PETROANALYTICS PROBLEM STATEMENT

Transcription

1 PETROANALYTICS PROBLEM STATEMENT Data Analytics is the prcess f discvering meaningful new relatinships, patterns and trends by shifting thrugh data using pattern recgnitin technlgies as well as statistical and mathematical techniques. A Data Analytic Tl uses the cmbinatin f pwerful Statistical Analysis and Data Mining equatins t predict the useful infrmatin fr a Petrleum retail utlet manager. As the Retail Outlets (RO s) are the prime areas fr btaining retail related data f any Oil Marketing Cmpany, develpment f data analytics fr mnitring varius patterns in the Retail Outlet is an essential factr. The Inputs t the system will be selectin f varius attributes by the User and the Output wuld be the cmputed results f the data analytics that wuld enhance the User s (Retail Outlet Manager, Reginal Office Manager, etc.,) decisin-making capability, which wuld be backed by cncrete facts. Since Retail Outlets (RO) are a majr pint f interactin with the custmers and their gldmine f data, the Oil Marketing Cmpany s treat the Retail Outlet as their premier face-uts. This generates the need fr in depth analysis f data fr higher peratinal capability f the Retail Outlet and als the study f custmer pattern at this level". Current Business backgrund & intrductin Oil Retailers have t d many tasks. Retailers have t analyze their custmers, develp strategies, chse markets and channel t cmpete, make Lcatin decisins. Retailers d design, purchase, price and prmte merchandise and services. They rganize their peratins and manage their emplyee and stres. Retailers create an atmsphere that is inviting t custmer and cnductive fr buying. It is very imprtant t understand the custmer behavir at retail utlet level. The develpment f Retail services in Oil & Gas Cmpany arises the need fr pwerful analytic tls in rder t analyze and interpret the huge amunt f data fr fact based Decisin-making. Develping pwerful Data Analytics Tls at the Retail Outlet Level culd satisfy the business demands f an il marketing cmpany. The main purpse is t integrate the Retail Management in Oil and gas Industry and t develp and custmize the varius Data Analytic Tls s that they culd be utilized t enhance the Operatinal Capability and Prfitability at the Retail Outlet Level. Data analytics can als identify pprtunities t change fueling patterns, leading t campaigns that can increase fuel sales during nn peak hurs f the day. Fr mst f the Retail players tday, Cverage is the key driver. The mre cities and utlets they launch their prgram in, the larger their member base naturally grws. After cverage begins t hit a plateau, data and analytics will take center stage as cmpanies plunge int the deep end f data mining fr elusive insight t drive their campaigns. In additin, analytics driven campaigns is the benefit f member database present. A cmpany culd launch a new prduct, a new fuel blend, a new facility at its utlets, a new partnership and reach ut t its best custmers using the results f data driven data analytics tl. Scenaris and prblems areas in retail Analytics T get the idea f prfitable custmers / Prfitable segments T get the idea f sequence f service t be prvided T help in taking decisins fr higher peratinal capability T help in achieving higher sales at the retail utlet level T knw the n. Of Prducts / Services per sale. T have plan n what type f merchandize/prduct mix t be kept in the Custmer-Stre

