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According to Simchi-Levi, Kaminsky, and Simchi-Levi, 2008, Meditech was spun off
from its parent company, Largo Healthcare Company, hoping that Meditech would thrive
without being pushed into the shadows of their parent company. Meditech soon began to thrive
in the endoscopic surgical instrument market and produce innovative, low-cost products. With
their major competitor being National Medical Corporation (Simchi-Levi et al., 2008). The
competition between Meditech and National Medical centered on the continuous development
and introduction of new products (Simchi-Levi et al., 2008). In this case study, specific questions
will be answered and analyzed, such as understanding the issues that Meditech has regarding
their products, the main driver of the issues, and the possible solutions to fixing the issues within
Meditech’s organization.
It has been explained that over the past several years, Meditech has introduced dozens of
new products into the market, however as Meditech is introducing the products with each
introduction of a product, there is a new update to a prior existing product (Simchi-Levi et al.,
2008). While it has been explained that the organization is planning to continue utilizing that
strategy, it has led to issues relating to the overall supply.
The main driver of the problems seen within the organization is the failure to utilize
Forecasting methods. It was explained that data to measure forecast accuracy within the
organization had not previously been tracked, and neither had forecasts and demand information
been monitored. It was also explained that another possible driving cause was panic ordering
both from dealers and affiliates. This was caused because a dealer is unsure whether or not the
product will be received in time and, as a result, increases its orders, hoping that Meditech will
deliver at least part of the order. The increased orders would cause demand to rise, which helps
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explain Meditech’s problems with demand consistently exceeding supply (Simchi-Levi et al.,
2008).
The customer service manager is the first to recognize the significant issues due to
Meditech’s specific organizational structure. It was explained that the planning, distribution, and
customer service department does everything that is required to ensure that the customers, which
are the hospitals, receive products when needed, and Dan Franklin, the customer service
manager, deals directly with everything from occasional customer complaints down to figuring
out various methods that can help improve the overall delivery and satisfaction to customers.
Customer Service representatives work with dealers and affiliates to keep them updated on
product delivery schedules and problems. The manager understood the customers’ frustrations
through various meetings with hospital managers (Simchi-Levi et al., 2008).
In order to fix the issues within Meditech, the organization needs to utilize forecasting
practices. It has been explained that demand forecasting plays a vital role in inventory planning.
In order to achieve efficient management of inventory, accurate forecasting has to be aimed first
(Mitrea, Lee, & Wu, 2009). This is vital because it has been proven that accurate forecasting can
help organizations allocate resources optimally (Zhang, Wu, Pan, Li, Ma, Zhang, & Wang,
2017). It will also be beneficial for the organization to focus on inventory control. By focusing
on the inventory, Meditech will know which items are in high demand and will be able to
prevent panic ordering in the future. Meditech will foresee the items that will be needed for
production, and as a result, the customers will have an increase in overall satisfaction.
References
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Mitrea, C. A., Lee, C. K. M., & Wu, Z. (2009). A comparison between neural networks and
traditional forecasting methods: A case study. International Journal of Engineering
Business Management, 1, 11. doi:10.5772/6777
Simchi-Levi, D., Kaminsky, P., & Simchi-Levi, E. (2008). Designing and managing the supply
chain : concepts, strategies, and case studies (3rd ed.). McGraw-Hill/Irwin.
Zhang, G., Wu, J., Pan, B., Li, J., Ma, M., Zhang, M., & Wang, J. (2017). Improving daily
occupancy forecasting accuracy for hotels based on eemd-arima model. Tourism
Economics, 23(7), 1496–1514. doi:10.1177/1354816617706852
