Evaluating the Risk of Raw Material Mills for Boeing
Raw materials plants are a source of significant risk and barriers for Boeing because the principle of operation and logistics does not meet modern requirements. First, factories cannot quickly balance the number of parts produced if requirements change (Sathe, 2019). This formulates overproduction or, vice versa, shortage if the demand value varies sharply. In addition, serial plants face several other problems, such as environmental issues and legislation, process changes, and union issues.
Moreover, this includes supply chain issues, facility issues, process change, disasters, and the expiration of facility leases. Next, the most significant risk factor for Boeing from raw materials plants was identified: the lack of aluminum and titanium for manufacturing processes to cover the demand. Boeing’s ability to access raw materials if the source is in a politically volatile territory could be compromised.
Weaknesses in Boeing’s Current Mill Risk Assessment Process
The main weaknesses of the current mill risk assessment process are the forecasting of TMX and the analysis of other important aspects based on inaccurate data. Although today, researchers are trying to reformat the data that should be considered in the analysis, it is still formulating a risk. This is because the primary goal of any data analysis is to search and discover patterns in the amount of data.
In business analysis, this goal becomes even broader. It is vital for any leader not only to identify patterns but also to find their cause. In this regard, if the analysis utilizes inaccurate data, the company’s activities may be threatened. Based on the analysis results, Boeing makes important decisions that affect performance and efficiency. The results could mislead development strategy providers when analyzed with inaccurate data, making things worse for Boeing.
Strategies for Improving the Mill Risk Assessment Process
First of all, to revamp the mill risk assessment process, one needs to level the root cause of the risk, namely the use of inaccurate data. The analysis should be based only on those datasets that reflect the actual area depending on the problem. In addition, it is essential to apply both regular and large datasets, which will allow one to identify trends and dependencies. Next, it is necessary to identify the main risks for Boeing, the potential risks, and the key reason for these risks (Sathe, 2019).
Inaccurate data were also used for forecasting, which should be reworked, and the main forecasting processes should be changed. Forecasting for Boeing using current strategies and data types can lead to incorrect results and significant financial losses. In addition, existing methods lead to misjudgment of raw material requirements, leading to accidents. To level the risk, it is necessary to change the assessment strategies to use more modern methods.
Addressing ThyssenKrupp Aerospace’s Late Deliveries and Inaccurate Forecasts: Solutions for Boeing
The issues behind ThyssenKrupp Aerospace’s (TMX’s) late deliveries have negative consequences for the company. The reasons are incorrect planning and analysis of inaccurate data. The results may be reduced efficiency, financial losses, and the worsening of other indicators.
Inaccurate forecasts are causing mills to receive incorrect information that cannot be fixed. This leads to malfunctions, shortages, overproduction, and other related problems. To solve these problems, Boeing needs to reform its analysis strategies and change its datasets. It is essential to correct these problems because they can lead to long-term negative consequences. This can affect Boeing’s financial condition, image, motivation, and development opportunities.
Reference
Sathe, P. (2019). Boeing’s strategic initiative: Raw material supply chain risk mitigation. WDI Publishing.