The Scenario

A University experienced significant setbacks when its automation systems, critical for supporting its business processes, were approaching the end of their lifecycle. This created operational risks, including system failures and downtime, which impacted the university’s ability to efficiently serve its clientele. The situation worsened after the exit of the university’s lead computer programmer, a key individual who had been employed on a contractual basis. His dissatisfaction with the offered salary led him to leave, leaving the university without the technical expertise needed for system upgrades.

Situation and Key Events

1. Automation System at Risk of Failure. The university's outdated automation systems were near the end of their life, risking failure and causing potential downtime that could disrupt operations.
2. Exit of the Lead Programmer. The university’s key programmer left due to dissatisfaction with his pay, creating a gap in technical expertise and halting system upgrades.
3. Inefficient Internal Solutions. To solve the problem, the university formed committees, but none could provide a solution. This caused delays, worsening the issue and frustrating clients.
4. Decision to Outsource the Original Programmer. After failed internal efforts, the university rehired the original programmer. He resolved the complex system issues and upgraded processes within just ten days.
5. Discrepancy in the Invoice for Services. The programmer invoiced P300,000 for his work, but explained that only P50,000 was for direct expenses. The remaining P250,000 raised questions, as the university sought to understand the value of the services beyond the basic costs.

Automation System at Risk of Failure

As technology ages, it naturally becomes prone to failures, especially in complex systems like business process automation. In this case, the University's automation system was reaching the end of its lifecycle, leading to operational risks. While the immediate focus might have been on preventing component failure, it is important to recognize that avoiding downtime is not just about replacing hardware but ensuring seamless integration and functionality.

While humans tend to underestimate automation reliability, several factors—such as the recency of automation errors and how participants view their own reliability in relation to the automation—play significant roles in shaping both reliability perception and the acceptance of automated advice (Hutchinson et.al., 2023).

Moreover, the university’s existing systems were integral to its business operations, and ensuring their stability and functionality would require expert knowledge of both the system’s infrastructure and business processes. The programmer’s deep understanding of the systems likely allowed him to efficiently identify and address potential points of failure—work that goes beyond simple maintenance.

Exit of the Lead Programmer

When the university’s top programmer left due to dissatisfaction with his salary and given the fact that his role is also only contractual, it created a significant gap in expertise. This points to a fundamental misjudgment by the university in undervaluing his role.

In the Philippines, contractual employees, also referred to as fixed-term or project-based employees, are individuals hired for a set duration or to fulfill a specific project. These employment arrangements are not meant to be permanent or ongoing. Both the employer and employee agree to the terms outlined in the fixed-term contract, which detail their respective rights and obligations, as well as the conditions for completion, payment, and termination of the employment (Arshad, 2024).

The impact of the programmer’s departure demonstrates the critical nature of his role. The fact that the university couldn’t move forward without him underscores his unique skill set and the specialized knowledge he possessed about the system. His eventual rehiring on a short-term contract suggests the university realized it had undervalued him initially.

Inefficient Internal Solutions and no Knowledge Transfer

When the programmer left, staff within the university tried to address this problem by setting up numerous internal committees. In trying to do so, they were unable to come up with any real solutions, demonstrating the obvious divide between broad IT knowledge and the nitty gritty of managing and developing complex business automation systems. This slow progress highlights the importance knowledge transfer (KT) in software, as sharing it provides the ability to spread necessary capabilities, information and experience between your employees. When implemented properly, it will ensure that the critical knowledge required to remain productive over time is retained and passed on, without a situation arising where operations grind to halt due to unforeseen skill gaps (Gallemard, 2023).

Without proper knowledge transfer, no one else possessed the specialized understanding required to resolve the problem. This delay not only contributed to inefficiencies but also demonstrated the limitations of general IT knowledge in managing such complex systems. In contrast, the programmer’s swift resolution upon his return highlighted the critical role his deep, specialized knowledge played, illustrating how effective KT could have prevented the issue.

Decision to Outsource the Original Programmer

After several failed attempts, the university chose to outsource the problem to the original programmer, who solved the issue in just ten days. This swift turnaround highlights his competence and ability to act under pressure, showcasing the value of his skill set.

