Operations Planning and Control: Forecasting Methods Overview
Chapter 4
Operations Planning and Control
1. Forecasting
3
Topics
•
Overview of forecasting
•
Forecasting methods
•
Forecasting Errors
What is forecasting?
•
Forecasting
is the process of estimating events whose actual
outcomes have not yet been observed.
•
The process of predicting future events based on past and
present information.
Predicting
[estimating]
future
[unknown]
events.
4
5
Characteristics of forecasts
•
Forecasts are
always wrong
. Should include expected value and measure of error.
•
Long-term
forecasts are
less accurate
than
short-term
forecasts. Too long term
forecasts are useless: Forecast horizon
•
Aggregate
forecasts are
more accurate
than
disaggregate
forecasts
Forecasting Role in Decision-Making and its Relationship
with Operations Management
External and
Internal Data
Objectives
And
Constraints
Managers
Forecasts
Updated
Forecasts
Actual
Performance
Planned
Performance
Operations
Resources
7
Forecasting Methods
1.
Qualitative methods
•
F
orecasting methods are based on judgments, opinions, intuition, emotions, or personal
experiences.
•
Executive Judgment
•
Sales Force Composite
•
Market Research/Survey
•
Delphi Method
2.
Quantitative methods
•
Forecasting methods are based on mathematical (quantitative) models, and are objective
in nature.
•
Naïve forecasting
•
Simple moving average
•
Weighted moving average
•
Exponential smoothing
•
Regression
•
ARIMA and
•
Two stage EWMA
8
•
Short-range forecast
•
Usually < 3 months
•
Job scheduling, worker assignments
•
Medium-range forecast
•
3 months to 2 years
•
Sales/production planning
•
Long-range forecast
•
> 2 years
•
New product planning
Types of Forecasts by Time Horizon
Design
of system
Detailed
use of
system
Quantitative
methods
Qualitative
Methods
Quantitative Approaches
Naïve Method
•
Demand in
next
period is the same as demand in
most recent
period
•
Easy but usually not good
10
Quantitative Approaches
Simple Moving Average
•
Assumes an average is a good estimator of future behavior
•
Useful when there is
little or no trend
•
Used for smoothing
•
11
Ft+1
= Forecast for the upcoming period, t+1
n
= Number of periods to be averaged
A t
= Actual occurrence in period t
Quantitative Approaches
Weighted Moving Average
12
•
Gives more emphasis to recent data
•
Weights
•
decrease for older data
•
sum to 1.0
Simple moving
average models
weight all previous
periods equally
Example
•
Determine forecast for periods
7
&
8
•
2-period
moving average
•
4-period
moving average
•
2-period
weighted moving average with t-1
weighted 0.6 and t-2 weighted 0.4
•
Exponential smoothing
with alpha=0.2 and the
period 6 forecast being 375
Problem Solution
Quantitative Approaches
Exponential Smoothing
•
Assumes the most recent observations have the highest
predictive value
•
gives more weight to recent time period
•
F
t+1
= Forecast value for time
t+1
•
A
t
= Actual value at time
t
= Smoothing constant
15
F
t+1
= F
t
+
(A
t
- F
t
)
Need initial
forecast Ft
to start.
Example
•
See the previous example…
17
Quantitative Approaches:
Linear Regression
A time series technique that computes a forecast with trend by drawing
a straight line through a set of data using this formula:
Y = a + bx where
Y = forecast for period X
X = the number of time periods from X = 0
a = value of y at X = 0 (Y intercept)
b = slope of the line
18
Quantitative Approaches:
Linear Regression…
•
Identify
dependent
(y)
and independent
(x)
variables
•
Solve for the slope of the line
•
Solve for the y intercept
•
Develop your equation for the trend line
Y=a + bX
19
Quantitative Approaches:
Linear Regression…
Linear Regression Problem
: A maker of golf shirts has been tracking the relationship between sales
and advertising dollars. Use linear regression to find out what sales might be if the company
invested $53,000 in advertising next year.
