Case Study: Enhancing Process Efficiency through Automation in an IT Multinational Company 🚀

Introduction

In my role as a Management Information (MI) Lead at a leading IT multinational company, I spearheaded a significant transformation program aimed at improving business processes and performance. My primary responsibility was to identify areas ripe for automation by analyzing business processes and performance data. This case study details an initiative that successfully automated a key process, leading to substantial improvements in efficiency and revenue.

Identifying the Problem 🔍

Initial Observations

During the initial phase of the transformation program, I identified a critical bottleneck within the Operations teams. A team of 10 full-time equivalents (FTEs) was tasked with reviewing and reporting on ticket quality. However, several inefficiencies hampered their efforts:

1. Low Sample Size: Due to the manual nature of the process, the team could only evaluate a small sample size of fewer than 10-30 tickets per week. This limited sample size raised concerns about data accuracy and reliability.
2. Ineffective Communication Strategy: The weekly reports generated by the team were not being utilized effectively during performance reviews. This lack of engagement meant that there was no feedback loop to drive necessary improvements.

Deep-Dive Analysis 🔬

Through detailed discussions with the team, I uncovered several key weaknesses:

• Manual Limitations: The manual process was time-consuming and constrained by the small sample size, leading to questionable data accuracy.
• Overloaded Metrics: The team used 10 different quality metrics, which added complexity without delivering clear insights.
• Lack of Feedback Loop: The results of the ticket quality reviews did not reach the intended audience effectively, preventing any meaningful action or resolution.

Developing the Solution 💡

Building the Business Case

Recognizing the need for improvement, I collaborated with the data analyst team assigned to the transformation program. Together, we evaluated the insights and concluded that the issue warranted further exploration for potential automation solutions.

Facilitating Collaboration 🤝

I facilitated review calls involving the team and the data analysts to assess the current process and explore viable automation options. This collaborative effort was crucial in ensuring that all stakeholders were aligned and that the proposed solution was feasible and effective.

Implementing Automation 🤖

The analysis revealed that 8 out of the 10 metrics could be automated. We decided to implement the Deming Cycle (Plan-Do-Check-Act) and lean management tools and techniques to guide the automation process. The key steps included:

1. Sanity Check on Metrics: We reviewed the existing metrics and confirmed that the 8 automatable metrics provided a realistic picture of the team’s performance.
2. Process Automation: We automated the manual tasks, eliminating the limitations related to sample size.
3. Efficiency Gains: The automation resulted in the equivalent of 10 FTEs’ work, significantly contributing to the financial objectives of the transformation program.
4. Enhanced Reporting: We developed a comprehensive report highlighting key performance metrics, which became integral to operational performance discussions and the MI framework.

Results and Impact 🌟

The automation initiative led to several positive outcomes:

• Improved Data Accuracy: Automation allowed for a larger sample size, enhancing the reliability and accuracy of the data.
• Increased Efficiency: The manual workload was significantly reduced, freeing up resources for other critical tasks.
• Effective Feedback Loop: The new report structure ensured that performance insights were effectively communicated, facilitating timely actions and resolutions.

Lessons Learned 📚

This initiative underscored several important lessons:

1. Data-Driven Decision Making: Always begin by analyzing available data to draw informed conclusions and initiate meaningful discussions.
2. Information Gathering: If data is unavailable, use methods like Day In the Life Of (DILO) or Week In the Life Of (WILO) to collect necessary information.
3. Collaborative Approach: Engage all relevant stakeholders to ensure alignment and feasibility of proposed solutions.
4. Continuous Improvement: Utilize frameworks like the Deming Cycle to guide the process of automation and ensure continuous improvement.

Conclusion 🎉

The successful automation of the ticket quality review process not only improved efficiency and accuracy but also reinforced the importance of data-driven decision-making and collaborative problem-solving. This case study exemplifies how strategic automation can lead to significant process improvements and contribute to overarching business objectives.