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 Forecasting Forecasting is the process of making statements about events whose actual outcomes (typically) have not yet been observed
Challenges Retailers face in Forecasting Scale of problems (Large number of items &stores to forecast) Intermittent demand (slow and erratic sales for many items at the store level) Assortment instability (frequent new-item introductions and seasonal assortment changes) Pricing and promotional activity
Forecasting techniques which are used to face the challenges Large-scale automated forecasting The objective in any business is to have the right product in the right place at the right time – and in the appropriate quantity   A large retailer may have tens of millions of store/item combinations   This automation minimizes staffing requirements, while permitting forecasters to focus on the "high value" forecasts that have the greatest impact on customer satisfaction and financial performance
Forecasting and revenue optimisation Revenue optimization systems help the retail planner make better decisions on regular product pricing, promotional activity and markdown pricing    Such systems are designed to optimize an objective (e.g., maximize revenue, maximize margin or minimize inventory)
Forecasting and replenishment A good replenishment policy takes into account the uncertainties of supply and demand, and makes store-level inventory less dependent on a highly accurate forecast
Forecasting which are done mainly in Retail Industry Sales Forecasting Sales forecasting is the process of organizing and analyzing information in a way that makes it possible to estimate what your sales will be. Factors that affect sales External Internal
External Factors Seasons Holidays Special events Competition, direct Competition, indirect External labour events Productivity changes Family formations Births and deaths Fashions or styles Population changes Consumer earnings Political events Weather
Internal Factors Product changes, style, quality Service changes, type, quality Shortages, production capability Promotional effort changes Sales Motivation plans Price changes Shortages, inventory Shortages/working capital Distribution methods used Credit policy changes Labour Problems
Demand Forecasting Demand forecasting is the activity of estimating the quantity of a product or service that consumers will purchase  Demand forecasting involves techniques including both informal methods, such as educated guesses, and quantitative methods, such as the use of historical sales data or current data from test markets  Demand forecasting may be used in making pricing decisions, in assessing future capacity requirements, or in making decisions on whether to enter a new market
 Focus Forecasting It uses several logical and easy to understand rules to project past data into the future Computer simulation program is used to measure the performance of the rules when compared with the actual demand
Few Software used in Retail Forecasting SAS Demand Forecasting for Retail It uses SAS software’s unique high-performance forecasting engine to automatically diagnose, model, execute and reconcile forecasts across multiple merchandise levels and locations It allows creation of forecast projects and manages forecasts with a built-in repository and versioning system SAS Demand Forecasting for Retail produces results at any level of the product and location hierarchies, down to the SKU/store level
Oracle Retail demand forecasting Designed to handle the challenges of real-world retail forecasting, including scaling to manage the large volumes of data involved The solution handles forecasting at the lowest level of detail (item-, by store-, by day-level), forecasting for new products, and forecasting for products with sparse sales, all at the lowest level with very little human intervention.  Forecasting provides a single forecast to drive the entire retail enterprise This integration brings together forecasts produced for replenishment, merchandise planning, financial planning, promotions, and pricing
SAP for Retail It uses sophisticated optimization and automation capabilities to help you strike the ideal inventory strategy – minimizing inventory ownership and maximizing customer service SAP Forecasting and Replenishment can help you improve inventory turnover and prevent out-of-stock conditions that result in lost sales and customer alienation
Conclusion  Forecasts will never be perfect, and sometimes they may not even be very good The goal of forecasting in retail should not be a foolish pursuit of perfection, but to generate forecasts that are as accurate and unbiased as we can reasonably expect them to be, and to do this as efficiently as possible Large-scale automation helps solve the problem of generating forecasts at granular levels of detail (such as store, item or week) However, there must still be a realistic assessment of the likely accuracy of forecasts at that level, and consideration of other strategies that can be used in conjunction with forecasting to best solve the business problem
Sandeep Dhankhar      :2010200 Sandeep Kumar Singh :2010201 Sankarshan Joshi         : 2010202 Saurabh Kumar            :2010203 Sayantan Banerjee       :2010204

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Retail Forecasting

