Phill,

I became very much familiar and skilled in flow (or lean) production during the eighties of the last century while I was working as a freelance consultant engineer. I was then involved with some well known automobile and electronic companies helping them in PT to become lean. Inventory related problems used to be plenty. This is perhaps the reason why I found your '5 Myths of Inventory Reduction' interesting but, I think, more oriented to people without a good knowledge of cost and operations management engineering and, in particular, FMECA analysis principles completed with a cause analysis in order to evaluate the economics of redesign.

I can’t see why you seem to dislike the optimization issue. When your problem is surrounded by constraints that force you to spend money to overcome some of them in exchange of some measurable benefit, I can’t see any acceptable reason not to apply maths to trade-off instead of some rule of thumb or what is, some times, known as common sense – which lacks very often! Taking the compressor example, it would be interesting to know how long the recovering period should be in order to make the alternative “no spares in inventory” become the most cost effective (in the compressor case it would be 4.5 days using maths). If this time period or a shorter one seems impracticable, one wouldn’t waste any more time with the issue. Yet, suppose that it would be possible to shrink that time period. How much would it cost? Would it be worth doing, that is, would it pay off? That is something you cannot answer in advance without using maths.

RCM like other qualitative methods are mandatory before you “dive” into maths, I agree. People are more receptive because they easily understand conclusions and become more involved towards solutions and strive for results. Maths comes some times afterwards and is normally and wisely, I should say, left to specialists. In materials management, there is often room for quantitative methods but in your '5 Myths of Inventory Reduction' there seems not to be, judging from the examples given. Please forgive me if I didn’t get your point straight.

Regards,

Rui

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Rui,

Thanks for the response…

How about another example: A cooling Tower system?

8 Cooling Cells, each with its own electric motor, gear box and fan.

The gear box is the issue.

Cost of the part = 40,000
Estimated life = 30 to 40 years
Characteristic life Beta = assume 1.5
No condition based task is available that guarantees with confidence some forecasting of failure - due to the installation/location of the gearbox.
6 of the gearboxes are 24 years old
2 of the gearboxes are 6 years old (2 Cells were added when the plant was expanded)
Failure cost is partial loss of production in the Summer and NO cost in the Spring, Fall, or Winter.
Failure cost in the Summer ~ $1000/hr to 1500/hr per failed Gearbox. (varies with ambient temperature – that’s why Summer is the issue)

Repair time with part in hand = 2 days
Repair time if part must be ordered = 40 days

Should we stock a gearbox and how many? Remember this is only an issue in Summer and the Gearboxes run 24/7, 365 days a year.

Hi Jaz,

I thought you could do it yourself by now... Anyway...

Estimated life should be the time interval that you estimate to have the equipment working for you before you have it scrapped or sold out because of a technology shift or an efficiency drop or any other reason. We normally call it "useful life". Is it what you mean when you say 30 to 40 years? I doubt...

quote:

Characteristic life Beta = assume 1.5

Do you mean 1.5 years?

Give me some time as I have to leave now.

Rui

Rui,

I wanted to challenge you . I thought I'd give a more complicated scenario. 8 Gearboxes installed in a cooling tower system, no spare cooling capacity during the summer months - so if a gearbox fails in the summer, or just before summer if no parts are in stock, then production rates will suffer. But if a Gearbox fails in the Fall, Winter, or Spring there is no consequence. This scenario is a curious one as now it is the probability of any one of the eight gearboxes failing and the probability of a failure occurring in the summer.

The remaining life, in the cooling tower system, I would estimate at 40 to 50 years.

My interpretation of Beta = 1.5 is an assumption that the probability of a gearbox failure increases slightly with time (i.e. the failure rate is not constant).

Jaz,

I accept the challenge with pleasure . The problem has to be equated in a different way from what I did in the compressor case. I think this time it will be easier to find a solution if I create a small simulation model, reason why I need some more time.

Now, with regard to your data, I think that when you say beta = 1.5 you don’t really mean the characteristic life (or the scale parameter of a Weibull distribution) but rather the shape parameter (or the characteristic life). Isn’t it so? A 1.5 shape parameter means that the instantaneous hazard rate increases slightly with time. But if it is so, you need to estimate the scale parameter (or the characteristic life) which is the time by which the probability of failure is 0.632 in the case of 2-parameter Weibull distribution. Otherwise, it will be impossible to proceed.

Rui

Rui,

Yes and Yes.

We estimated the scale parameter as 30 to 40 years (this is when the probability of failure is 0.632) and a shape parameter of 1.5.

Does this help?

