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Create export template for Anaplan users to import using months
Author: Tyler Beck is a Certified Master Anaplanner at TBLB LLC, and is currently a Team Lead at CVS Health.
Use case
An end user needs to export a template from Anaplan using Months as the time period in columns, and qualitative data in rows. When the template is exported as a CSV file the years in the dates turn to days instead of years in the Excel file. The import will result in error when mapping back to native time in Anaplan due to the date change.
Use case example
The module below is exported from Anaplan as a CSV file:
The CSV file is opened and the dates show the years as days, and the years are recognized as the current year DATE() in Excel as a default setting:
The dates in the CSV file will show a format of MM-DD-YYYY instead of the Anaplan format of MMM-YY. This is a major issue for clients who need to export a template to lookup the most recent data when manually inputting data is not feasible due to large data sets. When the CSV file is loaded back into Anaplan, the module will import the data into the wrong months or result in an error all together:
The import shows the error for “Invalid date or timescale identifier” because the expected native time years are shown as days in the CVS file.
Solution
Create a “Dynamic Time” production list in Anaplan apply it to the columns in a new export module as a template. Then import the template back into the input module’s native time using the custom fixed position pattern in the import configuration.
Solution example
* Create “Dynamic Time” as a production list so the dates can be changed in the Production environment: The list should include a special character in front of the MMM-YY month format that Anaplan uses for native time. The list in this example will use an apostrophe as the special character, and two years are applied to the list.
More than two years can be applied to the list, and subsets can be made for forecast and plan months as well.
* Apply Dynamic Time list to module and export the template for users to update values:
The export template should have all mapping qualitative data fields in rows and Dynamic Time in columns, and include empty rows so end users can create lookups for the data:
Also, remember to include the labels and update the “Name” to “Code” for the dimension in the rows:
Using codes will not only make using lookups easier in Excel, but is also more efficient for loading back into Anaplan by mapping codes to the dimensions.
Placing a special character in front of the native time period format will prevent the CSV file from defaulting to Excel’s MM-DD-YYYY format and now can be mapped to Anaplan’s native time.
* Create an import for the template using a custom fixed position pattern:
Map the codes to the correct dimensions, time as column headers, and the amount as a fixed line item.
Next, select the Time tab in the import configuration and select Periods for the timescale, then navigate to the custom fixed position pattern section and enter ?MMM-YY. Placing a “?” in front of the MMM-YY format will tell Anaplan to ignore the special character so the format will map back to native time.
* Run Import with new configuration to import the template into native time:
The import is now successful, importing all fields with no errors.
All values have been mapped to the correct dimensions in the correct native time period.
Conclusion
Creating an export template using a production flat list can allow end users to export a template to populate data to import back into Anaplan by placing a special character in front of the month names. This will allow end users to bulk upload large data sets when manually inputting the values is time consuming and tedious. Remember to always export the template to include all rows and to show labels, as well as changing the “Name” in the label to “Code” so numbered lists can be mapped seamlessly.
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How I Built It: Replacing list items
Author: Chris Allen is a Certified Master Anaplanner and Manager at Allitix Part of Accenture.
Hello, and thank you for checking out my ‘How I Built It’ video tutorial on replacing list items!
The video will walk you through the process of replacing list items. The key feature leveraged here is importing a new code into a placeholder list item to override its original code to match the list item the placeholder was used to represent for. I'll walk through all the necessary steps to successfully complete this task.
This is an important process I implement for projects dependent on SKU planning. A planner may want to create a new SKU in their SKU list, which does not currently exist and will not be established in upstream source system in near future. The SKU's actual code and display name is unknown until it's created in the source system. The new SKU list item acts as a placeholder until the new SKU is available in source system and imported into Anaplan. When the SKU becomes available, the placeholder SKU will need to transfer its data to it or in this case, we have the placeholder SKU transform into the SKU it was a placeholder for by replacing its code.
Check it out and let me know if you have any questions!
https://play.vidyard.com/sXHdwEN6ypncpJ4WaVcgQA
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How I Built It: User filters with variable hierarchy properties
Author: Erik Svensson is a Certified Master Anaplanner and a Principal Solution Architect at Anaplan.
Hello Anaplan Community!
Thank you for checking out my ‘How I Built It’ tutorial. In this video, I demonstrate a powerful technique for creating dynamic user filters. This solution gives your end-users the flexibility to filter a dimension by different attributes on the fly.
A great example is fashion assortment planning. In the "Tops” category a planner needs to filter by Neckline, while in "Footwear” a planner needs to filter by Upper Material. This model allows each user to select the specific attributes they want to filter by, providing a customized and highly flexible experience.
