I had a long talk with a senior IT professional in Chicago last week expressing his deep frustration that despite his long and distinguished professional career, he could not get interviews without certain certifications despite being able to teach the topics himself.

Many of you looking for jobs are facing the same problem whether you know it or not. Certifications are being used to screen out candidates because of the huge numbers of applicants. An $8/hr person or worse, a computer program, is being used to scan resumes into the little pile that the hiring manager will look at and the big pile that gets thrown away.

Certifications which are being used A LOT for this purpose are Six Sigma, Lean, PMP and increasingly in the IT field, ITIL. In some cases the companies truly [...]]]> A friend and colleague, Bill Bentley, writes about the use of professional certifications in the recruiting industry.

I had a long talk with a senior IT professional in Chicago last week expressing his deep frustration that despite his long and distinguished professional career, he could not get interviews without certain certifications despite being able to teach the topics himself.

Many of you looking for jobs are facing the same problem whether you know it or not. Certifications are being used to screen out candidates because of the huge numbers of applicants. An $8/hr person or worse, a computer program, is being used to scan resumes into the little pile that the hiring manager will look at and the big pile that gets thrown away.

Certifications which are being used A LOT for this purpose are Six Sigma, Lean, PMP and increasingly in the IT field, ITIL. In some cases the companies truly want these skills but in many cases they are nothing more than `wish list’ skills and objective facts that can be easily used by the resume screener for sorting.

If the jobs that you look at have these topics listed in the job descriptions you can be quite sure that without them, your resume is much more likely to end up in the big pile than the little pile. …

So do you really need that certification just to get the job?  Often an organization is trying to upgrade its bench strength by requiring certification, but how do you know it’s not just a charade and a meaningless recruiting filter.  Maybe you want to be sure your prospective employer is serious, and that might take some digging and fortitude to be able to walk away from a company that’s just pretending.

We’ve learned to categorize and sort our defects, problems, expenses by this [...]]]> In the late 1800s, economist and avid gardener Vilfredo Pareto established that 80% of the land in Italy was owned by 20% of the population. While gardening he later observed that 20% of the pea pods in his garden yielded 80% of the peas that were harvested. And thus was born a theory that has stood the test of time and scrutiny. The Pareto Principle or the 80:20 Rule has proven its validity in a number of other areas:

• 80% of sales come from 20% of your customers (definitely true in our case)
• 20% of your products account for 80% of your sales
• A small percentage of the population generate the largest health care costs
• Earnings come from 20% of your investments
• 80% of this blog’s traffic is to 20% of the postings (actually 33 pages of 180, or 18.3%)

We’ve learned to categorize and sort our defects, problems, expenses by this 80/20 rule so that we can focus or limited resources, attention, brain power on the ‘vital few’.

But in my experience you can’t let this thinking allow you to forget about the tail. Often the greatest opportunities or insights are found are out there in the mix.

Evolutionary Operations, first described by George E. P. Box and Norman R. Draper in in their book Evolutionary Operations – A statistical method for process improvement, New York: John Wiley and Sons, 1969.

EVOP is Continuous Improvement + Design of Experiments.

Basic idea is to replace the routine  operation of a process by continuous and systematic plan of slight adjustments of the control variables.  The effects of the adjustments are then evaluated just as with DOE.  The process is then shifted in the desired direction of improvement.  In many product and service processes it is impossible or very expensive to do DOE, especially where trials can be disruptive or the process owner would let you have the necessary time, materials, labor to run your experiments.  So rather than running experimental production runs you use actual production by shifting off the base point left, right, up, down all within "spec".

Given:

and

Now some math:

So the fixed repeating wheel will turn once in 2.86 days.  Production run sizes as follows:

Many business leaders fail to see the opportunities that an economic downturn provides.  To take advantage of opportunities you first need to make a rapid assessment of your vulnerabilities and then move quickly to minimize them.  Advantage can be gained by using process improvement tools to increase profitability and competitiveness:

Johann Carl Friedrich Gauss (1777-1855)  seen here on a German 10 Deutsche Mark banknote was a mathematician, astronomer, scientist noted for least squares, the normal distribution, and number theory.  Also known as the Gaussian distribution, the normal distribution is defined by the mean ("average", μ) and variance (standard deviation squared, σ2). The standard normal distribution is the normal distribution with a mean of zero and a variance of one.  Common applications include process modeling, queuing theory, safety stock and service level calculations, as was as statistical process control.

1. Ownership Rule – the person who first detects the problem is responsible for finding the root cause of the problem.

2. Quickly Rule – the problem must be dealt with and solved within 30 minutes, not put on a list or in a report for action at another time.