  • 1.  Forecasting Forecasting is the process of making statements about events whose actual outcomes (typically) have not yet been observed
  • 2. Challenges Retailers face in Forecasting Scale of problems (Large number of items &stores to forecast) Intermittent demand (slow and erratic sales for many items at the store level) Assortment instability (frequent new-item introductions and seasonal assortment changes) Pricing and promotional activity
  • 3. Forecasting techniques which are used to face the challenges Large-scale automated forecasting The objective in any business is to have the right product in the right place at the right time – and in the appropriate quantity A large retailer may have tens of millions of store/item combinations This automation minimizes staffing requirements, while permitting forecasters to focus on the "high value" forecasts that have the greatest impact on customer satisfaction and financial performance
  • 4. Forecasting and revenue optimisation Revenue optimization systems help the retail planner make better decisions on regular product pricing, promotional activity and markdown pricing Such systems are designed to optimize an objective (e.g., maximize revenue, maximize margin or minimize inventory)
  • 5. Forecasting and replenishment A good replenishment policy takes into account the uncertainties of supply and demand, and makes store-level inventory less dependent on a highly accurate forecast
  • 6. Forecasting which are done mainly in Retail Industry Sales Forecasting Sales forecasting is the process of organizing and analyzing information in a way that makes it possible to estimate what your sales will be. Factors that affect sales External Internal
  • 7. External Factors Seasons Holidays Special events Competition, direct Competition, indirect External labour events Productivity changes Family formations Births and deaths Fashions or styles Population changes Consumer earnings Political events Weather
  • 8. Internal Factors Product changes, style, quality Service changes, type, quality Shortages, production capability Promotional effort changes Sales Motivation plans Price changes Shortages, inventory Shortages/working capital Distribution methods used Credit policy changes Labour Problems
  • 9. Demand Forecasting Demand forecasting is the activity of estimating the quantity of a product or service that consumers will purchase Demand forecasting involves techniques including both informal methods, such as educated guesses, and quantitative methods, such as the use of historical sales data or current data from test markets Demand forecasting may be used in making pricing decisions, in assessing future capacity requirements, or in making decisions on whether to enter a new market
  • 10.  Focus Forecasting It uses several logical and easy to understand rules to project past data into the future Computer simulation program is used to measure the performance of the rules when compared with the actual demand
  • 11. Few Software used in Retail Forecasting SAS Demand Forecasting for Retail It uses SAS software’s unique high-performance forecasting engine to automatically diagnose, model, execute and reconcile forecasts across multiple merchandise levels and locations It allows creation of forecast projects and manages forecasts with a built-in repository and versioning system SAS Demand Forecasting for Retail produces results at any level of the product and location hierarchies, down to the SKU/store level
  • 12. Oracle Retail demand forecasting Designed to handle the challenges of real-world retail forecasting, including scaling to manage the large volumes of data involved The solution handles forecasting at the lowest level of detail (item-, by store-, by day-level), forecasting for new products, and forecasting for products with sparse sales, all at the lowest level with very little human intervention. Forecasting provides a single forecast to drive the entire retail enterprise This integration brings together forecasts produced for replenishment, merchandise planning, financial planning, promotions, and pricing
  • 13. SAP for Retail It uses sophisticated optimization and automation capabilities to help you strike the ideal inventory strategy – minimizing inventory ownership and maximizing customer service SAP Forecasting and Replenishment can help you improve inventory turnover and prevent out-of-stock conditions that result in lost sales and customer alienation
  • 14. Conclusion Forecasts will never be perfect, and sometimes they may not even be very good The goal of forecasting in retail should not be a foolish pursuit of perfection, but to generate forecasts that are as accurate and unbiased as we can reasonably expect them to be, and to do this as efficiently as possible Large-scale automation helps solve the problem of generating forecasts at granular levels of detail (such as store, item or week) However, there must still be a realistic assessment of the likely accuracy of forecasts at that level, and consideration of other strategies that can be used in conjunction with forecasting to best solve the business problem
  • 15. Sandeep Dhankhar :2010200 Sandeep Kumar Singh :2010201 Sankarshan Joshi : 2010202 Saurabh Kumar :2010203 Sayantan Banerjee :2010204