DEMAND PREDICTIVE MODEL FOR SMALL METALLURGICAL COMPANIES

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1 Jun 3 rd - 5 th 2015, Brno, Czech Republc, EU DEMAND PREDICTIVE MODEL FOR SMALL METALLURGICAL COMPANIES STAŠ Davd 1, LENORT Radm 1, PASTOR Otto 1, CHVĚJA Stanslav 2, SOBEK Jří 2 1 ŠKODA AUTO Unversty, Na Karmel 1457, Mladá Boleslav, Czech Republc, EU, stas@s.savs.cz, lenort@s.savs.cz 2 VÚHŽ a.s., Dobrá 240, Czech Republc, EU Abstract The typcal problems facng wth small metallurgcal companes are predcton errors, because ther markets are volatle and dffcult to predct. For that reason, the ablty to develop accurate demand predctons s crtcal n the ndustry. The paper presents a herarchcal mddle-term predctve model for small metallurgcal companes. The model s bult by top-down predctve approach and verfed by means of a case study n the company producng the specal rolled sectons. Weaknesses of the model are dentfed durng dscusson of the acqured results. A Generalzed concept of ANN predctve model s desgned for elmnaton these weaknesses. Keywords: metallurgcal company, demand predctve model, top-down approach, specal rolled sectons. 1. INTRODUCTION Small metallurgcal enterprses face many specfc problems related wth logstcs management (see e.g. [1], [2], [3], [4]). The typcal factor causng these problems s very dffcult demand predcton, because ther markets are volatle and dffcult to predct. For that reason, the ablty to develop accurate demand predctons s crtcal n the ndustry. The am of the paper s to desgn a herarchcal mddle-term predctve model for small metallurgcal companes and ts verfcaton n specal rolled sectons branch. 2. METHODOLOGICAL BASE Most companes deal wth multple levels of aggregaton and requre consstent predctons at all levels. Herarchcal predctng, a famly-based predctve methodology, s a centralzed predctve approach capable of satsfyng the varety of predcton nformaton requrements [5]. Predctons of tem and famly demands n the process are produced usng two approaches top-down and bottom-up, or a combnaton of the two (sometmes called mddle-out approach). The top-down approach entals predctng the completely aggregated seres, and then dsaggregatng the predctons based on hstorcal proportons [6]. These proportons may be accomplshed n varous ways as demonstrated by Gross and Sohl [7]. The top-down approach s recommended for strategc and tactc plans and budgets [8]. Thus, ths approach wll be used n desgned demand predctve model. 3. HIERARCHICAL DEMAND PREDICTIVE MODEL DESIGN The followng demand predctve model was created on the base of lterature revew n the area of predcton theory for the purpose of mddle-term predctons n small metallurgcal companes. Mddle-term predctons look ahead between three months and a year, and they are mostly used for Sales and Operatons Plannng (S&OP) process [9]. Base for utlzaton of top-down predcton approach n S&OP was defned by Lapde [10]. The predcton model s based on applyng sx man stages:

2 Jun 3 rd - 5 th 2015, Brno, Czech Republc, EU 1. Predcton structure determnaton determnaton of predcton levels, famles at any level (parents) and chldren wthn any famly. 2. Tme dmensons dentfcaton dentfcaton of the length and perodcty of the predcton. 3. 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 ntended predcton technques. 4. Generatng drect forecasts determnaton of drect and ndependent chld and famly forecasts whch can nclude tme seres analyss, predcton technques selecton, partal models buldng and evaluaton, models mplementaton, and predcton combnaton (f t s approprate). 5. Top-down process applcaton determnaton of derved chld predctons from the top level famly wth the followng proraton procedure [5]: Ratos calculaton calculaton of the rato of the drect chld predcton and the sum of the drect chld predctons comprsng ts famly: r n 1 DCF DCF (2) r rato of -th chld, = 1, 2,..., n DCF drect predcton of -th chld, n number of chldren n ts famly, Fnal predctons dervaton multplcaton of the fnal parent predcton by ths rato: FCF (3) FPF r FCF fnal predcton of -th chld, FPF fnal parent predcton 6. Trackng results contnuous trackng of how well the predctons compare wth the actual values observed durng the predcton 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 [11]. 4. CASE STUDY The possble utlzaton of the desgned demand predctve model was verfed n a company producng the specal rolled sectons. An overwhelmng majorty of producton s ntended for automotve ndustry. Thus, the case study s focused on ths branch Predcton structure determnaton In mddle-term horzon, the demand s predcted on three levels (see fgure 1). The top level ncludes the total demand of the plant. These are dvded nto two branch famles automotve and other branches. Fg. 1 Scheme of the plant predcton structure

