Drinking With Wrist Injury

Examples of indicators of supply chain to optimize

This message is available on the new blog from this link

"It's fine to give us tools of the LSS and the keys to understanding to implement them ... But I not know what I can optimize my business ...!?"

is what you need to tell you right now for some time if you're interested in the LSS on the net and more specifically on this blog.

To solve this problem here is a ticket where I'm going to recess some indicators of the supply chain and can be a project LSS.

Indicators for monitoring stock levels: The
stock level is followed by the rotation of stock. Stock rotation expressed in number of days until the product is in stock before being sold. It will be calculated on the basis of a stock J, divided by average sales per day.


Indicators for monitoring the reliability of forecasts:
The reliability of predictions concerns the forecast of sales and order forecasts. It gives the% of projected sales or orders established on N-1 compared to actual sales in N. It can be calculated in terms of turnover and / or quantity and established between the various links in the supply chain (Center bursting of the industrial point of sale, the retailer's distribution center). For example for a sales forecast of 160 and made 200, we get a gap forecast of 20% (40/200), which gives us a reliable forecast of 80%.

Indicators for monitoring service levels in the supply chain:
The service rate can be here on the command line, on the amounts or the number of pallets. The service rate is calculated by dividing what is delivered by what is ordered. This indicator will introduce the concept of "delivered on time" or "late (Untimely deliveries can not be counted.

Indicator Tracking backorders:
An indicator showing the number of days of delay may be introduced. It will be based on the number of days delay from the delivery date specified in the order (on line).

Monitoring Indicators rate customer service: For vision
rate service consumer side, two indicators can be used: one hand, the service rate in linear approach of calculating the number of days (or hours) where the product was available in linearly over a predetermined total upstream (in the month or week for example). On the other hand, the rupture rate in linear calculated on a number of statements about product availability. For example if the product was available 9 times over the past 10 surveys, the failure rate is 1 / 10 or 10%.

Indicators for monitoring possible:
Regarding the monitoring of deadlines, we have two indicators. The first defines the number of days (or hours) the period between the passage of the order and receipt. The second relates to the production. It makes the production time and is calculated by determining the number of days (or hours) elapsed between the production order and receiving the order.

Monitoring Indicators unplanned change:
Such monitoring can be achieved with several indicators. An approach
rush orders may be made by dividing the number of orders delivered within less than the contractual period, divided by the total number of orders.
A second indicator can detect changes in the number of changes on a business plan.
A final indicator allows to count the number of changes in the introduction of new products (dates of introduction, product launches etc ....)

indicator tracking unsold
This item may be followed by observing the rate of sale of obsolete products and / or the stock remaining at the end of promotional campaign .

Indicators for monitoring the distribution of products in stores:
monitoring indicators express mailing the presence of products in store. Digital distribution gives the% of stores with a reference product with respect to a universe of stores, a sign or a geographical area for example. The "value distribution" is digital distribution weighted by the turnover of the store.

Monitoring Indicators of Sales:
The retention rate of customers, changes in market share, changes in quantity of products sold by the buyer as well as changes in the volume of sales.

monitoring indicators of planning:
The performance of the planning can be measured by counting the number of days between the promotional plan from start of commercial activity in the store.
It can also be measured in days between the date of rebound orders and the date of supply of products.

Indicators for monitoring distribution constraints:
Optimizing distribution constraints focuses on filling the trucks and the cost of transport. Existing indicators may express a degree of filling. This is calculated by dividing the number of full truck (trucks are filled to more than 95% is considered complete) by the total number of trucks delivered.
can also define an average load per truck by dividing the number of pallets loaded with the number of places available in the truck. Inactivity trucks can also be assessed by counting the number of empty miles and dividing by the total number of miles traveled.
Regarding the cost of distribution: ratio of total transport costs reduced the turnover can be established.

monitoring indicators for data synchronization:
Regarding the synchronization of data as an indicator of compliance
bills can be analyzed. This is obtained by dividing the number of correct invoice lines by the number of invoice lines total.

