Business in Yasni Exposé of Vijay S. Sonawane

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Terminology and calculations   The following is a  list of the variables and the terminology used in this safety stock model: Normal distribution.  Term used in statistical analysis to describe a distribution of numbers in which the probability of an occurrence, if graphed, would follow the form of a bell shaped curve.  This is the most popular distribution model for determining probability and has been found to work well in predicting demand variability based upon historical data. Standard deviation.  Used to describe the spread of the distribution of numbers.  Standard deviation is calculated by the following steps: determine the mean (average) of a set of numbers. determine the difference of each number and the mean square each difference calculate the average of the squares calculate the square root of the average. You can also use Excel function STDEVPA to calculate standard deviation.  In safety stock calculations, the forecast quantity is often used instead of the mean in determining standard deviation. Lead time.  Highly accurate lead times are essential in the safety stock/reorder point calculation.  Lead time is the amount of time from the point at which you determine the need to order to the point at which the inventory is on hand and available for use.  It should include supplier or manufacturing lead time, time to initiate the purchase order or work order including approval steps, time to notify the supplier, and the time to process through receiving and any inspection operations.  Lead-time demand.  Forecasted demand during the lead-time period.  For example, if your forecasted demand is 3 units per day and your lead time is 12 days your lead time demand would be 36 units.  Forecast.  Consistent forecasts are also an essential part of the safety stock calculation.  If you don't use a formal forecast, you can use average demand instead. Forecast period.  The period of time over which a forecast is based.  The forecast period used in the safety stock calculation may differ from your formal forecast periods.  For example, you may have a formal forecast period of four weeks while the forecast period you use for the safety stock calculation may be one week. Demand history.  A history of demand broken down into forecast periods. The amount of history needed depends on the nature of your business.  Businesses with a lot of slower moving items will need to use more demand history to get an accurate model of the demand.  Generally, the more history the better, as long as sales pattern remains the same.  Order cycle.  Also called replenishment cycle, order cycle refers to the time between orders of a specific item. Most easily calculated by dividing the order quantity by the annual demand and multiplying by the number of days in the year. Reorder point.  Inventory level which initiates an order.  Reorder Point = Lead Time Demand + Safety Stock. Service level.  Desired service level expressed as a percentage. Service factor.  Factor used as a multiplier with the Standard Deviation to calculate a specific quantity to meet the specified service level. I have included a service factor table below or you can use Excel function NORMSINV to convert service level percentage to service factor.   Service Level Service Factor Service Level Service Factor 50.00% 0.00 90.00% 1.28 55.00% 0.13 91.00% 1.34 60.0 0% 0.25 92.00% 1.41 65.00% 0.39 93.00% 1.48 70.00% 0.52 94.00% 1.55 75.00% 0.67 95.00% 1.64 80.00% 0.84 96.00% 1.75 81.0 0% 0.88 97.00% 1.88 82.00% 0.92 98.00% 2.05 83.00% 0.95 99.00% 2.33 84.00% 0.99 99.50% 2.58 85.00% 1.04 99.60% 2.65 86.0 0% 1.08 99.70% 2.75 87.00% 1.13 99.80% 2.88 88.00% 1.17 99.90% 3.09 89.00% 1.23 99.99% 3.72     Understanding the statistical model and factoring in additional variables.   As mentioned previously, an understanding of the statistical theory behind this formula is necessary to ensure optimal results.  The statistical model uses the standard deviation calculation to describe the probability of a number occurring in reference to the mean in a normal distribution.  A table is then used to determine a multiplier to use along with the standard deviation to determine ranges of numbers which would account for a specified percentage of the occurrences. The multiplier is referred to as the number of standard deviations required to meet the percentage. The theory states that zero standard deviations added to the mean will result in a number in which 50% of the occurrences will occur below, one standard deviation added to the mean will result in a number in which 84% of the occurrences will  occur below, 2 standard deviations added to the mean will result in a number in which 98% of the occurrences will occur below, and 3 standard deviations added to the mean will result in a number in which 99.85% of the occurrences will occur below. In the safety stock calculation we will refer to the multiplier as the service factor and use the demand history to calculate standard deviation. In its simplest form this would yield a safety stock calculation of :  safety stock = (standard deviation) * (service factor).  