Process Improvements in Inventory Forecasting for the Retail Industry

Transcription

1 A Poit of View Process Improvemets i Ivetory Forecastig for the Retail Idustry Abstract Ivetory is oe of the biggest ivestmets a retailer makes; so ivetory maagemet is at the core of maagig costs. The best retailers usually achieve higher ivetory turs. However, specialty retailers typically take loger to tur their ivetory ad costatly look for ways to improve ivetory performace. Efficietly forecastig demad is a key competecy i the curret retail eviromet, where imports from foreig coutries ad operatios across multiple geographies ivolve log lead times. Accurate demad forecastig, backed by a efficiet supply chai of direct import ad distributio vedors, is critical to icreasig profit margis. Itroductio Demad forecastig is critical for the retail idustry: it helps maximize reveues ad profits while optimizig ivetory. Accurate forecasts also allow retailers to egotiate aggressively with suppliers ad vedors to price their products competitively. I a ideal retail eviromet, demad forecasts would meet the customers' exact demads, ad all products would ear maximum profits. However, demad forecastig is a complicated exercise ad ot usually highly accurate. This paper looks at various aspects ad processes to esure accurate demad forecastig. We believe that applyig the sciece of demad forecastig ad usig Lea Six Sigma techiques to improve the process will help compaies icrease ivetory turs ad lower ivetory holdig costs. Key Iputs for Demad Forecastig Most orgaizatios believe that the busiess case for forecastig demad is to meet customer requiremets at the right time ad at the right price. However, a mature forecastig process also helps a orgaizatio optimize the machies, staff, ad materials required to meet customer expectatios. The followig factors are importat iputs i desigig a optimized forecastig process. Historical Demad Historical customer orders are geerally used as the basis for estimatig demad. Although that might be the best data available for projectig customer requiremets, it is ot a perfect basis for projectig demad. I a ideal retail eviromet the umber of uits of demad forecast would be equal to customer orders. However i reality o orgaizatio ca perfectly meet customer demad ad some portio of it either remais ufulfilled or gets trasferred to other choices. As a result customer orders may ot give a accurate picture of demad. Demad ad Shipmets Past vedor shipmets records are also used as iputs i the process. But the shipmets may ot be fully cosumed; part of the ivetory might remai usold or be marked dow, ad therefore ot preset a accurate estimate of demad at the origial price. The Forecastig Process A good demad forecastig process follows a series of steps to achieve beefits at each stage of the supply chai, both o the demad ad the fulfillmet sides. With log time spas ivolved i hadlig global procuremet, a well-defied process becomes all the more critical.

2 Figure 1 explais the demad cycle with the results of oe stage flowig to the ext i a cyclical maer. The diagram also shows the depedecies of oe stage o the previous oe. Accurate Order Schedulers High i-stocks/ Optimized Ivetory Plaed Demad Low Safety Stock Loyal Customers Optimized Repleishmet Low Ivetory Optimized Warehousig Costs Optimized Shippig Costs Good Forecasts Lead to Multiple Busiess Beefits Figure 1: The Demad Cycle Uderstadig the Process of Demad Forecastig Demad forecastig ivolves multiple processes ad tools, as show i Figure 2. Merchadise Plaig Fiacial ad Assortmet Classify Demad Type from Previous Data Determie Resposiveess of Model Forecast Usig Statistical Techiques Maually Hadle Exceptios Complete Promotioal Effects o Forecasts Share Forecasts with Orgaizatio for Usage Figure 2: The Demad Forecastig Process I retail orgaizatios the demad forecastig process starts after the merchadisig orgaizatio makes decisios o what to merchadize ad the promotio pla. Oce the demad forecast is complete, the results are shared with ivetory maagemet ad supply chai aalysts i order to place orders from vedors. 2

