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Kumar Singh

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Modeling Warehouse Picking Bots Implementation Costs

The Bots are coming

And sooner or later, you WILL have them in your warehouse. Now when exactly you should leap is a question that is not as straightforward as many want you to believe. Technology is evolving so rapidly that if you are in specific industries, making a move too early may mean that within a couple of years, you may be stuck with a fleet of bots that are half as competent as those your competitor has. There is a methodological way to analyze what is the optimal time since that analysis incorporates internal and external factors, but that analysis is not within the scope of this article. In this article, I will share my insights on how you can approach building a cost model for warehouse picking bots implementation.

The Cost categories

As you can see in the illustration below, the costs of implementation of picking bots can be categorized into the three categories shown:

  • Subscription related costs
  • Deployment & Integration Costs
  • Employee training costs

As indicated in the illustration above, there are parameters that you will need to incorporate into your model for each category.

Subscription-based costs

  • Number of regular Operations bots
  • Number of additional bots for peak season
  • Subscription cost/bot/month
  • One-time deployment fees

Deployment & Integration costs

  • Initial deployment & Integration time
  • Salaried supervisors involved
  • Supervisor salaries
  • % of time supervisors will spend on integration
  • IT resources involved
  • % of the time these IT resources will spend on integration
  • Fully loaded IT salary
  • Layout modifications costs

Employee Training costs

  • Number of permanent full-time employees involved
  • Hrs of training/employee
  • Complete loaded hourly wage-Full time
  • Number of part-time employees involved
  • Hrs of training/employee
  • Full loaded hourly wage-Part time
  • Trainer Costs

Note that these buckets and parameters are indicative so you can think about structuring your model accordingly. This suggested approach is not cast in stone and can be modified per requirements.

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Kumar Singh

Kumar Singh is the founder of Designed Analytics LLC, which is focused on helping organizations explore how to leverage data and analytics to effectively compete, thrive and innovate. Kumar has over a decade of hands-on experience in supply chain and operations analytics. With over a decade of industry experience, he has worked across multiple industries, helping companies set up analytics centers of excellence. Post his industry experience, Kumar also did a stint in external consulting as a data science consultant with Boston Consulting Group (BCG). Kumar holds an MSc. in AI from Liverpool John Moores University, a Masters in Supply Chain from The Ross School of Business at The University of Michigan, Ann Arbor, an MBA in Operations Management from IIT Roorkee, India, and an undergraduate degree in Electrical engineering. Kumar is ASCM CPIM, CSCP, and CLTD certified and holds the PMP certification from PMI. He is also an AWS-certified Machine Learning Specialist and Microsoft certified in the Azure IoT developer specialty.

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