Production Challenges at Shuzworld - Term Paper Example

?. Company Analysis Memorandum Mr. Alistair Wu Operations Consultant Priority: Re: Production Challenges at Shuzworld Basically, the research is based on the recommendations on how to solve production challenges at Shuzworld. This can only be achieved through an analysis of the different aspects of operations within the organization. It is done through Shuzworld key issues. Introduction The production of goods in a manufacturing plant is an integrated process involving the interaction of people with machines. To increase production efficiency, the managers have the great responsibility of realigning operations including the production flow layout, making changes to the staffing plan, as well as other logistics such as warehousing and distribution. This memorandum looks at three key issues at the Shuzworld namely improvement of the current workflow, the introduction of a new project, and staffing plan in order to increase efficiency and output). The memorandum also looks at some short-term scheduling techniques and rules that can be used to achieve efficient movement of units through Shuzworld production systems. Re: Improvement of the Current Workflow The current workflow in the plant can be improved through the process of assembly line balancing. According to Heizer and Render (2011) in order to produce at a particular rate it is important that management is aware of the current work methods, tools and equipment that are in use. The length of time that each task requires must also so be determined. Additionally, the sequence in which each task should be performed must be identified. The precedence diagram which is shown below illustrates the precedence relationship among the tasks to be performed on the workboot assembly line, the time taken to carry out the tasks labeled A to H, and the tasks which can be done at the same time. Although the current Workboot Assembly Schedule indicates that there are eight tasks involved in the production, it is clear that tasks B and C can be performed simultaneously to save time. The same logic applies to tasks E and F such that performing these tasks simultaneously with further save the time spent on production. The restructuring of the production process in order to allow processing of multiple tasks can lead to reduced production time. Using this logic, 3 minutes will be saved between tasks A and D and the same amount of time saved between D and G. Therefore, the total amount of time saved after restructuring will be 6 minutes. Task C takes 3 minutes but when performed simultaneously with C (8 minutes), it will take 8 minutes to complete both tasks. Similarly, it will take 4 minutes to complete both tasks E and F thereby saving 3 more minutes. Therefore, the total time taken for production of Workboot will be reduced to 40 minutes. The correct number of workstations can be found after making a determination of the cycle time. The calculations are as follows: Cycle time = Production time available/units required = (8hrs/day* 60mins per hr)/48 units per day = 480/48 = 10mins/cycle The cycle time of 10 minutes can then be used to determine the minimum number of workstations as follows: Minimum number of work stations = Total task time (?ti)/cycle time = 46/10 = 4.6 or 5 stations The information suggests that the minimum number of work stations required is 5. However, the cycle time is 10 minutes and so only tasks that can be done in 10 minutes or less can be performed at the same work station. This means that Task A takes 10 minutes and will be done at one workstation; Task B and C can be done together in a total of 9 minutes at another workstation; Tasks D takes 8 minutes and can be done at another workstation; Tasks E, F and G takes a total of 10 minutes and can be done one workstation while Task H takes 9 minutes and will therefore be performed at another workstation. Therefore, a total of 5 workstations are required. The layout would therefore appear as follows: The most following tasks heuristic were used to assign jobs to workstations. This solution does not in any way violate the precedence relationships. The first workstation takes the full 10 minutes, while the second, third, fourth and fifth takes 9, 8, 10 and 9 minutes respectively The total idle time for this solution is 4 minutes and is made up of 1 minute at workstation 1, 2 minutes at workstation 3 and 1 minute at workstation 1. Using this setup it would take 50 minutes (5 x 10) to complete the task. Heizer and Render (2011) indicates that efficiency levels may be determined with the use of the following formula: Efficiency = Total task time/(Actual number of