Ford Motor Company -- Flowchart Analysis Ford

Ford Motor Company -- Flowchart Analysis

Ford Motor Company is one of the largest manufacturing companies in the global marketplace. Fiscally, Ford has had two major recent periods; one of prosperity, one of decline. Period #1, the late 1980s to 19979, showed a steady pattern of growth, about 5-7% per annum. Sales in 1998 were down 6%, up 14% in 1999, giving the decade an average of 5.4% growth and a profit margin slightly above the industry of 7-9%. Costs of goods remained consistent at around 72%, and debt/leverage ratios steady. Since 2000, though, and most especially since 2007, Ford has seen its sales and profits diminish, its international market share decline, and its ability to compete effectively erode. For the primary market segment of Ford, the automotive side, financial and marketing decisions are based on numerous trends: competitive pricing, cost of goods and projections, ability to recap money through subsidiaries (maintenance, Ford Credit, etc.), global vs. U.S. sales, and demographic trends) (Magee, 2004). Unfortunately, over the past decade, Ford has not read the market correctly in its ability to provide the successful vehicles that the public wishes to purchase, with the exception of its number one selling truck worldwide, the Ford F-150 (Wood, 2006, pp. 166-80).

One of the identified gaps in Ford's operations, causing a costly recall of 500,000 automobiles, was insufficient testing of certain engine components, causing certain engine parts to randomly catch fire. While the fix for these vehicles was relatively simple (1/2 day for most vehicles), the underlying product profile and marketing damage was significant (Crawley and Woodall, 2011).

In the previous assignment, we identified a simple flowchart with a four step process to manage function, feasibility, and safety for vehicles coming out of the assembly line process. Of course, this would not be practical to test every car, but an algorithm should be set up to look at a viable number of vehicles per X. For instance, if the recall affected 500,000 vehicles out of two million, then every 4-5th vehicle should be initially checked, and so on. This is the format discussed previously:

In a generic presentation, the testing process would have been formed from the following stages, as revealed in the chart below:

Testing of functionality

Testing of feasibility

Testing of safety

Bottlenecks - Bottlenecks occur in the manufacturing process when the performance of the system is limited by resources, components, or expertise. In the manufacturing process, it is important to ensure that there are enough materials and components to produce the product -- in our case, enough supplies and appropriate mechanical materials to assemble a vehicle. Henry Ford made history with the implementation of machine tooling and assigning a process that would limit bottlenecks. In the modern Ford plant, however, the breakdown surrounded the particular part(s) that were untested prior to assembly. Assuring that these parts are tested and available would, of course, reduce inefficiencies as well as potential bottlenecks in the process. One way Ford developed and instituted a process to reduce bottlenecks was to use a cause-and-effect analysis regarding the wait time for certain decisions. Once this model was analyzed, changes were made and performance increased (Spotlight on Decision Making, 2011).

The Learning Curve -- The concept of the learning curve is a way to think about the way an average person -- or persons doing a process, learn over time on a given activity. The idea is based upon the notion that repetition increases both skill and retention. Within the business model, using the learning curve as a measurement device ensures more efficiency and profitability. It allows organizations to make more informed decisions, and achieve balance between output and quality. For Ford, then, the implementation of the Quality Control measures within the assembly line process means the time required to complete a single task should decrease with repetition until it is at optimal levels. However, we must also make an assumption that the testing procedures for the appropriate parts have been honed enough so that there is a realistic expectation that the parts have been pretested prior to assembly (Spangler, 2008).