Supply chain management is a complex process that can be impacted by numerous factors.
Discussion: Meditech Surgical Case Study
Supply chain management is a complex process that can be impacted by numerous factors. Uncertainty and risk are inherent in every supply chain and management and mitigation of that risk is an effort that organizations undertake to increase reliability of the supply chain.
Uncertainty and risk factors that commonly impact the supply chain include variances in supply and demand, fluctuating inventory levels, forecasting inaccuracies, raw material and components availability, and external environmental impacts (Simchi-Levi et al., 2021).
Meditech has identified issues with both new product development and existing inventory supply challenges. Supply chain efficiencies can be created and controlled using accurate data and the lack of availability of data within Meditech is impacting the ability to create an efficient and reliable supply chain (Aryal et al., 2020). Big data analytics is an emerging trend that allows organizations to capture and utilize data and provide real-time visibility into the supply chain to create efficiencies (Aryal et al., 2020). Meditech lacks both historical data and accurate forecasting data to utilize in creating those supply chain efficiencies.
A system should be created that utilizes past historical data on new product launches and product sales, along with analyzing new product demand trends and the reduction of demand prior to products being retired to not only ensure that appropriate supply is available on new products, but also to ensure that a large amount of dead inventory is not incurred because of the retiring of older product models. Supply chain digitization can assist Meditech in capturing data and then utilizing that data to improve the supply chain model (Simchi-Levi et al., 2021).
2 Corinthians 9:10 (King James Version, 2017, 2 Corinthians 9:10) states “Now he that ministereth seed to the sower both minister bread for your food, and multiply your seed sown, and increase the fruits of your righteousness.” God is ultimately the one who supplies and ensures that will multiply to provide an adequate supply to meet the demand.
Root Cause Analysis
Accurate planning requires accurate input data. The concept of “garbage in and garbage out” is one that is in use at Meditech today, whereas the supply chain and production process is being managed both from a lack of data and a lack of accurate data. An input of poor data, or garbage in, results in the output of an unreliable supply chain plan, garbage out (Bansal et al., 2020).
The problems experienced at Meditech are both systematic and organizational in nature. Systematically there are too many systems in place that are not appropriating capturing data that can be utilized to create efficiencies and specifically reviewing data across systems has been identified as tedious, time-consuming and troublesome. Organizationally, anytime an issue is found because of poor customer satisfaction feedback, a review must take place to determine why the issue wasn’t identified before receiving feedback from the customer. Key performance indicators, or creation and measurement of improve performance drivers, should catch supply chain issues earlier in the process (Milliken, 2018). How sales forecasts measure against actual sales, how actual production is tracking to the planned schedule, and how delivery performance is tracking against goals are all performance variables that should be measured and identify problems earlier in the supply chain process (Milliken, 2018).
Problem Identification
The customer service manager, Dan Franklin, is the first individual to recognize the issue because of the lack of data availability, the lack of key performance metric identification and measurement, and the lack of leadership’s understanding of what is required in implementing a reliable supply chain. Given the lack of proactive management, the net result is that negative feedback is received by customer service from customers who are unsatisfied with the supply chain and delivery times at Meditech.
Simchi-Levi et al. (2021) states “Supply chain management is a set of approaches utilized to efficiently integrate suppliers, manufacturers, warehouses, and stores so that merchandise is produced and distributed at the right quantities, to the right locations, and at the right time, in order to minimize systemwide costs while satisfying service level requirements.” (p. 1). Meditech has failed to implement their supply chain strategy to achieve these goals.
Problem Resolution
Resolving supply chain problems are rarely a simple process. Supply chains are often comprised of highly complex networks of suppliers, customers, and employees that are impacted by many factors (Simchi-Levi et al., 2021).
The key to resolving any problem is to define the problem, define the root causes, and then analyze and plan for how to resolve the problem. The primary problem identified is growing customer dissatisfaction due to a failure in meeting customer demand (Simchi-Levi et al., 2021). The root causes shown that contribute to the problem include a lack of performance measure identification and management, a lack of proactive management for meeting demand requirements, a lack of data to support creating reliable production schedules, and a lack of accurate forecasting data to be utilized within the production planning process (Simchi-Levi et al., 2021).
Meditech should review its inventory control model, supply chain flexibility, supply contracts, distribution strategies, and information technology and decision-support systems to introduce improvements that reduce customer product delays and increase customer satisfaction (Simchi-Levi et al., 2021).
Conclusion
The Meditech case shows the complexity of supply chain management. While Meditech has introduced personnel and processes to design a supply chain system, and invariable has burdened significant cost in doing so, the result is growing customer dissatisfaction. Planning for supply chain efficiencies requires a complete understanding and development of a supply chain plan accounting for the entirety of the complex network structure. Failure to acknowledge the complexity and address each area accordingly introduces risk and failure.
References
Aryal, A., Liao, Y., Nattuthurai, P., & Li, B. (2020). The emerging big data analytics and IoT in supply chain management: a systematic review. Supply Chain Management, 25(2), 141-156. https://doi-org.ezproxy.liberty.edu/10.1108/SCM-03-2018-0149
Bansal, P.T., Gualandris, J., & Kim, N. (2020). Theorizing Supply Chains with Qualitative Big Data and Topic Modeling. Journal of Supply Chain Management, 56(2), 7-18. https://doi-org.ezproxy.liberty.edu/10.1111/jscm.12224
King James Bible. (2017). King James Bible Online. https://www.kingjamesbibleonline.org/
Milliken, A. (2018). Leveraging supply chain analytics to improve performance. Journal of Supply Chain Management, Logistics and Procurement, 1(1), 7-15. https://www-ingentaconnect-com.ezproxy.liberty.edu/content/hsp/jscm/2018/00000001/00000001/art00002;jsessionid=8omque58488f.x-ic-live-01#
Simchi-Levi, D., Kaminsky, P., & Simchi-Levi, E. (2021). Designing and managing the supply chain: Concepts, strategies, and case studies (Fourth ed.). McGraw-Hill Education.
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