Training on MRP & Forecasting

8/11/2019 Training on MRP & Forecasting

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Slides by

Rafia Zaman

Lecturer, BA Discipline

March 31, 2013

Supply Officers BasicProfessional Course 2013


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TOPIC: 1Material Requirement Planning


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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?


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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.


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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



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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.


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Time

Time Time

Time

Demand

Demand

Stable demandLumpy demand

Amounton

hand

Amounton

hand

Safety stock


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Overview of MRP


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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.


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Planning Horizon

1 2 3 4 5 6 7 8 9 10

Procurement

Fabrication

Subassembly

Assembly


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Developing MPS

Step 1. Calculate Projected On-Hand

Inventories.

Step 2. Determine the Timing and Size of MPS

Quantities.


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Developing a MPS: Step 1


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Developing a MPS: Step 2


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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.


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Calculation of ATP


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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.


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Product Structure Tree

Chair

Seat

Legs (2) Cross

bar

Side

Rails (2)

Cross

bar

Back

Supports (3)

Leg

Assembly

Back

Assembly


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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)


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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?


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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


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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


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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


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Check Supplement for MRP calculation


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TOPIC: 2Time Series and Forecasting


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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


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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


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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


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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


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Trend Component

Long-run increase or decrease over time(overall upward or downward movement)

Data taken over a long period of time

Sales

Time


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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)


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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


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Seasonal Component


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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


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Cyclical Component


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Irregular Component

Unpredictable, random, residual

fluctuations

Due to random variations of Nature

Accidents or unusual events

Noise in the time series


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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


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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


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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


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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


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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


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Moving Averages

Example:Five-year moving average

First average:

Second average:

(continued)

5

YYYYYMA(5) 54321

5

YYYYYMA(5)

65432


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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


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Check Supplement for calculation


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Thank You

Documents

Training on MRP & Forecasting