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applying six sigma to stock ordering process

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  • #46812

    Jason Phillips
    Participant

    Can anyone help me with this issue. We have been running out of parts in our assembly shop so have started to look at the reordering system. We have found that on many parts the reorder level is far less than usage. What we have done for each part is taken the last 12 months usage and worked out the average and the standard deviation and then used the formula below to work out the correct buffer stock quantity. By applying 3*Std deviation we will have enough stock to cover 99.99% of eventualities
    (Average weelky usage + 3 * Std Deviation) * lead time
    Is this the correct way of doing it – if not can someone anyone help?

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    #155229

    Vidyadhar
    Member

    Hi,
    I think using control limits is a fair way of doing things. However since it is the reorder process you would like to look at usage and consumption against the inventory, or waiting for stock
    The ideal situation would be stock arrival when all units are consumed with no inventory.
    However you would like to look at implications of cost on inventory or waiting for the order and take that call
    Look at a daily pattern on usage and try and map the seasonality for an order forecast.
    You would like to look at something like a Forecasting model for ATMs.

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    #155231

    GrayR
    Participant

    We have also used similar methods to determine the stocking level. You need to cover the variability in demand so that you don’t run out of stock.  In our case, we were reluctant to maintain 3 sigma limits for all parts because of the cost. So we went to +2 sigma for many parts — this works well in practice if the demand variability is not that great (Calculated as the coefficient of variation = std. dev./average).  For low volume parts, we even reduced the stocking quantity more.  Bascially, we felt that we shouldn’t run out of the high volume parts because we were trying maintain high service levels for low volume parts.
    Interestingly, you need to consider all sources of variability in the supply chain — both from the supply side and the demand side.  That is, if your supplying process produces on varying cycles and volumes, this also increases the amount of buffer stock needed.  This leads to one way of reducing the buffer stock needs  = level scheduling the supply process.

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    #155236

    Jason Phillips
    Participant

    Thanks GrayR
    You have confirmed what I thought – we have now gone ahead and adjusted our buffer stocks. It has increased inventory by 50K but in the scheme of things its nothing compared with the problems associated with late delivery etc.
     

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    #155653

    jim clark
    Participant

    Some caution… this is correct only if your demand rate has some level of normal approximation… if you have a very intermittent demand pattern you have to convert to a different model that factors in peak single order demand quanities

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    #155715

    AlexanderSO
    Participant

    Hi, I have some thing to add to the already said.
    Is very important to use the effect of the variation in the demand, however you can’t loose sight of the maintain cost.  For this reason I thing one should direct the efforts in a tactical manner, the method I use is this.
    1. Do an ABC clasification of your products (I use Paretto’s percentages A for the 80% B for the next 15% amd C for the last 5%), I do this with the last 12 months and review it every 4 months.
    2. Define the service level for each clasification, what I call the service level is the amount (percentage) of the normal distribution you want to cover, the 50% of the coverage is achieved with the aritmetic average, and is not really much advantaje in going to near the upper limit because the beneffit of a major inventory level is to low because you need much increase in your stock and gaing very few points in coverage, the values I use are 90% for A, 70% for B and 40% for C.
    3. Calculate the corrected average, what I call the corrected average is the value requiered to obtain the coverage of the normal distribution, as an addition you can check if your data fit into a normal distribution (you can use the asymetric factor and the cortuosis).  This is the inventory level that you shuol attain.
    4. Determine the lot size and the frecuency of the orders, I thing using the EOQ (Economic order quantity) as an initial approach is a good star, this is a very old theory, but it uses the cost factor, and this factor will always be important in any bussiness.
     
    Please let me know if this is usefull to you.
    Best regards.

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