1. How can a meal delivery service in India improve operations?
1.1. The Economic and Business Benefits of Data Oriented Accumen
Businesses and companies strive in a relentless endeavor to consistently improve in speed, efficiency, service, and their product and those unable to reach or attain an advantage in the capitalistic plight are set to be consumed by the market. With every company trying to find some unique insight into the market, proper business structure and analytics will allow companies to find and act on these unique insights providing lower costs, higher efficiency, better service and products giving them an edge and greater profitability.
1.2. Executive Summary
1.2.1 Exploratory Analysis
Exploring the most popular meals:
- Beverages
- Rice Bowl
- Sandwich
Streamlining business ventures: Within the cities 590 and 526 when looking at the last 25 weeks, it was determined that fullfillment center 91 should be closed due to the fact the the fullfillment center did not produce as relative to other centers within the same district, and for city 526, a new type B fullfillment center should be added due to the high quantity of orders it fullfills relative to the other fullfillment centers.
1.2.2 Marketing Analysis
The marketing campaign with the highest magnitude toward increasing revenue for a particular meal and increasing the number of orders is the email campaign despite having the greatest reduction in checkout price. Although feature meals do not increase the orders or revenue of a particular meal as much as the email campaign, it does not have as strong of negative relationship with checkout price allowing it to closely compete in overall increases of meal revenue.
1.2.3 Forecasting
Using a linear regression, a model was estimated with high predictive accuracy, a MAPE of 6% and less.
1.3. Methodology
Tasked with the goal of improving the overall operations of a food delivery service, this report will focus on the success of campaign methods, optimizing business operations by exploring cities with high quantities of fulfillment centers to reduce redundancy and to relieve points of stress, and create prediction models to assist with demand projections.
By looking at the different cities with high quantities of fulfillment centers, economic costs can be cut by reducing unnecessary expendatures and services can be improved by adding fulfillment centers where they are needed.
Moreover, statistical methods will be used to determine the effectiveness of the different campaign methods from the email campaigns to the meals featured on the website.
Finally, predictive modeling will be implemented to estimate future orders so that operation managers can anticipate and prepare for influxes of high demand and high demand products.
2. Most Popular Food Categories in Each Region
In chart 2.1, each food category is plotted by the percentage of within a relative region, and without a resounding doubt, for each region the most popular orders are beverages. Dominating certain markets with as much as 44.67% of all orders within a region. Aside from beverages, the following food categories maintain the highest proportioned order for the majority of regions RiceBowl, Sandwich, Salad, Pizza from second to fifth respectively.
3. Reducing Redundancy and Strain in fulfillment Coverage
3.1. City 590
Based on the chart 3.1, there is a large difference in the area covered by fullfilment center 91 and 41 and their proportion of orders serviced, seeing that they are both type C centers, their merge should be considered. This merge would allow the center to cover a larger area and more orders centralizing distribution reducing business overhead and optimizing equipment usage.
3.2. City 526
Once again, when looking at the lollipop chart for the proportion of orders filled in each fulfillment center in city 526, fulfillment centers 92 and 162 both cover a small area. Moreover, they cover a lower proportion of orders compared to other centers of the same type; thus, merging them should be a serious consideration.
Also, relative to the other fulfillment centers, center 146 has absorbed a lot of the burden in fulfilling customer orders, completing over 20.55% of total customer orders within the city. Thus, opening another type B fulfillment center can relieve strain on center 146 while improving customer service and order fulfillment efficiency.
4. Effectiveness of Different Marketing Campaigns
4.1 Outcomes of Email Campaigns
After graphing the Email Campaign over time, it is clear that the email campaign induces a higher number of orders for those meals, however, when looking at the checkout price there appears to be little increase to the checkout price of the meals orders. So, although the number of orders increases--with great volatility--the average amount of rupees spent on the meals decrease.
4.2 Outcomes of Featured Meals
Looking at the effects of featuring meals on the application, the featured meals actually have a descreased number of orders relative to the average number of orders for meals with only a handful of featured meals exceeding the number of standard orders. In regards to the average checkout price of meals by featured product, there is nota clear distinction between the average checkout price for featured meals against standard meals.
4.2. Statistical Analysis of Orders
4.2.1 Analysis of Variance for the Average Number of Orders in Relation to Promotional Methods
Testing the Beta Estimates for the model:
With an F-Statistic of 2930 and a p-value of 0.000, the model performs better than the null model and at least one Beta estimate is not null.
In the plot presented above, the terms representing the different campaigns both have positive relationships with the outcome variable orders; however, implementing both email and feature campaigns for a meal does not increase the orders as much one solid marketing campaign of either featuring a meal or an email campaign for a meal.
4.2.2 Analysis of Variance for Checkout Price in Relation to Promotional Methods