Smester 3 Angkatan 43Juni 2015

MAGISTER MANAGEMENT PRODUKSIUNIVERSITAS TRISAKTI

SUPPLY CHAIN MANAGEMENTFORCASTING DEMAND

Dosen :Dr. Dadang Surjasa

Prepared by:Indra Petrus Ambarita

(122131049)

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Integral Logistic ManagementBy : P A U L S C H Ö N S L E B E N

Supply Chain Management Work BookBy : F. Harrison

Forecasting is the ‘science’ of predicting demand. The forecast is generally formulated either from historical trends where past performance is ‘scientifically’ extrapolated into a future predicted trend, or frommarket information that considers potential sales in a particular market,perhaps as a result of some market-changing event.

FORCASTING DEMAND DEFENITION:

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The darker background

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FORCASTING PROCEDURE

Demand and Demand Forecast

CASE STUDY :1. DEMAND FORCASTING FOR FOOD AND FCMG INDUSTRY2. DEMAND FORCASTING FOR AUTOMOTIVE INDUSTRY3. DEMAND FORCASTING FOR FASION INDUSTRY

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OUTLINE CASE STUDY

Forecasting & Planning in the Food IndustryDrs. André Vriens MTD, Ir. Edward Versteijnen, EyeOn BV Business

Planning & Control Solutions,Croylaan 14, 5735 PC Aarle-Rixtel, The Netherlands Tel +31 492 388 850.

http://www.eyeon.nl/

Management SummaryOver the last years forecasting and planning in the food and FMCG industry has become ever more challenging.Companies are spending considerable time and resources improving the quality of the forecasting and planningprocesses and hence the quality of the output.

With this in mind, EyeOn has launched a knowledge network where companies in the food and FMCG industry share experiences and best practices on planning and forecasting. As a starting point for this network, interviews have been conducted with all participants to determine the current status in the industry. The interviews were conducted betweenMarch and June 2006. Since representatives from various parts of the value chain participated, good insight in the specific forecasting and planning issues throughout the industry could be achieved. In a network meeting in June 2006, the participants gave their feedback on the results of the survey and on a number of statements. This has beenincluded in the white paper.

This paper first describes the industry dynamics which puts high demands on forecasting and planning. In the second part, the key issues in business planning processes are identified: one number planning, promotion planning, upstream collaboration, the involvement of marketing and sales in the forecasting process, capacity bottleneck resolution, new product introductions and performance management were mentioned by the participants as most significant.Based on these issues 10 key ingredients are presented that guide the implementation of responsive forecasting and planning processesin the food industry. Together they compose a recipe to make planning and forecasting light!

Participating Companies : Aviko, BASF, Coca-cola, DSM, Friesland Foods, IFF, IOI-Loders Croklaan, Heineken, Grolsch, Heinz, Kloeckner Pentaplast, LU/ Danone, Masterfoods, Perfetti van Melle, Purac, Quest, Sara Lee, Suiker Unie, Tetra Pak, Unilever, Vion Food Group, Vrumona.

FOOD AND FMCG INDUSTRY

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Main player for foot and fcmg industri

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DEMAND TRENDS(Strong Price Erosion)

SUPPLY RENDS( Increased pricing raw materials

& energy)

So what! The impact on forecasting andplanning

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RESULT STRATEGY

Problem Solution

Industry Food & FMCG is very dynamics.Suppy dynamics :- Pressure on capacity utilization- Concentration / consolidation of suppliers.- ( Global) transfer of manufacturing.- Increased pricing raw materials & energy.- From supply/ capacity driven to demand

orientation.- Time to market and time to volume pressure.Demand dynamics- Strong price erosion.- The rise of private labels.- Consumer tendency to more variety,

freshmeness and healty products.- Variablility caused by promotions.- Short product life cycle and more new

product introductions.- Changing channels/ markets.

Key Forcasting and Planning Food & FMCG :

1. One-number planning2. Promotion Planning3. Upstream Collaborative

planning4. Capacity bottleneck resolution5. New product introduction6. Marketing and Sales

Improvement7. Performance management

Vehicle Retail Forecasting Demand and Inventory Management Case Study at Shanghai General Motors

Weiwei Zhong, Shanghai General Motor Company, Shanghai, P.R.China

ABSTRACTJust like any investment in business, Vehicle forecast and inventory management needs to serve the purpose of maximizing profit. For manufacturers, it is crucial to manage Supply Chain Operations as efficiently and profitably as possible. As we all know, Demand-Driven forecasting allows companies to sense demand signals through the synchronization of internal/external data. As the result, the accurate demand forecast will help the motorcar manufacture to support the inventory plan. However, in many cases inventory has turned into a major cash flow constraint thus making it necessary to optimize inventory using analytical and statistical methods inan integrated approach.

SAS® Supply Chain Solution (SCS) provides vehicle manufacturers the ability to calculate the accurate. Demand-Driven forecasting and optimal inventory replenishment policies, thus enabling them to keep the market share and maintain customer service levels while minimizing average total costs such as ordering, inventory holding and backorder penalty costs.

