Case Problem 155 Hosuki Hosuki: A Small Car Maker Competes

Case Problem155hosukihosuki A Small Car Maker Competes With Larger

Case Problem 15.5 involves Hosuki Hosuki, a small car manufacturer that competes with larger companies by building cars to order, utilizing technology, and close supplier partnerships. The company’s process includes a virtual bill of materials, collaboration with suppliers, and batch assembly. The problem requires creating a time-phased assembly chart for an order of 10 cars, and considering adjustments for additional customer orders for 5 cars within 5 days. It also involves analyzing inventory, lead times, and batch sizes, along with exploring alternative strategies of technology usage and public perception issues, with references to operational resource planning and organizational ethics.

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The case of Hosuki Hosuki exemplifies the complexities and strategic considerations that small manufacturers face when competing against established larger organizations within the automotive industry. By adopting a build-to-order model supported by advanced technology and close collaboration with suppliers, Hosuki aims to deliver customized vehicles swiftly and efficiently. This approach owes much to effective resource planning and supply chain integration, vital components highlighted in Russell’s (2020) discussion of operations management and resource planning strategies.

Creating a time-phased assembly chart involves mapping each step of the manufacturing process, starting from the receipt of customer orders to the final delivery of the vehicles. For Hosuki, the process begins with the procurement of parts, including chassis, powertrain components, body parts, and tires, each supplied by different vendors with specific lead times. The assembly steps, such as body stamping, chassis assembly, and final vehicle assembly, are scheduled sequentially, ensuring minimal delays. Given that each assembly process takes approximately half a day, and considering the batching of 10 vehicles per order, the total production time can be computed by summing the setup, processing, and waiting times for each stage, adjusted for the specific batch size and supplier lead times.

In practical terms, the assembly of 10 vehicles might start with scheduling the body stamping process, which requires detailed preparation and is limited by the machine’s operating hours. Since the stamping machine operates 8 hours daily, with specific setup and run times per part, optimization may involve staggering operations or extending working hours temporarily. Subsequent assemblies, such as the powertrain and chassis, depend critically on the timely arrival of components from suppliers like Supplier E (engine and transmission) and Supplier F (radiator and battery). Understanding supplier lead times—such as those for shocks, tires, wheels, and other parts—is crucial for accurate scheduling, as delays can compoundingly extend overall lead time; for this reason, real-time visibility into supplier systems is instrumental.

For the initial order of 10 vehicles, the assembly could be scheduled within a two-week window, allowing for parts procurement and assembly, with delivery aligned closely with customer expectations. As for the second scenario—filling an additional order for 5 vehicles in 5 days—significant adjustments are necessary. These could involve increasing batch sizes to accelerate production, pre-assembling some components, and adding shifts or overtime to speed up processes. Inventory levels would need to be increased temporarily to avoid delays caused by waiting for parts, especially for high-volume, long-lead-time components like wheels and shocks. To meet such tight deadlines, strategic procurement—such as maintaining safety stock or establishing expedited shipping agreements—would be essential.

Exploring alternative uses of technology might involve implementing a more sophisticated Enterprise Resource Planning (ERP) system capable of predictive analytics and real-time supply chain monitoring. For instance, integrating AI-driven demand forecasting would enhance the company's ability to anticipate customer needs and optimize inventory levels proactively. Additionally, utilizing digital twins—virtual simulations of the manufacturing process—could help identify bottlenecks and test multiple production scenarios to optimize throughput without costly physical adjustments. Such technologies would improve responsiveness and reduce lead times, thus enabling speedier order fulfillment.

However, these technological advancements come with public perception challenges centered on concerns about over-automation and transparency. Employing more automation might generate negative attitudes from customers wary of reduced employment or perceived loss of craftsmanship. Conversely, companies that emphasize the environmental benefits—such as reducing waste through predictive ordering—could positively influence customer perceptions. Transparency and ethical communication are critical; for example, openly sharing how technology improves product quality and safety can foster trust.

From a strategic standpoint, evaluating the “what if” scenarios—such as supply chain disruptions or technological failures—is vital. Contingency planning should include alternative sourcing, backup suppliers, and deployment of flexible manufacturing setups. The integration of Industry 4.0 technologies offers the potential for increased efficiency but requires careful change management to ensure employee acceptance and stakeholder confidence (Russell, 2020).

In conclusion, Hosuki Hosuki’s approach to production, leveraging strategic supplier partnerships and technology, provides a resilient foundation for meeting customer demands. Nonetheless, adapting to urgent orders requires proactive planning adjustments in inventory management, batch sizing, and tech integration. Ethical considerations and public perception must align with strategic ambitions, especially when deploying advanced technologies. The balance between operational efficiency and transparency through responsible innovation remains paramount for sustaining long-term competitiveness and stakeholder trust.

References

Russell, R. S. (2020). Operations Management (8th ed.). Pearson Education.

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BP. (2010). BP Code of Conduct. Retrieved from https://www.bp.com/en/global/corporate/sustainability/our-business/bp-code-of-conduct.html

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Choi, T. Y., & Hartley, J. L. (1996). An Exploration of Supplier Selection Practices. International Journal of Purchasing and Materials Management, 32(2), 36-44.

Deloitte. (2022). Industry 4.0 and Smart Manufacturing. Retrieved from https://www2.deloitte.com

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