Trusted Experts in Business Analytics BUSINESS ANALYTICS FOR DEMAND PLANNING: HOW TO FORECAST STORE/SKU DEMAND

Transcription

1 Trusted Experts in Business Analytics BUSINESS ANALYTICS FOR DEMAND PLANNING: HOW TO FORECAST STORE/SKU DEMAND September 2014

2 HOW DOES TM1 AND SPSS MODELER INTEGRATION WORK? In a QueBIT whitepaper titled Business Analytics for Demand Planning: Are You Ready For 2020? we discuss the importance of business analytics for demand planning. Specifically we detail how the integration of IBM Cognos TM1 and IBM SPSS Modeler allows organizations to converge financial modeling with predictive analytics. This follow-on paper dives deeper into how the integration works and provides examples of how TM1/SPSS is applied for demand planning in a retail environment. From a data flow perspective, a demand planner with TM1, could start from an Excel input schedule or TM1, TM1 Web or Cognos Insight. In Figure 1 we are using Excel where we can see sales and its seasonality. When we change the marketing spend for example, the new marketing data is passed seamlessly to SPSS Modeler which immediately computes the updated demand. The integration between SPSS Modeler and TM1 exploits turbo integrator which ensures that only the data that has changed is sent as SPSS already has historical demand and other variables. SPSS Modeler picks up the data, automatically selects the right statistical model, performs calculations and

3 puts the forecasted demand back in its database. Then the TM1 process pulls the forecast data back into the TM1 model which recalculates its results based on the new demand. In a matter of seconds an updated demand curve in the TM1 model shows any predicted changes in demand. An adjusted P&L is now available based on the change in the demand driver. TM1/SPSS INTEGRATES WITH LEGACY SOFTWARE A solution like TM1/SPSS is specifically built for both financial planning and forecasting, and also built to easily integrate into existing order management and supply chain planning systems. This is why companies with legacy order management/supply chain management solutions (e.g. JD Edwards), are choosing to incorporate a business analytics solution like TM1/SPSS into the process. In these instances the system is entirely Specifically built for financial integrated. SPSS Modeler extracts data from the order management/supply management systems, works on planning and forecasting, IBM the data to create a forecast and then pushes back the Cognos TM1 and IBM SPSS approved forecast into the order management/supply Modeler are designed to easily chain system every night. integrate into existing order management and supply chain TM1 AND SPSS FOR RETAIL DEMAND PLANNING planning systems. As an example, let s see how an electronics retailer (tablets, laptops and desktop computers), with 300 store locations and 5 distribution centers leverages TM1/SPSS. Due to their high carrying costs, rapid product obsolescence, and amount of cash tied up in the supply chain, they must maximize their ability to put product in the right place at the right time. Consequently, inventory accuracy and financial planning are paramount to optimize customer satisfaction and cash flow. Suppose we want to evaluate the impact of introducing a new laptop at half the price of similar products. Business analytics allows us to weigh the impact of individual features with other pieces of information and tie it all back to demand forecasts to determine which features will drive demand the most. By connecting price, screen size, resolution, processor speed, operating system, memory and storage to demand we can determine sales will change if we double the processor speed, or how demand will be impacted by doubling the processor speed and reducing the price by 10%. In addition, we can also incorporate store demographics, customers they want to serve in each location, and impact of marketing to create a more holistic forecast model. TM1 can also incorporate factors such

4 as number of employees and cost of living used into the predictive model to calculate some of the store expenses. Different stores in the same region may have completely different demographics and business analytics allows decision makers to get down to the store level to conduct what-if evaluations for different scenarios or generate store level forecasts to build product assortment and inventory levels for each store, and roll up the data to all the distribution centers. It is always best to start the data input at the most granular level possible where there is the tightest connection between demand drivers and actual demand. Generating a forecast at a higher level is feasible, but potentially less accurate than taking historical data at the store/sku level. The data is fed into the real time planning within TM1 which drives the P&L, the budgets and the revenue forecasts. Now we have the flexibility to roll up the forecast data to any level, district, distribution center, region, etc. TM1 allows decision makers to play what-if games in real time to evaluate the impact of infinite change variations, providing input for very intelligent planning. For instance, they can evaluate where they will get the best bang for their marketing spend. The model will change the marketing spend proportionately across the entire organization across every single store, or only at the distribution center or for a particular store, or even change spend for a particular product in a particular store. SPSS will do the calculation at the lowest level possible and show its impacts. HOW TO FORECAST DEMAND FOR A KNOWN STORE/SKU Let s look at how we project demand for a known store/sku. Based on TM1, we have a time series of weekly sales over the past, with whatever timeframe is available whether less than a year or multiple years. The data should include as many demand driver variables as possible for as long a period as is available. If we don t have an entire year s cycle, we can create a seasonality profile for that particular product and include that as a predictor into the model. The TM1/SPSS integration will allow us to quantify and predict the interplay between all the variables to determine impact on demand. The model looks at demand based on a number of inputs usually there could be between 40 or 50 predictors which might include:

