Date post: | 03-Jun-2018 |
Category: |
Documents |
Upload: | shazarafia |
View: | 223 times |
Download: | 1 times |
of 46
8/11/2019 Training on MRP & Forecasting
1/46
Slides by
Rafia Zaman
Lecturer, BA Discipline
March 31, 2013
Supply Officers BasicProfessional Course 2013
8/11/2019 Training on MRP & Forecasting
2/46
TOPIC: 1Material Requirement Planning
8/11/2019 Training on MRP & Forecasting
3/46
Material requirements planning (MRP):
Computer-based information systemthat translates master schedule
requirements for end items into
time-phased requirements for
subassemblies, components, and raw
materials.
Having a dinner party at home
hosted by you, which activities do
you have to perform? Do you match
your planning with MRP?
8/11/2019 Training on MRP & Forecasting
4/46
PARENT
Independent vs. Dependent Demand
Independent Demand
Demand for an item is created external to the
company
Independent demand The demand for an itemis unrelated to the demand for other items.
8/11/2019 Training on MRP & Forecasting
5/46
Dependent Demand
Demand for one item is related to the
demand for another item
Dependent demand for component parts is
based on the number of end items being
produced.
COMPONENT
Independent vs. Dependent Demand
8/11/2019 Training on MRP & Forecasting
6/46
Independent vs. Dependent Demand
8/11/2019 Training on MRP & Forecasting
7/46
Independent and Dependent DemandIndependent Demand
A
B(4) C(2)
D(2) E(1) D(3) F(2)
Dependent Demand
Independent demand is uncertain.
Dependent demand is certain.
8/11/2019 Training on MRP & Forecasting
8/46
Independent vs. Dependent Demand
Time
Time Time
Time
Demand
Demand
Stable demandLumpy demand
Amounton
hand
Amounton
hand
Safety stock
8/11/2019 Training on MRP & Forecasting
9/46
Overview of MRP
8/11/2019 Training on MRP & Forecasting
10/46
Master Production Schedule
MPS: One of three primary inputs in MRP;states which end items are to be produced,when these are needed, and in whatquantities.
Time Fences in MPS: Demand Time Fence andPlanning Time Fence
Cumulative lead time: The sum of the leadtimes that sequential phases of a processrequire, from ordering of parts or rawmaterials to completion of final assembly.
8/11/2019 Training on MRP & Forecasting
11/46
Planning Horizon
1 2 3 4 5 6 7 8 9 10
Procurement
Fabrication
Subassembly
Assembly
8/11/2019 Training on MRP & Forecasting
12/46
Developing MPS
Step 1. Calculate Projected On-Hand
Inventories.
Step 2. Determine the Timing and Size of MPS
Quantities.
8/11/2019 Training on MRP & Forecasting
13/46
Developing a MPS: Step 1
8/11/2019 Training on MRP & Forecasting
14/46
Developing a MPS: Step 2
8/11/2019 Training on MRP & Forecasting
15/46
AVAILABLE-TO-PROMISE QUANTITIES
The quantity of end items that marketing can
promise to deliver on specified dates.
The ATP inventory for the first week equals
current on-hand inventory plusthe MPS
quantity for the first week, minusthe
cumulative total of booked orders up to the
week in which the next MPS quantity arrives.
8/11/2019 Training on MRP & Forecasting
16/46
Calculation of ATP
8/11/2019 Training on MRP & Forecasting
17/46
Bill-of-Materials
Bill of materials (BOM): One of the three primaryinputs of MRP; a listing of all of the raw materials,
parts, subassemblies, and assemblies neededto
produce one unit of a product.
Product structure tree: Visual depiction of therequirements in a bill of materials, where all
components are listed by levels.
8/11/2019 Training on MRP & Forecasting
18/46
Product Structure Tree
Chair
Seat
Legs (2) Cross
bar
Side
Rails (2)
Cross
bar
Back
Supports (3)
Leg
Assembly
Back
Assembly
8/11/2019 Training on MRP & Forecasting
19/46
Product Structure tree indicates the components needed to
assemble one unit of product W. Determine component
needed to assemble 100 units of W
A
W
C (4)B (2)
D (2) E D (3)E (2) F
D
G (2)
8/11/2019 Training on MRP & Forecasting
20/46
BOM: Check it 20 units of X?
B(2)
X
D (3)C
E (2) F(3) H (4)G (2) E (2) E (2)
Item
Amount on
HandX 0
B 10
C 10
D 25
Item
Amount on
HandE 12
F 30
G 5
H 0
How many
additional unitsof E are
needed?
