Forecast business practice changes on your emissions with what-if analysis (preview)

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Microsoft Cloud for Sustainability Technical Summit May 2024

Important

Some or all of this functionality is available as part of a preview release. The content and the functionality are subject to change.

Note

This feature is included in Microsoft Sustainability Manager Premium.

What-if analysis is a custom AI model that allows you to forecast the impact of several business practice changes on your organization's carbon emissions footprint. It helps you to create more informed carbon reduction strategies and accelerate your overall sustainability goals. For example, you can forecast the impact of switching to renewable energy sources, such as wind or solar power, or switching suppliers through supplier-specific factors.

This article shows you how to create a what-if analysis forecast. It also provides considerations, details, and information to help you get the most out of your forecast.

The following video demonstrates how to use document analysis and what-if analysis:

Important considerations

Keep these considerations in mind while creating your forecast scenario.

  • Every scenario requires the following fields:

    • Name
    • Organizational unit
    • Data type
    • Calculation model
    • AR version

  • Facility isn't required for every scenario, but you can use it to further narrow down the data for your forecast.

  • All scenarios rely on their respective required fields when tracking activities for that category. For example, the Business travel scenario relies on the Distance field to accurately apply an increase or decrease percentage to the forecast.

Required fields

This table lists the category-specific required fields for each scenario.

To ensure successful forecasts for scenarios that mention one or more numeric columns, be sure to fill out the corresponding unit column. Also ensure that the calculation model you choose uses the same fields.

Scenario category Required fields
Industrial process Industrial process type
Mobile combustion Vehicle type, Fuel type
Stationary combustion Fuel type, Energy conversion ratio
Purchased cooling Facility, Is renewable
Purchased electricity Facility, Is renewable
Purchased heat Facility, Is renewable
Purchased steam Facility, Is renewable
1. Purchased goods and services Spend type
2. Capital goods Spend type
4. Upstream transportation and distribution Transport mode, Distance, Goods quantity
5. Waste generated in operations Material, Disposal method
6. Business travel Business travel type, Distance
7. Employee commuting Employee commuting type, Distance
9. Downstream transportation and distribution Transport mode, Distance, Goods quantity
12. End-of-life treatment of sold products Material, Disposal method

Create a what-if analysis forecast

To get started with what-if analysis, follow these steps:

  1. On the navigation pane, select What-if analysis (preview).

  2. On the What-if analysis (preview) page, select New scenario.

  3. On the New What-if scenario pane, enter the information for your scenario, and then select Save & Close.

  4. Select your created scenario, and then select Run scenario. After the scenario job completes, you receive an in-app notification alerting you of the results, with a hyperlink leading you to them. This page displays your scenario details and a graph visualizing your historic data and associated forecasts.

Screenshot showing a sample forecast.

Supported scenarios

What-if analysis supports the following scenarios. Each scenario has different levels of customizability that allow you to tailor the forecast to your organization’s data and needs.

Scope 1

  • Industrial process: The industrial process scenario allows you to forecast the impact of switching the Industrial process type for a particular Organizational unit or Facility assigned to Organizational unit.

  • Mobile combustion: The mobile combustion scenario allows you to forecast the impact of switching both the on-road and off-road vehicles being used. For example, you can forecast the emissions impact of switching from a diesel-powered fleet to a gasoline-powered one for a particular Organizational unit.

  • Stationary combustion: The stationary combustion scenario allows you to forecast the impact of switching the fuel being used at a particular facility. To provide a more accurate forecast, this scenario also requires you to provide an Energy conversion ratio.

    To calculate energy conversion ratios, divide the energy content of your existing fuel by the energy content of the fuel you’d like to forecast for. Alternatively, you might also use heating values. For example, if the energy content of your existing fuel is 33 MJ/kg and the new one is 38 MJ/kg, then your energy conversion ratio is around 0.87.

Scope 2: Purchased energy

All Scope 2 category forecast scenarios follow the same pattern: switch from nonrenewable to renewable energy, or vice versa, through the Is renewable field.

For these scenarios, ensure that you're choosing a calculation model that supports both nonrenewable and renewable energy source calculations. The easiest way is to use a conditional on the Is renewable field.

Screenshot showing a calculation model that supports both renewable and nonrenewable energy.

Scope 3

  • 1. Purchased goods and services and 2. Capital goods: Scope 3 categories 1 and 2 allow you to forecast the impact of switching the supplier from which you purchase goods and services. Before creating and running a forecast scenario for either of these categories, ensure you complete the following steps:

    1. Import any custom, supplier-specific emission factors needed for the forecast.
    2. Map all custom emission factors to a corresponding Spend type.
    3. Create or modify a calculation model to use the custom factors.