Workstations) ? (Largest assigned cycle time) x = 46/(5?10) = 92%. Therefore, the highest level of efficiency that can be obtained in order to complete the several tasks labeled A to H with a maximum cycle time of 10 minutes and utilizing 5 workstations is 92%. The following output shows the critical path indicating the shortest time period in which all activities can be completed. Activities A B C D E F G H Duration Minutes 10 6 3 8 3 4 3 9 Paths Path 1 1 0 1 1 0 1 1 1 37 Path 2 1 1 0 1 1 0 1 1 30 Path 3 1 0 1 1 1 0 1 1 36 Path 4 1 1 0 1 0 1 1 1 40 Longest/Critical path 40 The table indicates that the critical path is A, B, D, F, G, H. Therefore, there should be sufficient slack time at stations 2, 3 and 5 to act as a buffer against any unforeseen delays. Modern production flows require automation in order to shorten production periods while increasing production output. Automation of the production line will go a long way into achieving these objectives at the shoe production plant. The methods and tools applied in designing, analyzing, and optimizing, the production automation system are laid down according to the transport line control and supervision (Croci, Perona and Pozzetti, 2000). The Shanghai Shuzworld manufacturing plant should consider an innovative transport line that will move semi-finished products from one workstation station to another according as per the predetermined operating schedule. The automation system should enhance the modularity, integrability, scalability, and reconfiguration of the assembly line. This will in turn increase the overall plant flexibility. The acquisition of the latest technology will go a long way in increasing efficiency at the plant. This will lead to reduced operational costs, improved management, and new product designs. The current assembly line layout should be analyzed often and simplified in order to increase production efficiency (Nieble and Freivalds, 2002). The precedence graph is a useful tool for use in solving assembly line balancing problems. It provides a clear picture of the sequence of activities along with the time required to complete them. It also allows management to determine which activities can be combined in order to improve efficiency. Critical path analysis is also a useful tool in determining the shortest time period in which a project can be completed. It is complementary to Gantt charts and other project management tools. Re: The Initial and Ongoing Cost for the Maui Sandal Project The new Maui Sandal Project by Shuzworld is budgeted to produce 5 batches (10,000 sandals each) in the first month of production, 10 batches in the second month, and 15 in the third and 20 in the fourth month – a total of 50 batches. The hourly labor rate is US $1.08 and so the first batch is expected to take 1,000 hours. However, it has been reported by Pang that the production employees at the Shanghai production plant typically have an 80% learning curve. This implies that the number of direct labor hours for production will decrease by 20% each time the production quantity is doubled. The number of labor hours and production cost and learning curve for each batch of Maui sandals may be calculated by applying the Learning Curve Co-efficient Approach. The time to produce each batch may be determined using the following equation: TN = T1C Where, TN is the number of labor hours that is required to produce the Nth unit; T1 is the number of labor hours which is required to produce the first unit; and C is the learning curve coefficient. The following table shows the batch numbers, learning coefficients which were extracted from Heizer and Render (2011). Batch Number Unit time Total Time Number of labour hours required (TN) Time to produce batch Reduction in time for the Nth batch 1 1.000 1.000 1000 1000   2 0.800 1.800 1800 800 200 3 0.702 2.502 2502 702 98 4 0.640 3.142 3142 640 62 5 0.596 3.738 3738 596 44 6 0.562 4.300 4300 562 34 7 0.534 4.834 4834 534 28 8 0.512 5.346 5346 512 22 9 0.493 5.839 5839 493 19 10 0.477 6.316 6316 477 16 11 0.462 6.778 6778 462 15 12 0.449 7.227 7227 449 13 13 0.438 7.665 7665 438 11 14 0.428 8.093 8093 428 10 15 0.418 8.511 8511 418 10 16 0.410 8.921 8921 410 8 17 0.402 9.323 9323 402 8 18 0.394 9.717 9717 394 8 19 0.388 10.105 10105 388 6 20 0.381 10.486 10486 381 7 25 0.355 12.309 12309 355 26 30 0.335 14.020 14020 335 20 35 0.318 15.643 15643 318 17 40 0.305 17.193 17193 305 13 45 0.294 18.684 18684 295 10 50 0.284 20.122 20122 284 9 The table shows the time taken to produce each batch of Maui sandals up to batch 20 and the cost for every additional 5 thereafter. It also shows the additional time to produce each batch and the cumulative time