Hierarchical Sales Forecasting System for Apparel Companies and Supply Chains

Transcription

1 Radm Lenort, Petr Besta VŠB Techncal Unversty of Ostrava, Faculty of Metallurgy and Materals Engneerng, Department of Economcs and Management n Metallurgy, 17. lstopadu 15, Ostrava-Poruba, Czech Republc, E-mal: radm.lenort@vsb.cz, Herarchcal Sales Forecastng System for Apparel Companes and Supply Chans Abstract The typcal problems facng wth apparel companes and supply chans are forecastng errors, because fashon markets are volatle and dffcult to predct. For that reason, the ablty to develop accurate sales forecasts s crtcal n the ndustry. There are several research studes related to forecastng apparel goods, but very often only for one level. However, apparel companes and supply chans deal wth a number of levels at whch the forecasts could exst and requre consstent forecasts at all of them. The paper presents a herarchcal mddle-term forecastng system desgned for ths purpose on the bass of a lterature revew. The system s bult by the top-down forecastng approach and verfed by means of a case study n a partcular apparel company. The weaknesses of the system are dentfed durng dscusson of the results acqured. A generalsed concept of the ANN forecastng model s desgned for elmnaton these weaknesses. GENERAL PROBLEMS OF THE FIBRE AND TEXTILE INDUSTRIES Key words: apparel company, herarchcal forecastng, sales forecastng, top-down approach, forecastng system. Introducton The typcal problems facng wth apparel companes and supply chans are forecastng errors, because fashon markets are volatle and dffcult to predct. For that reason, the ablty to develop accurate demand forecasts s crtcal n the ndustry. Thomassey sums up the specfctes of apparel sales and related forecasts [1]: Horzon of forecasts two horzons of forecast are used n that branch: (1) a medum-term horzon (.e. one year) to plan the sourcng and producton and (2) a short-term horzon (.e. few weeks) to replensh, f necessary, and adjust the orders and delveres of local stores. Lfe cycle of tems sales forecasts are requred for basc and best sellng tems, whle fashon tems wth one shot supply are often not taken nto account n the forecastng process. Aggregaton of sales apparel companes commonly prefer the aggregaton of ther hstorcal data related to sales accordng to the herarchcal classfcaton. The famly level s very sutable for sales forecasts based on tme seres technques. At the lower levels, data are ephemeral; no hstorcal data are avalable. Seasonalty the seasonalty gves a global trend for sales, and ths should absolutely be ntegrated nto the forecastng system. There are several research studes related to forecastng textle and apparel goods, but very often only for one level or for several of the lowest levels [2-5]. However, apparel companes and supply chans deal wth a number of levels at whch the forecasts could exst and requre consstent forecasts at all of them. The am of the paper s: To desgn a herarchcal mddle-term forecastng system for apparel companes and supply chans on the bass of a lterature revew. To verfy the system by means of a case study n an apparel company, to evaluate ts weaknesses and to offer approprate ways for ther elmnaton. The benefts of searchng for new advanced forecastng systems n that branch can be expected at dfferent levels [1, 6]: Reducton of the bullwhp effect wthout major supply chan reorgansaton. Possblty for the suppler to smooth out producton, optmse ts resources, decrease costs, and mprove the effectveness of the retaler s sourcng strategy. Reducton of lost sales, markdowns and, consequently, ncrease the proft margn. Lterature revew Herarchcal forecastng Most organsatons deal wth multple levels of aggregaton and requre consstent forecasts at all levels. The pyramd n Fgure 1 shows a number of levels at whch the forecasts could exst. Herarchcal forecastng (HF), a famlybased forecast methodology, s a centralsed forecast approach capable of satsfyng a varety of forecast nformaton requrements [7]. Forecasts of tem and famly demands n the HF process are produced usng two approaches top- Lenort R, Besta P. Herarchcal Sales Forecastng System for Apparel Companes and Supply Chans. FIBRES & TEXTILES n Eastern Europe 2013; 21, 6(102):

