We are pleased to bring you the second part of Cornelio Abellanas’ first article on modeling the impact of variability on systems and processes. In the first article, Cornelio showed the impact of variability, in general, on processes as it relates to lead time, cycle time, work in process (WIP), and the impact to the customer. In this article, he applies the same approach, but with a focus on Just in Time Logistics.
Enjoy this article and learn more about Cornelio at the end of this article.
In the previous article we saw some effects of variability: accumulation of WIP before and after the high variability step and overall Lead Time increase.
In a situation like this we might decide to apply Just-In-Time Inventory in order to reduce the excess inventory and in this way reduce Lead Time. We can try this with the Monte Carlo Simulator.
We first reproduce the previous situation:
- Variation of step 2 = 40 and run for a while to reach stability and then apply JIT in the whole line by limiting Max queue in all steps to 200 (double the throughput of 100). This means that we are adding a constraint: each step will only process if there is room to store its output (output WIP + processed in next step ≤ 200).
Analyzing the results we see we have achieved our objective of reducing excess WIP and Lead Time but have caused a bigger problem: Customer dissatisfaction due to a decrease in % On Time and average Thruput.
By doing this we eventually eliminate the excess WIP which had accumulated before and after the high variability step (2). The elimination of the excess WIP After removes the protection it was providing to customer deliveries and therefore % On time drops: the customer is dissatisfied.
Another consequence of this action is that thruput drops in every single step to around 90% of capacity. Notice that all this is due to the variability in step 2 combined with the application of JIT in the line.
This drop in thruput starts in step 2 due to the fact that although its theoretical capacity is between 60 and 140, due to the JIT restriction, it will never receive more than 100 units from the previous step and will not be allowed to supply more than 100 to the next step this makes the effective capacity to vary from 60 to 100. Therefore this step has become a bottleneck for the whole line which explains why all steps downstream from 2 will be equally restricted.
The amazing fact is that also the steps upstream are restricted by this bottleneck in step 2. The reason is that when step 2 processes only 60 units it leaves only 60 holes in the input buffer which allows the previous step to process only 60 units.
So this variability bottleneck, combined with JIT, degrades the productivity of the whole line: both upstream and downstream.
Imagine you go to the line in this situation: the cause of the problem will not be evident. If you try to convince step 2 that it is causing a problem to the whole line the reply might be: Why do you say that? I am producing on average like everyone else!
This is again the "Virus" of variability at work.
By applying JIT in this case we have therefore exposed the customer to all the variability of step 2 and dropped the productivity of the complete line: a complete disaster!
Maybe you can work out alternative solutions to reduce WIP in this case of high variability and propose them in your comments.
About Cornelio Abellanas
Has a PhD in Telecommunications Engineering degree from the University of Madrid and a Master of Science from the University of Kent at Canterbury (UK). He is a Lean Six Sigma Black Belt and EFQM (European Foundation for Quality Management) assessor.
He has been Lean Six Sigma manager and a member of the Management Committee in Celestica Spain where he implemented 80 improvement projects per year with a total savings for the Company of 1% of the yearly revenue. Improvement projects yielded these savings by reducing operator time, optimising plant layouts, reducing equipment setup time, reducing admin and production lead times, increasing equipment availability, reducing operator and equipment defect rate, reducing work-in-process and component inventory, reducing scrap and rework, improving on-time delivery, etc.
He designed and implemented a line data collection system and real-time operator feedback which enabled self control in autonomous electronic board production cells.
He performed internal audits and achieved success in external audits for the Company with standards ISO 9000: 2000, AS9100 (Aerospace), ISO/TS 16949 (Automotive) and ISO 13485 (electro-medicine). He has developed and maintained the Quality System: Quality Manual, Process Value Stream Maps, Procedures, Corrective actions, Suggestion program, etc. for Celestica Spain. He has delivered EFQM training and assessment in companies Volvo Truck and Guzman.
He currently delivers Lean and Six Sigma training and leads Kaizen workshops in companies around Europe: IBM, AT&T, MSL, Philips, Volvo Truck, Ford, Celestica, IBC, Pt Pro, BP Solar, Italgres, PCS, General Dynamics, Telefónica International Wholesale Services, EMT Valencia, Importaco, Faurecia, Asac Pharma, etc. in France, Spain, Portugal, Belgium, Netherlands, Germany, Austria, Italy, Ireland, Greece, Switzerland, Sweden, etc.
He lectures at MBAs in local Universities.
He has managed and delivered courses at the IBM International Education Center in La Hulpe, Belgium on the topics: Total Quality Management, ISO 9000, EFQM, Stress Screening, Six Sigma, Statistical Process Control and Capability, Design of Experiments, Design for Manufacturability, Business Process Improvement, Lean Production, Kaizen, Theory of Constraints, etc.
He managed an Independent Business Unit which was responsible for production of a tape drive unit designed in IBM Tucson, USA and manufactured in a local IBM Spain subcontractor. He was Manufacturing Engineering Manager for banking control units in IBM Spain. He designed a matrix ticket printer for British Railways in Ventek Ltd., London (Datapoint Computers representative for UK). He worked as a Systems Engineer in logic design for Burroughs Machines Ltd. (now Unisys) in Cumbernauld (Scotland).
Contact information:
Polyhedrika CB
Valdelinares 2, 11a
46015 VALENCIA (Spain)
Mobile: (34) 678464624
Just In Time Inventory: Modeling The Impact of Variability with Monte Carlo Simulation is a post from: Lean Six Sigma Consulting
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