@ Supply Chain Management

I am on the road this week and so have little time at hand for blogging. But as I was flying to the east coast yesterday evening, I had an interesting thought. Change management projects and initiatives have a high failure rate, estimates that I have across put it about 60% failure rate. If anyone has a better number or figure for the failure rate for change management projects, send me a note.
Why?

The principal reason that I thought of yesterday was that change is not a part of the daily work schedule, part of the daily work objective – “If it ain’t broke, don’t fix it” creates a working environment that doesn’t emphasize change sufficiently. Instead change is something that happens when something goes wrong, has gone wrong and now is a festering sore or is about to go wrong and some alert group or individual recognized it just in time.
What I’d like to find out is whether change management failure rates are comparable for firms that have a continuous improvement culture and those who do not.

Tags: Change Management, Failure rates of Change Management projects, A culture of continuous improvement

Running down the path of metrics, I came across this take on the use of metrics in the business world. This article by Julie Fraser titled – Are metrics driving or killing your business? can be found at Manufacturing Business Technology.

The question is whether the behavior that most companies are rewarding actually helps performance, or hurts it.

Ms. Fraser’s line is not really what the right metrics are but whether the measured metrics actually help performance or hurt it. In other words, does it work or not?
Here’s the standard example given (and why am I not surprised that manufacturing gets the short end of the stick?)

Many talk about fact-based management, but outdated manufacturing metrics often conflict with corporate goals. For example, a plant that measures equipment utilization as the top indicator of success may in fact lead the company to carry excess inventory. In another scenario, minimizing overtime might hurt on-time shipments to customers and thus, overall revenues.

But both of these metrics are not manufacturing metrics but confusions arising from accounting. It is true that manufacturing measures equipment utilization is measured in addition to cycle times, changeover times and mean time to failure. However, utilization is measured for accounting reasons not because a manufacturing/plant manager is going to feel extremely accomplished because his/her machine was utilized 95% of the time. The concept of unit cost arises in accounting and not in manufacturing and everything else follows from that.
While Ms. Fraser indicates that measuring equipment utilization “may” in fact lead the company to carry excess inventory but that is not only because manufacturing managers are measured using the notion of efficiency but because unit costs are minimized in large batch production. Costs in production as it is/was measured is a fuzzy concept because it is not related to actual demand and the actual price paid by the customer but a weighted factor of allocation of overheads, raw material costs etc. In order to minimize unit costs using the accounting dimensions, utilization is a key lever and it has been pulled many a time.
But what can one make of the following set of metrics led dead-ends?

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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?

Tags: Updaing my Blog caching

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!

Tags: RSS feed restored

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…

Tags: Little’s law, Factory Physics, Supply Chain Physics, Behavior of manufacturing systems, Developing robust metrics, Metrics, Developing supply chain metrics

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.

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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?

Tags: Metrics, Maxager survey, SDExec.com, Production velocity, ROA, Return on Assets