49989 Operations Engineering: A New Assessment Answers

At first De Bono’s six hats technique is applied to so as to solve the problem of designing the new product and then the functioning of the production facility is discussed using Kanban plots and floor plans. The report then discusses about how the functions of the plant is managed and provides suggestion to improve the functions of the plant in-order to optimize the process of production. 

New Product Design

• De Bono’s Six Thinking Hats Technique

In-order to help improve parallel thinking this technique was developed by Edward de Bono and it helped in the process of thinking together (Karadag, et al. 2016). The six hats represent six different colours and each colour is for thinking in a unique style as mentioned here (Kivunja, 2015). White hat helps in the gathering of accessible data, yellow hat is for distinguishing the advantages that could be accomplished (Sheth, 2012), green hat is for imagination and includes advancement in the item, black hat is for disparities in the item with the goal that a more reasonable item is created (Taie and El Kamel, 2013), blue hat is for dealing with the objectives and manner of thinking lastly the red hat implies feelings or the nature that the new item would work (Dhanapal and Ling, 2013). ‘Model X’ is the latest product brought out by Tesla, Inc. ‘Model X’ is made on the same platform as that of Model S with the difference being that it is going to be a sports utility vehicle making it a new segment for battery powered electric cars. The detailed model is as follows.

White Hat:

• Tesla Inc., is famous all over the world with sales in more than 31 countries and the Model S was widely accepted.
• Marketing of Model X should be carried out effectively and proper advertisement should be made.
• There will be minimum additional cost involved as only the body has to be redesigned which could be done easily by Tesla.
• Find the appropriate shape and size of the body to be placed in the car without disturbing its aerodynamic balance.

• Checklist of the Design Review

The progress of ‘Sedan Model X’ project is evaluated using a design review checklist as shown below.

 Summary of the Design Review Outcome

New equipment is required for carrying out the manufacturing process of Model X. Fresh orders for procuring new equipment, components and new body has been and the necessary changes that has to be incorporated in the floor plan has been carried out. New storage facility is required for storing the new equipment and components space for the same is allocated outside the work area and the new storage unit has been ordered (Oahl and Beltz, 2008). All the new components required for manufacturing Model X could be supplied by our existing suppliers. 

New staff recruitment process has been completed and their joining orders have to be issued as soon as possible. The new products could be effectively distributed using the existing distribution network. A new marketing strategy is under development for promoting Model X. Financial analysis indicates that the new product would earn profit rather than losses and is therefore going to be a success in the market.

Avoiding Wastages by Identifying, Categorizing and Recommending Actions to avoid Wastage

Lean thinking is the process adopted to reduce wastage and to improve the manufacturing process. Another way of reducing wastage is by making good use of the unutilized skills of the work force (Akpolat, 2013) (Nicholas, 2011). Different types of wastages generated during the manufacturing process of Model X is as follows: 

• Wasting the skills of the employees.
• Stock Wastage.
• Wastage due to transportation.
• Increase in the movement involved in manufacturing process.
• Time Wastage.

The waste structures, main driver and prescribed moves that ought to be made to anticipate wastages are talked about in the accompanying table.

Development of Supply Chain Management

 A few parts of Supply chain administration concerning the 'Model X' venture is talked about in this segment. 

Supply Chain Integration Management (Li 2013). – It is accustomed to unite all the supply chain administration frameworks to foresee figures, it will be coordinated to the Product Lifecycle Management and the Enterprise Resource System. With the goal that the whole procedure of creation is nearly observed and controlled. 

Supply administration (Salunkhe et al., 2013) – Supply conjecture is completed so providers meet the creation plan on time. It is completed by looking into the accessible stock and the necessity of new stock. 

Demand administration (Ramnath et al., 2010) – It concerns the demands of the clients to Model X for which week after week conjecture are improved and techniques are chocked out. Estimates depend on the past deals and climate conjecture and regular demand for pie is additionally considered. 

Stock Management (McDonnell et al., 2015) – The machines are cleaned and support is done consistently so that there would not be any kind of shutdown which would influence general creation. Any such occurrences are recorded and the reason for the episode is discovered and cures are taken so the generation proceeds onward continuous. 

Manufacturing Management – Production for every week is assessed and the creation is booked according to demand. On the off chance that extra staff is required amid particular time or season, at that point they are utilized inside the accessible era.

