Sales Forecast and Store Analysis for Data Analytics

Sales Forecast and Store Analysis.pptx

1. Ice Cream Sales Forecasting and Store Analysis Ankit Arora, D21MCA16040

2. Introduction Forecasting plays a crucial role in the development of plans. It is an essential tool for the organization to know what level of activities one is planning before investment in inputs like man, material and machines. Forecasting as defined by the American Marketing Association is: “An estimate of sales in physical units or monetary value for a specified future period under proposed marketing plan or program and under the assumed set of economic and other forces outside the organization for which the forecast is made”. This work is about the Data Analysis and Visualization using Python and corresponding libraries/modules. Machine Learning is implemented for Predictive model building.

3. Business Opportunities In an Ice-cream store, customers want to have customizations for their Ice-creams. So, the stores updated the same in their systems and saved the sales records of their stores for three consecutive years. This practice is done for various stores in different regions. As a result, data for each store and region is stored for many years. Good analysis of this data can allow the management to make decisions that can help the company maximize profits as well as avoid losses by observing trends in the historical data collected from sales in each store. Example: Dominos Ltd. has several new products in research and development that are scheduled to go to market in FY [2023]. Now is the time: to identify the target market for these products. to understand how to best brand and position them. to identify demographic attributes for their success.

4. Need for Sales Forecasting Majority of the activities of the industries depend upon the future sales. Projected sales for the future assists in decision-making w.r.t. investment in machinery and market planning program. To schedule the production activity to ensure optimum utilization. To prepare the material planning to take up the replenishment action to make the materials available at right quantity and right time. To provide an information about the relationship between sales for different products as a function of time. Forecasting is going to provide a future trend which is very much essential for products design and development

5. Steps in Forecasting Process Step 1 Determine purpose of forecast Step 2 Establish a time horizon Step 3 Select a forecasting technique Step 4 Gather and analyze data Step 5 Prepare the forecast Step 6 Monitor the forecast “The forecast”

6. Types of Forecasting Judgmental - Subjective analysis of subjective inputs Associative models – Analyzes historical data to reveal relationships between (easily or in advance) observable quantities and forecast quantities. Uses this relationship to make predictions. Time series – Objective analysis historical data assuming the future will be like the past

7. Judgmental Techniques Opinion Survey Method Executive Opinion Method Customer and Distributor Surveys Marketing Trials Marketing Research Delphi Technique

8. Associative Forecasting Used for Short run forecasts Finds an association between predictor and predicted Regression – Identifies a statistical relationship between sales and one or more influencing factors, which are termed the independent variables. When only one variable is considered, it is called Linear Regression and the results can be shown as a line graph. When more than one variable is considered, it is called multiple regression. Mathematical representation of Linear Regression is y = a + bx Where, y = predicted (dependent) variable x = predictor (independent) variable b = slope of the line a = value of y when x = 0 (the height of line at the y intercept)

9. Time Series Analysis Time-ordered sequence of observations taken at regular intervals over a period Based on the past data arranged in the chronological order as a dependent variable and time as an independent variable. Time series method does not study the factors that influence the demand, in this method all the factors that shape the demand are grouped into one factor-time and demand is expressed as a series of data with respect to time. Common expression for Time Series Forecast is Y=TCSR Where Y = Forecasted Value T = Secular Trend - long-term movement in data C = Cyclic Variations - wavelike variations of long-term S = Seasonal Fluctuations - short-term regular variations in data R = Irregular Variations - caused by unusual circumstances

10. Benefits Better Control of Inventory Staffing Customer Information Use of Salespeople Obtaining Financing

11. Contact Information This project is submitted to Chandigarh University by Ankit Arora, D21MCA16040 for the partial fulfilment of Degree of Master of Computer Application (MCA) and holds the full rights over the project including it’s documentation and reports. In case of any query or concerns, one can reach out to [email protected] or [email protected] .