Sales Forecast and Business Management Strategy - ADDM (Advanced Data Decomposition Model)

Transcription

1 Delivering intelligent business solutions to World Class Retailers Soft Solutions Ibs AMS Sales Forecast and Business Management Strategy - ADDM (Advanced Data Decomposition Model) Embedded Cleansing engine: Theory and concepts 1/21

2 Table of Contents Table of Contents 2 1. Executive summary Main benefits Objectives Contents 3 2. Introduction Ibs Analytics embedded within Ibs - Suite What is a forecast? Overview of the general modeling process 7 3. Sale forecast Extract main movement and noise data Seasonal peaks modeling Trend modeling Trend modeling Regular sales forecast result Business Management Strategy Introduction to Elasticity Regular Elasticity Built Curve Promotion elasticity Flyer and commercials elasticity Halo and cannibalization Conclusion 19 2/21

3 1. Executive summary 1.1. Main benefits Ibs Analytics is based on a sales forecast engine developed by Soft Solutions to be a strategic tool for the retailer to anticipate consumers needs. It implements concepts of various scientific fields, like data mining, statistics, signal processing and mathematics, fitted to the business process of retailers. Moreover, Ibs Analytics is an embedded solution with real-time analysis, which provides accurate results with total transparency in simulations and set-ups. Indeed, these results can be obtained, through business-oriented reports, at various consolidation levels (item, segment, category, region, etc ) in easily understandable metrics. Based on the generated models (trend, seasonality, price elasticity ), ibs Analytics performs an average accuracy of ~75% at the item level and over 90% at the category level, for all kind of items including items with actually low turnover. Furthermore, Ibs Analytics has an impact on every level of the retail working chain: It strengthens the operational strategy and bring decision-making tools to fulfil objectives It avoids being over stocked or out of stock It provides an homogenous system to both analyze and predict needs It gives consistency to the operational strategy with pricing, assortment and marketing using the same forecast engine It induces a quick return on investment 1.2. Objectives This report aims to: Present detailed process of regular sales forecast ADDM (Advanced Data Decomposition Models) Provide comprehensible figures and tables to illustrate the goal of each step of the process; 1.3. Contents The following pages are structured into four sections: Overview of the sales forecast concepts Step-by-step baseline forecast process of ADDM ibs Analytics Advanced marketing policy impacts estimation of ADDM ibs Analytics Conclusions and References 3/21

4 2. Introduction 2.1. Ibs Analytics embedded within Ibs - Suite Achieving simulations in the retail business is one of the most strategic parts when defining and applying both marketing and operational policies. Indeed the ability to measure impacts of different decision is the key-part in order to take the right decision. Forecasting the sales has a central position as it interacts with every phase of the retailer framework (Fig 1). Therefore Ibs Analytics is an asset, which brings vulgarized science to business. Indeed, a coherent forecasting strategy among all departments and retail activities will induce a reduction in operational delay and cost due to impact of better forecast and a better visibility among various departments: Fig. 1. Sales forecast and the retailer workflow 4/21

5 Halo effect : Halo is a cross-category effect, which happens when the sales of item A is positively correlated to the sales of item B from another category. (i.e. Buying more beers and more chip) Ibs CATEGORY and Ibs REPORTING: Objectives Management set strategies, which impact the all business process. Ibs Analytics takes into account intelligent distribution of objectives in forecasting and provides a real-time follow-up on achievements. Ibs ASSORTMENT: Ibs Analytics is also useful in Assortment with assortment size optimization and halo & cannibalization measurements. Moreover, it computes benefits simulations in various metrics (units, sales, margins). Ibs CENTRAL and Ibs STORE: Operational Processes Optimization implies an ordering optimization in quantities, which depends of sales forecast, costs and rebates Cannibalization effect : Cannibalization is an intra- effect explained as an category increase in sales of item A induces a decrease in sales of item B (i.e. Buying more soda but less water) Ibs SPACE PLANNING: Products Display in the store plan or the store planogram with shelves constraints management is affected by the sales forecasts. Ibs PRICING and Ibs - PROMOTIONS: Sales & Marketing Policy Optimization in order to fulfil objectives is strongly connected to Ibs Analytics, which respects constraints & controls management and provides business indicators estimation. 5/21