Functional Improvements

• Overall Equipment Effectiveness Evaluation (OEE)

The following shows the OEE of the Robotic system 

Per day Scheduled time of operation = 10 h 

Time allocated for Breakdown = 30 min = 0.5 h 

Time allocated for Lunch break = 45 min = 0.75 h 

As per schedule break during evening = 30 min = .5 h 

As per schedule the net operating time = 8.25 h 

Total number of items manufactured = 50 

Total number of good items = 49 

Actual net operating time = 8 h 

Ideal Cycle time = Actual net operating time / Total items = 8 * 60 / 50 = 9.6 min/car 

Availability = Actual net operating time / net time as per schedule = 8/8.25 = 0.97 

Efficiency of Performance = total number of items manufactured * Ideal Cycle time / actual net operating time = 50*9.6 / (8*60) = 1 

Quality Rate = Good items/ total items manufactured = 49/50 = 0.98 

OEE = Efficiency of Performance * Quality Rate = 1 * 0.98 = 0. 98 

The Overall Equipment efficiency rate of the manufacturing plant is 98% 

• Improvement of OEE improvement

The quality rate is .98, the overall efficiency is 98% and the performance efficiency is 1, therefore if the breakdown has to be further reduced then all the parameters have to be improved as already the efficiency of the system is very high. But still there is scope for improvement which could be achieved by carrying out proper maintenance activity. It should be made sure that the production never stops due to unavailability of spares which should be available at all times.

New equipment should replace old equipment so that work does not come to a standstill due to the lack of equipment. All equipment should be cleaned properly at least once in a month. The workforce should be well trained to handle all the equipment and regular inspection should be executed to find the defects in the equipment (Sodkomkham and Chutima, 2016). 

• Evaluation of Yield, Defects and Defective Calculations

Polishing of the car (Model X) and final inspection forms the basis of this calculation

Polishing the car:

Total number of cars to be polished in a day = 50

Percentage of cars actually cleaned = 98%

Defective cars = 1 and Number of defects = 10 (0.2)

Non-Defective cars = 50

Yield (defective)₁ = non-defective cars / defective cars = 49 / 50 = 0.98

Defective per unit (DPU) = total defects / total items = 10/50 = 0.2

Yield (defect)₁ = e^-DPU = e^-0.2 = 0.8187 

Inspection of Model X

Total items = 49

Defective Model X = 0 (0%)

Defects = 15 (30%)

Non-defective D = 50

Yield (defective)₂ = non-defective cars / defective cars = 49 / 49 = 1

Defective per unit (DPU) = total defects / total items = 15/50 = 0.30

Yield (defect)₂ = e^-DPU = e^-0.3 = 0.74 

Rolled throughput yields

RTY (defective) = Yield (defective)₁ * Yield (defective)₂ = 0.98 * 1 = 0.98

RTY (defect) = Yield (defect)₁ * Yield (defect)₂ = 0.8187 * 0.74 = 0.6058

• Result’s Interpretation

When we say that the car is defective, it means that the car does not run, there may be a problem with the accelerator, instrument cluster or so on. But defect can be anything like a scratch in the body of the car, a puncture in the tyre, cracks in the window and so on. Therefore, it can be said that the car could be driven even if there are defects, which means that if the defects are identified during the inspection stage then they could be rectified and the car can be made to the form that was promised (Smith and Pretorius, 2012). 

Yield (defective) is the percentage of cars without any defects, which means that in the manufacturing process, an assumption is made that there are 20% defects in 2% of the cars and 98% of cars have been cleaned properly and have no defects in them. 

Therefore, only 49 cars or 98% of cars are considered in the second process as there is already one defective car having a total of 10 defects in it and thus the one defective car cannot be delivered to the customer. The average percentage of good defect free cars manufactured is given by RTY (defective) and it is 98%. Whereas, average percentage of cars with no defects is given by RTY (defects) which is 60.58%. This indicates that 98% of cars can be delivered whereas 2% of the car could not be delivered because they have 60% defects in them.

Analysis using Five Why Technique

The actual cause or root cause of finishing or delivering lower than expected number of cars could be determined using the 5 why technique (Fujita and Revetria, 2012).

Why is the manufacturing of cars lower than that scheduled? (Myszewski, 2013).

Because the water sprayed by one of the cleaning funnels have large amount of dust and fine particles in it.

Why is there dust and fine particles in the cleaning funnel? (Serrat, 2010)

Because the filter through which water flows before reaching the funnel is damaged.

Why is the filter damaged?

The filter is damaged because there is too much dust and fine particles in the water.

Why is there too much dust and fine particles in the water reaching the filter?

Because the tank connected to the cleaning funnel had cracked and thus the concrete and dust in its wall are carried by the water to the funnel.

Why was the crack not detected earlier? (Kohfeldt and Langhout, 2011).

Because the tank did not feature in the scheduled maintenance and cleaning process. 

Therefore, the root cause of failure was the lack of proper maintenance in the tank as it was not included in the root maintenance schedule and it should be included in the maintenance schedule so that the manufacturing process could be optimized and the efficiency of manufacturing is improved. 

• Fictitious Constraint’s Elimination

The manufacturing process could be optimized using five focussing steps and thinking processes (Pretorius, 2014). For implementing the process, it is assumed that a bottleneck occurs between painting and final inspection (Reid, 2007). Now eliminate the bottleneck by applying the five focussing steps. 

Constraint Identification (Watson et al., 2007) - In the production process 10 cars are scheduled to be polished at a moment of time. But only 5 spray painting equipment are available. Therefore, 5 cars are painted at a time The rest of them have to be kept in waiting without being affected by dust particles. 