3 Demad plaig of merchadise is usually doe for: Basic merchadise: This is the stadard basic assortmet of merchadise that is preset i stores all year roud. Historical sales data is usually available at the SKU level for this merchadise ad it is easier to idetify demad patters. Seasoal merchadise: Seasoal merchadise is preset i the stores for a specific time period ad chages every year. This makes forecastig at the SKU level very difficult. Uderstadig the Statistics of Demad Forecastig Key Aspects of Demad Forecastig Forecastig customer demad typically ivolves aalyzig past data ad usig statistical modelig to predict the future. Parameters typically used to arrive at fial forecasts iclude: Level: The base level of demad or sales is estimated after de-seasoalizig ad removig the effects of radom factors from sales. Tred: This is the chage i the sales level from the curret moth to the ext period. Seasoality: The seasoal idex is the aticipated icrease i sales due to chage of seaso. Causal Variables: Causal variables are exteral factors such as sales promotio, local weather chages, ad pricig volatility, oes that ifluece the forecast. Statistics of Forecastig Sice the data take over time is likely to show various radom variatios, smoothig techiques (such as Holt Witer's method) are used to reduce the effects of variatios. These techiques, whe used correctly, clearly reveal treds i demad. Other algorithms ca also be used, depedig o the required resposiveess to shifts i statistical patters. Compariso of Multiple Algorithms Table 1 shows these methods arraged i their order of resposiveess to chages i demad i the marketplace. Crosto is the least resposive ad Lewadowski the most. Algorithm Recommeded Use Crosto Method (Least Resposive) For forecast of products with itermittet demad Movig Average For short-term forecasts ragig from oe to four weeks Fourier Series For busiesses that are costat or chage at a costat rate Multiple Liear Regressio For costat busiesses ad some causal variables Holt Witers Whe seasoal ad tred compoets chage over time Lewadowski Method (Most Resposive) For early warig of treds, sice this method respods very fast to chages i the marketplace. Table 1: Algorithms for Demad Forecastig The resposiveess of a method gauges how fast it will react ad adapt to chages i the forecast based o iputs from the sales data. Highly resposive methods will chage fast ad re-forecast while less resposive algorithms will react slowly to market chages. I situatios where expesive software solutios are difficult to implemet, simple tools ca provide large beefits to small busiesses by reducig o-productive ivetory ad icreasig stocks i SKUs ad stores where sales are higher. I may orgaizatios, demad plaig is doe usig desktop spreadsheet applicatios. The plaers use past sales data ad simple averagig techiques to derive forecasts. Such processes are easy ad iexpesive to implemet, ad ca provide accurate results for busiesses where demad is stable ad sales do ot fluctuate heavily. More sophisticated software tools will also provide great results ad offer scalability as the orgaizatio expads. For busiesses where demad varies greatly ad the cost of goods is high, it makes good busiess sese to ivest i sophisticated forecastig software to optimize ivetory ad achieve higher margis. The Lea Six Sigma techiques described i the followig sectio ca be used to improve forecastig processes i all cases. 3

4 Lea Six Sigma for Process Improvemet Lea Six Sigma techiques are recommeded for implemetig process improvemets i demad forecastig without the cost of acquirig expesive software. These techiques help improve the demad forecastig process through five phases: Defie: Develop the problem statemet by usig Affiity Diagrams after collectig the voice of the customer. Idetify issues i the forecastig process, as well as iputs, by usig Failure Mode ad Effects aalysis. Icrease the value-addig steps by usig Value Stream Aalysis. This helps by itroducig critical value-addig decisio-makig steps early i the process to reduce rework further dowstream. Measure: Coduct Measuremet System Aalysis to idetify the accuracy of the measuremets coducted ad the validity of the iputs. This step helps validate forecast accuracy. Use statistical process cotrols ad tred charts to moitor the performace of the curret process. This will help track the rage withi which the curret system performs. Aalyze: Idetify process bottleecks ad iefficiecies. These ca be re-desiged to provide better iputs to the forecastig process. Uearth opportuities to streamlie approvals ad decisio-makig hierarchies. Desig ad coduct pilot tests o sample stores ad merchadise to test out solutios ad improvemets i the forecastig process. Improve: Validate all the iput data sources ad their completeess to esure correct output. Idetify correct baselies o which forecastig logic ca be based. Desig simple spreadsheet methodologies ad guidelies to achieve highly accurate forecasts without large ivestmets i software. Desig a pull-based system for efficiet process executio i merchadisig. This will eable perfect desig o perpetual ivetory. Cotrol: Implemet the solutio ad track performace. Perform statistical process cotrol ad measure forecast accuracy, forecast bias, ad forecast stability to esure that the process is performig as plaed. Moitorig Improvemets to Forecastig All improvemets to demad forecastig processes should be moitored closely by busiess uits. The key parameters that should be tracked iclude: Forecast Accuracy This key performace idicator (KPI) compares forecast ad actual sales for the statistical, promotioal, ad marketig compoets of demad forecastig. Forecast Bias Forecast bias qualifies forecast accuracy. A deviatio of the forecast from actual sales i a cosistet directio (over or uder forecast) is iheretly more 'correctable' tha a deviatio that is evely distributed above ad below sales. This measure is also called the 'trackig sigal'. Forecast Stability Forecast stability is the icrease i forecast accuracy as the actual period comes closer ad re-forecastig is doe multiple times. Ay deviatio from this gradual icrease of forecast accuracy reduces forecast stability. This idicates that some of the assumptios for forecastig eed to be chaged. 4

