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RSS Feed made superfast (or almost)

Just like the previous post mentioned, the RSS feed from my blog has been doing a sort of break dance – on sometimes, off other times. Moreover, the server performance in serving up the blog pages itself has not been as it was a few months ago. (Looks like my blog’s youth is showing – ;-))
I finally got down to the root of the problem after communicating with my web host. It does seem like a web server load issue and the recommendation from the customer support was to cache the PHP files for the purpose of “blazing speed”.
So, I’ve just installed the WP-cache plugin that does just that and I’ll follow the performance of the blog and its RSS feed a little closely for the next few days.
Now, I’m wondering whether I should cache my blog main Index page as well? And whether that is in the realm of the possible to begin with?

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RSS Feed restored

Over the last couple of days, there has been a recurring problem with the RSS feed at my blog. As of now, it looks like the feed has been recovered and is working just fine. Thanks for your patience!

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A little about Little’s law and Factory Physics

Little’s law is a result coming out of the field of queuing theory. If you’ve taken an introductory course in Operations Management or perused an introductory text in Operations or Industrial Engineering, I’m sure that you’ve come across this result. Simply stated,

The average number of customers in a stable system (over some time interval), N, is equal to their average arrival rate, Lambda, multiplied by their average time in the system, T, or:

What is interesting about this result is expounded further in the Wikipedia posting,

Although it looks intuitively reasonable, it’s a quite remarkable result, as it implies that this behavior is entirely independent of any of the detailed probability distributions involved, and hence requires no assumptions about the schedule according to which customers arrive or are serviced, or whether they are served in the order in which they arrive.

However, this is true only for stable systems which is a good place to begin with considering its generality of application. The scope of its application is not restricted to customer service scenarios but also to manufacturing as well as other operational environments. What is interesting to me about Little’s law is that it is well within the direction of what can be aptly termed factory physics. Furthermore, just as there is the very real need for a comprehensive undertanding of factory physics, there is an analogous need for understanding supply chain physics as well.
So what is Factory Physics? Factory Physics refers to a generalized field that deals with understanding the behavior of manufacturing systems. However, the real question is not whether the behavior of manufacturing systems is understood but whether decision makers situated in the firm fully understand the behavior of manufacturing systems? The question would analogously apply to the case of supply chain management as well.
The fundamental principles underlying factory physics are explored here and it is well worth a close reading. (The PDF link here provides a better representatio in my opinion)
In my next post, I will delve a little deeper into Factory Physics and what insights one can take away from those fundamental principles and find applications for them in a supply chain context. In the longer term, I think that this route offers a firm basis for developing robust metrics that are based on a sound appreciation for generalized laws of system behavior rather than taking the route of measuring what is measurable and observing what is observable. In my opinion, the former offers an empiricism that has been subsumed into the edifice of manufacturing or general operations while the latter offers empiricism on an ad-hoc and practical basis.
The journey continues…

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Metrics in the firm – Inside and Outside

I have spent the last few days thinking further about metrics (supply chain metrics, financial metrics and all sorts of other metrics) but I haven’t hit upon a foundational idea (or a set of ideas) to order all my thoughts. So the rummaging continues and I took some of that rummaging online:

A thought process when thinking about metrics (any sort of metric) is to ask, how the other metrics in the firm look like. An example of that would be stumbling upon such as this observation by Brad Feld,

Several years ago, some of y’all may remember an event called “the bursting of the Internet bubble.” Immediately preceeding this event, companies (and investors) focused on growth at any cost. This growth took various forms ranging from the one key financial metric that everyone cared about at the time (revenue) to non-financial metrics such as eyeballs, click-throughs, and affiliates. Shortly after the bubble burst, people started focusing on net income, cash flow, cash on hand, and other financial metrics. Not surprisingly, these were things that most rational business owners had paid attention to since – oh – the beginning of time.

Brad is talking about metrics in an industry that for a short period of time saw tremendous growth – the one financial metric used was revenue along with a host of other metrics such as eyeballs, click-throughs and perhaps even today – page views.
As Brad notes further down in his blog, the staple of financial metrics as used internally within a firm would be more along the lines of,

Monthly data we collect (and consolidated so everyone in the firm sees it on a weekly basis) includes revenue, cost of goods, operating expense, EBITDA, headcount, cash burn, cash on hand, debt, projected insolvency date, additional cash required to breakeven, and projected first quarter of profitabiity.

Another interesting take on operational metrics is the following that comes from the profession of lawyers (i.e. a service/consulting type of environment):

The Law Practice Business Model was introduced in 1984 by David Maister as a mathematical expression. Maister’s formula is as follows:
NIPP = (1+L) * (BR) * (U) * (R) * (M) where,
NIPP = Average partner income
L = Leverage (ratio of associates to partners)
BR = the “blended” hourly billing rate
U = Utilization (client hours recorded)
R = Realization (revenues divided by “standard value” of time recorded)
M = Margin (partner’s profit divided by revenues)

M. Thomas Collins notes further that

Maister’s model doesn’t tell the whole story. The financial manager has to be just as concerned about metrics that measure unbilled fees (work-in-process) and billed but uncollected fees (receivables). The managing partner has to be concerned about metrics that are not reflected in the financial numbers but will impact those numbers in the future. For example, are we opening new matters faster than we are closing old ones? Are the partners meeting their individual marketing goals? Is client satisfaction on track or veering off-course? While the Maister model is not the whole story, it is at the heart of the story.

