Blog March 25, 2024

Unpacking Supply Chain Data for Successful Automation Implementation

By Daniel Wang
March 25th | 2 min read

Supply chain automation is a game-changer for businesses, but to make it work, you need to focus on the right data points. Let’s break down what supply chain data really matters when deciding if automation is right for your supply chain.

Supply Chain Data: What Do I Need to Evaluate Automation?

Businesses seeking to enhance efficiency, reduce costs, and stay competitive are now prioritizing automation in distribution centers. However, businesses often face complex systems that require significant capital investments and careful planning for implementation. For this reason, carefully evaluating the true benefits of automation is essential, as challenges can sometimes obscure these benefits.

One common mistake businesses make when adopting automation is failing to establish clear objectives. Before engaging with vendors, it’s crucial to answer the question: What are my goals for automation? Whether it’s to resolve operational challenges, redefine tactical solutions, or advance a strategic objective, having well-defined objectives provides a benchmark for evaluating automation solutions.

Partners typically consider automation to achieve three primary objectives: reducing costs, improving throughput, and enhancing labor availability. Each objective demands a specific assessment approach with key data considerations for each.

 

Cost Reduction:

Automation often promises labor efficiency gains, but it’s critical to look beyond the rates promised for specific tasks and consider the holistic labor landscape. Oftentimes, the incremental efficiency gains compared to a streamlined conventional operation or even semi-automated solution is much slimmer than originally anticipated. Key data points to consider while calculating this include:

  • Current labor rates by function
  • Benchmark productivity rates
  • Labor improvements from alternative solutions
  • Cost of alternative solutions
  • Labor required for automation
    • Implementation costs and timeline
    • Posted labor rates
    • Additional labor processes
    • Systematic downtime (ex. set-up, changeover, maintenance)

 

Throughput Improvement:

Introducing automation can be a good way to improve your maximum throughput. However, efficiency gains are typically achieved through strict rulesets. Understanding these clearly and how they impact your processes are key to discerning if and how much you can increase capacity by. Key data points to consider are:

  • Current and maximum possible throughput
  • Efficiency losses at maximum throughput (What is the cost of each incremental labor unit at maximum throughput?)
  • Assessment of alternatives including conventional options
  • Evaluation of incremental gains and costs between alternatives

 

Labor Availability:

Automating tasks that are repeatable, strenuous, or awkward can be a good way to defend against low labor availability. Your staffing efforts may be limited by both your local supply and the attractiveness of your role. Key data points to think about when considering this benefit are:

  • Evaluation of current attendance rates.
  • Assessment of the labor pool in the region, especially during peak seasons.
  • Consideration of potential constraints during peak seasons, including scarcity, competition, quality, and flexibility of labor.
  • Examination of the impact of labor availability on customer service levels and potential missed demand opportunities.

 

Conclusion:

When exploring new technologies, a practical and objective-driven approach is crucial to gaining a competitive edge. Considering key variables, defining clear objectives, and conducting thorough evaluations help avoid sunk costs, delays, and underwhelming benefits. By defining clear objectives, performing comprehensive evaluations, and analyzing key data points businesses can make informed decisions that align with strategic goals.

 


Reach out to the LIDD team to learn more about automation and managing data for effective supply chain performance.

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