5 Beefits of Demad Forecastig The mai beefits of a improved process for effective demad forecastig are: Lower ivetory ad shippig costs: With the ability to procure ad distribute better, compaies ca maitai a lower ivetory of goods to achieve target sales. Orders to vedors ca be placed o time, avoidig higher ivetory holdig costs etailed by early orders ad lost sales due to late orders. Shippig costs are also lower, as a accurate umber of items will be bought ad shipped to distributio ceters. Uplaed shipmets across the various distributio ceters ad to stores will be reduced, leadig to lower freight charges. Lower markdow ad store labor costs: Fewer products will be marked dow, leadig to lower markdow costs ad less effort eeded to hadle low margi clearace products. Customer satisfactio: Apart from eablig retailers to better maage costs ad reduce uproductive ivetory, effective ivetory maagemet icreases customer satisfactio by providig the right products at the right time ad i the right quatities. Higher employee morale: Aother key beefit is a satisfied ad motivated store labor workforce because stores have to hadle less uproductive ivetory ad markdows that reduce store performace. This also reduces uecessary labor expediture to hadle uproductive ivetory. Strog partership with vedors: Better demad forecastig esures orders are placed early with vedors, allowig them to procure raw materials ad pla labor effectively. This reduces maufacturig costs, ad the the vedors may pass these savigs o to the retailer. As a result, the retailer achieves a higher profit margi while developig stroger parterships with vedors. All of this adds up to large savigs ad other beefits for retail compaies. Case i Poit A specialty retailer improved ivetory maagemet processes usig the techiques metioed i this paper. Busiess Challeges: The retailer eeded to chage these aspects of its ivetory forecastig process: Over-forecastig ad sales by approximately 17 percet o a average Usig stadard uplift for all divisios ad product categories Re-forecastig 7-21 times for each advertisemet Havig the ope-to-buy budget tied up i promotioal ivetory purchased due to over- forecast sales The Goals of the Project: The retailer aimed to: Improve the forecastig process ad clearly defie iputs Icrease forecast accuracy by 10 percet ad reduce multiple re-forecastig to a few limited milestoes Reduce ivetory ad store hadlig effort as well as re-work ad wasted effort The Solutio: The ew process eabled the retailer to reduce the umber of reforecastig milestoes ad also helped determie key iputs with plaers ad merchats early i the cycle. The ew structure elimiated multiple approval loops ad icorporated a sigle review with all the relevat stakeholders to esure upfrot team aligmet. Chage maagemet ad traiig was coducted to esure that all employees followed the stadardized process. Beefits: As a result of this iitiative, forecast variatio reduced from -24 percet to +4.4 percet over the duratio of the project, as show i Figure 4. 5

6 Reductio i % Variace to Uit for Promotio Sales Forecast 10% 0% -10% -20% -30% -40% -50% 13 t 12 t 11 t 10 t 9 t 8 t t7 6 t t5 t4 3 t t2 t 1-60% Figure 4: Tred of Reducig Variatio of Forecast for Promotioal Sales As a result of these chages, forecast accuracy icreased by 90 percet. The resultig savigs i ivetory costs were ow available for ivestmet i other product categories. Less effort was required to receive ad merchadize excess ivetory. Store persoel could focus more o customer service, ad this led to icreased sales. Coclusio I a competitive ad fast movig retail eviromet ivetory plaig ad forecastig eable lower costs ad greater efficiecies of operatio. However, ivetory maagemet is a highly ivolved process that icludes workig with vedors, busiess plaers, ad marketig teams to develop a sigle view of demad ad busiess plaig. This the eeds to be executed flawlessly. Demad maagemet is ow sophisticated eough to allow forecastig dow to the day ad the miute, as opposed to weeks ad moths. This also calls for mature eterprise processes ad eterprise architecture to esure that ivetory maagemet helps retailers exceed customer expectatios. About the Author Sarit Dey Sarit Dey is a maagig cosultat ad busiess architect i the Global Cosultig Practice at Tata Cosultacy Services (TCS). Sarit has more tha 14 years' experiece advisig seior maagemet at global compaies i improvig their busiess processes i the areas of retail supply chai, ivetory maagemet, retail merchadisig, plaogram implemetatio, store operatios, ad maufacturig. Sarit has a Master's degree i Maagemet from the Idia Istitute of Maagemet Calcutta ad a Bachelor's degree i Egieerig from Begal Egieerig ad Sciece Uiversity. 6

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