3. Actually Rule – if possible play back or recreate the process that occurred before the defect.

4. Support Rule – the person who detects the problem has primary responsibility for solving it, but supervisor and fellow workers can stop working and lend problem solving support.

5. Shut Up Rule – the discoverer is expected to solve the problem and be allowed time to discuss the problem and attempt to solve it.  Others can help but the supervisor or manager must keep quiet and give the person a chance to solve the problem.

From Dr Ryuji Fukuda

One team plastered their “war room” with all of their data, continuously rearranging their wall, retelling their story.  The other team began [...]]]> Recently read Edward R. Tufte’s The Cognitive Style of PowerPoint: Pitching Out Corrupts Within and initially dismissed his thesis as troglodyte.  Now sensitized, I’ve been watching for evidence of PowerPoint Abuse.  Found an unfortunate example with two parallel teams during a strategic capital equipment review.  Both teams were given the same mission and access to data: scrutinize the new capital equipment plans, challenge assumptions, collect new data and define cost reduction and risk mitigation plans.  Both teams were staffed with bright industrial, process, manufacturing, quality engineers who pulled on other subject matter experts in their data gathering.  Leadership effectively facilitated and guided both teams through the current state to future state diagnostic journey.  Significant productivity, utilization, overall equipment effectiveness opportunities were identified and tested over the two week full-time exercise.

One team plastered their “war room” with all of their data, continuously rearranging their wall, retelling their story.  The other team began typing their findings and abandoned their wall after a couple of days.  Individual leaders would visit with the teams randomly throughout the study period but never “walked the wall”, instead expected PowerPoint slides for the daily out briefs.  Attempts were made to reconcile the two teams leading up to a joint presentation to senior management.

Bottom line – what’s the new equipment price tag to support the new 5 year strategic operating plan?

One team argued for showing both the prior and new estimates as side by side stacked bar charts, the other team just a table listing the $9.6 million delta.

Despite coaching challenges the delta display won out.  Too bad because the Executive VP had remembered “the number” and misinterpreted the table.  Had the first team taken the EVP on a tour of their wall the message would have been clearer.

So since all items and situations are different I started using some statistics; (average demand * lead time) + (one sided Z factor * demand standard deviation) for the target inventory level; a little better approach.

Here’s another formula from Inventory Management Review;

Safety Stock: {Z * SQRT (Avg. Lead Time * Standard Deviation of Demand ^2 + Avg. Demand [...]]]> When I first learned inventory planning the math was rather simple.  On top of the cycle stock (expected demand during lead time) I would add a percentage or a number of days (or more likely weeks).  If the lead time was 2 weeks I  might carry 3 or 4 weeks.  I soon learned that demand for some inventory items is more volatile than for others, and some suppliers less reliable than others.  I’d rather have too much then not enough, and I’d never gotten in trouble for having a little too much.

So since all items and situations are different I started using some statistics; (average demand * lead time) + (one sided Z factor * demand standard deviation) for the target inventory level; a little better approach.

Here’s another formula from Inventory Management Review;

Safety Stock:
{Z * SQRT (Avg. Lead Time * Standard Deviation of Demand ^2 + Avg. Demand ^2 * Standard Deviation of Lead Time ^2}.
(note ^2 means ‘square’)
Reorder Point:
Average Lead Time*Average Demand + Z*SQRT(Avg. Lead Time * Standard Deviation of Demand^2 + Avg. Demand^2 * Standard Deviation of Lead Time^2) .

Over at QuickMBA ; To calculate the safety stock, first calculate the standard loss function, designated as L(z). This function is dependent on the values of the desired fill rate  f,  the demand  μ  and its standard deviation  σ ,  the time between orders  p,  and the replenishment lead time l : L(z)  =  ( 1 – f ) µ p / σ ( p + l )^1/2.  Once L(z) is known, z can be found in a look-up table and the safety stock can be calculated by:

Safety Stock  =  z σ ( p + l )^1/2

Here’s a new one recently published by Kent Linford in the APICS Magazine Nov/Dec 2006. 
SS = √ [( σFE)² x (LTI/FI)^beta + ( σLT)² x D²] x Z x (FI/OCI)^beta

Where:
SS = safety stock
FE = forecast error
LT = lead time interval
FI = forecast interval (pick a beta between 0.5 and 0.7)
D = average demand during lead time
Z = normal distribution service factor based on desired service level
OCI = order cycle interval

Dave Piasecki at InventoryOps.com uses;
safety stock = (standard deviation)*(service factor)*(lead-time factor)*(order cycle factor)*(forecast-to-mean-demand factor)

Jon Schreibfeder has another approach.

And so too does Joannès Vermorel.

Any questions? Got any other versions? What formula do you use?