The Statistical Process
The statistical process control is a design that is developed in a bid to ensuring that the standard processes take place with minimal errors (Chase, Jacobs & Aquilano, 2006). In this case, I have developed a process of an inventory for a store, military, or no other organization taking into consideration the factors that may hinder the process. By understanding the different bottlenecks in the inventory process, I will be able to minimize on the errors at the end of the process. In my week one assignment, I was able to analyze the steps that were involved in the inventory with the first step being data collection. The second step in the inventory process is resource planning whereby the records are updated using an enterprise resource planning (ERP) system to incorporate all the units within the store, or any other organization. The last step within the process is the evaluation of the data that has been collected and after it has been verified, it is stored for the purposes of reporting.
Control Limits
The control limits are used to explain the expected variation in the data that is to be collected from the process (Project Management Knowledge, 2010). One important factor to note about control limits is that the data collected has a lower limit and a higher limit, which must all be taken into consideration (Project Management Knowledge, 2010). In the inventory process, control limits are important because they help to ascertain whether or not the process has become success in all its aspects or not. Time is the most essential part of the process and the success of process will be determined by the time taken to complete the process (Chase, Jacobs & Aquilano, 2006). In week one, the time expected to be taken to complete the process to be nine hours, which is equivalent to 540 minutes, with comparison to the number of hours that are used in performing other processes within the store or organization. The table below will indicate the number of hours I spent on performing the inventory process from week one to week five, which I will use in calculating the control limits of the inventory process. The standard deviation for each of the samples taken will be multiplied by three considering that the number of deviations for the process is three.
The average time spent in the inventory process is 496 minutes and the standard deviation for the same is 152.7, which is to be multiplied by three making +or-458.1. Using the standard deviation, the inventory process’ control limit has a lower limit of 37.9 minutes and a higher limit of 954.1 minutes. On the other hand, the average number of minutes spent on other process within the store is 200 minutes and the standard deviation for the same is 254.9, which is to be multiplied by three making +or-764.7. Therefore, the other important processes in the store have a lower limit of -564.7 and a higher limit of 964.7. Looking at the number of hours spent in the inventory process and the number of hours spent in other processes in the other processes within the store, it is clear that it does not exceed the control limits that have been set. The store may have a lot of time for the inventory process, as well as, other processes. When I compare the average time of hours spent in the inventory process, I see that it is very close to the maximum goal that I had expected, which was 540 minutes.
Seasonal Factors
In terms of the seasonal factors that affected the inventory process in the store or organization, I would say that the process was not affected by any of the factors except for the historical analogy. In week one, the inventory process was conducted thrice a week, that is Monday, Wednesday, and Saturday but in the following weeks were based historical forecast whereby the inventory process was conducted for only two days a week. One important factor to note that the inventory process must be carried out in the during the first day of the week and the last day of the week to make sure that the goods that the system is up to date with regard to the employees and the goods within the store. The seasonal factor that affected the inventory process was directly proportional to the factor that affected the time spent in performing the other processes within the store considering that in the first week, the number of minutes spent in other processes within the store was 0, which was also the case for the second week. But looking at the number of minutes spent in the third to fifth week, one can see that at one point, it was higher than the time spent conducting the inventory process. This means that the two processes have a direct link towards the achievement of the intended goals and targets of the store (Chase, Jacobs & Aquilano, 2006).
Confidence Intervals
When using the confidence intervals, one is able to accurately point out the data to be used and the degree of confidence for each (which was 90%, 95%, and 99% for the inventory process and the other processes within the store) (RVLS, 2010). To determine the level of confidence for the two processes, the data collected from the first week to the fifth week will be used. The following table represents the confidence intervals.
The confidence of the inventory process, as well as, the other processes is a very important factor in determining the overall success rate of the process (RVLS, 2010). As can be observed, the higher confidence interval requires a higher amount of time needed. In the 99% confidence interval, the number of minutes that move at a higher margin than the confidence level of 90%.
Conclusion
In conclusion, the inventory process is a very important factor in determining the success rate of the store thus it is very important for the management of the store to come up with time management policies that are going to cater for the same. According to the observations that I was able to make within the 5 weeks of the process, the inventory process is co-related to the other processes within the organization and are both affected by seasonal factors as discussed above.
References
Chase, R. B., Jacobs, F. R., & Aquilano, N. J. (2006). Operations management for competitive advantage (11th ed). New York: McGraw Hill/Irwin.
Project Management Knowledge. (2010). Control limits. Retrieved from
RVLS. (2010). Confidence Intervals. Retrieved from