This is especially important in fast-moving industries where trends change quickly, and a one-size-fits-all filtering approach is too restrictive.
Key features:
* User-specific dynamic filtering
* Flexible attribute selection per user
Check it out and drop in a comment if you have any questions!
https://play.vidyard.com/ErQqG4YvqLwZTVhXm1uHYS
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How to build a Polaris reporting model in less than two weeks
Author: Hanwen Chen is a Certified Master Anaplanner and Professional Services Sr. Manager at Anaplan.
Over the past nine months, I have been involved in multiple Classic-to-Polaris conversion projects. One consistent requirement across these engagements is the need for scalable reporting solutions that support multiple natural dimensionalities. Customers are increasingly looking to Polaris to enable this type of reporting capability at scale.
This article demonstrates how you can quickly build a Polaris reporting model in less than two weeks by leveraging existing data from Data Hubs and Classic models. By reusing structured data and applying a streamlined setup approach, teams can rapidly enable scalable, multi-dimensional reporting in Polaris without rebuilding the entire model from scratch.
Common patterns in Classic models
From my experience, when reviewing existing Classic models that were not originally designed for reporting with multiple natural dimensions, two common patterns typically emerge:
* Flat data structures with additional attributes.
Data is often stored in a flat structure with additional attributes that describe the elements. It may also include dimensions such as Time. The flat structure typically serves as the data key and may be a concatenated list of multiple dimensions, such as project–department–account. Additional attributes describe other aspects of the dimension, for example, the region associated with a department or the category associated with a project.
* Incomplete or inconsistent dimension structures in Data Hub.
The Data Hub often lacks well-defined hierarchies or dimension structures that can support reporting directly. Without these, it becomes difficult to enable flexible multi-dimensional reporting.
If you observe these patterns in your Classic models, the following approach can help you implement a Polaris reporting model efficiently.
Solution configuration
* Report dimensions & data sources.
Start by identifying the dimensions required for reporting and the sources that provide the necessary data elements. For example, a report might include Time, Version, Cost Center, Product, and Region as key dimensions. These dimensions determine the structure of the reporting model.
Next, determine which systems, models, or module views will provide these dimension structures and data elements. Typically, this includes the Data Hub and existing Classic planning models. Clearly identifying dimensions and sources upfront ensures a smooth and streamlined setup process.
* Data Hub configuration.
The Data Hub serves as the central repository for master data and actuals. To prepare the Data Hub for Polaris reporting:* Configure dimension structures: Ensure flat lists exist to support the required reporting dimensionalities.
* Create output views: Build export views that structure the data for loading into Polaris. Well-designed export views minimize transformation work, simplify integration, and improve data load performance.
The Data Hub is critical because it standardizes dimensional structures and reduces complexity in the Polaris reporting model.
* Classic model configuration
Classic planning models provide plan and forecast version data. Before integrating with Polaris:* Prepare plan/forecast data: Ensure version data is structured and ready for export.
* Validate data elements: Confirm that all dimensions required for reporting are included in the Classic model and align with the Data Hub structures.
Proper preparation ensures the Polaris reporting model can consume version data efficiently without extensive transformations.
* Polaris reporting model setup.
Once the Data Hub and Classic model are ready, configure the Polaris reporting model:* Set up flat lists and hierarchical structures.
Create the reporting dimensions required in Polaris.
* Build modules to receive actual and version data.
Design modules to store imported data from the Data Hub (actuals) and Classic models (plan/forecast versions).
* Create processes to populate dimension data from the Data Hub.
Set up imports and processes to load dimension structures into Polaris.
* Create processes to load actual data from the Data Hub.
Import actuals prepared in the Data Hub export views.
* Create processes to load version data from the Classic models.
Import plan and forecast versions from Classic models.
* Set up bulk upload processes.
Enable bulk upload processes to load multiple versions of data as needed.
* Configure mapping and validation processes.
Set up mapping logic and validation modules and pages to ensure correct dimensional mapping and data integrity.
* Create reporting modules and report pages.
Include multi-dimensional reports, variance reporting (e.g., Current Forecast vs. Plan), and other analytical views to provide meaningful insights from the data.
Final thoughts
By leveraging existing Data Hubs and Classic models, teams can significantly accelerate the implementation of a Polaris reporting model. Instead of rebuilding data structures from scratch, this approach focuses on reusing structured data and aligning it with Polaris’ scalable dimensional architecture.
With the right setup, it is entirely feasible to stand up a functional and production-ready Polaris reporting model in less than two weeks.