2 8 Company Busness Market segment Product famly Product sub-famly Brand/model Packeg sze End tem End dem by customer End dem by customer by locaton Fgure 1. The forecastng pyramd [7]. down and bottom-up, or a combnaton of the two (sometmes called the mddle-out approach). The top-down approach entals forecastng a completely aggregated seres, and then dsaggregatng the forecasts based on hstorcal proportons [8]. These proportons may be accomplshed n varous ways, as demonstrated by Gross and Sohl [9]. The bottom-up approach nvolves forecastng each of the dsaggregated Forecast structure determnaton Tme dmenson dentfcaton Data collecton and manpulaton Generatng drect forecasts Tme seres analyss Model mplementaton Forecast combnaton Forecastng method selecton Top-down process applcaton Rato calculaton Trackng results Model buldng and evaluaton Fnal forecast dervaton Fgure 2. Herarchcal sales forecastng system desgned. seres at the lowest level of the herarchy, and then usng smple aggregaton to obtan forecasts at hgher levels of the herarchy [8]. Both of the approaches descrbed wll make sure that the resultant forecasts are consstent wth those at ether a hgher or lower herarchcal level. Herarchcal sales forecastng system for apparel companes and supply chans The followng sales forecastng system was created on the bass of a lterature revew n the area of forecastng theory for the use of mddle-term forecastng apparel companes and supply chans. Medum-term forecasts look ahead between three months and a year, and they are mostly used for the Sales and Operatons Plannng (S&OP) process [10]. There are two ways n whch top-down and bottom-up forecastng s useful durng S&OP; a process that s predcated on developng consensus-based demand and supply plans [11]: Development of a baselne forecast n order to take advantage of the accuracy that can be acheved by usng both types n conjuncton wth each other. To get the requste accountablty and commtment from all the organsatons nvolved n the S&OP process requres the forecasts to be aggregated and dsaggregated (and possbly translated) to varous levels to be revewed and revsed by each one, n terms they best understand. Usng gudelnes for herarchcal forecastng by Fledner [7] s the startng pont of the system desgned (see Fgure 2). The system desgned s based on the top-down approach, whch s recommended for strategc and tactcal plans as well as budgets [12]. The forecastng system s based on applyng sx man stages: Forecast structure determnaton determnaton of forecast levels, famles at any level (parents) and chldren wthn any famly. Tme dmenson dentfcaton dentfcaton of the length and perodcty of the forecast. Data collecton and manpulaton accessng and assemblng approprate data for each chld and famly; ther gettng nto a form that s requred for usng the forecastng technques ntended. Generatng drect forecasts determnaton of drect and ndependent chld and famly forecasts, whch can nclude: Tme seres analyss especally dentfyng the man component parts of the tme seres (trend, seasonal, cyclcal, and rregular) as one of the most mportant crtera for selectng sutable forecastng technques. Forecastng method selecton determnng the methods whch mght be good canddates for forecastng. It s recommended that more than one technque s used whenever possble, both from the group of avalable quanttatve and qualtatve methods. Model buldng and evaluaton determnng whch models (from the forecastng technques selected) provde the most accurate forecasts n terms of mnmsng forecastng error. If the models selected dd not yeld an acceptable level of accuracy, alternatve models would be selected. Model mplementaton generatng the actual model forecasts,.e. approprate forecasts are developed for the forecast horzon ntended. Forecast combnaton combnng the forecasts acqured f t s approprate. When two or more methods that have dfferent nformaton bases are used, ther combnaton wll frequently provde better forecasts than ether method alone. Wlson and Keatng recommend the weghted average of smple predctons for a combned forecast [13]: (1) CF combned forecast, w relatve weght of -th forecast (reflect forecastng error and must sum to 1.00), F forecast by means of n-th forecastng method, m number of combned forecastng technques, = 1, 2,..., m. Top-down process applcaton determnaton of chld forecasts derved from the top level famly wth the followng proraton procedure [7]:

3 Rato calculaton calculaton of the rato of the drect chld forecast and sum of the drect chld forecasts comprsng ts famly: 1 st Level Total sales 2 nd Level: Markets Inland Outland r = n = 1 DCF DCF (2) rato of -th chld, DCF drect forecast of -th chld, n number of chldren n ts famly, = 1, 2,..., n. Fnal forecast dervaton multplcaton of the fnal parent forecast by ths rato: FCF = FPF. r (3) FCF fnal forecast of -th chld, FPF fnal parent forecast. Trackng results contnuous trackng of how well the forecasts compare wth the actual values observed durng the forecast horzon. Over tme, even the best of technques and models are lkely to deterorate n terms of accuracy and need to be respecfed or replaced wth an alternatve method [13]. Case study Possble applcatons of the herarchcal sales forecastng system desgned were verfed by a case study of an apparel company whch belongs to the leadng tradtonal producers of clothng n central Europe. The most mportant range of products ncludes