6 Marketing : 2.2. What is a forecast? Marketing is defined as "the process of management responsible for identifying, anticipating and satisfying customer requirements profitably." by the Chartered Institute of Marketing. In retail, marketing includes advertising and various promotion strategies like: Price reduction offer Buy one Get one offer Bundle offer Extra bucks offer A sale is the result of the consumer perception of several Features (Fig 2) such as: The banner strategy, which has a huge impact on the consumer behavior by defining prices, promotion policy and marketing. The local microclimate, it is induced by the concentration of competitors and the type of the area (rural or downtown). The in-store availability and accessibility are also key features in a sale, with the assortment strategy and the planogram disposal. Some external factors are to be taken into account like unemployment, growth rate, inflation rate, which have an effect on the consumer purchase s budget. Moreover, household happiness can increase sales, as people are willing to buy. Although some of theses factors are hardly measurable, some others are well known and even decided by the retailer. Moreover, a sale can also be defined by some item s specifics: general trend, seasonal cycle, seasonal peaks, price changes, etc. Fig. 2. How to explain a sale? Sales quantity is an easily and available data from which information can be extracted to explain the sales. This approach, called modeling, is the one used in Ibs - Analytics for forecasting both regular sales and sales under special strategic rules. The goal of modeling in retailing is to be able to explain the quantity of item sold by the most significant variables aforementioned in order to build a model able to forecast the sales in various scenarios in the future. Ibs - Analytics uses the history of sales data to extract significant information such as the seasonal cycle, the seasonal peaks and the trend of the sales in order to provide a baseline of predicted sales. 6/21

7 2.3. Overview of the general modeling process Ibs - Analytics modeling process is divided in three (Fig 4): Data cleaning which aims to increase the data quality by removing erroneous events (available in a dedicated white paper. Visit our website Sales forecast engine, which will predict the regular sales (section 3) Business Marketing Management, which applies the effect of various marketing strategies on the sales (section 4) Fig. 4. General overview of Ibs - Analytics process 7/21

8 Bernstein polynomials: 3. Sale forecast Bernstein polynomials method implies to fit a general and complex polynomials function to the main movement of sales. Thus we obtain coefficients for each polynomial. Indeed, for each week, we are able to compute the difference between the Bergstein polynomials fitted function, called main movement, and the real observed data to obtain the residual data. From the history of sales cleaned during the previous step, Ibs - Analytics build a forecasting engine of regular sales. By applying data mining methods on these data, Ibs - Analytics extracts for each item several of its specifics components. Thus under the hypothesis that this item sales context do not change, the forecasting engine is able to predict future sales. The forecast engine is easily updated when fresh history is added, which allows a realtime follow-up. Furthermore, each part of the specific components modeled in the forecast engine can be manually corrected if need be. The whole workflow is described step by step in the following sections Extract main movement and noise data During this step, Ibs - Analytics applies a statistical method, developed by Bernstein (Bernstein ), to extract the main movement from the noise data (Fig 10). From a mathematical point of view, the main movement corresponds to the understandable part of the data, which can be explained statistically. The noise data are the residual component (yet) unexplained by the method, it refers to the chaotic part of the sales. The noise data are computed as the differences between the main movement and the cleaned data. The main movement represents the long-term behavior of the sales whereas the noise data corresponds to the short-term behavior. Fig. 10. Noise and Main movement extraction 8/21

9 Dynamic Alignment: From data of outliers detection, Dynamic Alignment method defines a scoring scheme based on the frequency of outlier to model seasonal peaks amplification and presence. Indeed, a perfect 52 weeks frequency will induce a whole amplified seasonal peak whereas a 50 weeks seasonal peak will be less amplified Seasonal peaks modeling As defined previously, seasonal peaks are redundant outliers with a defined frequency. Ibs -Analytics selected seasonal peaks with a year frequency (52 weeks). Once detected, the Dynamic Alignment method (Smith & Waterman 1981) is implemented as follow: the ratio of the amplitude over the main movement is computed for each peak and will be apply to the forecasted quantity during the same week next year. Fig. 11. Seasonal peaks modeling 9/21

10 3.3. Trend modeling Trend is described as the long-term element of sales data, which give the general direction of sales over a year. Using the main movement of the last year, this process will estimate, for each item, the trend for the coming year based on a weighted regression method. The trend is updated each week when fresh data are inputted in the database. Macroeconomics indicators can be included in the process for a better fitter trend. This is the most influent component when defining the longest-term behavior and thus making en absolute necessity to accurately estimate the trend. Indeed, this is a baseline on which other results will be applied. Fig. 12. Trend modeling 10/21

11 Discrete Cosine Transform: For each of the two year of history, a complex cosine function is fitted to the main movement. Then the three more influent cosine coefficients over the two last years are selected and the mean are computed. The seasonal cycle function is thus obtained from this three cosine coefficients Seasonal cycle modeling Like the season in a year, each item has its own seasonal cycle, which refers to a global repeated behavior over the years. Applying a Discrete Cosine Transform method (Ahmed et al. 1974), which is a sum of cosine functions, on our learning set, we obtain a cycle-smoothed curve corresponding to the yearly variation week by week. This seasonal cycle is a middle-term behavior, which takes into account the main movement of the learning dataset. Fig. 13. Seasonal cycle modeling 11/21