Additional tips and tricks in each configuration can further streamline building your Polaris reporting model. In a follow-up article, I will share these tips and tricks to help teams implement more efficiently.
Questions? Leave a comment!
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Other articles by Hanwen:
* The beauty of simplicity: any level of selection in a hierarchy
* The power of the ‘No’ version approach in Anaplan
* Data distribution design from Data Hub to multiple spoke production models
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How I Built It: Dynamic driver-based planning
Author: Chris Allen is a Certified Master Anaplanner and Manager at Allitix part of Accenture.
Hi Anaplan Community!
This ‘How I Built It’ video shares a dynamic feature to toggle between different planning methodologies corresponding to different lines on the P&L for driver-based planning.
It's a feature that can be applied to many different scenarios where a list item has unique line items related to it than its peers in the same list — to only show the related line items per the list item selected. The upside is decluttering a dashboard and allowing a good fit on one screen without much searching or scrolling.
Check it out and leave a comment with questions!
https://play.vidyard.com/qoByxyE7BZKSVJJzr62yEN
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Check out my other ‘How I Built It’ videos:
* How I Built It: Replacing list items
* How I Built It: Flagging new list items
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How I Built It: User Access Management
Author: Kevin Dale Bandelaria is a Certified Master Anaplanner and Solutions Delivery Head at OmniQuest, Inc.
Solution overview
The User Management process in Anaplan was developed to simplify how administrators set up and maintain model roles and selective access settings for users. Traditionally, configuring access in the backend can be tedious and error-prone, especially for large-scale implementations involving multiple regions or user groups. This solution brings that backend process into a structured, front-end experience, allowing administrators to manage user roles and data access through a single and intuitive interface within the app.
How it works
At the core of the setup are two modules: one for defining user-level configurations such as model access and hierarchy level, and another for managing the specific items to which users have selective access. Dynamic Cell Access (DCA) logic drives which parts of the input tables are editable based on user selections, ensuring consistency and control. A six-step process then streamlines the backend updates — resetting previous access, assigning new access, and syncing everything to the Anaplan Users tab with a single click.
Core benefits
This approach significantly reduces the time and effort needed for user onboarding and access maintenance. Instead of manually editing the Users tab, administrators can perform all actions from a guided interface, minimizing errors and removing the need for backend navigation. It also improves governance by enforcing structured inputs and ensuring that model roles and selective access levels follow the organization’s hierarchy and security design. Overall, it enhances scalability and provides a more user-friendly experience for workspace administrators.
Key system behaviors discovered
During development, several system behaviors were uncovered that are crucial for making this process work. For instance, when importing selective access data, Anaplan only accepts reference codes from numbered lists as text-formatted values — not display names or list codes. The process also relies on hidden “None” columns in the Users tab to properly reset user access. Another key finding was that save views must be flat, with all dimensions in rows; otherwise, imports won’t process correctly. Lastly, while there are displayed Write and Read columns inside lists that have selective access enabled, these are columns that cannot be imported into. These insights were instrumental in achieving a fully automated and reliable workflow.
The resulting framework provides a robust foundation for managing user access at scale, and it can easily be extended to handle additional logic such as read/write permissions or role derivations based on model selections. By moving complex backend processes into a guided front-end interface, this solution not only streamlines administration but also deepens understanding of how Anaplan handles user and access data under the hood. It’s a strong example of how automation and thoughtful model design can transform a common pain point into a seamless management experience.
Video
https://play.vidyard.com/F5P8PSFMmtJ8CB63Y6JN3j
Questions? Leave a comment!
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Discover how Anaplan turns complex workforce simulations into real-time insight
Author: Judee Dimapilis is a Certified Master Anaplanner and co-founder at Malaya Tech Consulting.
Preparing for a major business launch is always complex, especially in the gaming and leisure industry where coordinating different departments, opening phases, and staffing requirements add another layer of operational challenges.
Whether you’re gearing up for a soft launch, a grand opening, or a phased rollout across several sites, business leaders must answer critical questions like below:
❓ How many staff will each department need?
❓ When should deployment begin?
❓ When should hiring start?
❓ What is the manpower cost for each scenario, each department, and in some cases for each employee?
💡 These decisions are not just operational, they influence customer experience, staffing readiness and financial performance from day one. And to add, it reflects the culture of the company, values that customers feel, remember, and one that keeps them coming back.
This is precisely where Anaplan demonstrates its strengths, giving organizations the ability to model complex scenarios, strategically plan workforce requirements, and efficiently forecast budgets.