overcoats, jackets, and dresses desgned for prestgous world brands. The producton of clothes s charactersed by a hgh level of both technologcal processng and qualty. An overwhelmng majorty of producton s ntended for export. Forecast structure determnaton In the medum-term, sales are forecasted on three levels (see Fgure 3). The top level ncludes the total sales of the plant, dvded nto two market famles nland and outland, each of whch s further dvded nto four man product famles overcoats, jackets, dresses, and others. When focussng on prestgous world brands, the forecastng of foregn sales plays a key role. Fgure 3. Scheme of the company s forecast structure. Sales, ml. EUR 3 rd Level: Product famles Fgure 4. Tme seres plot for outland sales. Tme dmenson dentfcaton The forecast s carred out on a monthly bass. The length of the forecastng horzon s one year (from 1 January to 31 December),.e. 12 months. Data collecton and manpulaton Company sales are charactersed by rather sgnfcant seasonal fluctuatons. The lower sales perods are usually February & March and September & October. Autumn and wnter seasons are produced from Aprl to August, and sprng and summer seasons are produced from November to January. Partcularly the forecastng of foregn sales s performed for the purpose of S&OP. The perods wth lower sales are used for domestc market producton and for retal networks of the company. The perods of hgher sales must be covered by cooperaton. Sales of product famles ntended for foregn markets durng the perod of 2007 to 2011 (60 month) are demonstrated n Fgure 4. Wth regards to the senstve nature of nformaton, monthly sales data were determned by smulaton usng offcally avalable annual data of company sales. Overcoasts Jackets Dresses Others Tme, months Fgure 4 clearly shows a gradual decrease n foregn sales caused especally by the world economc crss and strong competton from low-cost countres (Asatc and south-east European countres). Although the sales of product famles of overcoats are gradually ncreasng, the sales of other product famles are contnuously decreasng. Generatng drect forecasts Usng the tme seres analyss n statstcal software STATGRAPHIC Plus 5.0, the sgnfcant seasonal and trend components were dentfed. Wth regards to ths fact, sutable adaptve and Box-Jenkns methodology technques were chosen for sales forecastng. Models were bult and evaluated usng statstcal software STATGRAPHIC Plus 5.0. The most accurate forecasts, n terms of mnmsng forecastng error were provded by Box- Jenkns models (see Table 1). The drect forecasts are presented n Table 2. Top-down process applcaton Use of the proraton procedure for obtanng the product famles level forecasts s stated n Table 2. 9

4 Table 1. The most accurate forecast models and ther error statstcs. Sales Outland Overcoats Jackets Dresses Others SARIMA model (2,1,2) (2,1,2)12c The fnal forecasts of the outland, overcoats, jackets, dresses, and others sales are graphcally presented n Fgure 5. The forecasts acqured at the product famles level are consstent wth the outland sales forecast. Dscusson of results (0,0,2) (2,1,2)12 Despte the fact that the tme seres analyss currently provdes a number of very (0,1,1) (1,0,2)12 (0,1,2) (2,1,0)12c (2,1,2) (1,1,2)12 RMSE MAE MAPE Sales, ml. EUR ME MPE Tme, months Fgure 5. Fnal forecasts for the frst and second level. hgh qualty tools for sales forecastng, ther applcaton n the apparel ndustry s lmted. The man reason s the fact that the use of hstorcal data for the purpose of forecastng does not take nto account other, n many cases absolutely essental, factors nfluencng future sales: Trade negotatons wth key customers customers address several apparel companes at the same tme usng pattern nqures before each season. Future sales depend on the ablty of the producer to prepare and to succeed wth a compettve offer. Economc development n sales markets the nfluence of the economc stuaton n the markets on sales s llustrated by the current world economc crss, whch has caused a sgnfcant drop n sales n all central European apparel companes. Competton from low-cost countres the current nfluence of mporters from low-cost countres s gradually decreasng. Partcularly producers n Asa are facng contnuous growth of labour costs and are not able to provde prces as low as n prevous years. Another reason of weaker competton from low-cost countres s represented by the persstent low qualty of goods offered. Weather wth regard to the character of apparel ndustry products, weather plays a sgnfcant role n the amount of sales. Strategc plans of the company strategc decsons related especally to the producton range and markets have a fundamental mpact on company sales. An extenson of the producton range and entry to new markets wll probably be assocated wth ncreasng sales and vce versa. That s why t s necessary to look for such tools of herarchcal forecastng n apparel companes and supply chans that make t possble to nclude not only hstorcal data concernng sales but also other factors havng an essental mpact on the forecast qualty. Sutable tools to be appled n ths sphere may nclude anartfcal neural network (ANN). These models can be exposed to large amounts Table 2. Top-down process for the product famles level. Drect forecasts (ml. EUR) Sum Ratos Fnal forecasts (ml. EUR) Month (ml. Outland Overcoats Jackets Dresses Others EUR) Overcoats Jackets Dresses Others Overcoats Jackets Dresses Others