2 T get the clarity in what new services t meet the demands f the varius custmer segments T identify the implicatins f lyalty prgrams / prmtin prgrams n prduct sales. Retail managers are nt in the psitin t crrelates effect f anther variable (such as envirnmental etc) n the sales The retail manager will prvide the details f perid, n which the system shuld retrieve data, use Analysis techniques.attributes are retrieved frm databases fr analysis purpses using Database Cnnectivity establishments accrding t the statistical algrithms f Predictability, flexibility, Trends analysis. The key areas f analytics als include Peak hurs f peratins Crrelatin analysis between Vlumetric sales f varius grades t Prduct grade price Impact f prmtinal schemes n sales vlume. Sales analysis (Trend Analysis) - In the sales analysis cntext, the real time analysis f the data shuld be enabled, which at present is available nly fr past data. Sales Frecasting Analysis Using equatins (bth shrt term and lng term frecast) - The frecasting requires frecasting analysis t be dne fr weekly, daily basis and yearly basis. High value custmer identificatin Ptential custmer identificatin Gap Analysis: This functinality must cver the perfrmance analysis between any tw retail utlets in terms f fuel sales and revenues generated. This gap analysis determines the perfrmance levels between tw retail utlets. Multiple crrelatins Analysis: The existing system pssess the crrelatin analysis between tw prduct grades. This shuld be enhanced with multiple crrelatin f prduct grades sales.there by the relatinship between effect f a grup f grade sales ver a particular grade can be undermined. Frecasting analysis using mathematical Mdel: Mdel is t be used t predict the shrt term sales using three parameters in this mdel namely pricing factrs, prmtinal cst incurred and past perid sales.this mdel must have advanced mathematical equatins t be slved by the system. Prfitability Analysis: This functinality examines the prfitability f the retail utlet n any particular day r any interval based n the varius parameters such as ttal sales n prduct grades and ther expenditures. sales rder analysis: This analysis lists ut the list f sales rder generated by the retail utlets at any dates and the list f prducts delivered n time.here the analysis can again be dne n prduct receipt times and average fuel quantity rdered between any intervals f time. New prduct grade frecasting: when a new prduct grade gets launched by the cmpany the sales data available fr the prduct will be ranging between ne mnth intervals t less than tw years. In such cases retail utlet existing analytics desn t have the functinality f frecasting.s, the cncept f single expnential smthing analysis and decmpsitin algrithms have t be develped. Tp list f prduct Analysis: This functinality is required t get infrmatin abut tp selling prducts which are either vlume wise (r) revenue wise.

3 Develp Analytics and mdel t slve the abve prblematic areas fr Oil retailers Mre infrmatin n Retail Business Envirnment In any retail utlet the transactins are basically made f primary and secndary activities, which may be mnetary r nn mnetary. The Primary business activity includes sales f Petrleum Prducts. The secndary activities are fcused n the extended services t the custmer, which are respnsible fr increase the ROI. Secndary activities catch the attentin f different custmers in rder t bst the prduct sale. The secndary activities als play a majr rle in the revenue generatin f the retail utlet. The primary activity cmprises f selling prducts like Gasline, Diesel, LPG, CNG, Lube ils etc. The secndary activity serves the arriving custmer using a variety f services which includes Cnvenience- stre, Vehicle Service, Air statin, Bill payment, Rest rm, Restaurant, Parking. Retail Managers are interested in the sales imprvement f petrleum prducts thrugh the value added services f the secndary activities. These secndary activities incur a variable cst n the retail utlet perfrmance. The majr players in il retailing acrss the wrld have custmer lyalty prgrams. The Oil cmpanies have intrduced their individual prmtinal schemes in the frm f varius custmer lyalty prgrams like petrcards etc. These cmpanies are fcused n their marketing effrts n building their retail utlet brand and services. Analytic Cnstraints Data analytics tl requires large amunt f data t shw accurate results in frm f graph. Hence the availability f such data which is btained as part f the real time business envirnment shuld be a validated data n which varius checks are perfrmed. This infrmatin may have t be btained frm the dealer f retail utlet [a case where Retail Outlet is nt perated by Oil Cmpany]. In case where the retail analytics wrks n incrrect and invalidated data the results f the systems wuld be a divergent frm the nrmal. Analytics f the data can be f tw lines namely industry specific metrics and cmpany specific metrics. The data analytic tl fcuses n the general industry metrics which can be applicable acrss all the il retailers irrespective f the cmpany specific metrics, thugh these requirements can be incrprated as per retail utlet requirements. The cnstraints that culd influence the bjectives are unknwn prfitable segments, unknwn idea n the sequence f services t be prvided, lack f clear plan n prduct mix t be kept in the Cnvenience stre and ineffective implementatin f lyalty prgrams / prmtinal schemes. In US market quality and price are cnsidered as an imprtant factr in il retailing. Hence imprtance will be given while designing the Data Analytic tl. The gegraphical lcatin f the retail utlet plays a significant rle in prduct sales. Revenue generatin will be higher, if retail utlet is present at busy lanes (i.e. free way r highway). Prmtinal schemes als play a significance rle in prduct sales at different lcatin. The systems available in the market are less pwerful, nn cst effective and cannt be easily integrated in the disparate cmputing envirnment. Data analytics require interfaces t the data warehuses f the retail utlet. The existing system thugh capable f prducing accurate results needs the abve mentined perfrmance criteria. Generatin f business analysis reprts (in frm f graphs) are shwn t the retail manager.here the relatinship between the dependent and independent variables shuld be shwn with their legends. The graph shws the effect f ther variables (randm) n business sales. A detailed analysis f the sales data can reveal many actin areas fr different variables. When there are multiple decisin variables, it is recmmended t take a systematic lk at all the variables. The

