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According to the Simchi-Levi et al. (2021) text, Meditech initially focused on manufacturing and
selling low-cost and innovative surgical endoscopy equipment, with the targeted customer base primarily being hospitals, material managers, and surgeon. The organization has undergone significant growth in a saturated market with the use of effective sales tactics, which has allowed
Meditech to become a leader within their industry with over 200 available products (Simchi-Levi
et al.,2021). Much of this success can be attributed to Meditech’s utilization of supply chain management methods, such as development chains, global optimization, and management of uncertainty/risk, that allows them to capitalize on the value and profit margins of their products. What are Meditech’s problems in introducing new products? In manufacturing ALL products?
Despite their success, Meditech faced several issues when introducing new products to the market. These included an inability to meet customer demand and delays in shipments of their products, which lead to frustration amongst their customer bases (Simchi-Levi et al. 2021). With shipments taking upwards of six weeks, medical providers and facilities were at risk of surgical equipment not being available for their patients. These issues were not limited to new products, but were often encountered by all Meditech products. As described by Simchi-Levi et al. (2021), the organization utilized a three-step process for their products: assembly, packaging, and sterilization. Many of the issues encountered were a result of the assembly face, which Meditech described as “manually intensive” due to the availability of various components needed (Simchi-
Levi et al. 2021).
What is driving these problems, both systematically and organizationally?
The issues faced by Meditech can essentially be broken down into two main categories: systemic
and organizational. The systemic problems faced by the organization are primarily due to their production planning and their scheduling process, which utilizes poor forecasting data for production and incorrect inventory levels for scheduling. When an external consultant was brought in, it was found that Meditech had excessive inventory in their warehouse which could be reduced by upwards of 40%. In terms of the organizational issues, many of these problems were a result of the decentralized nature of Meditech, with a plethora of pertinent information scattered across the organization rather than in a centralized location.
Why is the customer service manager the first person to recognize the major issues?
Dan Franklin, the customer service manager, was the first to identify issues with Meditech due to
his close relationship with customers who purchased their products. In his position, Mr. Franklin worked alongside customers for tasks such as updating orders and findings ways to improve delivery service. He identified several issues related to the distribution of products, such as the organization being unable to meet the demands of customers purchasing new products which then led to the discovery of issues surrounding forecasting orders and assembly.
How would you fix these problems?
To fix the problems at Meditech, I would first correct the forecasting models used by the organization. As stated by Morlidge & Player, “In the world of business today, any company that
is not able to forecast – to anticipate and to respond – risks loss (of money or opportunity) or in extreme cases failure” (Morlidge & Player, 2010). By implementing new processes, methods, and tools to the organization, greater emphasis can be placed on forecasting to mitigate issues such as excess or idle inventory. Hofmann & Rutschumann suggested a method of big data analytics as a means of improving demand forecasting, in which organizations examine “big data” or a combination of unstructured and structured data to discover patterns, correlations, and other trends that can aide organizations in making informed business decisions (Hofmann & Rutschumann 2018). I would also work to create a more centralized network within the organization, allowing both internal and external stakeholders to obtain the information they need when they need it. References
Hofmann, E., & Rutschmann, E. (2018). Big data analytics and demand forecasting in supply chains: a conceptual analysis. The International Journal of Logistics Management.
Morlidge, S., & Player, S. (2010). Future ready: How to master business forecasting.
John Wiley & Sons.
Simchi-Levi, D., Kaminsky, P., & Simchi-Levi, E. (2021). Designing and Managing the Supply Chain: Concepts, Strategies and Case Studies (4th ed.). New York, NY: Richard D. Irwin, Inc.
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