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Lean Manufacturing Simulation
Table of Contents
Lean Manufacturing is a manufacturing philosophy which if implemented correctly can shorten the time between the customer order and factory shipment by eliminating waste. It can be defined as the identification and removal of all non-value-added activities – its main goal is to eliminate the muda (waste). The philosophy identifies seven common issues with producing waste:
- Over production ahead of demand
- Employees waiting for the next processing step
- Unnecessary transport of materials
- Over processing of parts due to poor tool or product design
- Inventories are more than the minimum needed for production
- Unnecessary movement of employees
- Production of defective parts
Simulation is a vital technique to allow the representation of processes, people and technology in a computing model to help businesses reduce waste and lean manufacture. The simulation model mimics the operations of a business and is accomplished by stepping through events within a compressed amount of time, producing statistics of model elements and performance metrics which are evaluated by analysing the model output data.
Business processes are too complex and dynamic to be analysed by flowcharting and spreadsheet techniques, which is where simulation comes in. Simulation software enables businesses to quickly experiment with lean process improvement options and helps to pinpoint ways to reduce waste and add value to the customer.
The Simprocess modelling approach manifests this concept and builds on it by organising and analysing cost information on an activity basis. Waste can be generated by factors that might not usually be usually be thought as waste such as overproduction, high levels of inventory, items which need to be reworked, processing or waiting times and unnecessary movement.
So, ultimately simulation provides businesses with a technical way of understanding capacity, identifying bottlenecks and the choice of changing data to achieve new capacities, new goals and new logics before applying to the workforce which could prove costly.
The 8 most common wastes of lean manufacturing are as follows:
- Defects: products or services that are out of specification needing extra resources to fix which leads to increased costs and lost time
- Overproduction: where production exceeds customer demand and is considered the most harmful waste (including excess inventory & transportation)
- Waiting: when items aren’t in transport or processed for example employees waiting for materials, equipment being maintained etc
- Not utilising talent: underutilising employee skills and knowledge, limiting their potential; engaging employees in process improvement can reduce waste
- Transportation: occurs whenever products are transported from one location to another; the more transportation the more risk of damages, additional waiting time and costs
- Inventory excess: such as raw materials, work-in-progress or goods which are sitting idle is considered waste as it hasn’t yet contributed value to the end customer
- Motion waste: can occur due to inefficient layouts or searching for mislaid items which can consequently increase the damage to equipment or cause employee injuries
- Excess processing: any activity that isn’t needed to produce a functioning product and can occur during drawn out processes
Please refer to Appendix B for examples of Lean Manufacturing/Simulation Case Studies
To demonstrate how Lean Manufacturing and simulation can be used to increase productivity the following factory assembly was created using Simprocess software, based on the parameters set in the task. All report results were simulation within a 1-month period to generate sufficient results to analyse.
Appendix B refers to the full brief, outlining the parameters set, details of the factory layout, operations and how the product is assembled.
Figure 1 shows the initial model created. When running the simulation, the factory proved to be unproductive and inefficient. The basic model report can be found in Appendix C – Simulation 1.
Figure 1: The factory assembly process created in Simprocess Simulation software – detailing the manufacture of the table
Initially, the factory had produced 4,465 units with 2 tables remaining in the system and processed to the assembly. issue was not the processing of the raw materials, quantities or the sourcing of components; the issue lied within the productivity of workers and use of the forklift truck as this was not being optimised (see Figure 2).
Figure 2: Worker productivity in initial simulation, basic model
The above figure meant that worker productivity had to be optimised and a more streamlined dispatching process had to be implemented (relating to the Portakabin case study).
To improve the productivity of the factory the following parameters have been taken into account:
- Numbers of workers (assemble & forklift)
- Quantity of resources
- Batch forklift function
From simulation 2 – changing the move table process resource from ‘All members’ to ‘any one member’ enables a higher rate of productivity and the workers are no longer idle.