12 Pattern detection: Residual data are truncated in small sequences of defined length. For each sequence, the value of next week(s) is extracted. Then when forecasting, weekly variation is defined as a weighted mean of next week values from similar sequences, which are a pattern Weekly variation modeling Weekly variations are the remaining variations that are not modeled by other parts but contained in the residual data. They embody the mood of the consumer, the weather, special events and anything, which is not explained previously (Fig 14). This step extract relevant pattern with variable scales using the non-parametric pattern detection (Carbon & Francq 1995) applied on the residual data. This weekly variation correction is then added to the forecast. This process is the hardest and the less accurate of the engine as it tries to model randomness for a certain part. Fig. 14. Weekly variation modelling 12/21

13 3.6. Regular sales forecast result As a result, the forecast engine for regular sales is a sum of all the above process: First of all, Ibs Analytics sets the calculated trend (Fig15.a) Then, the seasonal cycle is applied (Fig15.b) After that, the seasonal peaks detected are adjusted (Fig15.c): At least, the weekly variations are added (Fig15.d) and the forecast values are obtained. Fig. 15. Building sales forecast Ibs Analytics performs very accurate results, computed when facing predicted values to the actual sales over the same weeks. Indeed, studies over more than one hundred items of several retailers show high accuracy, very satisfying in production conditions for the retail business. Thus Ibs Analytics, with the regular sales forecasting engine, offers a reliable solution in predicting future sales This forecasting engine can also be tuned to adjust forecast when the sales are under specific business marketing strategy, as explained in the next part. 13/21

14 4. Business Management Strategy 4.1. Introduction to Elasticity In economics, elasticity is computed as the ratio of the percent change in one variable to the percent change in another variable. This value measures the responsiveness of a function to changes in parameters in a relative way. Moreover, Elasticity is a tool independent of units and thus simplifies data analysis. In case of the retail industries, we are looking at the Regular Elasticity, which is computed as variation of sales to variation of price. In this context, Regular Elasticity measures the sales responsiveness to price change. Elasticity varies among items because some of them are essential to the consumer; others have an affective value, etc. Thus item can be divided in two: "Elastic" item shows an elasticity magnitude greater than one. "Inelastic" describe item with a magnitude of less than one. An example of inelastic items is items considered as necessities, which are more insensitive to price changes because consumers would continue buying these products despite price increases. At the opposite, a price increase of a good considered less of a necessity will decrease sales, making this item elastic Regular Elasticity In a stable state, Elasticity is defined as the ratio between variation of quantity sold over variation of price. This measures the response in sold quantity of an item to a price change. The simplest way is to admit a linear relationship between price variation and quantity variation but the change in quantity sold is not proportional to the price change. Elasticity has to be computed at an item or a segment level and a common elasticity function is shown in Fig 16 but varies from an item to another. Fig. 16. Classic Elasticity function Ibs Analytics implements this concept and adjusts the regular forecast engine when the sales are under specific management rules like a price change or a promotion allocation. 14/21

15 4.3. Built Curve This section explains how Ibs Analytics adjusts the regular sales forecast when a change in price at a specific forecasted week is defined by the retailer. Note that Ibs Analytics works under the hypothesis that an effect like a price change has an influence over a month. First of all, at an item level, sales data are consolidated to the segment level for the same reason explained in 3.1. For this part, Ibs Analytics selects only weeks where there were a price change in the sales history as learning set over two years of history. Then the elasticity function is computed for the segment and then tuned to the item level. Finally, Ibs Analytics constructs a Built curve, which reflects the diffusion of the price change s effect over the following three weeks. For example, the well-known Pantry Loading effect is happening when there is a price decrease and sales increase in the first week but then sales are decreasing as the consumer have stocked items during the previous week (Fig 17). Fig. 17. Built curve and Elasticity diffusion In the context of a price change, Ibs Analytics starts with the regular sales forecast as a baseline. Then, for a defined value of price change at a specific week, the associated elasticity coefficients are applied to this week and the three following (Fig 18). Using Ibs Analytics forecast engine under Price change strategy, one can now determine when the change should happen and by how much the price should be altered to achieve specific objectives. 15/21

16 Fig. 18. Sales forecast with Price change strategy 4.4. Promotion elasticity In this part, we are modeling the promotion elasticity of items, which is defined as the responsiveness in sale of an item when it is being promoted. It measures the effect of promotion during its effective week. There is many ways to promote an item (mark down, buy one and get one, etc ); every one of them can be transposed into a specific price decrease under definite conditions. Therefore, Ibs Analytics applies the previous concepts to promotion allocation strategy with the assumption that the promotion effect will only last one week. In this case, Ibs Analytics computes the elasticity function at the segment level, and then adjusts it at an item level. Finally, the elasticity coefficient corresponding to the chosen promotion allocation is then applied to the defined week (Fig 19). 16/21

