Business Finance Homework Help

I’m working on a supply chain discussion question and need support to help me understand better.

1-Hello Omar,

Export transactions are varied and often complex, requiring specialized skills and competencies. The firm may wish to launch new or adapted products abroad, target countries with varying marketing infrastructure, finance customer purchases, and contract with helpful facilitators at home and abroad. Accordingly, managers need to gain new, internationally-oriented capabilities in areas such as product development, distribution, logistics, finance, contract law, and currency management. Managers may need to acquire foreign language skills and the ability to interact with customers from diverse cultures.

2-According to Handfield, Jeong, & Choi (2019). Intuition is one of your most powerful decision-making skills when you’re torn between several possibilities. When making important decisions, business skills should always be considered, but there is just too much at risk nowadays to dismiss actual data-backed understanding. This is especially true now that data analytics and business intelligence applications are becoming more generally available. Scholars and practitioners have long stressed the importance of technology in enhancing supply chain information flows. The importance of information sharing in buyer–seller partnerships has been underlined appropriateness, and accessibility must all be part of our design solutions. We may create better design decisions that account for the subtlety of human variables that statistics cannot perceive by establishing the “why” with user behavior and then utilizing our intuition to guide us. We should use data as a guide rather than allowing it to dictate our strategy.

Based on Handfield, Cousins, Lawson, & Petersen (2015). If company’s strategies find it difficult to satisfy the wants of your customers, which is obviously counterproductive if your goal is to grow in a positive way. Even among major, well-known companies, strategy misalignment is common, but it is detrimental to growth, so if advancement is on your agenda, as well as that of your government colleagues and industry stakeholders, you must insist on specific improvements to supply chain procedures. Aligning your supply chain strategy to support industry growth does not entail accepting higher costs. The key is to concentrate your give chain team’s efforts on efficiency, value creation, and effectiveness. Prior research has emphasized the necessity of internal and external supply chain integration, but the supply management organization’s rising role in building this capacity has not been properly defined.

Handfield, R. B., Cousins, P. D., Lawson, B., & Petersen, K. J. (2015). how can supply management really improve performance? a knowledge-based model of alignment capabilities. Journal of Supply Chain Management, 51(3), 3-17. Retrieved from https://www.proquest.com/scholarly-journals/how-ca…

Handfield, R., Jeong, S., & Choi, T. (2019). Emerging procurement technology: Data analytics and cognitive analytics. International Journal of Physical Distribution & Logistics Management, 49(10), 972-1002. doi:http://dx.doi.org/10.1108/IJPDLM-11-2017-0348

3-There is often too much detail available to make a clear judgment about a lack of data. Conduct the following five steps in the data analysis process to develop your data analysis skills. The questions must be quantifiable, simple, and concise (Liberatore, Pollack-Johnson & Clain, 2017). Thinking about how you measure your data is equally critical, especially before the data collection period. The measuring method either supports or undermines your research later.

It’s essential to keep the following points in mind when you compile and organize the data: Before gathering additional data, consider whether information may be gleaned from current datasets or sources. The data collection process can be time-consuming and time-consuming, but it’s essential to start with a clear understanding of your goals and objectives (Handfield, Jeong & Choi, 2019). The information will help you make informed decisions about how to make the best use of your data. It can also be helpful to know how to respond to spikes in demand, such as when a customer requests more workers, are short-staffed, or the company needs to reduce its workforce. The right question to ask is: Can the company decrease its workforce without sacrificing quality?

Plan of time a file storage and naming scheme to help all team members communicate. Data processing tools and software are handy during this point. No matter how much data you gather, the chance will still play a role in the performance. Visio, Minitab, and Stata are all excellent software sets for sophisticated mathematical data processing (Liberatore, Pollack-Johnson & Clain, 2017). However, when it comes to decision-making software, nothing beats Microsoft Excel in most situations.

For a refresher on all the tasks Excel performs for data processing, we recommend this HARVARD BUSINESS overview class. We also recommend this business overview class for those who need a refresher on data processing and data analysis (Handfield, Jeong & Choi, 2019). Weigh the pros and cons of different data processing tools to help you better understand your data and data processing options. We’ll provide a step-by-step guide on how to get the most from your data in the next step of the analysis. The final step is to interpret your data to answer your question.

If your analysis of the results stands up under these problems and considerations, you’ve probably reached a helpful conclusion (Liberatore, Pollack-Johnson & Clain, 2017). By implementing these five steps, you will make smarter decisions about your company or government department. Moreover, your choices will be supported by data that has been thoroughly gathered and analyzed.

When firms examine the many performance measures available, there are several features that they should look for when picking metrics to aid in business choices.

Simple to Understand – A good measure is one that everyone who looks at it can readily understand (Elrod, Murray & Bande, 2013). It should be obvious what the metric truly measures and how it is calculated.

Quantitative – A key feature of a supply chain performance indicator is that it is stated by a number that is objective, i.e., obtained from facts rather than subjective.

Metrics What is Essential – Some metrics may appear significant, but the metric’s significance may be questioned when the data is reviewed. A performance metric used to make business decisions must measure critical data.

Instigates Correct Behavior – A good performance metric should compel the user to take the appropriate action (Elrod, Murray & Bande, 2013). For example, if a metric shows the number of orders processed per day, the correct action would increase the number of orders processed. In addition, however, the measure might occasionally inspire the user to act, albeit at the expense of other areas. For example, if the number of moves each day measures the warehouse personnel, they might raise the number of actions at the cost of the number of trucks loaded and processed.

Measurements Should Be Simple to Gather – Some firms use complex performance metrics that are time-consuming to collect and may need time away from line employees to prepare (Elrod, Murray & Bande, 2013). This is unhelpful, and measurements of this nature should be avoided.

References

Elrod, C., Murray, S., P.E., & Bande, S. (2013). A review of performance metrics for supply chain management: EMJ.Engineering Management Journal, 25(3), 39-50. Retrieved from https://www.proquest.com/scholarly-journals/review…

Handfield, R., Jeong, S., & Choi, T. (2019). Emerging procurement technology: Data analytics and cognitive analytics.International Journal of Physical Distribution & Logistics Management, 49(10), 972-1002. doi:http://dx.doi.org/10.1108/IJPDLM-11-2017-0348

Liberatore, M. J., Pollack-Johnson, B., & Clain, S. H. (2017). Analytics capabilities and the decision to invest in analytics. The Journal of Computer Information Systems, 57(4), 364-373. doi:http://dx.doi.org/10.1080/08874417.2016.1232995