Coursework Example: Ford Business Analysis

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Business Analysis- A Case of Ford

Every business decision made by Ford is influenced by data and analytics, from predicting the price of commodities to determining what the public wants, what the company will construct, where to source parts, and how to power its lineup of cars and trucks.

Information is being crunched behind the scenes by about 200 significant information and examination professionals from diverse fields (Ford, 2013). They are used by what Portage refers to as “examination centers of greatness” that are dispersed around the various businesses that make up the $134 billion company, including marketing, research, credit administration, and others.

These investigation professionals have been pressing crucial and key circle-back decisions since around 2007. (Passae, 2013). They worked on initiatives that eventually found solutions for problems including deciding which brands and models to discontinue, where to get supplies for parts, and how to cooperate with suppliers to adjust their inventory to facilitate transactions.

Passage’s most recent annual report, stock presentation, and quarterly financial data currently provide the rest of the story. In 2009, Portage made money for the first time in a long time, a year after disclosing a record $4.6 billion shortfall (Evans, 2017). It introduced 25 new vehicle lines that year, selling 2.3 million cars and trucks nationwide, making it the first brand to surpass 2 million sales since roughly 2007. In October, the company announced its seventeenth consecutively lucrative quarter.

All automakers use data analytics as a vital competitive tool. They analyze data about customers, production, vehicles, and more to forecast demand and fine-tune their product offers. For instance, General Motors has long been gathering information via its OnStar system, including vehicle diagnostic data (Maisel, 2013). But according to Thilo Koslowski, an automotive expert at Gartner, Ford is strongly emphasizing its analytic efforts on customer preferences, an area where it seems to be ahead of its rivals.

Business knowledge unites business analysis, information mining, perception, apparatuses, and framework, and best practices to assist firms in making more information-driven decisions (Srinivasan, 2012). When you comprehend its information and apply it to promote change, eliminate flaws, and react promptly to supply or market developments, you can decide your association’s associations in knowledge (Cooke, 2015). Current Business Knowledge arrangements are focused on flexible self-administration research, regulated information on reliable platforms, empowered business clients, and speed of understanding.

Ford, uses it in a way that makes its decisions depending upon it as it reduces the chance of any risks for the company to a very shorter proportion which will be beneficial for the company as it visualizes every aspect and makes it a preferable thing by the business (Negash, 2008).

Q2

• Descriptive analytics makes use of data gathering and information mining to provide an answer to the question, “What has occurred?” and provide insight into the past.
• Predictive analytics, which asks, “What might happen?” uses factual models and estimation techniques to anticipate what will happen.
• Prescriptive analytics makes use of reenactment and simplifying computations to provide direction on projected outcomes and react to the question, “How would it be a good idea for us to respond?”

Ford, at some point, uses each one of them but for future forecasting, it uses predictive analytics as it helps the business have a firm and stable standing in the future which will make the business forecast and be ready for the strategies and steps to be taken in the future which will be beneficial for the business and its consumers as well (Schmidt, 2006). Eliminating any doubts about any uncertainty and providing a precise and trustworthy forecast for the business.

References:

Cooke, B. and Alcadipani, R., 2015. Toward a global history of management education: The case of the Ford Foundation and the São Paulo School of Business Administration, Brazil.Academy of Management Learning & Education,14(4), pp.482-499.

Evans, J.R., 2017.Business analytics. Pearson.

Ford, D. and Mouzas, S., 2013. Service and value in the interactive business landscape.Industrial Marketing Management,42(1), pp.9-17.

Ford, D. and Mouzas, S., 2013. The theory and practice of business networking.Industrial Marketing Management,42(3), pp.433-442.

Maisel, L. and Cokins, G., 2013.Predictive business analytics: Forward looking capabilities to improve business performance. John Wiley & Sons.

Negash, S. and Gray, P., 2008. Business intelligence. InHandbook on decision support systems 2(pp. 175-193). Springer, Berlin, Heidelberg.

Schmidt, W.P., 2006. Life cycle tools within Ford of Europe’s product sustainability Index. Case study Ford S-MAX & Ford Galaxy (8 pp).The International Journal of Life Cycle Assessment,11(5), pp.315-322.

Srinivasan, V.R., 2012. Predictive business analytics.The Management Accountant,47(2), pp.211-215.

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