3 Demand (kg) Jun 3 rd - 5 th 2015, Brno, Czech Republc, EU Each of them s dvded nto nne product famles accordng to the sectons rollng mll operatng capacty (see fgure 2, kg/h) from A to I. Products for automotve branch plays a key role (80% of the producton) Tmes dmensons dentfcaton Fg. 2 Operatng capacty of product famles The predcton s carred out on a monthly bass. The length of the predcton horzon s one year (from 1 January to 31 December),.e. 12 months Data collecton and manpulaton Company demand s characterzed by relatvely sgnfcant seasonal fluctuatons. The lower demand perods are at the mddle and end of the year. The stuaton s related to holday months n the automotve ndustry. Partcularly predcton of demand s performed for the purpose of S&OP. Thus, the predctons are carred out n kg. Demand of product famles ntended for automotve ndustry durng the perod of 36 month are demonstrated n fgure Tme (months) Automotve A B C D E F G H I Fg. 3 Tme seres plot for demand from automotve ndustry

4 Jun 3 rd - 5 th 2015, Brno, Czech Republc, EU Wth regards to the senstve nature of nformaton, the monthly demand data were modfed. Fgure 3 clearly shows the gradual ncrease of demand from automotve ndustry caused especally by recovery of the automotve markets Generatng drect predctons Usng the descrptve and tme seres analyss n statstcal software STATGRAPHIC Plus 5.0, relatvely sgnfcant seasonal component were dentfed n automotve tme seres and n C, D, and E product famles tme seres. Other product famles tme seres have only random component wth/wthout trend component. Wth regards to ths fact, dfferent predcton technques were used for drect demand predctons. Models were bult and evaluated usng the statstcal software STATGRAPHIC Plus 5.0. The most accurate predcton models for each tme seres can be seen n table 1. The drect predctons are presented n fgure 4. Table 1 The most accurate predcton models Branch/Product Famles Predcton Technque Predcton Model Automotve SARIMA (1,1,1)x(2,1,0)12 wth constant A Smple exponental smoothng α = B Constant mean C SARIMA (1,1,0)x(2,1,2)12 wth constant D SARIMA (2,1,0)x(2,1,2)12 wth constant E SARIMA (1,1,1)x(2,1,0)12 wth constant F Lnear trend t G Constant mean H Constant mean I Lnear trend = t 4.5. Top-down process applcaton Fg. 4 Drect automotve demand predcton The fnal predctons used wth the proraton procedure (top-down process) are presented n fgure 5. The predctons acqured on the product famles level are consstent wth the automotve demand predcton.

5 Jun 3 rd - 5 th 2015, Brno, Czech Republc, EU Fg. 5 Fnal automotve demand predcton 5. DISCUSSION OF RESULTS Despte the fact that the tme seres analyss currently provdes a number of very hgh qualty tools for demand predctons, ther applcaton n specal rolled sectons branch s lmted. The man reason s the fact that the use of hstorcal data for the purpose of predcton doesn t take nto account other, n many cases absolutely essental factors, nfluencng the future demand. Ths factors are demand development n automotve and metallurgcal markets, strategc plans of the company, and trade negotatons wth key customers. That s why t s necessary to look for such tools of herarchcal predctng n ths branch that make t possble to nclude not only the hstorcal data concernng demand but also other factors havng essental mpact on the predcton qualty. Sutable tools to be appled n ths sphere may nclude the artfcal neural networks (ANNs). These models can be exposed to large amounts of data and dscover patterns and relatonshps wthn them [14]. The generalzed concept llustrated n fgure 6 can be used for demand predctng n the sngle levels of herarchy n the specal rolled sectons branch. Fg. 6 Generalzed concept of ANN predcton model Inputs for an ANN predcton model can be dvded nto fve groups: 1. Inputs A hstorcal data regardng demand of the metallurgcal company. 2. Inputs B data takng nto account the automotve market development (e.g. n form of automotve market ndexes).