If your lead time, order cycle time, and forecast period were all the same and if your forecast was the same for each period and equaled the mean of the actual demand for those periods, this simple formula would work great. Since this situation is highly unlikely to occur you must add factors to the formula to compensate for these variations.  This is where the trouble lies. You must add factors to adapt this theory to work with your inventory, however, each factor you add compromises the integrity of the original theory. This isn't quite as bad as it sounds. While the factoring can get complicated you can keep tweaking it until you find an effective solution.  Your final formula will look like:  safety stock = (standard deviation)*(service factor)*(lead-time factor)*(order cycle factor)*(forecast-to-mean-demand factor). There is not a general consensus on the formulas for these factors; in fact, many calculations do not even acknowledge the need for them.  I will give some recommendations for these factors, however, I strongly suggest you test and tweak them with your numbers to arrive at something that works for you. Lead-time factor.  This is necessary to compensate for the differences between lead time and forecast period.  The standard deviation was based on the forecast period, a factor is necessary to increase or decrease the safety stock to allow for this variance.  A formula you can try is lead time factor = square root (lead time/forecast period). Order cycle factor.  Since longer order cycles result in an inherent higher service level you will need to use a factor to compensate for this.  A formula you can try is Order cycle factor = square root (forecast period/order cycle). Forecast-to-mean-demand factor.  Remember that the original statistical model was based upon the mean of the distribution. Substituting a forecast for the mean in the calculation of standard deviation creates a problem if the forecast mean and the actual demand mean are not close and also if the forecast varies between forecast periods (seasonality, sales growth).  Sorry but I don't have a canned formula for this one that I feel confident enough with to publish.  The actual formula used will vary based upon the types of variances and the method for standard deviation calculation used. Minimum Reorder Point.  For slow moving products and especially if the lead time is short, you may want to program in a minimum reorder point which is the equivalent of one average sale. Lead-time Variances.  You may have noticed that I have only discussed demand variations in this model. While you can use this model for predicting variations in supply, I have found that supply variations tend to be far too random and unpredictable.  Supply problems tend to be related more to a vendor than an item and the severity of the variations do not fall into the pattern of a normal distribution. The safety stock calculated for demand variation will also cover for some supply variations, however, the best way to deal with variable supply is to have a high level of communication with the vendor and not to count on safety stock.  You may find that certain items which are critical to your operation may require a safety stock calculation based upon the nature of the supply chain of the specific item. While all of these factors and their potentially detrimental effect upon the integrity of the original formula may leave you feeling less than confident with the results of this model, you should realize that these factors would be necessary in any method of calculating safety stock which takes a scientific approach to meeting service levels  while maintaining minimal inventory levels. It is very important to thoroughly test the model prior to final implementation to ensure it is working correctly and to determine impact on inventory levels and cash flow.  It's also a good idea to start with a higher service factor initially and gradually reduce it until your actual service levels meet your objectives. You will never find perfection in determining probability, however this type of formula is certainly more effective than the previously mentioned keep it simple approaches.