This generated the following results:
Due to the forklift being idle for a large amount of time, it was decided to increase the number of workers to 3 and share the workload by setting the ‘MOVE_TABLE’ function to ‘Any One Worker’:
The implementation of lean manufacturing by making the value flow at the pull of the customer prevents and eliminates waste in the simulation process. The following changes to the simulation can be considered to reduce this:
Levelling production to match demand
Reducing end-to-end cycle time to less than customer’s expectation of a reasonable wait time
Using just-in-time delivery schedules with suppliers
Fixing all problems at the source – accepting the higher short term cost of doing this
Reducing change over time so that batch sizes can be reduced cost effectively
Empowering and training employees to increase communication
To eliminate waste, all the above simulation process changes should be considered together, keeping one variable the same, with a wide range of parameters (batch sizes, schedules, acceptable cycle times, resource quantities etc).
Entities to change:
- Number of resources
- Number of workers
- Number of forklifts
- Batch production
Graph displaying the product output comparison between Simulation 1 and the Optimal Simulation:
In conclusion, Simprocess has been utilised effectively and has helped to streamline Make A Table Ltd (MTL) manufacturing to create a more profitable, leaner and satisfying working environment with increased productivity and worker satisfaction.
- 0% stock left in the system
- 0% assembly waiting time
- Number of products produced in a month increased by ___%
- Workforce increased by ___% creating more jobs and employability in the area, contributing to the local economy, making workers feel more satisfied
- Manufacture optimised ___% through batch production
- Annual profit of the factory increased by __%
Lean Manufacturing case studies
- Global Manufacturing. (2014). Top 10: Lean manufacturing companies in the world. Available: https://www.manufacturingglobal.com/top-10/top-10-lean-manufacturing-companies-world. Last accessed 30/10/2018.
Wate reduction techniques
- Simul8. (2017). Implementing Lean Manufacturing with Simulation. Available: https://www.simul8.com/manufacturing/lean-process-improvement. Last accessed 01/11/2018
Supply Chain planning for GSK with Simulation Software
- anylogic. (2018). Supply chain planning for GSK with Simulation Software. Available: https://www.anylogic.com/supply-chain-design-for-vaccine-manufacturer/. Last accessed 04/11/2018
- FutureStateSolutions. (2010). Lean Manufacturing Case Study: Office Furniture. Available: http://www.futurestatesolutions.co.uk/lean-manufacturing-case-study—office-furniture.html. Last accessed 04/11/2018.
- Lean Manufacturing Tools. (2015). Benefits of Lean Manufacturing | Why Implement Lean?. Available: http://leanmanufacturingtools.org/63/benefits-of-lean-manufacturing/. Last accessed 04/11/2018.
- Business Case Studies. (2016). Lean production at Portakabin A Portakabin case study. Available: http://businesscasestudies.co.uk/portakabin/lean-production-at-portakabin/just-in-time-production.html. Last accessed 04/11/2018.
F. Hosseinpour and H. Hajihosseini . (2009). Importance of Simulation in Manufacturing. World Academy of Science, Engineering and Technology. Vol:3 (3), 261-263.
Tuncer I. Ören. (1994). Artificial intelligence in simulation. Annals of Operations Research. 53 (1), 287–319.
Thomas L. Jackson (1996). Implementing a Lean Management System. 2nd ed. Oregon: Norman Bodek. Chapter 1.
Make a Table Ltd (MTL) is a manufacturer of dining tables. The design is shown in Figure C1. The company management wanted to check if there could be an opportunity to improve the productivity of the manufacturing process by focusing on the final assembly. As shown in Figure C1, the dining table has wooden surface and 4 aluminium legs.
Figure A1: The design and components of the table
Figure A2: Final Assembly
The Final assembly: The legs are assembled to the wooden surface and the table is packaged for shipping. This will take on average 10 minutes with a standard deviation of 1.5 minutes (see Figure A2) using one worker. A forklift truck will be used to move the tables to the finished-products-store ready for customers/distributers. A forklift truck will be driven by one worker and this will take on average 4 minutes with a standard deviation of 1 minutes.