period for all batches. The learning curve which provides a clearer picture of the levels of reduction in time is shown below. The learning curve would be steepest over the first 4 to 5 batches of Maui sandals to be produced and that efficiency levels increase at a lower rate as additional batches are produced. This is also confirmed by the immediately preceding table. The table below shows the how labor cost and labor hours over the next four months in relation to the production of Maui sandals. Budgeted Labor Hours and Labor Cost to Produce Maui Sandals Over the Next 4 Months Month Batches Labor hours Labor Cost ($) 1 5 3,738 4,037.04 2 10 4,773 5,154.84 3 15 5,509 5,949.72 4 20 6,102 6,590.16 Total 50 20,122 21,731.76 The table shows that as the hours fall the cost of production falls with it. In Month 1 a total of 3,738 labor hours were required to produce 5 batches at a cost of $4,037.04 - $807.41 per batch. Ten (10) batches were produced in Month 2 and this required only $1, 117.80 more and works out to be $515.48 per batch. Fifteen (15) batches would be produced in Month 3 and this requires only $794.88 more than the cost to produce the ten batches that would be produced in Month 2 at a cost of $396.65. A total 20 batches would be produced in Month 4 at a cost of $6,590.16 which would be just $640.44 more than the cost of producing the 15 batches produced in Month 3 – at a cost of $329.51 per batch. This indicates that the average cost of producing each batch fell over the period and will continue to do so in the future but at a declining rate. The learning curve was used as the decision tool because it illustrates how the levels of efficiency can be improved overtime as more batches of Maui sandals are produced. The coefficient approach to the learning curve was used to calculate the labor hours required because it takes into consideration every unit of production when compared with the arithmetic approach. Additionally, the table of coefficients can be found in some textbooks such as Heizer and Render (2011). It would therefore make the job easier for almost anyone who needs to check the accuracy of the figures as well as to determine how it works. Re: Staffing Plan The staffing of machine operators requires proper planning in order not to compromise production efficiency. Although machines do most of the work, man labor also plays an integral role in the production of shoes. In other words, machines and people should work in an integrated manner in order to increase production (Mahmood et al, 2010). In order to keep production cost down I would recommend that the assignments for jobs be carried out using the following steps. Original Table Step 3 - Subtraction - use the smallest uncovered number except those where lines intersect to which the number should be added Machine operator/ Job A ($) B ($) C ($) D ($) Machine operator/ Job A ($) B ($) C ($) D ($) 1 $10 $12 $10 $11 1 $0 $3 $0 $1 2 11 9 11 11 2 1 0 1 0 3 9 8 11 9 3 0 0 2 0 4 10 8 9 10 4 1 0 0 0 Step 1a – Row- Subtract lowest number in each row from all other number in the relevant row Step 2- Lines - cover all zeros with minimum lines possible Machine operator/ Job A ($) B ($) C ($) D ($) Machine operator/ Job A ($) B ($) C ($) D ($) 1 $0 $2 $0 $1 1 $0 $3 $0 $1 2 2 0 2 2 2 1 0 1 0 3 1 0 3 1 3 0 0 2 0 4 2 0 1 2 4 1 0 0 0 Step 1b - Column -subtract the smallest number in each column from the other number in the relevant column Step 4 - Assignments Machine operator/ Job A ($) B ($) C ($) D ($) Machine operator/ Job A ($) B ($) C ($) D ($) 1 $0 $2 $0 $0 1 $0 $3 $0 $1 2 2 0 2 1 2 1 0 1 0 3 1 0 3 0 3 0 0 2 0 4 2 0 1 1 4 1 0 0 0 Step 2 - Lines -cover all zeros   Machine operator/ Job A ($) B ($) C ($) D ($) Machine operator/ Job A ($) B ($) C ($) D ($) 1 $0 $2 $0 $0 1 *       2 2 0 2 1 2   *     3 1 0 3 0 3       * 4 2 0 1 1 4     *   The above table shows the assignment of Operators A, B, C, and D to four jobs. The total cost of which would be $37 as shown in the table below. Job Operator Cost 1 A $10 2 B $ 9 3 D $ 9 4 C $ 9 Total cost $37 This staffing plan was arrived at using the assignment method. Only one worker is assigned to each job. Using the original table the assignment method seeks to create an opportunity cost of zero by repeatedly subtracting the lowest cost from each row or column. See Steps 1a and b above. We then draw the minimum number of lines to cover all the zeros in the table as shown in Step 2. After doing so we find that only three lines are needed and so the solution is not optimal. The smallest uncovered number is 1 and this is subtracted from all other uncovered numbers and added to numbers at the intersection of lines in Step 3. We then