Quantitative Approaches
ARIMA Method
•
A
uto
R
egressive
I
ntegrated
M
oving
A
verage is part of the
linear
models
that is capable of representing
both stationary and non-
stationary time series.
20
Note that
stationary
processes vary about a fixed level, and
non-
stationary
processes have no natural constant mean level.
AR
IMA continued…
Autoregressive Models
AR(p)
•
It takes the form of
•
Autoregressive models
are appropriate for
stationary
time series, and the
coefficient
β
0
is related to the
constant level
of the series.
21
An
AR(p)
model is a regression model
with lagged values
of the dependent variable in the independent variable
positions
, hence the name autoregressive model.
ARI
MA
continued…
Moving Average
MA(q)
•
It takes a form
•
An MA(q) model is a regression model with
the dependent variable, Yt, depending on
previous values of the errors rather than
on the variable itself
.
22
AR
I
MA
continued
ARMA(p,q) Models
•
A model with
autoregressive
terms can be combined with a model having
moving
average
terms to get an
ARMA(p,q)
model:
•
ARMA(p,q)
models can describe a wide variety of behaviors for
stationary
time series.
23
•
Note that:
•
ARMA(p,0) = AR(p)
•
ARMA(0,q) = MA(q)
ARIMA continued
ARIMA(p,d,q) Models
•
Models for non-stationary series are called
Autoregressive Integrated Moving Average
models, or
ARIMA(p,d,q)
, where
d
indicates the amount of differencing.
•
The first step in model identification is to determine whether the series is
stationary
.
•
If the series is
not stationary
, it can often be converted to a stationary series by
differencing
: the original series is replaced by a series of differences and an ARMA model
is then specified for the differenced series (in effect, the analyst is modeling changes
rather than levels).
24
Quantitative Approaches
Two stage EWMA
25
•
Stage one
•
Stage 2
Measures of Forecast Error
B. MSE = Mean Squared Error
C. RMSE = Root Mean Squared Error
A.
MAD = Mean Absolute Deviation
Ideal value =0 (i.e no forecasting error)
D.
MPE = Mean Percentage Error
2. Aggregate Production Planning
The Role of the Aggregate Plan
Aggregate Planning
•
Based on composite (representative) products:
•
Simplifies calculations
•
Forecasts for grouped items are more accurate
•
Considers trade-offs between holding inventory & short-term
capacity based on workforce
Aggregate Production Planning
•
Purpose: specify the combination of production rate, workforce level,
and inventory on-hand that satisfies the forecasted demand at the
lowest cost.
•
Production rate: quantity of product produced per unit of time (autos/day).
•
Workforce level: number of workers required to meet a specific level of
output.
•
Inventory on hand: unsold units carried over from one period to the next.
13-6
Managing Demand
•
Pricing
•
Advertising and Promotion
•
Backlogs and Reservations
•
Develop Alternative Products
13-7
Managing Supply (Capacity)
•
Overtime/Undertime
•
Hiring/Firing of Personnel
•
Temporary/Part-time Personnel
•
Subcontracting
•
Adjusting Inventories
•
Adjusting Lead Times
13-5
Aggregate Planning: Objectives and Approaches
•
Objectives:
•
Match Supply and Demand (Effectiveness)
•
Minimize Costs (Efficiency)
•
Approaches
•
Reactive approach:
•
Allow volume forecasts based on Marketing plan to drive production planning
•
Proactive approach:
•
Coordinate Marketing & Production plans to level demand using advertising & price
incentives
Aggregate Plan Strategies
•
Chase strategy:
•
Match the production rate to meet the demand rate by adjusting the
workforce level (hiring/firing) as the demand rate changes. Minimize
finished good inventories by matching demand fluctuations.
•
Level strategy:
•
Use a stable workforce working at a constant production rate. Use
inventories and backorders to absorb demand peaks and valleys.
Pure Aggregate Strategies
Chase Plan Example
•
Chase hires and fires staff to exactly meet each periods demand
•
Period 1 = (500 units x .64 std.)/160 = 2 people, need to fire 16 people
Level Plan Example
•
Level production rate= 28,000 units/7 periods= 4000 units
•
Level workforce= (4000 units x .64 std.)/160 = 16 people
Hybrid Strategies
•
Combine elements of the chase/level strategies with other options:
•
Stable workforce but variable work rate (overtime/undertime).