Costing: Each worker will cost the factory £15 per hour and £6000 per year as a fixed cost per worker. The factory is employing 2 workers per shift in the final assembly section of the factory. The forklift truck costs £8 per hour and a fixed annual cost of £600 for insurance and maintenance.
Number of working hours: The factory and is operating 24 hours a day, 7 days a week using three shifts system to manufacture the tables.
Assumptions: You can make any other reasonable assumptions about the cost, customers demand or time of any process as required.
Chrysler increases revenue by $1m per day – without increasing costs
Leading automotive manufacturer Chrysler utilized simulation software to meet increased demand and grew revenue by $1,000,000 per day at its Brampton plant, without increasing costs. The Brampton assembly plant builds 3 models of car with an annual output of 200,00 units. With the release of the newmodel, a substantial rise in customer demand was to be expected and as a result, the plant was tasked with improving daily production rate from 930 to 969 units to meet this increase. Simul8 (2017).
Steve Lin, a member of Chrysler’s simulation team used simulation software to understand the impact of changing line speeds and using simulation software it soon became evident that two specific machines were causing the bottleneck.
The simulation enabled the team to rank all manufacturing lines based on their performance and easily identify the poorest performing lines to optimize and remove costly bottlenecks. Running the simulation for one month meant that the results generated would be as accurate as possible. The management team were able to utilize the simulation to help make decisions to improve productivity and met the target of producing 39 extra units per day.
Lean Production at Portakabin
A further example of various lean manufacturing techniques working seamlessly together to benefit a manufacturing company can be seen in the following case study. The Portakabin lean production process encourages waste reduction by building a modular, lean process. The off-site construction and installation of completed and fitted out modules means that fewer workers are required on site and there is less transport needed. Portakabin has been able to reduce the volume of waste by 60% over the last three years; achieved by:
- Cutting out waste from the manufacturing system
Portakabin have though carefully about how it can improve design to reduce waste in manufacturing, examples include:
- Maximising the use of raw materials ensuring design tessellates
- Re-use of materials
- Changes in materials used
- Precise cutting of materials with no errors
- Employee satisfaction meaning less errors
- Pre-sized cutting of standard parts, no trimming required
- Recycling waste
- Recycles 65% of waste generated and staff are always educated to think about recycling and sustainability
- Set up waste management teams and regularly given refresher courses
- Pallets used for transporting Portakabin products
Futurestate Solutions Ltd – Office Furniture Store
Reviewing the furniture case study, it became apparent that the office furniture company employed Business Solutions FutureState in 2010 to develop a simulation to develop lean manufacturing into their organisation. This was due to their large material and energy price increases, lack of space, poor staff morale and product lead times taking too long amongst other reasons too. Futurestate Solutions Ltd (2014).
The solution was to utilise Lean Manufacturing, Improvement Teams, Waste Elimination, Stock Control, DFMA and Kanban Stock Control. The combined effect of these lean manufacturing methods leads to the Office Furniture Store having an increased output of productivity and number of items processed. The following results were taken from the Futurestate case study generated by the Office Furniture Store after Lean Manufacturing was optimised.
- Increased productivity in areas to double capacity
- Regained approx. 40% of floor space in 200,000sq/ft factory
- Improved turnover per employee by 38%
- Reduced standard lead time from 6 weeks to less than 2 weeks
- Transformed the culture to one driven by continuous improvement
- Improved communication and cascading of key business objectives
- Provided staff with training in various Lean Manufacturing techniques enabling problem solving in situ
- Redesigned products for manufacture and assembly
Basic Model: Simulation 1
Optimal Productivity Model: Simulation
INSERT TEXT REPORT OF OPTIMAL PRODUCTIVITY SIMULATION
The following tables show interpreted results, parameters and unit cost price from the ten simulations.
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