    Note

    We recommend that you store all supplier-specific factors in a single factor library so that your corresponding calculation model can dynamically choose the correct one based on the mapped Spend type.

    After you complete these items, use the Spend type: current and Spend type: new fields to map your current and proposed suppliers, ensuring that they align to the first two steps.

  • 4. Upstream and 9. Downstream transportation and distribution: Scope 3 categories 4 and 9 provide some of the broadest flexibility in terms of forecast scenarios. For each of these categories, the scenario allows you to forecast the impact of switching the Transport mode being used and the corresponding Distance traveled and Goods quantity being transported.

    For Distance traveled and Goods quantity, provide an estimate of the percentage increase or decrease you expect to see as a result of changing the Transport mode. For example, if switching from Plane to Ship, you might see a decrease in distance traveled and an increase in the quantity of goods being transported. Positive integers represent increases and negative integers represent decreases.

    Note

    Decreases can't be greater than 100%.

    Any increases or decreases to Distance traveled or Goods quantity are applied on the forecast at the aggregated monthly level.

  • 5. Waste generated in operations and 12. End-of-life treatment of sold products: Scope 3 categories 5 and 12 allow you to forecast the impact of changing the way you dispose of a particular material. For example, you might forecast the impact of composting your food waste as opposed to sending it to a landfill.

  • 6. Business travel and 7. Employee commuting: Scope 3 categories 6 and 7 allow you to forecast the impact of changing the mode of transport that members of your organization use for business travel or employee commuting through the Business travel type and Employee commuting type fields, respectively. Much like categories 5 and 12, categories 6 and 7 also allow you to enter an estimated percentage of increase or decrease to distance traveled.

    Note

    Any increases or decreases to Distance traveled are applied on the forecast at the aggregated monthly level.

Forecast aspects

  • Existing strategy: The existing strategy forecast is a view into your projected emissions if you were to change nothing about the current way you generate emissions for that category. For example, if you're forecasting the impact of switching from coal to biofuel for a particular facility, the existing strategy forecast represents the projected emissions of continuing to use coal.

  • New strategy: The new strategy forecast is a view into your projected emissions if you were to switch to the new business strategy represented by your forecast scenario. For example, if you're forecasting the impact of switching from coal to biofuel for a particular facility, the new strategy forecast represents the projected emissions of switching to biofuel.

  • Prediction intervals: Prediction intervals represent the estimate of an interval in which a future observation falls with a certain probability (we use 95% confidence), given the historical data. Prediction intervals essentially represent the uncertainty associated with a forecast.

Model failures and informational messages

This section explains errors or problems you might have with forecasts.

We made some adjustments in order to generate this forecast

Screenshot of the adjustments message.

  • Switching to a fallback forecasting method: We use a fallback forecasting method in case the number of historical data points and/or the data quality required to fit (S)ARIMA or ETS models is insufficient. There are two specific cases in which it becomes necessary to switch to a fallback methodology:

    • Too many missing data points in an otherwise relatively uniformly spaced historical data time series
    • Irregularly spaced historical data

  • Data uniformity check and frequency adjustment: Before forecasting, your data is aggregated on a monthly level to generate a monthly baseline and what-if forecast. However, if upon aggregation the data doesn't present a relatively uniform monthly cadence, further aggregation to two, three, four, or six months are attempted. If it isn't possible for the series to achieve relative uniformity under these adjustments, then a simpler fallback model is used for forecasting.

Unable to generate forecast

  • Historic data is too sparse: To ensure a successful forecast, we require your historical data to have a frequency of at least one data point every six months. If your data is more sparse than that interval, the forecast fails.

  • No or too few historical data points: The what-if analysis forecasting models require at least six data points (after frequency adjustment, described earlier in Data uniformity check and frequency adjustment) to successfully generate a forecast.

Screenshot of the no data points error message.

Screenshot of the too few data points error message.

Supported time series forecasting models

Sustainability Manager supports (Seasonal) Auto Regressive Integrated Moving Average ((S)ARIMA) and Error Trend Seasonality (ETS) univariate time series forecasting models for generating forecasts on activity data. The model selection framework selects the best forecasting model based on the historical activity data. The generated activity-level forecasts go through the calculation model to transform them into emissions-level forecasts.

ARIMA and ETS are the most widely used time series forecasting methods. ETS models rely on the descriptions of trend and seasonality in the data, while ARIMA models describe the autocorrelations in the data. To learn more about these models, refer to Chapter 7 (Exponential Smoothing) and Chapter 8 (ARIMA models) of the Forecasting: Principles and Practice textbook.

On certain occasions, such as when the historical data is too little or highly irregular, a simple fallback model is selected, instead of ARIMA or ETS.