Wednesday, November 19, 2008

Successful Project Management Spite Challenging Times


Plato's Dodecadron by ~John M. Kennedy T. on deviantART


QUALITY MANAGEMENT AND CONTROL FOR PROJECTS

EVERYTHING SHOULD BE MADE AS SIMPLE AS POSSIBLE, BUT NOT SIMPLER
- ALBERT EINSTEIN (Minsky, 1986)

INTRODUCTION


My participation in the forum did not decline, over the implementation of the "Ephesus project", I made space in my schedule, every two days or so, to read new posts, and to contribute by providing my point of view and experiences in the field. I learned from my fellow participants quite a bit over those last months, in turned out that most of us were implementing projects and that we were looking for more support, knowledge and ideas on how to deal with the nuances of every project. It was a point in which we all wanted to hear from each other about what were the experiences and final results of those projects. Somehow, from the exchange of our special cases, we found out that we were writing most about on the importance of assessing the project strategic quality factors and concluding that at the end they were the direct result of the decisions that we made based on our project analysis skills. Therefore, what I am about to discuss in here is my views on the most effective decision analysis and the evaluation methods that could be utilized by today’s organizations facing tremendous economical and cultural changes and challenge.


THE QUEST FOR QUALITY



When we actually refer to quality, what we mean is the degree or level in which something (whereas be an object [like a project], an animal or a person) is perceived to be superior, somewhat around “there” or inferior to a prearranged average, standard, norm or index. I often warm “qualifiers” about their biases; but this is inevitable, we are all biased somehow by the lenses that we use to observe our distorted “realities”, and so what we tend to believe is what we see in that reality out there on the spot in from of our eyes. Anyways and perhaps, we can agree upon in that to a higher quality of a service or product provided is followed or supported proportionally by a higher measure of the customers’ satisfaction; for instance, the customers will consume more of our products or services, or they will not return them or make complaints or they will do it but with a notoriously lower rate or frequency. All these aforementioned indicators only means more business and so we are very interested in determining and hearing what quality means from the genuine voice of our stakeholders. At least I do. Albeit, this equation seems to be so easy to formulate, becomes cumbersome to understand when, we face the question about how to interpret what all those percentiles mean for our projects' outcomes. It is because, what is good for “Peter”, it does not mean necessarily that it is good for “Paul”. Hence, the customer services and the fulfillment departments appear to be always full of problems at all the times, it is a drainage of resources and human energy and sometimes these departments can only cope with the demands imposed to them. I do not quite remember where I have heard this but it made a lot of sense for me when I did, so as manner of corroborate with what I am stating above, I am writing it in here, “In theory, there's no difference between theory and practice. In practice there is.” There for let’s see the analysis and its methods.


ANALYSIS AND METHODS


Analysis is mental process. The idea of dividing and object into its parts for understanding how the object is built or how the parts work or relate to the whole is ancient. It documented utilization I dated back from the times of the ancient Greeks; inclusively, before Aristotle or Plato, well it can be argued that also the Ahmes papyrus, a document that was probably written in Egypt on its Middle Kingdom, that is, 2000-1800 BC and which claims to be a “thorough study of all things, insight into all that exists…" is an indication of the utilization of higher order thinking skills from the Bloom’s taxonomy but logic and argumentation seem to have been developed by the Greek culture. Back to these years and days though, we could find several labels with radical names or types of analysis; for instance, chemistry analysis, financial analysis, and evenly calculus receive the name of analysis, when what we are talking about is the study of functions. Nevertheless, the most probably basic conjecture about what every type of analysis means is to do as anatomists do when focused in their quest for understanding the structure of the subject of study; the anatomist divides it in smaller parts. The Project Manager [PM] dissects a project up to the most minimum details. Using appropriated tools, the PM is able to elaborate and control the schedule and the budget in detail because the PM analyzes the project in work packages within different levels of the Work Breakdown Structure WBS. (Kerzner, 2006) As there are several ways to kill flies, there are several methods to solve problems; as well, to envision effective strategic plans. These methodologies are known as decision analysis Methods. Kerzner, op. cit. stated that a critical question that project managers and executives face is, “…How to measure the return of investment [ROI] as a result of implementing a project... ;” he added, “The actual measurement can be described in both qualitative and quantitative terms.” (chap. 23) Therefore, no matter which the decision analysis method chosen by the PM, an additional method should be implemented to measure or assess the progress and outcomes of the project’s strategic plan.


