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Automating the Model Building Process

By Dan Kogan  January 10, 2019

The Why: Reasons for Automation

The holy grail of supply chain design is to have a ‘living model’ at your fingertips.  From this living model, you can quickly test scenarios by pulling the supply chain levers under consideration and assessing the impact to key metrics.  While extremely valuable insights are unlocked by applying scenarios to the living model, this metaphoric pot of gold is far-too-often at the end of an onerous trudge through data management and baseline modeling.

With such extensive effort expended on data management and baseline modeling, the ultimate question becomes, “how can you accelerate the time to value?”  As with any defined process, data management and baseline modeling can be largely automated.  Moreover, the upfront effort of such automation may be less than you would think.

Whether supply chain design is a core competency or a support function within your organization, there are likely some common themes across all of your modeling initiatives.  Perhaps they consistently revolve around decisions of capital expenditure, how to best serve a new market or the impact of rationalizing assets.  While the investments, geographies, property and equipment in question may be different each time, the structure of the automation pertains less to the data and more to the business question itself.  To this end, automating the model building process creates value in the following manner:

Repeatability over time

Repeatability over time is perhaps the most obvious reason to automate.  If you know you will be refreshing a model with any regularity, why reinvent the wheel again later?  Broadly, there are two use cases for refreshing a model – routine-driven and event-driven – and while the former is generally known from the start to require a future refresh, the latter is often realized only after the fact.  Below are some examples of each:

  • Routine-driven (i.e. annual capital budgeting; weekly sales and operations planning)
  • Event-driven (i.e. fuel price spike; unexpected disaster such as a port strike or facility fire; political uncertainties; availability of new commercial real estate)

Repeatability across markets

The quality of the insight you provide through your models will stimulate a thirst for more within your organization (“Congratulations for your hard work, you’ve been rewarded with more work!”).  Once you’ve proven your value through a specific project, it will be prevalent in leadership’s collective mind to call upon you for similar value in the future.  Perhaps the examples below sound familiar:

  • Geographies (i.e. “Can we get a site selection analysis for Australia just like we did for Brazil?”)
  • Business units (i.e. “I’d like us to rationalize sourcing assignments for glass, just like we did for aluminum.”)

Remember, while the input data itself may change, the structure of the model and the vast majority of the corresponding automation pertains more to the type of business question itself.  Furthermore, your leadership will be able to more quickly digest standard outputs in a standard manner to how they were constructed for prior projects.

Repeatability across team members

Help someone else the way you wish someone had for you.  Help set yourself up for success.  Help your team grow and evolve.

  • Enable coverage during planned or unexpected out-of-offices (“I can’t believe Dan became a finalist on Jeopardy a week before the analysis is due!”)
  • Facilitate specialization and division of labor (i.e. allow more senior team members to focus on modeling approach and scenarios, by transitioning execution of a data workflow to more junior team members)
  • Enable quick recall of an older project (“It’s been about a year since I did this, but the Data Guru workflow will help us remember why we did it this way.”)

Again, standardization drives benefits through an easy-to-follow workflow, self-documented through the use of Data Guru to visually display pathways of intuitive icons representing specific data transformations and the respective inputs and outputs of each process step.

Repeatability within a project

Even in circumstances where design projects are legitimately ‘one-off’ requests, the low barrier to automation coupled with the likely need for iterative re-querying of data and adjusting filters and modifying aggregation structure may still prove a net benefit within a single project.

  • Even if the model approach and structure is not-fully-defined (is it ever?), Data Guru makes experimenting and testing a highly modular and configurable activity.
  • Data Guru’s self-documenting nature provides visibility to key data decisions and assumptions used in the model-building process.

In the aforementioned circumstances, automating the model building process accelerates the time to unlock the valuable insights desired by you and your stakeholders.  With this, the question truly becomes, what are your reasons not to automate?

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