I hope that this small sample of indicators Supply Chain will give you ideas for starting LSS project. Feel free to leave comments if you have any doubts about the soundness of an indicator selection ...! ;-)

Nys Unemployment Weekley

Accuracy, Repeatability, Reproducibility: GRR for friends ... VS

This message is available on the new blog from this link

So far I am doing so to make available the concepts ... But here we begin to return to the concepts a bit more obscure to managers hungry for Operational we are ... ;-)

If I tell you this: it is because this step appeared to me at first, relatively useless. The example of Smarties, by way of training, helping me do not particularly to assess the appropriateness of the tool ...

I'm going now to explain why at first appeared to me useless tool, then the path that led me to judge it necessary step in the DMAIC Measure.

G (Gauge = accuracy) R (repeatability) & R (Reproductibility) measures the reliability of the measurement system Y . In
transactional processes we have to do mostly with indicators that are derived from the information system. Also, if a decision tool (BW, BO, Pentaho ... etc..) I realize the same query twice (repeatability): it is little chance that the tool I provide two different measurements! Similarly for reproducibility, if someone other than me throwing the same query, I can not imagine the system provide me with a different value than it previously took me out ...! Finally with respect to the accuracy of the measurement ... Even so, the measure proved partially wrong: what would my power to identify from which the gap measure?

is why I was skeptical: I do not understand the value of verifying the reliability of data transmitted by an information system that is specifically designed to provide reliable indicators.

But if the system is reliable, the human it is not ... And if the information processing is performed by system upstream of this processing chain we have a human being who feeds the system. So it is necessary to verify the variability of information that is indicated in the system.

Consider an example in customer service (hotline) we have that store operators in disputes. Disputes that are recorded are assigned to a specific coding. If, according to the operators where the mood of the operator, interpretation and codification of the case which is devoted to change: we will encounter problems. For example, if litigation resulting from mistakes of VRPs were recorded in the proceedings from the customer service (to the order entry example): when we work on reducing litigation generated by the customer service: we will be annoyed because Are we may not evolve as we predicted (since it includes errors of VRP (which are outside the project LSS).

Now: how?
As much as possible: it is preferable to obtain paper documents that are the source of treatment. In our example we can imagine that calls litigation followed a confirmation by fax from the client to the statement of its case. It must then collect a sample of these statements and in our view if the request falls under what we want to measure is included. Here is a chart that illustrates my example:

This example illustrates that the measurement accuracy and repeatability ... (For measurement of reproducibility, it had been that I feel that all operators were asked to treat each case ...). Here we get 80% reliability of our measurement system. That's enough! The practice of LSS project set at 80% the threshold (if we consider that our indicator is reliable at 80% minimum, we will act on at least 80% of what causes us to think that our actions will have a significant impact on the indicator measured).

If I had obtained a reliability rate of less than 80% would have required that I carry out actions to retake the test successfully (staff training, clarification of coding, resegmentation classifications of disputes etc ....)
The test
GR & R is a "go right" to enter the next step "analysis." This step is crucial and if the reliability test of the measurement system does not pass then It absolutely must not move forward on data analysis. Because if I analyze data that are poorly coded upstream: so my analysis is wrong.

I realize that this concept of reliability of the indicator is not easy to assimilate ... also feel free to ask questions or leave comments! ;-)

Sliding Door For Reptile Cage

Pareto Benford - 3rd installment of "War of the indicators"

This message is available on the new blog from this link

I had decided to return to the fundamentals of the LSS but I am definitely passionate about the struggle being waged " Indicators Famous "and" Indicators of the shadows. " On side we have the couch potatoes and illusionists Average, Min and Max and the other we have the needy and robust median, quartiles, standard deviation. So far it seems very difficult to decide ... Then, the ring, two laws of distributions come do the show ...! ;-)

To my right, built like an ox, down from its 20-80 we have: Mr. Pareto ...
my left lying like a log, precision random: Mr. Benford ...