4 systematic lk shuld be seen as an art f seeing varius relatinships, their interrelatinships in the whle s that thse structures are visible. In retailing f il industry, the margins are very thin. Als the retailers face intense cmpetitin frm their cmpetitrs. Any retailer wuld like t knw abut the predicted sales fr the prducts f varius grades fr the cming perid f mnths. The demand number fr each prduct fr the frward mnth r a week is ne f the key inputs which are als being called as PDP (Pre-dispatch planning) schedule. This input actually gives the rate at which the refinery perates fr the next mnth. The fllwing inputs give demand numbers i.e. frecasted data f the retail utlet. Histrical data fr the same perid and the latest sales Marketing inputs based n marketing plans t intrduce any prmtinal schemes Frwarded prduct prices in the cming mnths In mathematical terms, time series analysis is the effective technique used fr frecasting f sales. Time series Analysis accunts fr the fact that data pints taken ver time may have an internal structure such as Aut crrelatin, Trend r seasnal variatin.the usage f time series mdel is f tw fld T Obtain an understanding f the underlying frces and structure that prduces the bserved data T fit a mdel and prceed t frecasting, mnitring r even feedback and feed frward cntrl.

5 Hints and pints t cnsider in yur slutin: (1) In United States there culd be 6 grades f gasline, diesel etc. s cmpanies wuld like t crrelate the sales f grade fuel. This wuld give them an idea t intrduce new grade fuels r ablish if any. (2) Sales f sme retail utlets prducts might be high in sme lcalities during schl timings etc. (3) Sales vlume might change during peak summer times and wuld be lw during winter seasns. It can g high during festival seasns. It can fllw cyclic and acyclic patterns (4) T prmte sales, a RO manager culd reduce the price f fuels n display utside frm his nearest RO cmpetitr (5) Part f prmtinal campaign wuld invlve inviting custmers fr free car wash, granting fuel pints, gifts etc. (6) An additin f a new fuel statin (retail utlet) culd impact the sales f the existing retail utlets. (7) Custmers are identified using unique custmer numbers (fleet cards), credit cards r vehicle number (8) Oil cmpanies in United States tend t increase their sales by imparting prmtinal schemes. Very ften sales Maximizatin is taken as the single largest target f retailing. Here sales vlumes are increased thrugh greater prmtinal effrt. The increased sales prmtin has sme additinal cst t it which has nullified the effect f imprved realizatin n accunt f mre number f units sld. Analytics must help the cmpany t knw the extent f success f its prmtinal schemes, there by giving them scpe t plan their prmtinal activities. Since prmtinal perids generally ranges between 1-20 days. (9) Fuel Prices can change ver week, mnths etc. In United States fuel prices culd change even 6 times a day.