Riordan Process Improvement Plan Essay Example for Free

Riordan Process Improvement Plan EssayTime is always sorrowful forward making it difficult to execute daily processes slowly. Travelling is a daily process that takes much clip and resources. Time spent on locomotion can be known as waste beat as the main goal is to transport from point A to point B without analyzing or performing actions on other tasks. Multitasking is non advisable meaning a highschool focus should be on the road and other road users confirming it is illegal. The process if done as quickly as possible can reduce the motorcycle time leaving extra time for more juicy processes. The activity to drive from home to built in bed is graphically shown infra in the form of a flowchart.Currently time taken to execute the activity is not efficient. Certain processes be occupying heavier proportion from the total cycle time. A process improvement plan is drawn not only to analyze and reduce genuine time provided alike not forgetting to achieve a safe trip.St atistical Process ControlData below tabulates five hebdomads of travelling time from home to office. The next step is to deduce whether the data is efficient by running a test. Statistical process control (SPC) tests random warnings from processes to determine the productiveness is perfectly efficient (Chase, Jacobs Aquilano, 2006). The test graphically depicts the upper control limit (UCL) and lower control limit (LCL) of each the average mean and average stretch graphs. Average of time taken and range from each workweek in combination with the range and average factors are requirements to calculate both limits. Graphs with the limits first, plot the periodical average mean and average range. Observation is made from the graphs to decide on whether or not all sample data is deep down the control limits. The sample data that either is higher than the UCL or lower than the LCL will be the overuse time. Value of data is not only under observation but also the pattern of the char t is also under monitoring. The pattern of a stable chart is sample data closely plotting around the mean data. Patterns that edge an increase toward the UCL or decrease toward the LCL or erratic behavior must undergo investigations (Chase et al., 2006).The both chart depicts that the average of total time and range is within the UCL and LCL. The observation only concludes that the current data is allowable but not perfectly efficient. The pattern of the data in the average mean chart depicts a run of three plots above central line. The practice to avoid the first weeks traffic congestion is to leave from home reaching office exactly at 9.00 a.m. The second and third week changes practice as work is piling up and requires more setup time.The pattern of the data in R chart depicts an increase. The final plot reaches a range nearly to the UCL. The reason is the zero value recording of total cycle time on Monday.Seasonal FactorsThe data above is in normal tabulation manner meaning no trips involving external variables or environmental factors intervention is taken into consideration. extraneous variables present itself in seasonal or cyclic durations. The latter is easily taken into consideration as the operation time is constant but the actor makes it harder to analyze any given length of duration. Seasonal usually associates with duration of the year involving particular activities (Chase et al., 2006). The trip from home to office isunder different seasonal influences.The fasting period of the Muslims is a major influence in the trip. Traffic is much lighter not only for the trip to the office but also from the office especially on the weekends.. Vehicles on the main route and highway are slight reducing driving time. The drive is much smoother requiring less petrol eliminating the duration to drive to the petrol station and fill petrol.Holidays season is another major influence in the trip. Academic institutes such as schools, colleges and universities a re undergoing final examination. Institutes deem holidays reducing the morning. Vehicles belonging to school bus drivers, college or university students and instructors reduce allowing working adults to use the routes and highway freely. The current assumptions are made relying on past personal experience of the last five years.Finally observation relying on past personal experiences has shown that in the initial week traffic is at the highest at peak hours but reduces by the end of the month. Employees tend to stay late at office at the final week of the month mostly because of the need to complete monthly closing reports. Amount of cars reduces as the weeks run in a monthly cycle.Total cycle time needs to be as less and independent as possible. Cycle time that easily reacts under any influences will make decisions harder to conclude as observations are not consistent. Seasonal factor is the adjustable correctional value in a given time series of the season of the year. The table b elow records the seasonal factor that adjusts the next months cycle time to 300 minutes comparing to the current 347.14 minutes.Confidence IntervalsConfidence intervals are brackets that the veritable population occur base on the confidence levels (NIST SEMATECH, n.d., para. 2). 95% is set as the confidence level for the above data. The sample size is below 15 and the chart below depicts the distribution of average mean for the five weeks being normal (University of Phoenix, 2010, Estimation and Confidence Intervals, p. 305).The distribution scale put to use is the t-distribution consoling the above conditions. The interval that encloses the true population parameter in a 95% confidence level base on the current data is from 61.98% to 79.57%. destructionThe process undergoing the plan records a nearly stable result from the (SPC) within the control limits, producing seasonal factors for next month forecast and nearly a high confidence interval for its confidence level. The process is still open for modifications as the plan has point out areas for improvements. The SPC patterns requires the data to be graphically stable, the average mean are not to be heavily leaning against the seasonal factors and the confidence interval must increase so that the quickest cycle time is achievable.ReferencesChase, R. B., Jacobs, F. R., Aquilano, N. J. (2006). Operations management for competitiveadvantage (11th ed.). New York McGraw Hill/Irwin.NIST SEMATECH (n.d.). What are Confidence Intervals? Product and Process Comparisons.Retrieved fromhttp//www.itl.nist.gov/div898/handbook/prc/section1/prc14.htmUniversity of Phoenix. (2010). Statistical Techniques. Retrieved imperious21, 2010 from University of Phoenix, QNT 561 Applied Business Research Statistics