5 Inputs A Inputs B Inputs C Inputs D Inputs E of data and dscover patterns as well as relatonshps wthn them [14]. The generalsed concept llustrated n fgure 6 can be used for sales forecastng at the sngle levels of the herarchy n apparel companes and supply chans. Inputs for an ANN forecastng model can be dvded nto sx groups: Inputs A hstorcal data regardng sales of the apparel company or supply chan. Inputs B data expressng the success of the apparel company or supply chan n negotatons wth key customers (e.g. as a rato of the quantty nqured and actually ordered). Inputs C data takng nto account apparel goods market development (e.g. n form of apparel market ndexes). Inputs D data related to the compettveness of low-cost countres (e.g. as the amount of labour costs n the apparel ndustry). Inputs E data takng nto account weather. Inputs F correcton of sales forecasts acqured by the ANN forecastng model related to the strategc plans of the company. Concluson Artfcal neural network model Sales forecast Inputs F Fgure 6. Generalsed concept of ANN forecastng model. The current stuaton on the apparel markets s charactersed by relatvely hgh fluctuatons of customer demand, whch makes the forecastng process at varous company levels more and more dffcult, especally from the pont of vew of qualty forecasts used n S&OP. In many apparel companes, ths process runs almost purely on the bass of the experence of company managers. That s why a complex herarchcal sales forecastng system based on the top-down forecastng approach and use of forecastng technques, whch would make t possble to nclude not only hstorcal data concernng sales progress but also other factors havng an essental mpact on the forecast qualty, should be desgned and appled. Based on the fndngs acqured, future research work can be defned n the followng drectons: Verfcaton of the generalsed ANN concept for sales forecastng at sngle levels of the herarchy. Identfcaton of specal forecastng methods and models for famles whch are charactersed by sporadc sales the use of tradtonal forecastng models for the famles descrbed s very problematc. Acknowledgement The work was supported by specfc unversty research of the Mnstry of Educaton, Youth and Sports of the Czech Republc No. SP 2012/42 and SP 2013/49. References 1. Thomassey S. Sales forecasts n clothng ndustry: The key success factor of the supply chan management. Internatonal Journal of Producton Economcs 2010; 128, 2: Fnal forecast 2. Frank C, Garg A, Sztandera L, Raheja A. Forecastng women s apparel sales usng mathematcal modelng. Internatonal Journal of Clothng Scence and Technology 2003; 15, 2: Thomassey S, Happette M, Castelan JM. A global forecastng support system adapted to textle dstrbuton. Internatonal Journal of Producton Economcs 2005; 96, 1: Thomassey S, Happette M, Castelan JM. A short and mean-term automatc forecastng system - applcaton to textle logstcs. European Journal of Operatonal Research 2005; 161, 1: Ozbek A, Akaln M, Topuz V, Sennaroglu B. Predcton of Turkey s Denm Trousers Export Usng Artfcal Neural Networks and the Autoregressve Integrated Movng Average Model. FIBRES & TEXTILES n Eastern Europe 2011; 19, 3: Bakalarczyk S. Innovaton of the Polsh Textle Sector wth Respect to Antbacteral and Bacterostatc Textles. FI- BRES & TEXTILES n Eastern Europe 2012; 20, 2: Fledner G. Herarchcal forecastng: ssues and use gudelnes. Management and Data Systems 2001; 101, 1: Hyndman RJ, Ahmed RA, Athanasopoulos G, Shang HL. Optmal combnaton forecasts for herarchcal tme seres. Com pu ta tonal Stat st cs and Data Ana lyss 2011; 55, 9: Gross CW, Sohl JE. Dsaggregaton methods to expedte product lne forecastng, Journal of Forecastng 1990; 9, 3: Takala J, Malndžák D, Straka M, et al. Manufacturng Strategy Applyng the Logstcs Models, Vaasa: Vaasan ylopsto Unversty of Vaasa, Lapde L. Top-down & bottom-up forecastng n S&OP. The Journal of Busness Forecastng 2006; 25, 2: Kahn K. B. Revstng top-down versus botton-up forecastng. The Journal of Busness Forecastng 1998; 17, 2: Wlson JH, Keatng B. Busness Forecastng wth Accompanyng Excel-Based ForecastX TM Software. McGraw-Hll Companes, Levenbach H, Cleary JP. Forecastng: Practce and Process for Demand Management. Duxbury, Receved Revewed