ORG 716 Week 7 Emerging Trends and Issues Example

Algorithms on the Line and Engineers at Home: Emerging Issues for a Global Device Maker's Organization

[Student Name]

University of Phoenix

ORG/716: Organizational Theory and Design

Week 7 Assignment

[Instructor Name]

[Date]

Lakehead Medical Components, its plans, people and figures are composites written for a model paper.

Over six weeks, this analysis has followed Lakehead Medical Components, the invented molder and machinist of device parts, through its structure, leadership, cultures and people systems across four plants. The company now faces changes its leaders describe as unlike anything in its history: AI-based visual inspection, an algorithm that schedules operators, hybrid work for engineers and customers asking it to move production from Malaysia to Mexico or the United States. This paper asks what organizational theory can say about these changes and where it needs to develop.

Four Trends at One Company

AI visual inspection. Since last year, a camera and machine-learning system inspects molded catheter components at St. Paul, catching about 98 percent of defects against about 91 percent for human inspectors and working at line speed. Twelve inspector roles are affected.

Algorithmic scheduling. At Tijuana, a scheduling system assigns operators to lines each shift based on certifications, attendance history and demand, replacing supervisors' judgment. Supervisors say they no longer know why operators are placed where they are.

Hybrid work. Since 2021, process and quality engineers in Minnesota work from home two days a week. Engineering productivity measures are stable, but plant managers report that engineers are less available when problems occur on the floor.

Nearshoring. Two customers representing 22 percent of revenue have asked for second sources in North America, citing supply risk. Moving production from Penang, the best-performing site, would cost about $14 million and set back cell practices built over five years.

New Forms or Old Problems?

Puranam et al. (2014) argued that every organization must solve the same universal problems: dividing work into tasks, allocating tasks to people, providing rewards and providing information. They proposed that a form of organizing is new when it offers novel solutions to one or more of these problems, not merely because it uses new technology. Applying this test, algorithmic scheduling is a new solution to task allocation, replacing supervisors' judgment with rules applied by software. AI inspection is a new solution to task division, shifting inspection from people to machines and leaving people to handle exceptions. Hybrid work changes how information is provided, reducing the informal exchanges that coordinate engineers and operators. Nearshoring is an old problem, location of production, driven by new concerns about supply risk.

Most of what feels new at Lakehead is a new answer to an old question about who does what and who knows what.

Algorithms at Work

Kellogg et al. (2020) reviewed research on algorithms in the workplace and described how they enable new forms of control: directing workers through recommendations and restrictions, evaluating them through recording and rating and disciplining them through replacement and rewards. They noted that algorithmic control can be more comprehensive, instantaneous and opaque than earlier forms and described how workers resist through individual and collective tactics. At Tijuana, operators report that the algorithm repeatedly assigns them to the least preferred lines after an absence, which they experience as punishment without explanation. Supervisors, stripped of an important source of authority, cannot explain or override it. Turnover among recently absent operators rose after the system launched.

Automate or Augment?

Raisch and Krakowski (2021) described an automation-augmentation paradox: organizations can use AI to automate tasks, taking humans out, or to augment them, combining human and machine judgment, and the two depend on each other over time, since automation requires human knowledge to build and augmentation can shift into automation. For inspection, Lakehead's first plan was to cut the twelve inspector roles. An augmentation option would keep six inspectors as reviewers of the system's flagged parts and trainers of its models, preserving the human knowledge needed to update the system when new parts are introduced and satisfying FDA expectations for validated human oversight.

Hybrid Work and Coordination

Hybrid work for engineers reduces the informal, face-to-face exchange that Week 3 identified as a lateral coordination mechanism. Galbraith's information processing view, applied in Week 3, would predict that reduced coordination capacity must be replaced by other mechanisms. Lakehead has added scheduled floor hours and a video link from each line, with mixed results. The case shows how an established theory continues to explain a new practice.

Nearshoring and Institutional Pressure

Customers' demand for North American sources reflects both a rational concern with supply risk and institutional pressure, as large device firms copy each other's resilience programs. Moving work from Penang tests the people systems analyzed in Week 6, since the practices that make Penang successful took years to build and may not transfer, as Week 5 found.

Who Gains and Who Loses

Engineers gain flexibility from hybrid work; operators lose some access to engineering help. Inspectors' jobs are at risk under automation but could be redesigned under augmentation. Supervisors lose authority to algorithms. Penang's workforce faces job losses if work moves. Organizational choices, not technology alone, determine these outcomes.

What Leaders Are Deciding Now

The leadership team must make three decisions in the coming year. On inspection, it is weighing automation against augmentation; the analysis above favors keeping six inspectors as reviewers and model trainers. On scheduling, it can keep the algorithm as it is, give supervisors the right to override it with a recorded reason or make its rules visible to operators; research on algorithmic control suggests that transparency and a human appeal path reduce resistance. On nearshoring, it can move work from Penang, add capacity in Tijuana while keeping Penang's volume or offer customers dual qualification without moving existing parts. Each decision trades efficiency against the human knowledge and relationships earlier weeks showed to be the company's real assets.

Effects on the Hybrid Design

These trends also test the structure recommended in Week 3. Algorithmic scheduling centralizes a decision that the hybrid design meant to push to sites. AI inspection creates a new kind of expertise, model training, that no site yet owns. Hybrid work weakens the lateral links that the design depends on. Leaders should place each new technology within the decision-rights map rather than letting it redraw the map by default.

Gaps in Theory

The analysis suggests three areas where theory needs development. First, how should regulated organizations design human oversight of AI systems that must be validated, when the systems learn and change? Second, how does algorithmic task allocation affect supervisors' authority and the informal structure that natural system theory emphasizes? Third, how do multisite firms preserve practices built at one site when institutional pressures push production elsewhere? These questions will inform the research gap in Week 8.

Conclusion

Lakehead's emerging issues are largely new solutions to the old problems of dividing work, allocating tasks, rewarding effort and sharing information. Research on new forms of organizing, algorithmic control and automation and augmentation explains much of what the company faces, and established theories such as information processing and institutional theory still apply. The gaps lie where regulation, learning systems and multisite practices meet.

References

Kellogg, K. C., Valentine, M. A., & Christin, A. (2020). Algorithms at work: The new contested terrain of control. Academy of Management Annals, 14(1), 366-410. https://doi.org/10.5465/annals.2018.0174

Puranam, P., Alexy, O., & Reitzig, M. (2014). What's "new" about new forms of organizing? Academy of Management Review, 39(2), 162-180. https://doi.org/10.5465/amr.2011.0436

Raisch, S., & Krakowski, S. (2021). Artificial intelligence and management: The automation-augmentation paradox. Academy of Management Review, 46(1), 192-210. https://doi.org/10.5465/amr.2018.0072