Set Up CRM Analytics for Manufacturing
Set Up CRM Analytics for Manufacturing is a guided Setup wizard that deploys a pre-built CRM Analytics app containing dashboards, datasets, and dataflows tailored to Manufacturing Cloud.
Definition
Set Up CRM Analytics for Manufacturing is a guided Setup wizard that deploys a pre-built CRM Analytics app containing dashboards, datasets, and dataflows tailored to Manufacturing Cloud. The app reads from standard Manufacturing Cloud objects such as Sales Agreement, Account Forecast, and Account, then enriches them with order and product data to give sales operations a starting view of demand, revenue, and agreement performance without building anything from scratch.
The wizard is intended to compress what would otherwise be several weeks of dashboard design and dataflow authoring into a single afternoon of configuration. It is the right starting point for any org rolling out Manufacturing Cloud where the analytics team wants a baseline before customizing. The deployed dashboards are fully editable, so most orgs treat the wizard output as the foundation rather than the final state.
Inside the Manufacturing Analytics app
Dashboards the wizard creates
The deployment installs a set of dashboards covering the core Manufacturing Cloud workflows: Sales Agreement Performance shows actual versus contracted volumes and revenue across periods; Account-Based Forecasting visualizes forecasted versus actual revenue by account and product family; Run-Rate Business tracks recurring orders against committed quantities; and Product Performance breaks down volume by SKU. Each dashboard is multi-page with filter controls for product family, account hierarchy, time period, and sales agreement type. Out of the box, the dashboards refresh nightly. For sales operations leaders who want intraday visibility, the dataflows can be reconfigured to run hourly within the Analytics Studio after the initial setup completes.
Datasets and how they are built
The wizard creates around eight to ten datasets, each scoped to a specific business question. Sales Agreement Performance Dataset combines Sales Agreement, Sales Agreement Product, and Account Forecast rows. The Order Performance Dataset combines Order Product, Order, and Account rows from the standard Order object model. Each dataset is materialized from the source objects via a dataflow defined in JSON, viewable and editable through the Analytics Data Manager. The datasets are not real-time copies of the underlying records; they are snapshots refreshed on the dataflow schedule. Stakeholders who expect the dashboards to show live record changes need to be re-educated on this snapshot model during onboarding.
Dataflow schedules and refresh cadence
Each dataset feeds from a dataflow that runs on a schedule. The default schedule is once per day, typically scheduled overnight in the org's time zone to avoid contention with business-hours user activity. The wizard sets this default but does not enforce it; any admin with Manage CRM Analytics permission can change the cadence. Running dataflows more frequently increases the load on the org's data sources, the CRM Analytics dataflow runs allowance, and the user perception of dashboard freshness. There is no built-in incremental dataflow for Manufacturing in the wizard output, so every refresh is a full extract. For very large orgs, this becomes a planning factor for nightly batch windows.
Sharing and permissions for the deployed app
The wizard deploys the app into the CRM Analytics environment with default sharing rules tied to the Manufacturing Cloud user permission set. Users who hold that permission set get viewer access to the app; admins with Manage CRM Analytics get editor access. Field-level security on the underlying objects is respected through dataset-row-level security defined in the dataflow, so a user who cannot see specific Account records on the standard UI will not see those accounts in the dashboard filters either. After deployment, the typical change is to add a custom sharing rule for the executive team or a specific business unit, which is done through the app's sharing dialog in the Analytics Studio.
Customizing after the wizard completes
The wizard output is editable in two layers. The dashboards are SAQL- and Dashboard-JSON-backed and can be edited in the Dashboard Builder by anyone with the right permission. New widgets, filters, and pages can be added without rerunning the wizard. The dataflows are also editable, but changes there require Analytics Builder expertise. Best practice is to clone the wizard-deployed dashboards before modifying them so future re-runs of the wizard (after a Manufacturing Cloud upgrade introduces new fields) can refresh the baseline copy without overwriting your customizations. The standard pattern is: wizard dashboards as read-only reference, customized clones as the production assets shown to users.
Prerequisites the wizard does not check for
The wizard assumes the org already has Manufacturing Cloud licensed, the relevant permission set assigned to the running user, and at least one Sales Agreement created with line items. If those prerequisites are missing, the wizard will run but the dashboards will appear empty, which leads to a frustrating debug cycle. Before launching the wizard, confirm that test Sales Agreements exist, that Order and Order Product data is loaded for the same accounts, and that Account Forecasts have been generated. A 30-minute prerequisite check up front saves a half-day of stakeholder demos that go badly because no data shows up.