8/11/2019 Training on MRP & Forecasting
21/46
Inventory Records
One of the three primary inputs in MRP Includes information on the status of each
item by time period
Gross requirements
Scheduled receipts
Amount on hand
Lead times
Lot sizes
8/11/2019 Training on MRP & Forecasting
22/46
Inventory Records
Gross requirements Total expected demand
Scheduled receipts
Open orders scheduled to arrive
Planned on hand
Expected inventory on hand at the beginningof each time period
8/11/2019 Training on MRP & Forecasting
23/46
Inventory Records
Net requirements Actual amount needed in each time period
Planned-order receipts
Quantity expected to received at the beginning
of the period
Planned-order releases
Planned amount to order in each time period
8/11/2019 Training on MRP & Forecasting
24/46
Check Supplement for MRP calculation
8/11/2019 Training on MRP & Forecasting
25/46
TOPIC: 2Time Series and Forecasting
8/11/2019 Training on MRP & Forecasting
26/46
The Importance of Forecasting
Governments forecast unemployment, interest rates,
and expected revenues from income taxes for policy
purposes
Marketing executives forecast demand, sales, andconsumer preferences for strategic planning
College administrators forecast enrollments to plan for
facilities and for faculty recruitment
Retail stores forecast demand to control inventory
levels, hire employees and provide training
8/11/2019 Training on MRP & Forecasting
27/46
Common Approaches
to Forecasting
Used when historical data are
unavailable
Considered highly subjective
and judgmental
Common Approaches to
Forecasting
Causal
Quantitative forecasting methodsQualitative forecasting methods
Time Series
Use past data to predict future
values
8/11/2019 Training on MRP & Forecasting
28/46
Time Series
What is a time series?
a collection of data recorded over a period of time
(weekly, monthly, quarterly)
an analysis of history, it can be used by
management to make current decisions and plans
based on long-term forecasting
Usually assumes past pattern to continue into thefuture
8/11/2019 Training on MRP & Forecasting
29/46
Basic Business Statistics, 10e 2006 Prentice-Hall, Inc.
Chap 16-29
Time-Series Components
Time Series
Cyclical
Component
Irregular
Component
Trend Component Seasonal
Component
Overall,
persistent, long-
term movement
Regular periodic
fluctuations,
usually within a12-month period
Repeating swings
or movements
over more thanone year
Erratic or residual
fluctuations
8/11/2019 Training on MRP & Forecasting
30/46
Trend Component
Long-run increase or decrease over time(overall upward or downward movement)
Data taken over a long period of time
Sales
Time
8/11/2019 Training on MRP & Forecasting
31/46
Downward linear trend
Trend Component
Trend can be upward or downward
Trend can be linear or non-linear
Sales
Time
Upward nonlinear trend
Sales
Time
(continued)
8/11/2019 Training on MRP & Forecasting
32/46
8/11/2019 Training on MRP & Forecasting
33/46
Seasonal Component
Short-termregular wave-like patterns
Observed within 1 year
Often monthly or quarterly
Sales
Time (Quarterly)
Winter
Spring
Summer
Fall
Winter
Spring
Summer
Fall
8/11/2019 Training on MRP & Forecasting
34/46
Seasonal Component
8/11/2019 Training on MRP & Forecasting
35/46
Cyclical Component
Long-termwave-like patterns
Regularly occur but may vary in length
Often measured peak to peak or trough totrough
Sales
1 Cycle
Year
8/11/2019 Training on MRP & Forecasting
36/46
Cyclical Component
8/11/2019 Training on MRP & Forecasting
37/46
Irregular Component
Unpredictable, random, residual
fluctuations
Due to random variations of Nature
Accidents or unusual events
Noise in the time series
8/11/2019 Training on MRP & Forecasting
38/46
Linear Trend Equation
The long term trend of many business series often approximates a
straight line
Linear Trend
YT= B
0+ B
1X
Year
Sales in
millions (Y)
1990 0.2
1991 0.4
1992 0.5
1993 0.9
1994 1.1
1995 1.5
1996 1.3
1997 1.1
1998 1.7
1999 1.9
2000 2.3
8/11/2019 Training on MRP & Forecasting
39/46
Additive Time-Series Model forAnnual Data
Used primarily for forecasting when
components assumed to be independent
Observed value in time series is the addition
of components
iiiii ICSTY
where Ti= Trend value at time i
Si= Seasonal value at time i
Ci= Cyclical value at time i
Ii= Irregular (random) value at time i
8/11/2019 Training on MRP & Forecasting
40/46
Multiplicative Time-Series Model
Used primarily for forecasting when
components assumed not be independent
Allows consideration of seasonal variation
where Ti= Trend value at time i
Si= Seasonal value at time i
Ci= Cyclical value at time i
Ii= Irregular (random) value at time i
iiiii ICSTY
8/11/2019 Training on MRP & Forecasting
41/46
Smooth out the random variations to get anoverall impression of the pattern ofmovement over time
Calculate Moving Average, Weighted MovingAverage
Smoothing Techniques of Time Series
8/11/2019 Training on MRP & Forecasting
42/46
Moving Average
Useful in smoothing time series to see its trend Basic method used in measuring seasonal fluctuation
Applicable when time series follows fairly linear trend
that have definite rhythmic pattern
A series of arithmetic means over time
Result dependent upon choice of L (length of period for
computing means)
Examples:
For a 5 year moving average, L = 5
For a 7 year moving average, L = 7
8/11/2019 Training on MRP & Forecasting
43/46
Moving Averages
Example:Five-year moving average
First average:
Second average:
(continued)
5
YYYYYMA(5) 54321
5
YYYYYMA(5)
65432
8/11/2019 Training on MRP & Forecasting
44/46
Weighted Moving Average
A simple moving average assigns the same weight to each
observation in averaging
This method looks at past data and tries to logically attach
importance to certain data over other data
Weighted moving average assigns different weights to each
observation
Most recent observation receives the most weight, and the
weight decreases for older data values
In either case, the sum of the weights = 1
8/11/2019 Training on MRP & Forecasting
45/46
Check Supplement for calculation
8/11/2019 Training on MRP & Forecasting
46/46
Thank You