scholarly journals Fourier Analysis for Demand Forecasting in a Fashion Company

10.5772/56839 ◽  
2013 ◽  
Vol 5 ◽  
pp. 30 ◽  
Author(s):  
Andrea Fumi ◽  
Arianna Pepe ◽  
Laura Scarabotti ◽  
Massimiliano M. Schiraldi
Keyword(s):  
Moving Average ◽  
Demand Forecasting ◽  
Fourier Method ◽  
Weather Conditions ◽  
Economic Factors ◽  
Fast Fourier Transform Algorithm ◽  
Fashion Industry ◽  
Forecasting Method ◽  
Short Lifecycle ◽  
Socio Economic Factors

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 clothing sectors. 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.

scholarly journals Planning Production Capacity Using Time Series Forecasting Method and Linier Programming

2018 ◽  
Vol 7 (2) ◽  
pp. 20
Author(s):  
M. Tirtana Siregar ◽  
S. Pandiangan ◽  
Dian Anwar
Keyword(s):  
Average Method ◽  
Moving Average ◽  
Demand Forecasting ◽  
Production Capacity ◽  
Mean Absolute Percentage Error ◽  
Programming Method ◽  
Percentage Error ◽  
Absolute Percentage Error ◽  
Forecasting Method ◽  
Planning Production

The objectives of this research is to determine the amount of production planning capacity sow talc products in the future utilizing previous data from january to december in year 2017. This researched considered three forecasting method, there are Weight Moving Average (WMA), Moving Average (MA), and Exponential Smoothing (ES). After calculating the methods, then measuring the error value using a control chart of 3 (three) of these methods. After find the best forecasting method, then do linear programming method to obtain the exact amount of production in further. Based on the data calculated, the method of Average Moving has a size of error value of Mean Absolute Percentage Error of 0.09 or 9%, Weight Moving Average has a size error of Mean Absolute Percentage Error of 0.09 or 9% and with Exponential Method Smoothing has an error value of Mean Absolute Percentage Error of 0.12 or 12%. Moving Average and Weight Moving Average have the same MAPE amount but Weight Moving Average has the smallest amount Mean Absolute Deviation compared to other method which is 262.497 kg. Based on the result, The Weight Moving Average method is the best method as reference for utilizing in demand forecasting next year, because it has the smallest error size and has a Tracking Signal  not exceed the maximum or minimum control limit is ≤ 4. Moreover, after obtained Weight Moving Average method is the best method, then is determine value of planning production capacity in next year using linier programming method. Based on the linier programming calculation, the maximum amount of production in next year by considering the forecasting of raw materials, production volume, material composition, and production time obtained in one (1) working day is 11,217,379 pcs / year, or 934,781 pcs / month of finished product. This paper recommends the company to evaluate the demand forecasting in order to achieve higher business growth.

scholarly journals Logit analysis of socio-economic factors influencing people to become fishermen in the central region of Ghana

2011 ◽  
Vol 56 (1) ◽  
pp. 55-64 ◽  
Author(s):  
Henry Acquah ◽  
Isaac Abunyuwah
Keyword(s):  
Central Region ◽  
Weather Conditions ◽  
Structured Interview ◽  
Economic Factors ◽  
Logit Analysis ◽  
Access To Credit ◽  
The People ◽  
Study Results ◽  
Credit Lack ◽  
Socio Economic Factors

This study analyzes the socio-economic factors that influence people?s decision to become fishermen in the central region of Ghana. Using a well structured interview schedule, a random sample of 98 people from Elmina in the central region of Ghana was selected for the study. Results from the descriptive statistics analysis of respondents identified fishing as a family business, minimum skills requirement and ready market for fish demand as factors that motivated majority of the people into fishing. Lack of storage facilities, access to credit, lack of government assistance and unpredictable changes in weather conditions on sea were the main constraints to fishing activities. Results from the logistic regression model indicated that household size and access to credit were significant factors that positively influenced people?s decision to become fishermen. The regression analysis further revealed that engaging in other income generating activity and being educated significantly reduces the probability to start fishing business.

scholarly journals Analisis Penjualan Baju Seragam Sekolah di Konveksi Hanifah Collection

2017 ◽  
Vol 3 (2) ◽  
pp. 61
Author(s):  
Ahmad Fazarudin ◽  
Ahmad Nalhadi ◽  
Gerry Anugrah Dwiputra
Keyword(s):  
Average Method ◽  
Moving Average ◽  
Demand Forecasting ◽  
Exponential Smoothing ◽  
School Uniforms ◽  
A Value ◽  
Forecasting Method ◽  
A Company