5 Marketing spend Features: resolution, memory, operating system Price changes Product life cycle Weather and seasonality Geographic location: Cost of Living, educational level and income Store customer demographics: is this an area that traditionally buys a lot of this product, a lot of value shoppers? The TM1/SPSS integration will allow demand planners to quantify and predict the interplay between all the variables to determine impact on demand. The model gauges the importance of each element and links the features together in such a way that it will predict demand for any product in any store. These models form the basis for assortment planning which allows us to determine which products should be offered in which stores. Assortment rationalizations are driven by SPSS. As an output, the model gives the relative importance of each element based on how they drove historical demand. In our example, marketing spend drives demand. The next step is to calculate upper/lower confidence bounds for the forecast to have confidence in the forecast. For example, we can determine the best and worst case scenario based on a 95% confidence level. We are now ready to simulate changes in the demand drivers. Depending on the driver we adjust, modeler automatically chooses a particular model for a store/sku combination. Demand driver adjustments can come from TM1. For example, we can enter what marketing spend will be, run the new data through the model, and simulate the effect of adjusting marketing up or down. We can do the same with weather. What if we have an unseasonably cold spring in our southeast store what will that do to our overall demand? The chart below shows the model predicted. The blue line is actual demand, and the red line is what the model predicted. The right hand side is the future forecast for the next 52 weeks. The spike is slightly higher than the prior but has similar but not identical shapes as the model has adjusted demand. Though this example is a store/sku forecast, we can roll up the forecasts to any geography or supply chain level. These will be more accurate because they were generated from lower level forecasts. Looking at the 95% confidence intervals gives us a sense of where we expect the actuals

6 to be relative to the forecast. Then we can take confidence intervals into TM1 and indicate best and worst case scenario based on the 95% - TM1 handles multiple scenarios very nicely. Based on predicted demand, we are now able to review the impact of the changes on P&L, cash flow and balance sheet. Change any demand driver in TM1 and instantly see the potential impact on financial performance. For instance, we could use TM1 data spreading to change marketing spend and see the impact. Or more likely we could change marketing spend across all products in a select store to evaluate the results. Alternatively we can go into that same store/sku level and adjust the marketing spend there. Even though we may not track marketing spend at the SKU level, TM1 lets us change numbers at all levels of the hierarchy. Because Modeler computes its model at the lowest possible level, whatever we change in TM1, Modeler will generate new forecasts at the store/sku level and gets results back. Then once again, TM1 allows us to look at that forecasted demand and see the impact on P&L, balance sheet, cash flow and anything else. HOW TO FORECAST DEMAND FOR A NEW STORE/SKU Now let s evaluate how a retail store would generate a predictive model for new product introductions. It is a real challenge to introduce a new SKU because we have no historical data on which to determine what sales will be. The typical approach to this problem is to find a similar peer product and model the sales according to that product. But it is also possible that we ve never had this type of product in a similar store, or that there is no similar product in the product catalog.

7 TM1 and SPSS models forecast demand for a new store/sku in the same way that they create forecasts for existing products. With historical information already available in the database, we can run new data through the model to generate new predictions. The model will gauge the importance of each of the new products elements and link the features to existing products to predict demand for a new product in any store. For example, it will quantify and predict the interplay between the new product s features and the price. Just as with existing store/skus, we can then input different what-if scenarios into TM1 which will run through SPSS Modeler. If we introduce a new product in this store at $499, we can predict the number of units sold. If we increase the price of any existing product by $20, how will sales be impacted? If we change marketing spend, how will this affect first year sales for any new product introduction? How will all this change given seasonality? We can play with the combination of price and units options to determine the optimal price to maximize the revenue. Only a predictive model can help deduce these subtleties to then identify the price sweet spot. IMPRESSIVE AND MEASURABLE RESULTS QueBIT recently applied TM1 and SPSS Modeler at a F500 retailer with a very large SKU base. The objective was to align their supply chain with actual demand by using an accurate demand forecast. As a result, the retailer: Reduced overall inventory by 7% Increased inventory turn by 4%, a very large number considering their massive inventory Increased sales by 13% These impressive results showcase the benefits of business analytics because retailers are seldom able to decrease inventory and also increase sales and inventory turn. The TM1/SPSS integration also allows the retailer to model what-if scenarios in real time, adjusting parameters to instantly see the impact of changes on inventory turn, inventory costs and sales.

8 CONCLUSIONS Companies that deploy the integrated TM1 and SPSS Modeler benefit from being able to: Calculate the ideal price points, discounts, and promotions by product, store or channel to maximize profits. Plan for inventory demands across locations and channels to have the right product in the right location at the right time. Lower inventory levels while reducing out of stocks, improving sales and reducing transportation costs. Determine the best responses to shifting customer demands. Improve decision-making through what-if scenario analysis. Evaluate the impact of marketing campaigns on sales. The combination of these capabilities has a direct impact on the bottom line due to optimal use of working capital, higher inventory turn, less product obsolescence, lower inventory carrying costs, improved financial planning and improved cash flow. This increase in productivity and competitiveness means higher profits. ABOUT QUEBIT Trusted experts in business analytics strategy and implementation, QueBIT is dedicated to helping organizations improve their agility to make intelligent decisions that create value. An IBM Premier Partner, QueBIT has conducted hundreds of successful implementations of IBM Cognos TM1, IBM Cognos BI and IBM SPSS we are one of few partners that offer such a broad range of analytics solutions. Financial, sales, marketing and operations departments in over 350 organizations in all types of industries say QueBIT s singular approach to business analytics produces tangible results which is why we are repeat recipients of IBM s Business Analytics Partner Excellence Awards. Contact our team to learn more about how QueBIT s integrated Demand Planning solution maximizes revenues through forecast accuracy while controlling costs at info@quebit.com.