Constraint exploitation (Watson et al., 2007) – 5 cars cannot be kept close to the painting area as it is not a good practise and paint might fall on the cleaned car which may disturb the quality of painting to be done on the car. But the advantage is that the painting process would never stop when there are enough cars to paint. 

Subordinate other procedures to above decision (Watson et al., 2007)– The time taken by each process in the workplace or manufacturing floor should coordinate with the time taken to paint the car. The advantage here is that there is a reduction in the burden of cleaning as there are more clean cars and the staff held up in cleaning could be utilized elsewhere.

Constraint Elevation (Watson, et al., 2007) – Now if 5 more painting machines are bought the problem could be easily solved and it is the best way as painting machines don’t cost much.

Inertia as a Constraint (Watson, et al., 2007) – As soon as the new painting machines arrive the production should return to the usual way and there should be no delay due to any type of inertia.

Conclusion

‘Model X’ which is the new car to be launched by Tesla, Inc., can be manufactured effectively if the procedure mentioned in the report is followed correctly. A detailed analyses of the effectiveness of the product has been made and the effect the product would have on the present and future of the company has been evaluated carefully. All factors indicate that the product is going to be a great success and would earn profit. The report also presents an effective supply chain, floor plan and Kanban plots. The report also suggests ways to improve the production and indicates that the new product would be great success and will be an asset to the company in the coming years. 

References

Akpolat, H. (2013). Operation Engineering Lecture Slides, UTS Subject 49989. Sydney: UTS.

Dhanapal, S. and Ling, K.T.W. (2013). A Study to Investigate How Six Thinking Hats Enhance the Learning of Environmental Studies. IOSR Journal of Research & Method in Education. 1(6), pp. 20-29.

Fujita, H. and Revetria, R. (2012). New Trends in Software Methodologies, Tools and Techniques. Amsterdam: IOS Press.

Karadag, M., Saritas, S. and Erginer, E. (2016). Using the ‘Six thinking hats’ model of learning in a surgical nursing class: Sharing the experience and student opinions. Australian Journal of Advanced Nursing. 26(3), pp. 59-69.

Kivunja, C. (2015). Using De Bono’s Six Thinking Hats Model to Tech Critical Thinking and Problem Solving Skills Essential for Success in the 21^st Century Economy. Creative Education. 6, pp. 380-391.

Kohfeldt, D. and Langhout, R.D. (2011). The Five Whys Method: A Tool for Developing Problem Definitions in Collaboration with Children. Journal of Community & Applied Social Psychology. 22(4), pp. 316-329.

Li, Z. (2013). Design and Analysis of Robust Kanban System in an Uncertain Environment. Karlsruhe: KIT Scientific Publishing.

McDonnell, L.R. et al. (2015), Implementation of A Visual Kanban Method for Process Management in The Greta Environment. Business Logistics in Modern Management. 13, pp. 187-196.

Myszewski, J.M. (2013). On improvement story by 5 whys. The TQM journal. 25(4), pp. 371-383.

Nicholas, J. (2011). Lean production for competitive advantage: a comprehensive guide to lean methodologies and management practises. New York: Productivity Press.

Oahl, G. and Beltz, W. (2008). Engineering Design: A systematic Approach. 2^nd Edition. London: Springer.

Pretorius, P. (2014). Introducing in-between decision points to TOC’s five focusing steps. International Journal of Production Research. 52(2), pp. 496-506.

Ramnath, B.V., Elanchezhian, C. and Kesavan, R. (2010). Application of Kanban System for Implementing Lean Manufacturing. Journal of Engineering Research and Studies. 1(1), pp. 138-151.

Reid, R.A. (2007). Applying the TOC five-step focusing process in the service sector: A banking Subsystem. Managing Service Quality: An International Journal. 17(2), pp. 209-234.

Salunkhe, R.T., Kamble, G.S. and Malage, P. (2013). Inventory Control and Spare Part Management through 5S, KANBAN and Kaizen at ABC Industry. IOSR Journal of Mechanical and Civil Engineering. pp. 43-47.

Serrat, O. (2010). The five ways technique. Washington: Asian Development Bank.

Sheth, M. (2012). Six Thinking Hats. Asian Journal of Management Research. 2(2), pp.814-820.

Smith, M. and Pretorius, P. (2012). Exposing the False Paradigm used in Management Decision Making. The South African Journal of Industrial Engineering. 12.

Sodkomkham, T. and Chutima, P. (2016). Lean Six Sigma Application in Rear Combination Automotive Lighting Process. IOP Conf. Series: Materials Science and Engineering. 131, pp.1-9.

Taie, E.S. and El Kamel, A.A. (2013). Six thinking hats as a creative approach in managing meetings in hospitals. Journal of Nursing Education and Practice. 3(9), pp. 187-200.

Watson, K.J., Blackstone, J.H. and Gardiner, S.C. (2007). The evolution of a management philosophy: The theory of constraints. Journal of Operations Management. 25, pp. 387-402.