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Specialty Packaging Corporation, Part A Ali – Azhar – Dame - Ira

Specialty packaging corporation, part a

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Page 1: Specialty packaging corporation, part a

Specialty Packaging Corporation, Part A

Ali – Azhar – Dame - Ira

Page 2: Specialty packaging corporation, part a

About Polystyrene

Polystyrene (PS) is a synthetic aromatic polymer made from the monomer styrene, a liquid petrochemical. Polystyrene can be rigid or foamed. General purpose polystyrene is clear, hard and brittle. It is a very inexpensive resin per unit weight. It is a rather poor barrier to oxygen and water vapor and has a relatively low melting point. Polystyrene is one of the most widely used plastics, the scale of its production being several billion kilograms per year. Polystyrene can be naturally transparent, but can be colored with colorants. Uses include protective packaging (such as packing peanuts and CD and DVD cases), containers (such as "clamshells"), lids, bottles, trays, tumblers, and disposable cutlery.

As a thermoplastic polymer, polystyrene is in a solid (glassy) state at room temperature but flows if heated above about 100 °C, its glass transition temperature. It becomes rigid again when cooled. This temperature behavior is exploited for extrusion, and also for molding and vacuum forming, since it can be cast into molds with fine detail.

Page 3: Specialty packaging corporation, part a

Problem IdentificationJulie Williams wants to : Select the appropriate forecasting method

and estimate the likely forecast error. Which should she choose?

Forecast quarterly demand for each of the two types of containers for the years 2007 to 2009.

Improve supply chain performance, as SPC had been unable to meet demand effective over the previous several years.

Page 4: Specialty packaging corporation, part a

Supporting TheoryForecasting ClassifieldQualitatif Primarily subjective and rely on human

judgment.

Causal The demand forecast is highly corelated with

certain factors in the environment

Simulations Imitate the consumer choices that give rise to

demand to arrive at a forecast

Time Series Use historical demand to make a forecast

Multiplicate : level x trend x seasonal factors

Additive : level + trend + seasonal factors Mixed : (level trend) x seasonal factors

Forecast Method Applicability

Moving average No trend or seasonality

Simple exponential smoothing

No trend or seasonality

Holt’s model Trend but no seasonality

Winter ‘s model Trend and seasonality

Page 5: Specialty packaging corporation, part a

Supporting TheoryBasic Approach to Demand Forecasting

Understand the objective of forecasting

Integrate demand planning and forecasting

Understand and identify customer

segment

Identify the major factors that influence the demand forecast

Determine the appropriate forecasting techniqueEstablish

performance and error measure for the

forecast

Page 6: Specialty packaging corporation, part a

Supporting TheoryBullwhip Effect

Page 7: Specialty packaging corporation, part a

Analysis 1

Over the several years, they had been unable to meet demand

Understand the objective of forecasting

Integrate demand planning and forecasting

Establish a collaborative forecast using data from the SPC and Customer

Have two produts, black and clear plastic

Have quarterly historical demand plastic container

Page 8: Specialty packaging corporation, part a

Analysis 1

Understand and

identify customer segment

Identify the major

factors that

influence the

demand forecast

Summer Fall

Page 9: Specialty packaging corporation, part a

Analysis 1

Increasing volume (‘000 lb) in every quarter each years.

Historical demand of plastic containers influence by seasonal demand

Determine the appropriate forecasting technique

Establish performance and

error measure for the forecast

MSA (Mean Square Error) MAPE (Mean Absolute Percentage

Error)

Page 10: Specialty packaging corporation, part a

Analysis 2

Year Quarter Black Plastic Demand

Clear Plastic Demand

2007

I 6,759 5,929II 5,154 15,158III 5,366 8,149IV 13,864 4,190

2008

I 7,620 6,488II 5,790 16,555III 6,009 8,883IV 15,476 4,559

2009

I 8,481 7,048II 6,426 17,951III 6,651 9,617IV 17,087 4,928

REGRESI LINIERBlack Y = 8,886.63 + 853.79xClear Y = 15,001.69 + 700.61x

Year Quarter Sumbu X Black plastic demand

CMA

SEASONAL RATIO INDEX

DESEASONALIZED SALES = Sales/Season

al Index (Sumbu Y)

TREND (Y = 8,886.63 + 853.79x)

TREND AFTER ADJUSTMENT

BY THE SEASONAL

INDEX = Trend x

Seasonal Index

WEIGHT

2002 I 1 2,250 0.5 0.25 8,926.81 9,740.42 2,455 II 2 1,737 1 0.19 9,325.34 10,594.21 1,973 III 3 2,412 1 12,982.25 0.19 0.19 12,821.17 11,448.00 2,154 IV 4 7,269 1 14,331.38 0.51 0.47 15,402.99 12,301.79 5,805

Time series CMA Seasonal and trend Ekstrapolasi regresi linier

Forecasting method

Page 11: Specialty packaging corporation, part a

Analysis 3

Julie Williams used optimum forecast to meet unpredictable demand influence by seasonal demand (response supply chain objective)

Ord

ers

0Time

Sales from store

Ord

ers

0Time

Store’s orders to

wholesaler

Manufacturer’s orders

to its suppliers

Ord

ers

0Time

Wholesaler’s orders to manufactur

erO

rders

0Time

Retail Store

Whole -

saler

Manuf-

acturer

Supplier

Page 12: Specialty packaging corporation, part a

Analysis 3 Coordination

mechanism for reducing supply chain dynamic instability by using information sharing, channel alingment and operational efficiency

Page 13: Specialty packaging corporation, part a

Recomendation

Page 14: Specialty packaging corporation, part a

Lesson learned Company should understand the role of

forecasting for both an enterprice and a supply chain.

Manage unpredictable demand with coordination mechanism by using information sharing, channel alingment and operational efficiency.

Page 15: Specialty packaging corporation, part a

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