Rui, I think engineering students at bachelor degree level are not thought numerical methods or the Weibull distribution. Do you? Perhaps only those who take mathematics course will learn these subjects. One more thing is that when in the University, the subjects are thought in a very theoritical manner without showing where these will be applicable & useful which can give motivation to learn the subjects.

Jaz,

Yes it does help. Thanks.

Josh,

Numerical methods are taught in Engineering courses during the first year at the University, but Reliability is part of the content of only two branches of some Mechanical Engineering courses existent in PT: Product Engineering and Industrial Engineering. It also exists, to my knowledge, at the Air Force Academy and at The Navy Academy.

Process engineering courses (where equipment and installations maintenance is included more from an organizational perspective) do not cover Reliability for the time being, reason why I teach this subject as professional training so often at companies’ sites or at a specialized Institute (ISQ). It is my firm belief that all professionals in the field of operations management should go through the reliability and maintainability concepts. It gives you a broader picture of the interactions of technical and management issues and enables you to make better choices in the pursuit of increasing levels of operational efficiency and safety. The last but not the least, higher levels of self-satisfaction with everyday’s work will consequently surface.

Regards,

Rui

Jaz,

I will work on the cooling tower case on the week end. It is promised.

Regards,

Rui

Dear All,

I am going to post this as a new thread also. However, as you guys are talking about inventory stock levels ectetera I thought it may be of interest here.

I hope it is of some use.

Best regards,

inventory.xls (444 Kb, 24 downloads) Sawtooth Inventory Analyser

Rui,

Thanks again for your interest in these case studies. I am very curious of the results.

I believe I have learned from the compressor valve case. I hope I will be able to understand this more complicated cooling water tower gearbox case.

And I too believe that one must start with an "RCM" type process to first appreciate where efforts in spare part analysis are most required.

Following Daryls´s initiative I would like also to contribute to this forum with a calculating tool - a very straightforward one -, which allows you to determine when to order a certain critical component to be kept in stock (just in case) and how much to order. I use to give this Excel file among others to the attendees of my courses on material management. I hope it will be usefull for some of you.

Regards,

Rui

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Spares.xls (37 Kb, 22 downloads)

Hi Jaz,

Here´s my answer to your case. I am sorry for the delay.

If data are not exactly the way you want, please just let me know.

I hope it is of help.

Regards,

Rui

PS: I have just made some corrections in what the english idiom is concerned and I attach a revised document. I am sorry for the inconvenience.

Rui

This message has been edited. Last edited by: <Rui Assis>,

Cooling_tower_1.pdf (51 Kb, 23 downloads)

Here is one more EXCEL file, this time demonstrating the use of the MRP (Materials Requirement Planning) algorithm. MRP allows one to plan just-in-time (the latest possible) parts needed for preventive maintenance actions. Although it is embedded in CMMS software, I thought it could be of interest to see the rationale behind it through the observation of the formulas that I coded in EXCEL.

Change data (pale blue cells), see what happens and interpret the results.

Regards,

Rui

This message has been edited. Last edited by: <Rui Assis>,

MRP.xls (60 Kb, 24 downloads)

Rui,

Wow. Thank you for the analysis. I really appreciate your time and effort. I am curious as to how you performed the analysis: What software you used (off the shelf, self made excel spreadsheet)? How much time it took? Etc.

Hi Jaz,

I am glad that you liked my work. It was a pleasure to spend about 6 hours trying to find out a solution for such an interesting problem. I used a self-made Excel spreadsheet as I always do.

I gave the case still some more thought this afternoon while I was at the gym, and I dropped a few more lines covering the transient conditions. I think the case is now complete. Let me know if you want to change data.

Good work and regards,

Rui

PS: I replaced the document (I had forgotten to label table 5 in english)

This message has been edited. Last edited by: <Rui Assis>,

Transient_conditions.pdf (26 Kb, 18 downloads)

Rui,

Apologies for taking so long to get back to you. There are a number of issues in your comments of 16 August so I will try to address them in order.

quote:

I found your '5 Myths of Inventory Reduction' interesting but, I think, more oriented to people without a good knowledge of cost and operations management

It seems from the above and from your other comments that you are assuming that the '5 Myths' is an approach for inventory reduction. It is not. It is an observation of behaviour and forms part of an overall process - the Inventory Cash Release Process. The part you read is an extract from my book that explains the whole Inventory Cash Release Process. The '5 Myths' came about from observations of clients and the things they did or said to justify their behaviour in relation to inventory. In most cases their behaviour was self serving and did not drive the most appropriate outcome for the company. I think that you would agree that this is the case with the examples you read.