•
Subcontract production or hire part-time or temporary workers to cover
short-term peaks.
Preliminary Considerations
•
Identify the point of departure:
•
How much capacity is currently in use?
•
Identify the magnitude of change needed
•
Identify the anticipated duration the modified capacity is necessary
Developing the Aggregate Plan
•
Step 1-
Choose strategy: level, chase, or Hybrid
•
Step 2-
Determine the aggregate production rate
•
Step 3-
Calculate the size of the workforce
•
Step 4-
Test the plan as follows:
•
Calculate Inventory, expected hiring/firing, overtime needs
•
Calculate total cost of plan
•
Step 5-
Evaluate performance: cost, service,
human resources, and operations
Aggregate Planning Costs
•
Basic production costs (fixed and variable): material costs, labor costs,
overtime pay.
•
Production rate-change costs: hiring, training, layoff/firing,
adding/cutting shifts.
•
Inventory holding costs: cost of capital, storage, insurance, taxes,
spoilage, shrinkage, obsolescence.
•
Backlog costs: expediting, loss of customer goodwill, loss of sales
revenue from cancelled orders (due to product unavailability).
Aggregate Planning Techniques
•
Trial-and error (usually employing spreadsheets): costing out
various production planning scenarios to determine which has the
lowest cost.
•
Mathematical approaches:
•
Linear programming.
•
Linear decision rule (LDR).
•
Heuristic approaches.
Plan for Companies with Tangible Products –
Plans
A, B, C, D
•
Plan A
:
Level aggregate plan
using inventories and back orders
•
Plan B
:
Level plan
using inventories but no back orders
•
Plan C
:
Chase aggregate plan
using hiring and firing
•
Plan D
:
Hybrid plan
using initial workforce and overtime as needed
Problem Data for Plans A, B, C, D
Plan A - Level Using Inventory &
Backorders (Table 13-5)
•
First calculate the level production rate (14400/8=1800)
Plan A
Evaluation
•
Back orders were 13.9% of demand (1380)
•
Worst performance was period 2 at 21% of demand
•
Marketing will not be satisfied at these levels
•
Workable plan for operations
•
No employees hired or fired, no overtime or undertime needed, and output
is constant
•
No human resource problems are anticipated
Plan B – Level, Inventory but
No Backorders (Table 13-7)
•
Set the level rate equal to the peak cumulative demand/period
Plan B
Evaluation
•
Plan B costs $240K (16%) more than plan A and has ending
inventory of 7980 units
•
To be fair, Plan B built 1920 additional units ($192K) which will be
sold later
•
Plan B costs $2.58 more per unit (2.5%)
•
Marketing satisfied by 100% service level
•
Workable Operations and HR plan- hire 12, no OT or UT, and level
production
Plan C – Chase Using Hires and
Fires (Table 13- 9)
•
The production rate equals the demand each period
Plan C
Evaluation
•
Costs an additional $2 per unit more than Plan B
•
Marketing is satisfied again by 100% service level
•
From Operations and HR standpoint, not easy to implement:
•
Need space, tools, equipment for up to 120 people in period 6 and only
have 60 people in period 4
•
High training costs and potential quality problems
•
Low morale likely due to poor job security
Plan D– Hybrid, Initial Workforce
and OT as Needed (Table 13-12)
•
This is basically a level plan using OT to avoid backorders
Plan D
Evaluation
•
Cost is only $.61 (.6%) more than Plan A with a reasonable increase
in ending inventory (+1440)
•
Marketing is satisfied as well with 100% service level
•
Not difficult for Operations to implement
•
Does not need excessive overtime
•
Uses overtime in just periods 1 and 2 (7%, 20%)
•
Aggregate Plan Objective
:
Keep customer service high and costs
low
Aggregate Plans for Service Companies with Non-
Tangible Products- Plans E, F, G
•
Options remain the same – level, chase, and hybrid plans
•
Overtime and undertime can be used
•
Staff can be hired and fired
•
Inventory cannot be used to level the service plan
•
All demand must be satisfied or lose business to a competing
service provider
Problem Data for Plans E, F, G
(Table 13.4)
Plan E – Level with Staffing for Peak
Demand- (Table 13-14)
•
Staff of 69 people creates excessive UT (30%)
•
Cost per service call is $46.15
Plan F – Hybrid with Initial Workforce
and OT as Needed (Table 13-16)
•
Costs reduced by $77K and undertime to 20%
•
Cost per service call reduced to $41.13 (-$5.02)
Plan G – Chase Plan with Hiring and
Firing (Table 13-18)
•
Total cost reduced by $114K over Plan F, utilization improved to 100%, and cost per service call $33.72 (-$7.41)
•
Workforce fluctuates from 30-69 people- morale problems
•
Solution??
