Anaplan Connect API

Anaplan Connect enables a user with Administrator credentials to automate the running of Anaplan actions, avoiding the need for Anaplan GUI’s

What is Anaplan Connect?

Anaplan Connect is an API Client with a command-line interface that supports the following types of Anaplan actions:

  • Import
  • Export
  • Delete – eg. Delete from List using Selection to remove specific items from a list.
  • Process – a combination import/export actions

Why use Anaplan Connect?

  • Anaplan actions can be run outside the normal Anaplan GUI. Actions can be scheduled to run automatically at a defined interval.
  • Limiting the need for user import Dashboards and having a more direct feed to lists and module input values
  • Quicker and cheaper to set up than other integration tools

How to use Anaplan Connect

  1. The Anaplan Connect API can be downloaded from Anapedia.
  2. Create the required actions in your model.
  3. Create your batch (.bat) files to call your commands. Each batch file needs to contain:
    1. Workspace ID. This is unique and will not change.
    2. Model ID. This is unique to each model and will not change.
    3. Set the Operation Type, which includes:
      1. Anaplan Action. A specific Import or Export
      2. Credentials: The User Name and Password, unless you are using Certificate-based Authentication

When writing your Batch file there are good example files included within the Anaplan Connect Client install and the support pdf on Anapedia to assist you.

What can Anaplan Connect do?

  • Run an Import Action to import data direct to your Model from databases such as Sequel and Oracle or from simple text .txt or .csv files.
  • Delete data, List items using Selection.
  • Export module or List content to Excel workbooks, txt or csv files

Alternatives to Anaplan Connect

In addition to Anaplan Connect, there are several data integration connectors to support integration between Anaplan and third-party applications, including:

  • Dell Boomi
  • Informatica
  • MuleSoft
  • SnapLogic
  • Tableau

Anaplan also provides integration support with SalesForce.com, allowing any SalesForce.com user access to Anaplan from within SalesForce.com.

If you’re looking for more information regarding Anaplan integration approaches, please get in touch with us.

Dynamic Cell Access in Anaplan

Last week I was given a customer requirement which allowed me to use a new Anaplan feature – Dynamic Cell Access.

My remit was to limit a user to be able to map/configure only a single option from the specific list’s items and disable the other options.

For example, if the user needed to set a ‘Proficiency level’ for a particular job role, the functionality had to disable the other options once one option had been selected or leave all options open, writeable.

The Proficiency Level for an Area Manager for a particular role had to be set as one of the following, Expert, Practitioner or Foundation level and once one had been set to TRUE the other options/list items should be read only.

What is Dynamic Cell Access?

Dynamic Cell Access (DCA) is a powerful tool in an Anaplan Architects toolbox giving them the ability to set Read, Write or no access, to a cell, an entire row or column.

When I investigated further, I looked at the Anaplan’s tutorial app and was impressed by the variety of uses and how helpful it could be.

With DCA, a model builder can provide the ability to control a users’ read or write access from a dashboard, rather than the old and more conventional way of a Workspace Admin setting this access via the settings tab.

For example, if your model had a list of Regions but only certain users should see or have write access to particular data for specific Regions items.

By creating a module dimensioned by users and a particular list – in this case our Regions list – and using Boolean line items, we can build a dashboard that displays a nice user-friendly way of setting these permissions to Read or Write or not showing the cell values at all.

I was also very impressed after looking at the Learning App on the various uses from straightforward to the more complicated solutions, on how DCA can add value to a model’s design.

Some Examples…

Summaries and Subtotals

You can use DCA to apply different security at different levels of a hierarchy. Very powerful if you want to suppress total levels for certain users. For example, in a line which calculates employee salaries, you could use DCA to control a users’ ability to see individual salaries, and then also use DCA to hide the summary totals from them (as this might allows them to calculate the missing values!)

Controlling Workflow and User Experience

When Actual data for a year is mixed with Forecasting data, in the months where Actuals existed a planning adjustment line item could be made read only or invisible so nothing could be entered into these cells, eliminating the need for validations.

Another good use of DCA could be where we want to create a workflow. For example, approval might be required before the next steps of data entry can begin. Lines Items can be set to read only until approval had been granted.

The uses in fact are endless and I for one will be incorporating more Dynamic Cell Access into my future Models

Using Anaplan for Supply Chain Planning

We hear from many prospective customers about the challenges they face with accurately and efficiently conducting their supply chain planning. One area where we’ve gained a lot of experience is helping customers enhance their S&OP processes. Historically, these businesses would have relied on Microsoft Excel and Access solutions to complete their planning, but the reality of using these tools is often far from ideal. The sort of challenges they face are as follows:

• Capturing inputs from multiple users across the business is tricky. Spreadsheets end up being emailed around the organisation and then linked back together in a confusing web of linked spreadsheets.
• The level of granularity that can be achieved through a spreadsheet may not be enough for the business to plan accurately.
• Building in the ability to calculate a statistical forecast or seasonality can be tricky in Excel.
• Sharing the results of a forecast across the business can be slow, especially when different versions of the truth emerge. It can then be very time consuming to produce reporting packs which may end up needing to be redone if the plan gets updated.