go back to Step 2 and again draw lines through all zeros. A total of four lines are needed and so the optimal solution has been obtained and so the assignments can be done as shown above. Job 1 would be assigned to Machine operator A. The cost of performing Job 3 is the lowest for Machine operator D and Job 4 the lowest for Machine operator C. It therefore means that Machine operator B will perform Job 2. This decision analysis tool (the assignment method) was used because it represents an effective way of assigning jobs to resources. The objective is to minimize costs and it provides a simple method of assigning machine operators to jobs in order to achieve lower production cost. Re: Outline of Short-term Scheduling Techniques and Rules to Achieve Effective Movement of Units According to Heizer and Render (2011) by using short-term scheduling techniques, capacity decisions, aggregate planning and master schedules can be translated into job sequences and specific assignments of the factors of production. Scheduling is important because it allows for a more efficient movement of goods through the production process and to the consumer. It leads to better utilization of assets and lower cost. It also leads to additional capacity as a result of faster throughput and improvement in customer service as a result of faster deliveries. A very useful method that could be utilized to schedule jobs through Shuzworld production systems is the Critical Ratio technique. Heizer and Render (2011) indicates that the critical ratio is an index number which is calculated by dividing the time remaining until the due date by the work time that is left to complete the job. The jobs with the lowest critical ratios are scheduled before those with the highest ratios. The formula for calculating the critical ratio (CR) is as follows: CR = Time remaining/Work days remaining = (Due date – Today’s date)/Work (lead) time remaining. Heizer and Render (2011) also indicates that the critical ratio does the following: Helps to determine specific job status Establish job priorities Relates stock and make to order jobs Automatically adjust job priorities based on demand and job progress Tracks the progress of jobs through the production system Heizer and Render (2011) also indicates that Johnson’s rule may also be used to sequence two or jobs which use the same machines or work centers. It is also used in order to minimize the total production time as well as the idle time. The rule is as follows: List all jobs along with the time to completion Choose the job with the shortest time to completion. If it is located in the first work center then it should be done first. Otherwise it should be done last if it is in the last work center. Eliminate jobs as soon as they have been scheduled. Repeat the first two steps while moving towards the center of the sequence. The example below explains how Johnson’s rule works using two machines or work centers. The table shows two machines or work centers to which jobs are assigned. There are a total of 4 jobs labeled A, B, C and D. The job with the shortest time to completion – Job A is located in the second work center and so it will be dealt with last. Job B is next in terms of time. It is located in the first work center and so it will be completed first. Next in line is Job C. Since it is in work center 2 it will be completed just ahead of Job A while Job D which takes the longest time will be completed after Job B. Conclusion These recommendations indicate that challenges in the production system can be resolved by using various decision tools and scheduling techniques. In light of this I have utilized a Gantt chart for scheduling work flow, the assignment method – a linear programming technique to assign workers to jobs, and the arithmetic approach to the learning curve to indicate how labor hours would decline as the number of batches produced is increased. I have also outlined scheduling techniques and rules that can be used to achieve efficient movement of units through Shuzworld production systems. These should go a far way in dealing with some of the challenges faced. References Croci, F., Perona, M. and Pozzetti, A. (2000). Work Force Management in Automated Assembly Systems. International Journal of Production Economics. Vol. 64 No. 1-3 pp. 243-255 Mahmood, H., Rahman, M. N., Deros, B, and Ghani, J.A ., (2010). Improving Production Line Performance: A Case Study. Applied Mechanics and Materials. Vol. 44 – 47, pp 4136- 4140 Nieble, B, and Freivalds, A. (2002). Methods, Standards, and Work Design. McGraw-Hill Heizer, J. and Render, B. (2011). Principles of Operations Management. 8th ed. USA: Prentice Hall Read More