DDDDDDDRRRRRRRRrrrrrrrrrrriiiiiiiinnnnnnnnnnngggggggggg!

Well ... then I think you're conditioned enough to get into the thick of it. It was a few years, I was required to establish a computing cost of logistics products stored on a platform. So I had masses of apportionment for each item based on key distribution (also called work units). You agree, for logistics such as storage or shipping it was appropriate to take into consideration the volume of parcels. Also most of our calculation reallocated charges based on volume (volume stored, the volume received, shipped volume ... etc). So I made a pretty matrix in excel, with beautiful macro to update the zany and after a few days of work I before my eyes production costs by product ... ... Oh miracle!

Then came the time of the analysis ... And there: problem! I found myself with items that generated staggering costs ... A little research, I finally realize that no measures were inconsistent products ... :-( I called
the logistics manager and asked him to check the products that I had identified ... He returned with the good things ... So I redid the crunching and such ... zinzin articles dating back to surface with errors ...!
The problem is that it was impossible to ask to remeasure all 6000 products stored on platform! So I had me a nice dilemma: how to be sure of the reliability of my data without having to measure all of my products and redo endless iterations on taking new measures .. .!?

My first instinct was to turn to the Pareto law. As everyone knows Pareto highlighted a distribution law that is "arch-used" in business. This law says for example that 80% of my sales are generated by 20% of my clients. I decided to leave the 20% of my items that generate 80% of my volume. Also I leave the 80% of my items in stock representing 20% of my value Global stock. So if I meet my two files I end up with items that have the highest volumes with the lowest values so I can identify aberrations! In one detail that I almost had assumed (somewhat debatable ;-) my bulkiest items were necessarily the most expensive. And actually I could identify new aberrations ...

So: I relaunched the zany ... and always the same ... He left with errors Articles ... But I had another problem: the errors were smaller in magnitude because I corrected the 20% of my article that generated 80% of my erroneous volumes (Pareto is sacred!;-P).

After a few days the light brightened. If the law of Pareto distribution was limited by my problem: when I was going to use another distribution law, the latter much less known, but much more efficient for the problem I had to solve: Benford's law .

For those who do not know: Benford's law refers to a random observation of the phenomenon that we reserve nature. In any digital distribution (the list price of a supermarket, the list size of all trees in the territory, the list of net wages inhabitants of a country ... etc.) if we take the first digit (different from 0) and we account for these figures: while we have consistently said distribution:


not strange ...!? Whatever you take as distribution, if you count the number of 1 in first position, 2 in first place ... etc, you will always get this%. Even stranger is that there are more than 1 2 of 2 than 3 ... etc.. But it's like this ... To the extent that the distribution is random it would look like this.

I therefore decided to compare my distribution with that of Benford and here's what I got:
The result was obvious! I had too much of 5 and not enough three. So I made an extraction of all my items with a 5 in the first volume of my character and I review the list. I quickly see that all items of a family (those shoes) were the same size. The ingratitude of the task had led the people in charge of measures to copy-paste. They are justified by the fact that all the shoe boxes were the same size. In a proportion, perhaps, but you can not take the Benford's law, by default, so ...! The shoeboxes are not the same size when it comes to dress shoes, fishing boots or booties for children ... etc..

After measuring each of the references to the family shoe found in the warehouse, I redid an analysis of my new layout and here is what it gave:
This time I seemed reliable file ... I remoulins the zany and obtained success with the calculation of my costs back as expected ... ;-)

For leisure I LSS projects in a post to come, you talk about the tool that tests the reliability of data for registration thereof (GR & R) but often project starts with data already now registered and in such cases it is necessary to verify consistency of the data before providing any analysis ...!

Bravo to you Mr. Benford ... You won this battle hands down! ;-P