✅ Simulate every scenario with ease
Planners start by inputting key variables, including:
* Opening dates (soft launch, grand opening, or phased rollouts)
* Deployment lead times by department
* Hiring lead times by role
* Employee cost assumptions
* Override options — If needed, planners can override opening dates per department for special cases
Sample input tables
➡️ Opening Category Table
Opening Category
Opening Date
Soft Launch
01-Aug-2025
Grand Opening
15-Sep-2025
➡️ Department Setup Table
Department
Opening Category
Months Before Opening (Deployment)
Months Before Deployment (Hiring)
Override Opening Date
Hotel – Wing 1
Soft Launch
2
1
—
Hotel – Wing 2
Grand Opening
3
2
10-Sep-2025
F&B – Wing 1
Soft Launch
1
1
—
F&B – Wing 2
Grand Opening
2
1
—
Gaming Floor 1
Soft Launch
4
2
—
Gaming Floor 2
Grand Opening
3
2
—
➡️ Employee Cost Table
Employee Role
Monthly Salary
Benefits %
Training Cost
Gaming Attendant
2,000
10%
300
Hotel Staff
2,200
12%
200
F&B Service Staff
1,800
8%
150
With these inputs, Anaplan instantly calculates:
* Target deployment dates per department
* Hiring start dates
* Staffing requirements per role
* Cost simulations down to the individual employee
* Roll-up budgets for departments, cost centers, and the entire company
This gives teams a clear picture of the financial and operational impact of every staffing and scheduling decision before it happens.
✅ Real-time simulation down to the employee level
A standout capability of Anaplan is its ability to simulate costs at per employee level. Powered by the combination of Hyperblock and Hypermodel technologies, Anaplan can:
* Manage large, multidimensional calculations
* Run detailed staffing simulations for each employee
* Scale easily as departments, roles, and scenarios expand
💡 For gaming and leisure organizations, determining the exact cost impact of hiring 10 gaming attendants vs. 15, this level of granularity is essential.
Sample output report
1 - Deployment and hiring timeline summary
Department
Opening Date Used
Deployment Date
Hiring Start Date
Total Staff Needed
Hotel – Wing 1
01-Aug-2025
01-Jun-2025
01-May-2025
45
Hotel – Wing 2
10-Sep-2025
10-Jun-2025
10-Apr-2025
38
F&B – Wing 1
01-Aug-2025
01-Jul-2025
01-Jun-2025
52
F&B – Wing 2
15-Sep-2025
15-Jul-2025
15-Jun-2025
40
Gaming Floor 1
01-Aug-2025
01-Apr-2025
01-Feb-2025
80
Gaming Floor 2
15-Sep-2025
15-Jun-2025
15-Apr-2025
95
2 - Employee-level cost simulation
Department
Avg Cost per Employee
Staff Count
Total Manpower Cost
Hotel – Wing 1
2,420
45
108,900
Hotel – Wing 2
2,350
38
89,300
F&B – Wing 1
2,050
52
106,600
F&B – Wing 2
1,980
40
79,200
Gaming Floor 1
2,150
80
172,000
Gaming Floor 2
2,200
95
209,000
3 - Company-wide cost roll-up
Total Cost
Hotel Division
198,200
F&B Division
185,800
Gaming Floors
381,000
Company Total
765,000
✅ Effortless roll-ups from employee to company level
Once costs are calculated at the employee level, Anaplan’s hierarchical lists automatically aggregate data into:
* Department totals
* Cost center totals
* Company totals
No manual consolidation. No risk of version inconsistencies. No more crashed worksheets. Just one reliable source of truth and real-time financial information.
4 - Executive dashboard highlights
Anaplan dashboards give leaders visibility into staffing readiness, hiring timelines, and budget status.
For eg.
🔵 Soft launch staffing readiness: 92%
🟡 Grand opening staffing readiness: 78%
🔴 Critical gap: Gaming Floor 2 requires hiring to begin earlier
💰 Budget status: On track; variance +2.4%
⚙️ Next action: HR to begin recruitment for Gaming Floor 2 by 15-Apr-2025
✅ From individual employees to strategic insights
Anaplan enables organizations to analyze workforce plans at the most granular level and then automatically roll them up to higher totals.
* Detailed visibility at employee level
* Department or cost center-level budget oversight
* Company-wide projections
This empowers leaders to align staffing, schedules, and budgets for every launch scenario with confidence.
✅ Real-time results for agile decision-making
Whether planning for a soft launch, grand opening, or phased rollout across hotel wings, F&B outlets, and gaming floors, Anaplan brings clarity to complexity.