Compare smaller permanent workforce, more OT??
Aggregate Planning Bottom Line
•
The Aggregate plan must balance several perspectives
•
Costs are important but so are:
•
Customer service
•
Operational effectiveness
•
Workforce morale
•
A successful AP considers each of these factors
3. Master Production Schedule
Planning Links to MPS
Role of the MPS
•
Aggregate plan:
•
Specifies the resources available (
e.g
.: regular workforce, overtime,
subcontracting, allowable inventory levels & shortages)
•
Master production schedule:
•
Specifies the number & when to produce each end item (the anticipated
build schedule)
•
Disaggregates the aggregate plan
Objectives of Master Schedule
•
The
Master Scheduler
must:
•
Maintain the desired customer service level
•
Utilize resources efficiently
•
Maintain desired inventory levels
•
The
Master Schedule
must:
•
Satisfy customer demand
•
Not exceed Operation’s capacity
•
Work within the constraints of the Aggregate Plan
Developing an MPS
•
The Master Scheduler:
•
Develops a proposed MPS
•
Checks the schedule for feasibility with available capacity
•
Modifies as needed
•
Authorizes the MPS
•
Consider the following example:
•
Make-to-stock environment with fixed orders of 125 units
•
There are 110 in inventory to start
•
When are new order quantities needed to satisfy the forecasted demand?
The MPS Record
•
Projected Available
= beginning inventory + MPS shipments -
forecasted demand
•
The
MPS row
shows when replenishment shipments need to arrive to avoid a stock out (negative projected available)
Revised and Completed MPS Record
Evaluating the MPS
•
Rough-cut capacity planning:
•
An estimate of the plan’s feasibility
•
Given the demonstrated capacity of critical resources (e.g.: direct labor &
machine time), have we overloaded the system?
•
Customer service issues:
•
Does “available-to-promise” inventory satisfy customer orders? If not, can
future MPS quantities be pulled in to satisfy new orders?
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•
Step 1: Determine the Planning factors:
•
Labor Factors
•
Machine Factors
Step 2:Calculate the Workload Generated
by This Schedule
Step 3
: Calculate the Capacity Needs for Each Resource
for Each Time Period
Step 4
: Calculate Individual Workcenter
Capacity Needs Based on Historical
Percentage Allocation
Using the MPS to “Order Promise”
•
The authorized MPS is used to promise orders to customers
•
The MPS table is expanded to add
customer orders
and
available-to-promise
rows (inventory to satisfy new
orders)
•
ATP
Action
Bucke
t
= (beginning inventory + MPS shipment) – (customer orders before next replenishment).
Available in period 1
•
ATP=MPS shipment – Customer orders between current MPS shipment and next scheduled replenishment.
Available in periods 3,5,7,8, & 11
Example of Revising the ATP MPS Record:
A customer calls marketing willing to purchase
200 units if they can be delivered in period 5. The two tables below show how the system
logic would first slot the 200 into period 5 and then how the order would be allocated
across periods 1, 3, and 5 and adjusting the ATP row.
Stabilizing the MPS
Thank you!
Questions?
