Post on 20-Jun-2015
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Specialty Packaging Corporation, Part A
Ali – Azhar – Dame - Ira
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.
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.
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
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
Supporting TheoryBullwhip Effect
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
Analysis 1
Understand and
identify customer segment
Identify the major
factors that
influence the
demand forecast
Summer Fall
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)
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
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
Analysis 3 Coordination
mechanism for reducing supply chain dynamic instability by using information sharing, channel alingment and operational efficiency
Recomendation
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.
Terima Kasih