By turning complex workforce planning into real-time insights, gaming and leisure companies can:
* Align staffing and budget with operational needs
* Make faster, more strategic decisions
* Execute every launch smoothly and successfully
💡 Anaplan transforms scenario planning into a strategic advantage — helping organizations plan smarter, act faster, and perform better at every opening.
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Interviewing and onboarding Anaplanners
Author: Andrew Barnett is a Certified Master Anaplanner and Vice President at PJT Partners.
Having worked at several firms in the Anaplan ecosystem, both on the partner side and as a customer, I’ve seen firsthand how critical it is to hire and develop the right Anaplan talent. Bringing an experienced Anaplanner onto your team and successfully onboarding new model builders are crucial steps in growing an Anaplan capability. In this post, I’ll share personal insights on what I’ve seen work (and not) in interviewing experienced Anaplanners and in training up new ones from scratch.
When interviewing candidates with Anaplan experience, I focus on three key areas: technical skills, relevant experience, and personality/culture fit. Covering all three gives a more accurate view of the candidate’s suitability for the role and the team.
Technical assessment: In my experience, technical interviews for Anaplan roles usually take one of three forms: a take-home modeling exercise, a knowledge test (written or verbal Q&A), or a live problem-solving session. Each has pros and cons, but the live exercise tends to be the most revealing.
Experience: Beyond technical ability, I ask about the candidate’s Anaplan project experience. What types of models have they built, and in what business areas? What was their role in those projects? This helps me gauge depth of practical knowledge and whether their background aligns with our needs.
Personality/team fit: Anaplan modeling is collaborative — model builders work closely with end users, stakeholders, and other Anaplanners. I look for strong communication skills, a problem-solving mindset, and a constructive, low-ego approach. A few targeted behavioral questions often provide a clear signal on how they’ll show up day-to-day.
Of the technical assessment methods listed above, the live problem-solving exercise has given me the best insight into a candidate’s capabilities. There’s nothing like watching someone tackle an Anaplan problem in real time to reveal their true skill level.
For this, I’ll prepare a simplified real-world scenario and ask the candidate to troubleshoot it with me live. As they work through it, I observe how they navigate the model, isolate the issue, and explain the reasoning behind each step.
This approach shows how a candidate thinks on their feet. Strong candidates will methodically identify assumptions, test hypotheses quickly, and keep the end-user outcome in mind. I’ve seen highly certified candidates struggle in a hands-on test, while others with fewer credentials excel, reinforcing my belief that performance in a live exercise matters more than badges alone. If you can include a live exercise in your hiring process, I highly recommend it; it’s the closest proxy for real work you’ll find in an interview.
Skilled Anaplanners are in high demand, so many teams will need to grow their own talent. Whether you’re upskilling an internal employee or hiring someone new to Anaplan, a structured onboarding program is critical. The best approaches I’ve seen combine Anaplan’s learning resources with realistic internal simulations.
I’ve seen two firms handle it particularly well:
* “Basics + Project” approach (Akili): Early in my career, before today’s structured training ecosystem existed, new model builders started with foundational Anaplan training to cover the essentials followed by a sample project. In this sample project, new hires received data files and business requirements that resembled a client use case and were asked to build a simple model to meet those needs. After a short build period, they presented their solution to the team, walking through why they made the design decisions they did. This was an incredibly effective way to accelerate learning and build confidence. It also gave managers a practical view of who was ready for more complex work and who needed additional support.
* Comprehensive blended program (Allitix): Years later, I saw an even stronger approach that intentionally fused Anaplan’s structured learning path with internal simulations. The agenda included the formal Anaplan certification track alongside other important Anaplan courses, followed by a sample project. What I appreciated most was that this program wasn’t just for entry-level model builders. It also included more advanced sample projects for experienced hires and people looking to move into more senior roles. That type of tiered development is rare, and it’s a powerful way to create a consistent bar for progression while keeping high performers engaged.
The common thread between these successful programs is the marriage of theory and practice. Formal training gives you the vocabulary, patterns, and best practices. Hands-on simulation make you apply that knowledge.
This mirrors how people learn to code: the fastest growth happens when you build something real that matters. The same is true in Anaplan. You can understand model design principles conceptually, but you only internalize them when you wrestle with real data, tradeoffs, and stakeholder expectations.
Investing in thoughtful interviewing and onboarding for Anaplanners pays off. When hiring experienced talent, go beyond standard Q&A and check how they solve problems in the moment. When building new talent, pair Anaplan’s learning resources with structured, real-world simulations that reflect the work your team actually does.
In my experience, teams that get these two processes right build stronger models, earn trust faster, and scale their Anaplan capabilities with far less friction.
Good luck and happy planning!
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