Forecasting is a crucial process in operations management, involving the estimation of future events based on past and present information. This chapter covers the significance of forecasts, characteristics of forecasting, role in decision-making, various forecasting methods (qualitative and quantitative), types of forecasts by time horizon, and quantitative approaches like the Naïve Method. Understanding forecasting is essential for effective decision-making and planning in operations management.
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Presentation Transcript
Chapter 4 Operations Planning and Control
Topics Overview of forecasting Forecasting methods Forecasting Errors 3
What is forecasting? Forecasting is the process of estimating events whose actual outcomes have not yet been observed. The process of predicting future events based on past and present information. Predicting [estimating] future [unknown] events. 4
Characteristics of forecasts Forecasts are always wrong. Should include expected value and measure of error. Long-term forecasts are less accurate than short-term forecasts. Too long term forecasts are useless: Forecast horizon Aggregate forecasts are more accurate than disaggregate forecasts 5
Forecasting Role in Decision-Making and its Relationship with Operations Management Objectives And Constraints External and Internal Data Planned Performance Forecasts Managers Operations Updated Forecasts Actual Resources Performance
Forecasting Methods 1. Qualitative methods Forecasting methods are based on judgments, opinions, intuition, emotions, or personal experiences. Executive Judgment Sales Force Composite Market Research/Survey Delphi Method Quantitative methods Forecasting methods are based on mathematical (quantitative) models, and are objective in nature. Na ve forecasting Simple moving average Weighted moving average Exponential smoothing Regression ARIMA and Two stage EWMA 2. 8
Types of Forecasts by Time Horizon Quantitative methods Short-range forecast Usually < 3 months Job scheduling, worker assignments Medium-range forecast 3 months to 2 years Sales/production planning Long-range forecast > 2 years New product planning Detailed use of system Design of system Qualitative Methods
Quantitative Approaches Na ve Method Demand in next period is the same as demand in most recent period Easy but usually not good 10
Quantitative Approaches Simple Moving Average Assumes an average is a good estimator of future behavior Useful when there is little or no trend Used for smoothing A + A + A + ... + A + t 1 - t - t 2 - t n 1 F = + t 1 n Ft+1 n A t = Forecast for the upcoming period, t+1 = Number of periods to be averaged = Actual occurrence in period t 11
Quantitative Approaches Weighted Moving Average Gives more emphasis to recent data A w = F + + w A + w A + ... + w A + t 1 1 t 2 1 - t 3 - t 2 n - t n 1 Weights decrease for older data sum to 1.0 Simple moving average models weight all previous periods equally 12
Example Period 1 2 3 4 5 6 7 8 Actual 300 315 290 345 320 360 375 Determine forecast for periods 7 & 8 2-period moving average 4-period moving average 2-period weighted moving average with t-1 weighted 0.6 and t-2 weighted 0.4 Exponential smoothing with alpha=0.2 and the period 6 forecast being 375
Problem Solution Period Actual 2-Period 4-Period 2-Per.Wgted. Expon. Smooth. 1 300 2 315 3 290 4 345 5 320 6 360 7 375 340.0 328.8 344.0 372.0 8 367.5 350.0 369.0 372.6
Quantitative Approaches Exponential Smoothing Assumes the most recent observations have the highest predictive value gives more weight to recent time period Ft+1 = Ft + (At - Ft) Ft+1= Forecast value for time t+1 At = Actual value at time t = Smoothing constant Need initial forecast Ft to start. 15
Example See the previous example
Quantitative Approaches: Linear Regression A time series technique that computes a forecast with trend by drawing a straight line through a set of data using this formula: Y = a + bx where Y = forecast for period X X = the number of time periods from X = 0 a = value of y at X = 0 (Y intercept) b = slope of the line 17
Quantitative Approaches: Linear Regression Identifydependent (y) and independent (x) variables Solve for the slope of the line Solve for the y intercept Develop your equation for the trend line Y=a + bX 18