Hanifah Collection is a company engaged in the convection of school uniforms. The fluctuating number of requests each month creates its problems in determining the amount of production. This study aims to find a method that matches the data pattern as the basis for determining the amount of output in the next period. The technique used in this study is the forecasting method of Moving Average, Exponential Smoothing and Triple Exponential Smoothing with parameter level errors of each way using MAD, MSE, and MAPE. From the results of this study, there is a moving average method with the most appropriate method in determining demand forecasting in the next period with a value of MAD of 172.22, MSE of 46624.34 and MAPE 46624.34.

scholarly journals IMPLEMENTASI PERAMALAN PENJUALAN IKAN LAUT UNTUK OPTIMASI PERSEDIAAN BAHAN BAKU (Studi Kasus di UD Harum Bungah Gresik)

2021 ◽  
Vol 6 (2) ◽  
pp. 59
Author(s):  
Khanifatus Sa'diyah ◽  
Narto Narto
Keyword(s):  
Manufacturing Industry ◽  
Raw Materials ◽  
Moving Average ◽  
Demand Forecasting ◽  
Raw Material ◽  
Forecasting Method ◽  
National Economic ◽  
Marine Products ◽  
Sea Fish ◽  
Weighted Moving Average

Indonesian marine waters have high marine resource resources. One of Indonesia's seafood commodities is fish. With proper management and utilization, marine products become one of the promising business opportunities for the community, so that fisheries become one of the supporting sectors of national economic development. UD Harum is one of the businesses engaged in the fisheries sector as a supplier of marine fish raw material needs to meet the needs of the manufacturing industry. To optimize production planning to meet industry demand, forecasting of sea fish sales data forecasting in the previous period is needed to anticipate a shortage of raw materials. The purpose of this forecasting is to implement forecasting using the Single Moving Average (SMA), Weighted Moving Average (WMA) and Centered Moving Average (CMA) methods in forecasting sea fish sales at UD Harum and to find out the best forecasting results to increase sea fish sales at UD Harum. Forecasting results show forecasting using the Single Moving Average (3-monthly) and (5-monthly) methods respectively 8107.67 kg and 8399.4 kg. For the Weighted Moving Average (3-monthly) and (5-monthly) methods, the results of forecasting are 7268,963 kg and 7443,452, respectively. As for the Centered Moving Average (3-monthly) method with forecast results of 8107.67 kg. The forecasting method chosen to optimize sales is the Centered Moving Average method with a forecast value of 8107.67 kg and has the smallest forecasting error compared to other forecasting methods with a MAPE value of 0.30875 and MPE of -0.1720.

scholarly journals Time Series Analysis to Predicting Demand of Roasted Coffee Production

2019 ◽  
Vol 10 (5) ◽  
pp. 26
Author(s):  
Agatha Rinta Suhardi ◽  
Shendy Amalia ◽  
Shinta Oktafien ◽  
Siska Ayudia Adiyanti ◽  
Siti Komariah ◽  
...  
Keyword(s):  
Mean Squared Error ◽  
Moving Average ◽  
Demand Forecasting ◽  
Moving Averages ◽  
Roasted Coffee ◽  
Coffee Production ◽  
Absolute Deviation ◽  
Forecasting Method ◽  
Excess Production ◽  
Production Demand

Consumer demand conditions for fluctuating roasted coffee and ineffective production planning often lead to excessive production. Excess production will lead to wasteful costs and maintenance of quality on roasted coffee. Production demand forecasting is the basis for making production demand decisions. The purpose of this study is to predict the number of production requests for the next period and determine the most suitable forecasting method in determining the amount of roasted coffee production demand. The object of the data taken is roasted coffee. Analysis methods use moving averages, weighted moving averages, and exponential smoothing. In determining the most suitable forecasting method based on the Mean Absolute Deviation (MAD) forecasting value and the smallest Mean Squared Error (MSE) of each method used. The results of this study indicate that the most suitable forecasting method is using a Weighted Moving Average with a three-month period and forecasting roasted coffee production for November 2016 of 38.3 kg.