As you would be aware, inventory management is not an isolated activity performed by people with 'good knowledge of cost and operations management', it is an outcome from the activities of a wide number of people that actually influence the result. This includes engineers, storeroom personnel, trades people, finance and procurement. By understanding the behaviours of this group one can then identify when all the good work being done by people such as yourself is being undone.

quote:

I can’t see why you seem to dislike the optimization issue.

It is not that I dislike optimisation it is that people refer to the use of an algorithm as optimisation when it is not. True optimisation requires consideration of all the system and behaviour issues impacting the outcome not just a calculation based on a single set of data. If the answer to the optimisation of inventory was as simple as completing a calculation taught in first year university then one would expect that everyone would have a satisfactory result. But they don't. So the answer must be in something else.

Back in the 80's when you were involved with 'lean' the world was a much simpler place. A simple algorithm was sufficient because there was less pressure in business. Today, the path for making smart decisions and achieving improved inventory performance is far more complex.

The development of ERP systems, the awareness of supply chain management, the relative reduction in the cost of transport, the improved velocity in the supply chain and the increase in competition on a global basis have all conspired to make the basic algorithm approach a sub standard business solution. In the 21st century a more holistic approach is required that addresses all of the issues and addresses all of the constraints.

quote:

I can’t see any acceptable reason not to apply maths to trade-off instead of some rule of thumb or what is, some times, known as common sense – which lacks very often!

Of course there is still a place for the algorithm and I am sure that your 'maths' has helped the people on this thread solve their immediate problem. To be clear, however, the Inventory Cash Release Process is not just 'common sense'. It was developed using scientific method including hypothesis falsification and experimental methods.

It also important to understand that the Inventory Cash Release Process is a process and not a stand alone tool or technique. A process is a superior approach because it enables the opportunity to change behaviour and ultimately be embedded in an organisation. Hence the result is lasting rather than a one off. I am sure that your experience in 'lean' would back this up. The use of a process is the exact approach needed to counter the so called common sense of which you speak.

As you can tell I get quite excited about providing companies with solutions that they can implement and use and that provide true optimisation on a lasting basis. No offence is meant in anything I have written and I trust that in the spirit of learning none is taken.

If you are interested in learning more (or if any other reader is interested) read the article in Reliability Magazine in May/June titled When Inventory Optimization Doesn't Optimize. Otherwise I am happy to share more with the readers of this forum.

Hi Phill,

Thank you for your extensive explanations.

Please don’t take me wrong. I much appreciate and admire people like you that have the skill and work trying to convince people to change their behaviour towards operations management by fostering rationale in their everyday’s work. I am not and never was good at it. That’s why I made my way differently and chose since long not to become involved in organization processes and teams work. I prefer to work alone. I selected quantitative methods as a tool for a living as consultant engineer in operations economy (and reliability and maintainability more recently) and professor in the academic world. I have at least the ability to see when quantitative methods are the ultimate answer to a problem – how would you deal with Jaz’s cooling tower case otherwise? What I dislike the most is to hear comments on the theory side and the practice side issue. I am firmly convinced that they are complementary of each other – the problem resides often in not applying the right proportion or despising one of the sides.

Maths and philosophy are quite close to each other, as you know, and they provide me a sense of reward and wellbeing at this stage of my life.

I wish you continuous successes,

Rui

Rui,

I have accepted your challenge

Here is my review of the cooling tower problem.
The cooling towers are obviously critical in summer
Cost of failure $48k - $60k (in summer)
There are 8 cooling towers and they have adopted a run to failure strategy so failure is inevitable
Time for delivery is excessive (38 days) and I assume cannot be reduced.

To me this says, if the supply time cannot be reduced significantly then you must hold one unit.

Analysis time - 30 seconds

Now the real question is - is there an alternative way to run the plant or will the vendor supply a unit on consignment?

Both of these must be explored before investing in the gerabox.

(Of course I might also ask why a run to failure strategy is seen as appropriate on a critical piece of plant!)

In fairness I must say that I spent 10 years maintaining plant and equipemnt in a highly seaonal industry where the conditions were similar to this case study. Summer failure was unacceptable as we could sell all that we made. Winter failures rarely raised an eyebrow!

Hope this alternative view helps provoke some thinking.

Cheers,

Rui and Phillip,

Thank you both for your thoughts and ideas on this issue.

As the reliability coordinator for this plant it is nice having the two answers as different peers will ask the same questions but will expect as varied answers as provided in this thread. In the end I believe it is about building confidence in the decision making ability by being able to defend a position. If confidence is acquired then there is more opportunity to ensure decisions are implemented (i.e. the resources will be granted).