Appropriate Level 2 Metrics for Feets Company
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Introduction
Metrics refers to the different varieties of data points extracted using a wide range of methods. Over the years, metrics have been used by organizations and companies to establish performance, operation and accounting analysis. Property models and industry standards govern an organization’s metrics as they come in a wide range of varieties and can be used in different scenarios depending on the objective of an organization. Analysts then draw their recommendations and take on the business from the metrics. Information collected in the metrics allows stakeholders and the organization’s internal managers to manage strategic projects (Kusrini et al., 2019). An organization can adopt different types of metrics. More often than not, the embraced metrics depend on their SCOR model level. This paper will discuss the level two metrics for Feets Athletics wear distribution chain company, the implementation, and recommendations.
Three levels prove essential in measuring and standardizing the supply chain performance metrics. Regardless of how a company chooses to operate, the metrics enable them to examine and gauge themselves with other companies (Lima-Junior & Carpinetti, 2019). The level two metrics deal with the supply chain configuration, comprising segments, geographies, and products. It includes subsets under the primary categories in level 1 and serves as its diagnostics. Since the metrics at level two are high, they are examined over multiple SCOR procedures. Level two metrics help identify the level 1 metrics performance gap and evaluate performance variation against the plan (Fauzi et al., 2019). Feets company can configure its supply chain and implement its operations strategy.
Since Feets company deals in trendy shoes, it is vital that they pay attention to the manufacturer’s cycle time to meet the trends customers adopt at the retailing end. Buyers use customer preference to determine what to order from Feet stores, the quantity and shoe sizes. Configuring the supply chain to suit the buyer’s future expectations of the shoe trend will likely yield more profit and allow the company to retain its relevance in the market (Ahoa et al., 2018). Since Feet’s company prioritizes efficiency more than responsiveness, it may embrace M1, a make-to-stock. The Feets company can also adopt an M2 configuration. This scenario is the build-to-order where the Feets company produce shoes according to customer order in just-in-time trend. To balance out the supply and demand of shoes from the store, the shoe company works closely with the buyers to adapt to customer experience and transform their orders based on real-time customer data (Fauzi et al., 2019). Once they are ready, they are transported directly to the buyers’ store. Therefore, getting a scope of fashion and customer preference over a particular time frame is crucial for the Feet’s footwear company.
Conclusion
The configuration of the supply chain by Feets company allows it to measure performance gaps in the business and develop effective techniques to close these gaps. It gives a clear perspective on its orders between the vendors and the buyers of Feets company, keeping track of the vendor’s delivery dates and buyers’ pick-up dates and balancing them out. The company can also drop outdated practices that have become obsolete and trends that would hinder the supply chain performance and drag it down through losses. For this reason, the company should positively embrace supply chain configuration despite the costs that may come with it as it would have long-term positive impacts.
References
Ahoa, E., Kassahun, A., & Tekinerdogan, B. (2018, July). Configuring supply chain business processes using the SCOR reference model. In International Symposium on Business Modeling and Software Design (pp. 338-351). Springer, Cham.
Fauzi, A. R., Ridwan, A. Y., & Juliani, W. (2019, August). Supply Chain Performance Measurement System Development for Shoes SME using Subcontract Production Strategy Based on Integrated SCOR-BSC Model. In IOP Conference Series: Materials Science and Engineering (Vol. 598, No. 1, p. 012126). IOP Publishing.
Kusrini, E., Caneca, V. I., Helia, V. N., & Miranda, S. (2019, December). Supply Chain Performance Measurement Using Supply Chain Operation Reference (SCOR) 12.0 Model: A Case Study in AA Leather SME in Indonesia. In IOP Conference Series: Materials Science and Engineering (Vol. 697, No. 1, p. 012023). IOP Publishing.
Lima-Junior, F. R., & Carpinetti, L. C. R. (2019). Predicting supply chain performance based on SCOR® metrics and multilayer perceptron neural networks. International Journal of Production Economics, 212, 19-38.