AUTOMOTIVE INDUSTRY

INTRODUCTIONThe motorcar manufacture SGM identifies the same problem. Further analysis reveals that inventory levels are high and turns are below most major competitors. In addition a technology change and a proliferation of models amplify the issue.The corporate goal is a reduction of inventory across the order fulfillment process in excess of 30% with no negative impact on service levels. Customer feedback reveals that a key to customer satisfaction is on time delivery and any deviation from promised dates has a negative impact on customer satisfaction.

Background

Characteristic of Vehicle Inventory Optimization

STEP1.Using the SAS Demand Forecast Server to predict

STEP3.Using the SAS Inventory Optimization Method

In order to offer system solutions for weekly operational decision, the stock management model and order parameters have been defined and solved. Service level ,safety stock level, normal delivery ability, emergency delivery ability, the most optimized delivery cycle time, the probability of different delivery situation and management cost (including the cost of replenishment, holding and backordering) are all introduced in the total cost model of automotive supply logistics. Ultimately, this powerful solution provides essential decision support by helping manufacturers answer the three fundamental questions of inventory management:

How to plan different package location in VSC or Dealer? Which items have crossed inventory thresholds and should be reordered? How much should be ordered?

STEP4: Inventory Optimization Result from SAS® Supply Chain Solution

Although counterintuitive, it in fact is possible to reduce inventory while improving service levels simultaneously using our proven inventory management methodology.SAS inventory management methodology attacks inventory from two directions: Optimizing inventory levels while viewing the existing order fulfillment process as a given constraint. Changing the fundamental order fulfillment process across the entire system

CONCLUSION

SAS® Supply Chain Solution can perform quickly with hundreds of thousands of items, which dramatically eases complicated processes such as large-scale vehicle prediction and inventory planning. Thesecapabilities help our company SGM increase sales share and reduce inventory costs and achieve target customer service levels.

Problem Solution

In Automotive Manufacture Industry:

Inventory levels are high and turns are below most major competitors. In addition a technology change and a proliferation of models amplify the issue.

Demand-Driven forecasting allows companies to sense demand signals through the synchronization of internal/external data to maximizing profit.

In Automotive Manufacture Industry:

Accurate demand forecast will help the motorcar manufacture to support the inventory plan.

SAS® Supply Chain Solution (SCS) provides vehicle manufacturers the ability to calculate the accurate.1. Maintain 300 Model packages and about 1,200 dealers. SCS lack the detailed historical data, even and factors, tracking and analysis.2. Minimize the impact caused by fluctuations and Optimize the buffer to ballance fluctuation.3. Make vsc as a pool and to quick response customer demand.SAS function :

a. Using the SAS Demand Forecast Server to predictb. Vehicle Result from Demand Forecast Server.c. Using the SAS Inventory Optimization Methodd. Inventory Optimization Result from SAS® Supply Chain

Solution

Fourier Analysis for DemandForecasting in a Fashion Company

Andrea Fumi1, Arianna Pepe1, Laura Scarabotti1 and Massimiliano M. Schiraldi1,*1 University of Rome “Tor Vergata” - Department of Enterprise Engineering, Roma, Italy

* Corresponding author E-mail: schiraldi@uniroma2.itReceived 1 June 2013; Accepted 15 July 2013

Abstract

In the fashion industry, demand forecasting is particularly complex: companies operate with a large variety of short lifecycle products, deeply influenced by seasonal sales, promotional events, weather conditions, advertising and marketing campaigns, on top of festivities and socio-economic factors. At the same time, shelf-out-of-stock phenomena must be avoided at all costs. Given the strong seasonal nature of the products that characterize the fashion sector, this paper aims to highlight how the Fourier method can represent an easy and more effective forecasting method compared to other widespread heuristics normally used. For this purpose, a comparison between the fast Fourier transform algorithm and another two techniques based on moving average and exponential smoothing was carried out on a set of 4-year historical sales data of a €60+ million turnover medium- to large-sized Italian fashion company, which operates in the women’s textiles apparel and clothingsectors. The entire analysis was performed on a common spreadsheet, in order to demonstrate that accurate results exploiting advanced numerical computation techniques can be carried out without necessarily using expensive software.

Keywords Demand Forecasting, Fourier Analysis, Fast Fourier Transform, Fashion

FASHION INDUSTRY

Problem Solution

In the fashion industry, demand forecasting is particularly complex: companies operate with a large variety of short lifecycle products, deeply influenced by seasonal sales, promotional events, weather conditions, advertising and marketing campaigns, on top of festivities and socio-economic factors. At the same time, shelf-out-of-stock phenomena must be avoided at all costs.The entire analysis was performed on a common spreadsheet, in order to demonstrate that accurate results exploiting advanced numerical computation techniques can be carried out without necessarily using expensive software.

Given the strong seasonal nature of the products that characterize the fashion sector, this paper aims to highlight how the Fourier method can represent an easy and more effective forecasting method compared to other widespread heuristics normally used.

For this purpose, a comparison between the fast Fourier transform algorithm and another two techniques based on moving average and exponential smoothing was carried out on a set of 4-year historical sales data of a €60+ million turnover medium- to large-sized Italian fashion company, which operates in the women’s textiles apparel and clothing.

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