What is Supply Chain Management Best Practice? GILMORE SAYS: If a process is common across a company, then surely there is a «best way» to do it most of the time within that enterprise. And if a process is common across businesses generally, it would seem there is an opportunity for Best Practice - or is that commoditization? --------------------------------------------- ----------------------------------- What do you say? Send us your comments We all hear a lot of talk about supply chain and logistics «Best Practices», including from me. But what are they, really? Are they truly useful? This column was spawned, in part, from a panel discussion I moderated more than a year ago on Best Practices. It went in a direction I don't think the panelists or the audience expected. By the end, we were discussing not Best Practices per se, but whether the concept was really meaningful. Somewhat to my surprise, neither the panelists nor audience, at least in this case, thought it really was. One consultant on the panel at one point near the end went so far as to say «Best Practice is baloney.» Now, in fairness, this was a discussion centered around distribution center operations, and I think processing in a DC tends to be pretty situation specific, making (perhaps) the use of Best Practices less clear. To further think through this, we decided to get the opinions of a number of supply chain and logistics experts. Ralph Drayer, ex-Chief Logistics Officer at Procter & Gamble and who now runs Supply Chain Insights, thought I was batty for even questioning the concept of Best Practice: «Shame on you! Of course there is such a thing as Best Practices,» Ralph told me. «The fact is that every situation is NOT really that unique, and believing so only adds to unnecessary complexity, cost and consumer value erosion.» «That's why the consumer goods to retail industry pulled together under ECR [Efficient Consumer Response] and the Global Commerce Initiative to develop and publish Industry Best Practices for common processes," he added. "P&G did the same thing internally as we globalized our operations. A Best Practice is developed by a group of expert users who share their knowledge and experience to define the best method of operating a common process.» There is strong merit in that perspective, to be sure. If a process is common across a company, then surely there is a «best way» to do it most of the time within that enterprise. And if a process is common across businesses generally, it would seem there is an opportunity for Best Practice - or is that commoditization? Gene Tyndall, well-known consultant and SC Digest Contributing Editor (and a friend of Drayer's) had a somewhat different view: «The term «Best Practices,» and the relentless pursuit of them, has caused more trouble than benefit. Everyone believes they need to find them, but then they cannot even define one, much less adopt it,» he said. «Even if you find one, it will change very soon, as someone else tops it.» He added: «The trick, when you find one, is to "adopt and adapt" the practice to your unique situation. This is what people struggle with. I have argued for years that Dell and Wal-Mart (and others) do indeed have some, but others cannot adopt and adapt them. High-techs have struggled to do so, and K-Mart failed miserably. Others just say that their business models are different, which is a cop-out. He also stressed the role of metrics: «Best Practices without performance measures, or metrics, are useless. Just like benchmarks, which without practices or processes are also useless.» Jim Tompkins of Tompkins Associates, whose company runs a benchmarking consortium, agreed with Tyndall's last point, focusing on the «result» aspect: «A Best Practice is a process that produces the best benchmark for a specific task," Tompkins said. "So, if the task being considered is inventory accuracy and one determines that 90% of the companies like my company, which have a benchmark of 99.8% or higher for inventory accuracy, utilize cycle counting, then cycle counting would be a best practice for my company. Furthermore one could look into the specifics of the best practices of cycle counting to gain more insights into how to best perform cycle counting.» Ed Marien, well-known to many from his supply chain leadership at the University of Wisconsin and on-going consulting work, also focused on using benchmarking and metrics right. «The problem with many Best Practice comparisons is that they forget the metrics side,» he said. «The problem with many benchmarking studies is that the focus is upon the metrics, which may not be defined the same across companies or industry comparisons are made based upon metrics only, without considering the How To's.» I think I will make a "Part 2" of this column in a few weeks, incorporating some of your feedback. Netting it out here, though, I like the simple way Stephen Craig of transportation consultants CP Consulting answered when I asked him about whether there was such a thing as Best Practice. He answered: «I don't know if there is Best Practice, but there is clearly Good Practice.» SCDigest Technology Editor Mark Fralick took a similar tack, and maybe even summed it up best. In working with clients, he said, «I don't worry so much about Best Practice as I do in eliminating Bad Practice.» Now that's something I think we can all agree on. We only included a portion of the excellent feedback we received from our commentators. You can read their full comments nearby in News and Views - you'll enjoy them. I would like to do a follow up to this - help us out by sending your thoughts on the topic of «best practices.» How would you define best practices? How can they be best applied? When a «best practice» is promulgated across an industry, does that make it a commodity capability/process? What else do we need to consider here? Let us know your thoughts at the link below .

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