one period to the next
• An example from Garment Manufacturing
– Produce at the rate of 9000 metres of cloth everyday during the months of January
to March
– Increase it to 11,000 metres during April to August
– Change the production rate to 10,000 metres during September to December
– Carry 10% of monthly production as inventory during the first 9 months of
production.
– Work on a one-shift basis throughout the year with 20% over time during July to
October

Aggregate Units for Capacity
Examples
Sl. No Product
Aggregate Unit of
capacity
1 Phenyl Acetic Acid Metric tonnes
2 Data Entry Systems Numbers
3 Mini computer Value (ex-factory) in Rs.
4 Printed Circuit Board Square Metres
5 Alloy Iron Castings Metric tonnes
6 Cement Metric tonnes

Why is it necessary?
• Demand fluctuations
• Capacity fluctuations
• Difficulty level in altering production rates
– Production systems are complex and varying the rate of
production requires prior planning and co-ordination with
supplier and distributor
• Benefits of multi-period planning
Aggregate Production Planning is done in an organisation to match the
demand with the supply on a period-by-period basis in a cost effective
manner

Targeted Demand
to be fulfilled
Arriving at effective
Period-by-period
Demand to be met
Arriving at
Period-by-Period
Supply Schedules
-
Actual period-by-period
Supply Schedules
Forecasting
Alternatives for
Modifying demand
Alternatives for
Modifying supply
Aggregate Production Planning Framework

Alternatives for managing demand
• Reservation of Capacity
– Hospital Appointment system
• Influencing Demand
– Special Tariffs
• Late night calls are cheaper
• Midnight flight to Bombay is cheap
– Differential Discount Structures
• Senior Citizen Discount
– Limited period special offers
• Happy Meal (Selected time of day)

Alternatives for Managing Supply
• Inventory Based Alternatives
– Stock out, Backordering/Backlogging
– Carrying Inventory
• Capacity Adjustment Alternatives
– Hiring/Lay-off of workers
– Varying shifts
– Varying Working Hours (OT,UT)
• Capacity Augmentation Alternatives
– Sub-contracting/Outsourcing
– De-bottlenecking
– Addition of new capacity

Aggregate Production Planning Alternatives
Description of the alternative Costs
Alternatives for
managing demand
Reservation of capacity Planning and Scheduling costs
Influencing Demand Marketing oriented costs
Alternatives for
managing supply
Inventory based alternatives
(a) Build Inventory Inventory holding costs
(a) Over Time/Under Time OT premium, Lost productivity
(b) Vary no. of shifts Shift change costs
(c) Hire/Lay-off workers Training/Hiring costs, Morale issues
Capacity augmentation alternatives
(a) Sub-contract/Outsource Transaction costs for sub-contract
(b) De-bottleneck Annualised de-bottlenecking cost
(c) Add new capacity Annualised cost of new capacity

Forecasting
Aggregate
Production
Planning
Master
Production
Scheduling
Materials Plan
Capacity Plan
Actual
Production
Market
Labour &
Resources
Vendors
Material
Inflow
Order
Inflow
Resource
availability
Master Production Scheduling Linkages with
APP & Forecasting

Dis-aggregation process in MPS
An illustration
18000
Silver 20
Gold 40
Platinum 70
Month 1 Month 2 Month 3
Forecast 100 120 140
Firm Order 120 90 30
Forecast 200 240 180
Forecast 80 100 90
MPS Quantity
16000 19700 16300
Capacity Planned using APP
Capacity Required/unit
Silver
Planning Horizon
Type of
service
Demand
status
Shaded area represents MPS Qty.
Capacity required (for
MPS Quantity)
Gold
Platinum

Dis-aggregation process in MPS
An illustration

Chapter Highlights
• Aggregate Production Planning (APP) serves to translate the business plans into
operational decisions
• The decisions include
– amount of resources (productive capacity and labour hours) to commit,
– rate at which to produce
– inventory to be carried forward from one period to the next
• APP is done to match the demand and the available capacity on a period-by-
period using a set of alternatives available to modify demand and/or the supply
• Alternatives for modifying demand include reservation of capacity and methods
of influencing (changing) the demand during a period
• Alternatives for modifying the supply include inventory variations, capacity
adjustment and capacity augmentation

• APP exercise employs the two generic strategies; chase and level
production. A chase strategy is often found to be expensive and hard to
implement in organisations
• In reality a mixed strategy using a combination of alternatives is
employed in an APP exercise. It uses a variety of alternatives for
modifying supply.
• The structure of a transportation model lends itself to studying the APP
problem
• Linear programming can also be used to model the APP problem
• MPS involves dis-aggregation of product information and ensuring the
required capacity and material are available as per the plan

Production Planning and Control

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