Climate of the Reformation: droughts and anomalous weather in the 1500s-1510s in Europe

2020 ◽  
Author(s):  
Andrea Kiss ◽  
Mariano Barriendos ◽  
Rudolf Brázdil ◽  
Chantal Camenisch ◽  
Silvia Enzi ◽  
...  
Keyword(s):  
Western Europe ◽  
Weather Conditions ◽  
Temporal Analysis ◽  
Economic Consequences ◽  
Economic Factors ◽  
Documentary Evidence ◽  
The Reformation ◽  
Rapid Spread ◽  
Spatial Temporal Analysis ◽  
Socio Economic Factors

<p>In the 1500s-1510s an unusually high number of significant droughts in Central and Western, and partly in Southern Europe; the years 1502-1504, 1506-1507, 1513-1514 and 1516-1518 were dry particularly in Central and Western Europe. Droughts, interspersed with wet years marked even by significant floods and other weather-related extremes, and frequent hard winters were mainly responsible for the reduced or poor crop and hay harvests in multiple years. These circumstances, in combination with other socio-economic factors, contributed to the increased social tension of the period, manifesting itself in major peasant uprisings, and might have acted as a catalyst in the timing and rapid spread of the Reformation.</p><p>The first part of the presentation is concentrated on the reconstruction and spatial-temporal analysis of the droughts (and hard winters) using documentary evidence – in comparison with the tree-ring based hydroclimate reconstruction (OWDA: Cook et al. 2015) and the multiproxy-based reconstruction of Central European precipitation (Pauling et al. 2006).</p><p>The most significant groups of socio-economic consequences are analysed in the second part of the presentation, with special emphasis on discussing the possible cumulative effects of the anomalous weather conditions during the period on the ongoing transformation of the late-medieval society and economy and the Reformation itself.</p>

scholarly journals Demand Forecasting for Multichannel Fashion Retailers by Integrating Clustering and Machine Learning Algorithms

Processes ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 1578
Author(s):  
I-Fei Chen ◽  
Chi-Jie Lu
Keyword(s):  
Demand Uncertainty ◽  
Prediction Models ◽  
Demand Forecasting ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Fashion Industry ◽  
Fast Fashion ◽  
Product Demand ◽  
Learning Machines ◽  
Forecasting Method

In today’s rapidly changing and highly competitive industrial environment, a new and emerging business model—fast fashion—has started a revolution in the apparel industry. Due to the lack of historical data, constantly changing fashion trends, and product demand uncertainty, accurate demand forecasting is an important and challenging task in the fashion industry. This study integrates k-means clustering (KM), extreme learning machines (ELMs), and support vector regression (SVR) to construct cluster-based KM-ELM and KM-SVR models for demand forecasting in the fashion industry using empirical demand data of physical and virtual channels of a case company to examine the applicability of proposed forecasting models. The research results showed that both the KM-ELM and KM-SVR models are superior to the simple ELM and SVR models. They have higher prediction accuracy, indicating that the integration of clustering analysis can help improve predictions. In addition, the KM-ELM model produces satisfactory results when performing demand forecasting on retailers both with and without physical stores. Compared with other prediction models, it can be the most suitable demand forecasting method for the fashion industry.

DINAMIKA KELOMPOK TANI ANUGERAH MOLLO DI DESA NETPALA KECAMATAN MOLLO UTARA KABUPATEN TIMOR TENGAH SELATAN

2020 ◽  
Vol 21 (1) ◽  
pp. 71-80
Author(s):  
Tanggu Dedo Yeremias ◽  
Ernantje Hendrik ◽  
Ignatius Sinu
Keyword(s):  
Formal Education ◽  
Secondary Data ◽  
Nonparametric Test ◽  
Primary Data ◽  
Economic Factors ◽  
Dynamic Level ◽  
South Central ◽  
Group Members ◽  
Relationship Of ◽  
Socio Economic Factors

ABSTRACT This research has been carried out in the Anugerah Mollo Farmer Group, in Netpala Village, North Mollo District, South Central Timor Regency, starting in March - April 2019. This study aims to determine: (1) The dynamic level of the Anugerah Mollo Farmer Group in Netpala Village, North Mollo District, South Central Timor Regency, (2) Relationship between Socio-economic factors of farmer group members and the level of dynamics of the Anugerah Mollo Farmer Group in Netpala Village, North Mollo District, South Central Timor Regency. Determination of the location of the study carried out intentionally (purposive sampling) The type of data collected is primary data obtained from direct interviews with respondents guided by the questionnaire, while secondary data is obtained from the relevant agencies. To find out the first purpose of the data analyzed using a Likert scale, to find out the second purpose of the data analyzed using the Sperman Rank statistical Nonparametric test. The results of this study indicate that: (1) The level of dynamism of the Anugerah Mollo Farmer Group in Netpala Village, North Mollo District, South Central Timor Regency, is in the very dynamic category of 84%, (2) The relationship of socio-economic factors is only one of the five variables that are significantly related namely land area with a coefficient of rs 0.278 and t = 1.782 count greater than t table 1.699 (p> 0.05), while other social factors such as age, formal education, number of family dependents, and experience of farming show no significant relationship with the level of dynamism of Anugerah Mollo Farmers Group in Netpala Village.

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