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Time Variation and Lead Time Increase: A Vicious Circle

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Time variation and WIP cause a vicious circle that increases value stream lead time making it difficult to keep pace with changing customer demand. Time is a continuous variable which can be easily recorded without the need of special equipment: PCs or smartphones used to collect data can automatically record a timestamp. When it comes to analyse cause and effect between process parameters and results time is key. Time Variation Causes Accumulation of WIP You can experience this effect in exercise 7 with a simulator  : This WIP Increase Causes a Lead Time Increase As seen in the same simulator: This is due to the fact that items have to wait in the queues formed before steps 2 and 3. Long Lead Times Cause an Amplification Upstream of the Market Variation  This effect is well illustrated in the Beer Game  which can be experienced with various simulators in the web. The game was first described by Peter Senge  in The Fifth Discipline . A small variation in the market demand is amplified

Test/ Repair Loop: Potential Value Stream Bottleneck

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  A Test/ Repair loop can become a bottleneck for the whole Value Stream when Test First Pass Yield is lower than planned. A drop in FPY is normally caused by problems upstream in the Value Stream as seen in   https://polyhedrika.blogspot.com/2020/12/defect-generation-and-detection.html Test/ Repair loops are often absent in Value Stream Maps in spite of the potential to become the bottleneck for the total process. Download this Excel example file   TestRepair.xlsm   to your PC from OneDrive folder   Polyhedrika You must close all open Excel files before you open this one and you should enable Macros . To simulate 1 hour operation just press  F9 . Press the  RUN 100  button to run 100 cycles. To reset (put all Work-In-Process to zero and set default values) press  Ctrl + r You can only write in the yellow cells. This Test-Repair loop is part of a Value Stream with a Throughput of 100 units/ hour (Takt = 36 seconds) therefore you need to work out the minimum required Test and Repair cap

Lot Size and Constraints

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System constraints are key when it comes to optimizing our value stream. The process bottleneck limits the overall throughput and it determines such things as manufacturing lot sizes. With this simple manufacturing line simulation you can experience the effects of alternative solutions in order to maximize profit. When resources are shared by several products there is a setup time when changing product and you have to decide what is the optimal production lot size to optimize the process. Download this Excel file TOCeng5.xlsm from OneDrive folder Polyhedrika Close other Excel files before you open this one and enable Macros. Process Objective Run the simulator to obtain the maximum profit after one simulated week. You have an initial capital of 1000 € which you can use to buy materials to feed the blue, green and orange machines. The green machine performs 3 operations: b , c and d in sequence. All parts should be processed through all 3 so you should decide what manufacturing lot

Theory of Constraints with Excel Solver

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  With this simple value stream to produce products A and B we want to illustrate the way to maximize profit taking into account the system constraints . Each product is made by assembling two sub assemblies, one of them common to both products. We have three special purpose workstations with a corresponding operator trained for the specific job: Manufacturing Test Assembly The line operates one shift with 2400 productive minutes per week. The fixed costs, which include the salaries of the three operators, are 8000 € per week. Sales Price: A: 90€  B: 100€ The maximum weekly market demand is: 200 As  100 Bs Total materials cost: A: 50€  B: 40€  We want to know how many products A and how many B we should produce to maximize profit. System Constraints Time available per week for each of the three operators is 2400 minutes Weekly market demand: 200 A and 100 B. Product Margin We want to maximize overall profit so we calculate the margin of each product by subtracting price minus materials