DECISION ANALYSIS METHODS

As we have discussed, the PM has to use two main activities to define the quality of the project outcomes, i.e., analysis and evaluation. Both are use to make decisions under the presence of uncertainty and so for decreasing the chance of loss, reworked or scrap, and therefore the heart of the PM’s inquiry is what to do for bringing science to project management for making sounded and prudent decisions when one is facing risky situations and environments. [Human] Intelligence is the ability to solve problems, Minsky (1986) stated in his seminal work “Society of Mind” about using knowledge for solving problems, “The most efficient way to solve a problem is to already know how to solve it. Then one can avoid search [for it] entirely.” (p. 75) However, to how to solve problems is basically to go beyond our pure intuition and empirical knowledge, we probably would want to use both as we do in Bayesian Analysis, but first we have to recognized something that George Pólya, the Hungarian born mathematician understood well: First the analysis, meaning a problem has many parts. Then we must acquired knowledge that we might not have at the time, think in projects like the NASA, first man in the moon, who knew how to do that? Or what previous empirical knowledge those engineers had about it? Some knowledge derived or was based on pure research, certainly not by the experience of having sent a man to the moon already. To solve of our problems, we also need a way to save and compiled information and data so we can understand their crucial interrelationships. Hopefully, we would find some cause-effect relationships or some correlations beyond the mere chance factors, but we must make sure about the support that we bring to our claims or conclusions. The process needs to be tested, retested and reviewed it. Finally, we need to be able to convert the learning, whether from success or [mostly from] failure in a technical body of knowledge [BOK], standards or guidelines and problem solving systems as with the case of the Decisions Analysis Systems which essentially are aimed to be employed in similar situations in the future. (Minsky, 1986) Aufmann, Nation, & Clegg (2003) noted how Pólya was able to synthesize in one of his ten books on mathematics, entitled precisely, “How to Solve it” [published in 1945], the essence of problem solving which for the most part has been applied in many diverse disciplines. (chap. 1) I think that Project Management is not an exception in this case, as I can see how the many Decision Analysis Methods and Evaluation Methods are just a variation of Pólya’s four principles. [1st] Understand the problem; [2nd] devise the plan [this is the hard part]; [3rd] implement the plan; and [4th] Review and extend [evaluation and evolution]. As Project Management is continuously embraced more and more by many industries and managers, as also the figure the Pólya is becoming recognized like a pivotal figure in bringing about a simple and coherent method to solve problems. (Aufmann, Nation, & Clegg, 2003).



A decision is not exactly a solution. With frequency a project appears to be in a volatile state. It can change in both directions, like in any marriage, for the better or for the worse. So while the project plan is being implemented, we have learned [throughout many cases] to applied measures to control the project and thus preclude it to go into the wrong direction. We implement projects aimed to make appropriated changes, in business, in the non-profit or public arenas, as in government too; the main idea is to meet or exceed the expectations of the customers under those costs that have been budgeted under predetermined agreements, either for profit or to meet certain quality requirement. Thus the ISO 9000 series quality definition is, “The totality of feature and characteristics of a product or services that bears on its ability to satisfy stated or implied needs.” (Kerzner, 2006, p. 35) The success of an organization depends of its understanding of the definition of quality according to their customers. Quality is a continuous improvement process for retaining, increasing or bringing back customers. Therefore, every decision analysis and evaluation method that the PM would select is oriented and spins around the concept of quality.

The major decision analysis and evaluation methodologies has expanded in a complex interdisciplinary studies, we have the branch of Decision Theory and Decision Analysis. There are three categories of decision-making: (a) Certainty, (b) Risk, and (c) Uncertainty. Making decisions under ‘a’ is the easiest, because we are sure 100 percent that we know what our chances are and which ones shall be the payoffs for each of the different possible outcomes. Therefore, from knowing the payoffs for every state of nature, the PM can create a matrix to understand this relationship more clearly. Every action, in Decision Theory, is a strategy planned correspondingly to every identified state of nature or possible outcome of which the PM has no control over. Therefore, the work of making decisions under certainty is, besides to build a matrix, to determine, by simple inspection though, the best way to produce the best results with the minimum amount of losses, investment and/or efforts. Here, simply there are not probabilities. Nevertheless, and ‘a’ situation very rarely happens, I tend to believe that the likelihood of such situations approach to zero meaning it is extremely unlikely that a PM would face such situation. In any case and most likely, the PM will have to make decisions, under (b) risk, (c) uncertainty of both.