17 Fig. 19. Sales forecast with promotion allocation strategy 4.5. Flyer and commercials elasticity Dealing with advertising, Ibs Analytics also measures the impact of communication on sales. In flyer media, a coefficient is defined for each of the following type of pages: odd, event, front page and back page. Thus to extract information from these data, Ibs Analytics attributes an adjustment coefficient to apply to the sales forecasts (Fig20) based on weeks with promotion in two years history. Moreover, even the place on each page or the size of the pictures can have an effect on sales. Further studies on the subject will improve Ibs Analytics model and thus its accuracy rating. Fig. 20. Flyer coefficients 17/21

18 4.6. Halo and cannibalization Correlations among item s sales exist and these effects are called (Fig21): Halo cross-category when the sales of item A is positively correlated to the sales of item B from another category. (i.e. Buying more beers and more chips) Cannibalization intra-category when the increase in sales of item A induces a decrease in sales of item B (i.e. Buying more soda but less water) Fig. 21. Halo and cannibalization These linkages can be extracted from point of sales data or loyalty programs for example. Ibs Analytics implements an association rules method called A priori (Agrawal et al. 1994) to quantify correlation between items. Measuring these effects will improve our forecast engine as it will allows more precise forecasts on levels higher than the item level. Indeed, as for now, Ibs Analytics is item centered and a promotion on an item won t have any effect on the forecast of another item. 18/21

19 5. Conclusion To summarize, Soft Solutions has developed a dedicated module Ibs Analytics for data mining to offer a decision-making tool by anticipating consumer s need. It implements concepts and robust methods from scientific researches and tuned them for the retail business. As shown, Ibs Analytics gathers and cleans data from sale data history to provide a non-erroneous learning set. Extracting information from these cleaned data, Ibs Analytics builds an accurate model able to precisely forecast regular sales with a reasonable error rate in real time simulation. Under defined business marketing strategies, Ibs Analytics can also adjust the forecast engine by data mining more information from data. Ibs Analytics is an embedded and accurate solution with real-time analysis and total transparency in simulations and setups. Results are obtained, through friendly-users reports, at various consolidated levels in easily understandable metrics. Theirs accuracies vary from 75% to 99%, even with items showing slow dynamics. To conclude, Ibs Analytics is an asset to build solid business management strategies over the time and to achieve specific objectives because it is associated to every level of the retail working chain. Indeed, it provides help during operational strategy decision and gives follow-ups on objectives. Plus, by anticipating the consumers need, overstocked or out-of-stock situations can be avoided. Moreover, the needs are analyzed and predicted with the same homogenous system. Finally, using the same sale forecast engine for pricing, assortment and marketing, the operational strategies are strengthened and more consistent. Thus due to all the reasons above, Ibs Analytics induces a quick return on investment. 19/21

20 References: N. Ahmed, T. Natarajan, and K. R. Rao, 1974, "Discrete Cosine Transform" J. Bernstein, 1971, "Modules over a ring of differential operators. Study of the fundamental solutions of equations with constant coefficients" T. F. Smith & M. S. Waterman, 1981, Identification of common molecular subsequences M. Carbon and C. Francq, 1995, Nonparametric estimation of probability density and regression function - Nonparametric forecasting R. Agrawal and R. Srikant, 1994, Fast Algorithms for Mining Association Rules T. Brya, 2000, MAPE-R: An Empirical Comparison with MAPE R. Hyndman, 2006, Another Look at Forecast-Accuracy Metrics For Intermittent Demand E. Del Castillo, 2007, Process optimization: a statistical approach P. Goodwill, 2006, The Process of Using a forecasting Support System M. Leonard, 1999, Promotional Analysis and Forecasting for Demand Planning. A practical time-series approach J.W. Taylor, 2007, Forecasting daily supermarket sales using exponentially weighted quantile regression (2007) S.H. Chang, 1971, Estimation of Forecast Errors for Seasonal-Style-Goods Sales V. R. Nijs, M. G. Dekimpe, J.-B.E. M. Steenkamp, D. M. Hanssens, 2001, Category-Demand Effects of Price Promotions M. Kumar, N.R. Patel, J. Woo, 2002, Clustering Seasonality Patterns in the presence of error R. Paap, 1999, PromoCast. A new forecasting method for promotion planning 20/21

21 For more information: Website: o Soft Solutions o Soft Solutions Analytics 21/21