The speed and efficiency with which the programmer fixed the problem further justify his high value. His ability to diagnose, fix, and upgrade the system in such a short period speaks volumes about his expertise. The fact that the system, which had stumped multiple committees, was repaired so quickly by the programmer reinforces the idea that his work was not just about time spent on tasks but the knowledge, precision, and high-level problem-solving skills he brought to the table. This expertise saved the university from prolonged downtime, potential reputational damage, and further inefficiency.

Discrepancy in the Invoice for Services Rendered

After completing the work, the programmer submitted an invoice for P300,000. When asked about the breakdown, he mentioned that P50,000 was spent on direct expenses, which left the university questioning the remaining P250,000. This discrepancy might seem concerning, but there are multiple reasons to justify this cost.

Justifying the Remaining P250,000:

1. Expertise and Knowledge. What the programmer charged for was not just the time or materials used, but the years of experience, specialized skills, and intricate knowledge that allowed him to resolve the issue so quickly. Just as a highly skilled doctor charges significantly more than a general practitioner, a specialist in business process automation brings valuable expertise that comes with a premium cost.
2. Speed and Efficiency. The fact that the problem was solved in just ten days is a testament to the programmer’s efficiency. The university was likely losing money every day that the system was not functioning properly due to the loss of productivity and dissatisfied clients. By solving the issue so quickly, the programmer saved the university from much larger potential losses.
3. Value-Based Pricing. The programmer provided a solution that no one else could deliver. His value lies not in the number of days he worked but in the resolution of a critical problem that no other internal team could solve. Pricing based on the value delivered—especially when the outcome has such a high impact—is a common practice in high-skilled technical fields. His fee is a reflection of the business impact of his solution, not just the materials or time involved.

The DIKW Model: Something the University could’ve applied

The DIKW (Data, Information, Knowledge, Wisdom) model can help the University address their automation issues by turning data into actionable solutions. First, data from the failing systems, like error logs or system reports, can be analyzed to extract useful information. This information, combined with the programmer’s expertise, becomes knowledge that identifies the root causes of the problem and guides the necessary system upgrades. The programmer's unique skills allowed him to quickly move from information to knowledge, enabling him to fix the issue within ten days (The DIKW Model for Knowledge Management and Data Value Extraction, 2024).

By outsourcing the programmer, the university demonstrated wisdom, recognizing that their internal teams lacked the expertise to solve the problem. While the direct costs were P50,000, the remaining P250,000 reflects the value of the programmer's experience in preventing further downtime and inefficiencies. The DIKW model shows that the real value of data lies in making smart decisions and taking action, which the university achieved by bringing back the programmer to restore their systems.

Conclusion

In this case, even though the direct costs for materials and time were relatively low, the programmer's skills and the value he brought to the university justify the extra P250,000 in his invoice. The university realized they couldn’t solve the problem without him, showing that his payment was not just about covering expenses but also about the important problems he fixed. The programmer's specialized skills and quick work helped the university avoid serious problems, making his fees reasonable.

Moreover, the programmer’s ability to fix the system not only solved the immediate issues but also helped the university improve for the future. By learning from him, the university can understand its systems better and take steps to prevent similar problems later on. This investment in his expertise ultimately makes the university stronger and better prepared for technology challenges in the future, offering a good return on the money spent.

References

• Arshad, S. (2024, July 15). Guide to Contractual Employees in the Philippines. RecruitGo. https://recruitgo.com/blog/guide-to-contractual-employees-in-the-philippines/

• ‌Gallemard, J. (2023, March 2). The Basics of Knowledge Transfer: A Beginner’s Guide. Smart-Tribune.com; Smart Tribune. https://blog.smart-tribune.com/en/knowledge-transfer#:~:text=Knowledge%20transfer%20(or%20KT%20meaning,other%20individuals%20in%20a%20business.

• Hutchinson, J., Strickland, L., Farrell, S., & Loft, S. (2023). The perception of automation reliability and acceptance of automated advice. Human Factors, 65(8), 1596-1612.

• The DIKW model for knowledge management and data value extraction. (2024, March 19). I-SCOOP. https://www.i-scoop.eu/big-data-action-value-context/dikw-model/#:~:text=The%20DIKW%20model%20or%20DIKW,we%20do%20in%20digital%20transformation.