Creating Value Through Operations
Choose Amazon as the organization.
learn how to apply forecasting and demand models as part of a business operations plan.
Choose 2 quantitative elements that you would like to research in relation to the organization that you selected for your business plan. These elements may be related to products, services, target market, consumer preferences, competition, personnel, resources, supply chain, financing, advertising, or other areas of interest. However, at least one of these elements should be related to a product or service that your organization is planning to offer.
Develop forecasts by implementing the following approach:
Collect data, including old demand forecast (subjective data) and the actual demand outcomes.
Establish the forecasting method (from readings). Decide on the balance between subjective and objective data and look for trends and seasonality.
Forecast future demand using a forecasting method.
Make decisions based on step 3.
Measure the forecast error where applicable. Look for biases and improve the process.
Write a 350- to 525-word paper evaluating the findings from the supported data points above, and explain the impact of these findings on operational decision making. Insert charts and supporting data from Excel and other tools in your paper.
Forecasting Demand for Amazon's New Grocery Delivery Service: A Case Study
Introduction:
Amazon, a leader in e-commerce, is considering expanding its offerings with a grocery delivery service. This paper will explore how forecasting techniques can be applied to estimate demand for this new service and inform operational decisions. We will analyze two key quantitative elements:
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Decision Making based on Forecasts:
By analyzing historical trends and seasonality, Amazon can make informed decisions about:
- Historical Demand for Groceries: Analyzing historical online grocery sales data from market research firms or industry reports.
- Prime Membership Growth: Assessing the growth rate of Amazon Prime memberships, a potential indicator of customer base for the new service.
- Historical Demand for Groceries:
- Prime Membership Growth:
- Naïve Forecast: This simple method uses the most recent sales figure as the forecast for the next period. It's a good starting point for establishing a baseline.
- Seasonal Naïve Forecast: This method takes into account seasonality by calculating an average demand for each period (e.g., month) over the past few years. This average is then used as the forecast for the corresponding period in the upcoming year.
| Year | Online Grocery Sales (Millions) | Naïve Forecast (Millions) | Seasonal Naïve Forecast (Millions) |
| 2020 | 100 | - | - |
| 2021 | 120 | 120 | - |
| 2022 | 150 | 150 | - |
| 2023 (Forecast) | - | - | (Calculated based on historical seasonal averages) |
- Warehouse Capacity: Forecasts can inform decisions on warehouse space required to store and distribute groceries efficiently.
- Delivery Fleet Management: Demand forecasts can help determine the number of delivery vehicles needed to meet customer needs.
- Inventory Management: Forecasting helps in optimizing inventory levels to avoid stockouts or overstocking.
- Marketing and Promotions: Demand forecasts can be used to plan targeted marketing campaigns and promotions during peak grocery buying seasons.
- Competition: Analyzing competitors' market share and growth can provide further insights into customer preferences.
- Consumer Demographics: Understanding the demographics of Prime members can help tailor the service to specific customer segments.
- Economic Conditions: Economic factors can influence consumer spending habits, and should be factored in.