Quantitative Approaches: Linear Regression Linear Regression Problem: A maker of golf shirts has been tracking the relationship between sales and advertising dollars. Use linear regression to find out what sales might be if the company invested $53,000 in advertising next year. Sales $ (Y) (X) 1 130 32 4160 2304 2 151 52 7852 2704 3 150 50 7500 2500 4 158 55 8690 3025 5 153.85 53 Tot 589 189 28202 9253 Avg 147.25 47.25 Adv.$ XY X^2 Y^2 XY n X Y = b 2 2 X n X 16,900 22,801 22,500 24964 ( )( ) 28202 4 47.25 147.25 2 = = b 1.15 ( ) 1.15 9253 4 47.25 ( ) = = a Y b X 147.25 47.25 = a 92.9 = + = + Y a bX + 92.9 1.15X = 87165 ( ) = Y 92.9 1.15 53 153.85 19
Quantitative Approaches ARIMA Method Auto Regressive Integrated Moving Average is part of the linear models that is capable of representing both stationary and non- stationary time series. Note that stationary processes vary about a fixed level, and non- stationary processes have no natural constant mean level. 20
ARIMA continued Autoregressive Models AR(p) = + + 2 ,..., + + + It takes the form of Y Y Y Y 0 1 1 2 t t t p t p t = response variable at time observation (predictor variable) at time regression coefficients to be estimated error term at time t Y Y t t = t k t k = = i t Autoregressive models are appropriate for stationary time series, and the coefficient 0is related to the constant level of the series. An AR(p) model is a regression model with lagged values of the dependent variable in the independent variable positions, hence the name autoregressive model. 21
ARIMA continued Moving Average MA(q) = + It takes a form An MA(q) model is a regression model with the dependent variable, Yt, depending on previous values of the errors rather than on the variable itself. ... Y t q 1 1 2 2 t t t t q = = = response variable at time constant mean of the process regression coefficients to be estimated error in time period - = Y t t i t k t k MA models are appropriate for stationary time series. The weights i do not necessarily sum to 1 and may be positive or negative. 22
ARIMA continued ARMA(p,q) Models A model with autoregressive terms can be combined with a model having moving average terms to get an ARMA(p,q) model: Y Y Y = + + + + + ... ... Y t q 0 1 1 2 2 1 1 2 2 t t t p t p t t t q ARMA(p,q) models can describe a wide variety of behaviors for stationary time series. Note that: ARMA(p,0) = AR(p) ARMA(0,q) = MA(q) 23
ARIMA continued ARIMA(p,d,q) Models Models for non-stationary series are called Autoregressive Integrated Moving Average models, or ARIMA(p,d,q), where d indicates the amount of differencing. The first step in model identification is to determine whether the series is stationary. If the series is not stationary, it can often be converted to a stationary series by differencing: the original series is replaced by a series of differences and an ARMA model is then specified for the differenced series (in effect, the analyst is modeling changes rather than levels). 24
Quantitative Approaches Two stage EWMA Stage one = + (1 ) Y Y Y 1 t t t = + (1 ) Y Y Y 1 t Stage 2 t t = + Y Y Y t t t = dY Y Y 1 t t t 25
Measures of Forecast Error n t=1 A - F t t A. A. MAD = Mean Absolute Deviation MAD = Mean Absolute Deviation MAD = n 2 n t=1 ( ) A -F t t B. MSE = Mean Squared Error B. MSE = Mean Squared Error MSE= n C. RMSE = Root Mean Squared Error C. RMSE = Root Mean Squared Error RMSE = MSE n t=1 ( ) A -F D. D. MPE = Mean Percentage Error MPE = Mean Percentage Error t t 1 n MPE= F t Ideal value =0 (i.e no forecasting error)
Aggregate Planning Based on composite (representative) products: Simplifies calculations Forecasts for grouped items are more accurate Considers trade-offs between holding inventory & short-term capacity based on workforce
Aggregate Production Planning Purpose: specify the combination of production rate, workforce level, and inventory on-hand that satisfies the forecasted demand at the lowest cost. Production rate: quantity of product produced per unit of time (autos/day). Workforce level: number of workers required to meet a specific level of output. Inventory on hand: unsold units carried over from one period to the next.