The above mathematic expression is really a current snapshot and I assume that such a snapshot is taken at the end of some period such as a month or quarter. In fact, it can be characterized as a trailing metric (the stock market analogy would be ) and as such is not forward looking because it doesn’t take into account an unfolding reality.


Read the rest of this entry »

How supply chain metrics are used?

SDExec.com reports on the results of a survey carried out by Maxager. The principal finding of the survey was,

Many manufacturers believe that it is important to measure the speed at which products are made, but very few have systems in place to do so

The survey results amply show that while everyone is aware that there is a problem, few have a solution:

The survey results showed that although respondents overwhelmingly (92 percent) believe that analyzing the speed with which they produced profitable products was important, 71 percent don’t have software or systems in place to do so. The result is that very few manufacturers (5.7 percent) have the ability to use a metric that is aligned with return on assets (ROA).

Here is a snapshot of Maxager’s solution to the problem:

Combining production velocity with margin produces a profit-per-minute metric. Being time-based, this metric is directly linked to ROA. It can be used at an operational level to measure the profitability of individual products, customers, deals, markets, sales regions, salespeople and production facilities. Then, everyday decisions about which products to make, who to sell them to and where to make them can be made collaboratively to maximize annual corporate profits and ROA.

What do you think? Does it work?

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Supply Chain Metrics – A first cut

If you’ve come across the term KPIs or Fill Rate or Inventory turns, chances are that you’re aware that all these terms fall (not exclusively though) under the rubric of a topic called Supply Chain Metrics. In this first post about Supply Chain Metrics (of what I will be hoping is a series of posts), I want to assemble an intersection of the most common Supply Chain Metrics as might be observed in practice. Beware, there is no uniform standard for these metrics across firms and so terms that mean one thing in one firm might mean something approximately the same with slight differences.
Here’s an initial list of metrics that I have assembled
Supplychainmetric.com
1. Back orders
2. Cycle Time
3. Fill Rate
4. Inventory Classification (ABC)
5. Inventory Turns
6. On time shipping and delivery
7. Perfect Order
The Power of Metrics (DMReview)
DMReview organizes supply chain metrics using the following four dimensions:

1. Response-Time Metric (timeliness dimension)
2. Visibility Metric (process efficiency dimension)
3. Productivity Metric (productivity dimension)
4. Shrinkage Metric (profitability dimension)

Building and leveraging Metrics Framework to drive Supply Chain Performance (Infosys)
They outline the key characteristics of the right metric as including – Reliability, Validity, Accessibility and Relevant. They also elaborate that:

• Metrics are most useful when embedded in a metrics model that represents a business process
• The criticality of a metric is determined by the process performance insight that it provides
• Metrics need to be assigned to roles that have process execution, monitoring and tracking responsibilities

Supply Chain Benchmarking (AMR Research)
AMR Research focuses on 8 high-level operational processes:

* Request to quotation
* Order to delivery/cash
* Perfect order fulfillment
* Inventory management
* Source and make (with cash to cash)
* Operational planning
* New product development time
* Supply chain management costs

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Is an IBM and SAP Marriage in the Works?

SCDigest reports on strong rumors about merger talks between IBM and SAP.

These are just rumors, to be sure, and have been circulating at some level for almost a year.
SAP has strongly denied the rumors during that time, though chairman Hasso Plattner unintentionally put some fuel in the fire last May by saying to the German Financial Times last year that: “There are only three potential buyers [of SAP]: IBM, Microsoft and Google. Of all companies, I don’t see anyone else. If shareholders think that a combination, and not independence, is better, then it will happen.”


From the services model that IBM follows, it makes sense to acquire a behemoth like SAP purely for its installed base and then sell all sorts of services to them. But the larger question is – what’s the room for growth here? From a software sales point of view, the market is pretty much saturated. My own view of the ERP behemoth is that given the utter complexity of something of the order of SAP/Oracle and the implicit insistence that the firm adapt to SAP’s version of reality – there is quite an opportunity for an intelligent class of enterprise software to make deep inroads.
Whatever the big honchos at IBM are thinking, I’m skeptical of such a merger simply because of revenues from any sort of installed base growth. The market space where there is some growth potential seems to be:

IBM and SAP have an existing partnership to bring ERP to the small and mid-sized company market. Penetrating these smaller companies has been a key marketing goal of SAP for the past few years.

The question is – why pick an elephant (or a sheared down version of an elephant) to run what needs to be, strategically and execution-wise, a nimble organization? Any new entrant in the enterprise software space needs to enter via the small and mid-sized company market because that’s where the behemoths are concentrating their efforts.
Old Chinese (Confucius) saying: “Do not use a cannon to kill a mosquito.”
This makes for an exciting few years ahead.

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About me

I am Chris Jacob Abraham and I live, work and blog from Newburgh, New York. I work for IBM as a Senior consultant in the Fab PowerOps group that works around the issue of detailed Fab (semiconductor fab) level scheduling on a continual basis. My erstwhile company ILOG was recently acquired by IBM and I've joined the Industry Solutions Group there.

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