Mentat Technology has worked with several customers to deliver S&OP solutions in Anaplan. We’ve helped these customers remove the limitations presented by their old solutions and realise the benefits of implementing a cloud-based, integrated model. In turn, these companies have gone on to roll-out Anaplan across other parts of their organisation such as Finance, thereby leveraging the benefits of connected planning.
Our customers have reported significant reductions in the time it takes to produce forecasts, and that has allowed them to increase their forecasting frequency. Some customers have chosen to implement statistical forecasts that greatly improve the accuracy of their sales forecast, to the point where the accuracies of these statistical forecasts beat those of their own sales team!

Read more about how Mentat Technology have helped Tarmac use Anaplan for their supply chain planning.
If you would like to learn more about how Mentat Technology can help with supply chain planning, please contact us.
Read more about Supply Chain planning in Anaplan 

Anaplan Best Practices – Large Data Volumes

How can we use Anaplan to reduce cell sparsity from 360 million, to only 5 million cells?

It is a very common scenario where our clients need to be able to load transaction level data into Anaplan. In most cases this is due to bottom-up calculations, external system restrictions or low-level allocations/mappings. Large volumes of data significantly impact Anaplan model size and can even affect the performance, therefore we would like to share a couple of design techniques that will make the model efficient.

Staging lists

The requirement to import transaction level data doesn’t mean such high level of details has to be permanently stored within the model. In the vast majority of cases the main driver for high volume data loads are low level allocations and mappings between multiple data sources, but after the initial processing the details are no longer required. In such a scenario, using a staging list approach is the best design solution.

Staging lists process:

1. The data is initially loaded (via user action or automated process) into the model
2. System validates the data and runs low level calculations
3. User reviews results and validations (optional backup export can be enabled at this stage as well)
4. User submits the results by running a process (dashboard button)
5. The system aggregates the data using predefined unique key to a level that provides all required attributes and assures optimal size and performance efficiency
6. Source data loaded in p1 is being removed from the model

The design provides high quality bottom-up results and eliminates ineffective workspace utilisation and performance loses.

Multi-level Numbered lists

Multi-dimensional data cubes are the default way in Anaplan to deliver required results, but in some cases these prove to be inefficient or even not possible – this is when numbered list hierarchies prove to be the best solution.
To illustrate the efficiency of the technique we will use an example of a Claims triangle report for the purpose of Solvency II reporting.
In order to build the report, we typically need the following details: Reserving Class, Policy, Risk Code, Year of Account, Development Period. The example volumes are as follows:
– Reserving Cl = 30 items
– Policies = 20,000
– Risk Codes = 80
– YoA = 15
– Dev Period = 15
If all the attribute lists were added to a module it would result in following size for each measure (Res Cl is removed from calculation as it’s a direct parent of policy):
20,000 x 80 x 15 x 15 = 360,000,000
Even this simplified and fairly low volume example results in significant report size.

The key for efficient design is to understand the data – in this example, it is logical that not all the policies will have values for every YoA or development period, let’s assume an average of 7 years/periods per policy, on top of that a single policy usually does not have more than 5 unique risk codes. Following numbered list hierarchy would be created:
– L1 Reserving Class
– L2 Policies
– L3 Risk Codes (unique combination of RCs applicable for each policy)
– L4 YoA (unique combination of valid Policy, RC and YoA)
– L5 Dev Period (unique combination of valid Policy, RC, YoA and Dev Periods)
The total size of a single measure using above mentioned assumptions would be:
30 (Res Cl) + 20,000 * 5 * 7 * 7 = 4,900,030
As we can see the numbered list approach is significantly more efficient and the results can easily be exported in a pivot-friendly format or sent to other external systems.

These two techniques prove to be very powerful when processing large volumes of data and can either be used individually or in conjunction to provide the best solution for our client.

Anaplan Partner Hub

This week we attended Anaplan’s first full day Partner Hub in London. It was a chance to learn more about the platform changes being planned for release in the short, near and long-term and to also hear about their vision for how they see the product evolving over the coming years.

There was too much covered during the day to share everything, but some of the highlights for us were the demonstrations we saw of the new workflow functionality and Excel add-in. Anaplan have clearly invested a lot of time and resources in redeveloping both of these – and the results are impressive.

The new Anaplan workflow, or ‘Tasks’ as we will probably come to know it, allows planning cycles to be much more task driven. Instead of accessing their planning dashboards within the models as they would have done previously, users can receive an email notification which will take them to a new Tasks portal. From here they can see all tasks assigned to themselves and complete each one in order – without leaving the portal.

The new Excel add-in currently in development has made huge improvements since the current release. One major change in the new build is that the add-in allows users to manage the data they retrieve themselves, rather than relying on a model builder to create the appropriate views within the model. As a result, users can be more selective about what data they pull in to Excel from Anaplan. We have also seen big improvements in how data can be pivoted and sliced once it is in Excel.

If you would like to understand more about upcoming changes to Anaplan, please contact us.