When to skip the wizard and build from scratch
The wizard is the right call when the org wants standard manufacturing analytics on standard Manufacturing Cloud data. It is the wrong call when the org has heavily customized objects, when revenue lives outside Manufacturing Cloud (in an ERP or a custom Revenue object), or when the existing analytics team has already built dashboards that the business has approved. In those cases, the wizard introduces dashboards that compete with the existing ones and confuses users. Skip the wizard, study the dataflow JSON it produces in a sandbox, and use it as a reference architecture for the team's custom build.
Run the CRM Analytics for Manufacturing wizard
The wizard is delivered as a guided Setup flow that takes between fifteen and forty-five minutes to run depending on data volume. Execute it in a sandbox first, validate the dashboards with the business, and then re-run in production. Below are the steps from a clean sandbox through to a deployed, validated app. The wizard expects a working Manufacturing Cloud configuration on top of CRM Analytics Plus; running it before either of those is in place creates dashboards that load but show nothing useful. The right order is licensing, permission sets, sample data, then the wizard itself, then a customization pass with the business.
- Verify prerequisites in the sandbox
Confirm Manufacturing Cloud is licensed and the running admin user has the Manufacturing Cloud Admin permission set and the CRM Analytics Plus permission. Confirm Sales Agreements, Account Forecasts, and Orders exist with realistic data. Run a sample report on Sales Agreement Products to confirm the data is queryable. If anything is missing, load test data first using the standard Data Loader or create records manually before launching the wizard.
- Launch the wizard from Setup
Navigate to Setup, search for CRM Analytics, and select Set Up CRM Analytics for Manufacturing. The wizard presents a single screen with an explanation of what will be deployed, the prerequisites it checks for, and a Start button. Click Start. The wizard begins by inspecting the org and may prompt for permission assignments if it detects missing pieces. Follow the inline prompts; the wizard logs every action it takes for later review.
- Configure dataset filters and naming
The wizard prompts for an app name, a description, and optional date or product filters that scope the deployed dashboards. For most rollouts, accept the defaults and rename only the app to match the org's naming convention. Choose whether to schedule the dataflow immediately or defer to the next day. Click Deploy and wait. The deployment usually completes in five to fifteen minutes depending on data volume; the first dataflow run after deployment is what populates the dashboards with actual numbers.
- Validate, customize, and promote to production
Once deployment finishes, open the Analytics Studio and navigate to the Manufacturing Analytics app. Walk through each dashboard with at least one sales operations stakeholder, taking notes on missing filters or columns. Clone any dashboard that needs customization and edit the clone, leaving the wizard output untouched as a baseline. After sandbox sign-off, re-run the wizard in production. The wizard is idempotent for the dataset and dataflow creation but will overwrite any wizard-named dashboards, which is why clones are the right pattern for production customization.
- The wizard does not check that source data exists. Running it against an empty Manufacturing Cloud setup produces dashboards that load successfully but show zero everywhere.
- Re-running the wizard overwrites the wizard-named dashboards. Always clone before customizing so the next run does not erase your edits.
- Default dataflow schedule is nightly. Stakeholders expecting real-time numbers need to be set straight before the first demo or expectations will not match reality.
- Row-level security is inherited from the underlying object permissions, but only if the dataflow uses the security predicate. Customized dataflows that drop the predicate accidentally expose data.
- The wizard requires CRM Analytics Plus, not the base CRM Analytics license. Orgs on the base SKU see the wizard but cannot complete deployment.
Trust & references
Cross-checked against the following references.
- CRM Analytics for ManufacturingSalesforce Help
- Manufacturing Cloud OverviewSalesforce Help
Straight from the source - Salesforce's reference material on Set Up CRM Analytics for Manufacturing.
- CRM Analytics for ManufacturingSalesforce Help
- Create a CRM Analytics AppSalesforce Help
About the Author
Dipojjal Chakrabarti is a B2C Solution Architect with 29 Salesforce certifications and over 13 years in the Salesforce ecosystem. He runs salesforcedictionary.com to help admins, developers, architects, and cert/interview candidates sharpen their fundamentals. More about Dipojjal.
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