6 Jun 3 rd - 5 th 2015, Brno, Czech Republc, EU 3. Inputs C data takng nto account the metallurgcal market development (e.g. n form of metallurgcal market ndexes). 4. Inputs D data expressng the success of metallurgcal company n negotatons wth key customers (e.g. as a rato of nqured and really ordered quantty). 5. Inputs E correcton of demand predctons acqured by ANN predcton model related to the strategc plans of the company (e.g. modernzaton of equpment, ntroducton of new products). CONCLUSION The current stuaton on small metallurgcal companes markets s characterzed by hgh fluctuatons of customer demand. Ths makes the predctve process n varous company levels more and more dffcult, especally from the pont of vew of qualty predctons used n S&OP. In many small metallurgcal companes, ths process runs almost purely on the bass of experence of the company managers. That s why the complex herarchcal demand predctve system, based on top-down predcton approach and use of predcton technques whch would make t possble to nclude not only the hstorcal data concernng demand progress but also other factors havng essental mpact on the predcton qualty, should be desgned and appled. REFERENCES [1] SANIUK S., SAMOLEJOVÁ A., SANIUK A., LENORT R. Benefts and barrers of partcpaton n producton networks n a metallurgcal cluster - research results. Metalurgja, Vol. 54, No. 3, 2015, pp [2] KRAMARZ M., KRAMARZ W. The dentfcaton of zones of amplfcaton of dsruptons n network supply chans of metallurgc products. Metalurgja, Vol. 54, No. 1, 2015, pp [3] VILAMOVÁ Š., MIKLOŠIK A., KOZEL R., SAMOLEJOVÁ A., PIECHA M., WEISS E., JANOVSKÁ K. Regresson analyss as an objectve tool of economc management of rollng mll. Metalurgja, Vol. 54, No. 3, 2015, pp [4] SPISAK J., LISUCH J., ROHACOVA A. Technologcal logstcs - tool of optmalzaton of heat treatment processes of raw materals. In Metal 2011: 20th Annversary Internatonal Conference on Metallurgy and Materals, Ostrava: Tanger, pp [5] FLIEDNER G. Herarchcal forecastng: ssues and use gudelnes. Management and Data Systems, Vol. 101, No. 1, 2001, pp [6] HYNDMAN R. J., AHMED R. A., ATHANASOPOULOS G., SHANG H. L. Optmal combnaton forecasts for herarchcal tme seres. Computatonal Statstcs and Data Analyss, Vol. 55, No. 9, 2011, pp [7] GROSS C. W., SOHL J. E. Dsaggregaton methods to expedte product lne forecastng, Journal of Forecastng, Vol. 9, No. 3, 1990, pp [8] KAHN K. B. Revstng top-down versus botton-up forecastng. The Journal of Busness Forecastng, Vol. 17, No. 2, 1998, pp [9] TAKALA J., MALINDŽÁK D., STRAKA M. et al. Manufacturng Strategy Applyng the Logstcs Models, Vaasa: Vaasan ylopsto Unversty of Vaasa, [10] LAPIDE L. Top-down & bottom-up forecastng n S&OP. The Journal of Busness Forecastng, Vol. 25, No. 2, 2006, pp [11] LEVENBACH H., CLEARY J. P. Forecastng: Practce and Process for Demand Management. Duxbury, 2006.