Managing Demand Pricing Advertising and Promotion Backlogs and Reservations Develop Alternative Products 13-6
Managing Supply (Capacity) Overtime/Undertime Hiring/Firing of Personnel Temporary/Part-time Personnel Subcontracting Adjusting Inventories Adjusting Lead Times 13-7
Aggregate Planning: Objectives and Approaches Objectives: Match Supply and Demand (Effectiveness) Minimize Costs (Efficiency) Approaches Reactive approach: Allow volume forecasts based on Marketing plan to drive production planning Proactive approach: Coordinate Marketing & Production plans to level demand using advertising & price incentives 13-5
Aggregate Plan Strategies Chase strategy: Match the production rate to meet the demand rate by adjusting the workforce level (hiring/firing) as the demand rate changes. Minimize finished good inventories by matching demand fluctuations. Level strategy: Use a stable workforce working at a constant production rate. Use inventories and backorders to absorb demand peaks and valleys.
Chase Plan Example Chase hires and fires staff to exactly meet each periods demand Period 1 = (500 units x .64 std.)/160 = 2 people, need to fire 16 people
Level Plan Example Level production rate= 28,000 units/7 periods= 4000 units Level workforce= (4000 units x .64 std.)/160 = 16 people
Hybrid Strategies Combine elements of the chase/level strategies with other options: Stable workforce but variable work rate (overtime/undertime). Subcontract production or hire part-time or temporary workers to cover short-term peaks.
Preliminary Considerations Identify the point of departure: How much capacity is currently in use? Identify the magnitude of change needed Identify the anticipated duration the modified capacity is necessary
Developing the Aggregate Plan Step 1- Choose strategy: level, chase, or Hybrid Step 2- Determine the aggregate production rate Step 3- Calculate the size of the workforce Step 4- Test the plan as follows: Calculate Inventory, expected hiring/firing, overtime needs Calculate total cost of plan Step 5- Evaluate performance: cost, service, human resources, and operations
Aggregate Planning Costs Basic production costs (fixed and variable): material costs, labor costs, overtime pay. Production rate-change costs: hiring, training, layoff/firing, adding/cutting shifts. Inventory holding costs: cost of capital, storage, insurance, taxes, spoilage, shrinkage, obsolescence. Backlog costs: expediting, loss of customer goodwill, loss of sales revenue from cancelled orders (due to product unavailability).
Aggregate Planning Techniques Trial-and error (usually employing spreadsheets): costing out various production planning scenarios to determine which has the lowest cost. Mathematical approaches: Linear programming. Linear decision rule (LDR). Heuristic approaches.
Plan for Companies with Tangible Products Plans A, B, C, D Plan A: Level aggregate plan using inventories and back orders Plan B: Level plan using inventories but no back orders Plan C: Chase aggregate plan using hiring and firing Plan D: Hybrid plan using initial workforce and overtime as needed
Problem Data for Plans A, B, C, D A B 4 5 6 7 8 9 Cost Data Regular time labor cost per hour Overtime labor cost per hour Subcontracting cost per unit (labor only) Back order cost per unit per period Inventory holding cost per unit per period Hiring cost per employee Firing cost per employee $12.50 $18.75 $125.00 $25.00 $10.00 $800.00 $500.00 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 Capacity Data Beginning workforce (employees) Beginning inventory (units) Production standard per unit (hours) Regular time available per period (hours) Overtime available per period (hours) 90 0 8 160 40 Demand Data (units) Period 1 Period 2 Period 3 Period 4 Period 5 Period 6 Period 7 Period 8 1920 2160 1440 1200 2040 2400 1740 1500 Total Number of Periods 8