Blaise Pascal, the French philosopher, mathematician and inventor, a true polymath, devised the Expected Value System; I see this system as the direct precursor of the Decision Trees and Influential Diagram [ID] or Decision Network Methodologies. Pascal calculated the expected value by multiplying the number of all the identified of the all possible actions times the probability of each of their outcomes. Later, other scientist based the Decision Analysis Models, by focusing on the concept of utility, i.e. the relative satisfaction from or desirability of the acquisition or consumption of products or services. Not surprising that most of the latest discoveries and applications of Decision Theory comes from economics. (Arsham, 2008)


The Decision Trees and the IDs are both Decision Analysis and evaluation tools because they show to the decision maker the presence of alternatives for following or taking a course of action, the risks or uncertainties and the measurement of the outcomes. (Hasson, 2005) Another method is the Montecarlo Process or Analysis, AKA Montecarlo Method, is really a set of approaches denominated under the same name and having some commonalities among them, such as they determine a set of possible variable inputs and use these inputs randomly to sum the results of the every calculation into the last result. Kerzner, op. cit, explained that Montecarlo is applied to risk management to quantify the risks factors associated with the project’s cost, schedule or performance. (p. 735)
Making decisions under the presence of risk involves probability; therefore, the PM usually employs mathematical models to make educated decisions as well. Thus, the PM can use Expected Monetary Value or EMV for short, which is the product of the likelihood of occurrence and the gain or loss that will result thereof. (Expected Monetary, 2008) In addition, the Expected Value of Perfect Information [EVPI] and the Expected Opportunity Loss, [EOL] are both utilized for making decisions when the PM is facing uncertainty. We have other mathematical models such as the Minimax, the Maximax and the Maximin. Their names indicate the bias place in these realistic methods, whereas be on the maximum return or on the minimum loss or negative consequence.

CONCLUSION


The aforementioned Decision Analysis and Evaluation Methods are prescriptive in nature and for such they provide guidelines on how to make prudent decisions by following some logical structures or analytical methods. The other way around is to study how and why people make mistakes, so there are the Descriptive Methods too. Experience or empiricism as intuition should also be part of the decision-making process, but I do no think these methods should or could have a more predominant role than the systems that are based on logical, mathematical and statistical inference. Reason beats memory and experience. The Bayesian Method, like the Game Theories as well, takes other interesting routes. For example, in Bayesian Method the probability is subjective, which means these method studies and equates the attitudes, believes, and perception of the decision-maker into the process of figuring out possible outcomes for a set of determined plans of action. In contrast, the Game theory takes in consideration what other people could or might do under certain specific set of circumstances, including possible emotional theaters and behavioral patterns, based on the assumption that people barely change their behavior, especially while participating in games or markets, matter of fact this is also apply to wars scenarios. When a PM is on the field and running a project he must use all these tools and methods, inclusive the PM’s self, taken as instrument now, becomes the most important tool since he has to put all these theories together and in the spur of a very constraint time frame and with the zeitgeist of the project just to make it happen as expected by all the stakeholders and recursively by the same PM too.  



REFERENCES

Arsham, H. (2008). Tools for Decision Analysis: Analysis of Risky Decisions. Retrieved June 21, 2008, from http://home.ubalt.edu/: http://home.ubalt.edu/ntsbarsh/opre640a/partIX.htm#rtopicaljava
Aufmann, R., Nation, R. D., & Clegg, D. K. (2003). Applied College Algebra. New York, New York: Houghton Mifflin Company.
Expected monetary value. (2008). Retrieved June 24, 2008, from BusinessDictionary.com: http://www.businessdictionary.com/definition/expected-monetary-value.html
Hansson, S. O. (2005, August 23). Decision Theory: A Brief Introduction. (D. o. Technology, Ed.) Retrieved June 19, 2008, from http://www.infra.kth.se/: http://www.infra.kth.se/~soh/decisiontheory.pdf
Kerzner, H. (2006). Project Management: A system Approach to Planning, Scheduling, and Controlling (9th ed.). Hoboken, NJ: John Wiley & Sons, Inc.
Minsky, M. (1986). The Society of Mind. New York, NY: Simon and Schuster, Inc.

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