Plan A - Level Using Inventory & Backorders (Table 13-5) First calculate the level production rate (14400/8=1800) D Plan A: Level Aggregate Plan, Using Inventories and Backorders E F G H I J K L M 3 4 5 6 7 8 9 Compute Level Production Rate Total Demand 14400 0 14400 1800 Less: Beginning Inventory Total Net Demand Average Demand Per Period <-- Production Rate for Level Plan 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 Compute Workforce Needed Units per Employee per Period 20 90 0 0 Employees Required Number to Hire Number to Fire Period Detailed Plan Computations 1 2 3 4 5 6 7 8 Total 14400 Demand (units) (net of beg. Inventory) Cumulative demand (units) Period production (units) Cumulative production (units) Cum.Dem. Minus Cum.Prod. Ending Inventory (units) 1920 1920 1800 1800 120 0 120 2160 4080 1800 3600 480 0 480 1440 5520 1800 5400 120 0 120 1200 6720 1800 7200 -480 480 0 2040 8760 1800 9000 -240 240 0 2400 11160 1800 10800 360 0 360 1740 12900 1800 12600 300 0 300 1500 14400 1800 14400 0 0 0 14400 720 1380 Backorders (units) Cost Calculations for Plan A Regular time labor cost Overtime labor cost Inventory holding cost Back order cost $1,440,000 $0 $7,200 $34,500 Hiring cost Firing cost Total Cost $0 $0 $1,481,700
Plan A Evaluation Back orders were 13.9% of demand (1380) Worst performance was period 2 at 21% of demand Marketing will not be satisfied at these levels Workable plan for operations No employees hired or fired, no overtime or undertime needed, and output is constant No human resource problems are anticipated
Plan B Level, Inventory but No Backorders (Table 13-7) Set the level rate equal to the peak cumulative demand/period D Plan B: Level Aggregate Plan, Using Inventories but No Backorders E F G H I J K L M N 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 Period Detailed Plan Computations 1 2 3 4 5 6 7 8 Total 14400 Demand (units) (net of beg. Inventory) Cumulative demand (units) Cumulative demand/periods Period production (units) Cumulative production (units) Cum.Dem. Minus Cum.Prod. Ending Inventory (units) 1920 1920 1920 2040 2040 -120 120 0 2160 4080 2040 2040 4080 0 0 0 1440 5520 1840 2040 6120 -600 600 0 1200 6720 1680 2040 8160 -1440 1440 0 2040 8760 1752 2040 10200 -1440 1440 0 2400 11160 1860 2040 12240 -1080 1080 0 1740 12900 1842.857 2040 14280 -1380 1380 0 1500 14400 1800 2040 16320 -1920 1920 0 16320 7980 0 Backorders (units) Compute Level Production Rate and Workforce Needed Production Rate (units) Units per Employee per Period Employees Needed Number to Hire Number to Fire 2040 20 102 12 0 Cost Calculations for Plan B Regular time labor cost Overtime labor cost Inventory holding cost Back order cost $1,632,000 $0 $79,800 $0 Hiring cost Firing cost Total Cost $9,600 $0 $1,721,400
Plan B Evaluation Plan B costs $240K (16%) more than plan A and has ending inventory of 7980 units To be fair, Plan B built 1920 additional units ($192K) which will be sold later Plan B costs $2.58 more per unit (2.5%) Marketing satisfied by 100% service level Workable Operations and HR plan- hire 12, no OT or UT, and level production
Plan C Chase Using Hires and Fires (Table 13- 9) The production rate equals the demand each period D Plan C: Chase Aggregate Plan, Using Hiring and Firing (no overtime) E F G H I J K L M N 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 Beginning Number of Employees Units per Worker per Period 90 20 (used to compute workforce size requirement each period) Period Detailed Plan Computations 1 2 3 4 5 6 7 8 Total Demand (units) (net of beg. Inventory) Production per period (units) Employees needed in period 1920 1920 96 6 0 2160 2160 108 12 0 1440 1440 72 0 36 1200 1200 60 0 12 2040 2040 102 42 0 2400 2400 120 18 0 1740 1740 87 0 33 1500 1500 75 0 12 14400 720 78 93 Number to hire Number to fire Cost Calculations for Plan C Regular time labor cost Overtime labor cost Inventory holding cost Back order cost $1,440,000 $0 $0 $0 Hiring cost Firing cost Total Cost $62,400 $46,500 $1,548,900
Plan C Evaluation Costs an additional $2 per unit more than Plan B Marketing is satisfied again by 100% service level From Operations and HR standpoint, not easy to implement: Need space, tools, equipment for up to 120 people in period 6 and only have 60 people in period 4 High training costs and potential quality problems Low morale likely due to poor job security
