🧮ERP: Implementations (Part XVII: Taming the Monsters)

ERP Implementations Series

 

Given their extensive scope, duration, investment and complexity, ERP implementations are probably one of the most complex endeavors pursued by organizations. Moreover, they are often a matter of endurance with many junctions, skirts, turns, shortcuts, ups and downs, a continuous carousel in which the various issues tend to misbehave like little monsters, many of them haunting one’s dreams unexpectedly during and long after implementations.

Probably, the main drivers are the scale and mass of such projects as they touch all or most important aspects of organizations. Just consider the typical project done for a single department and multiply its complexity by a constant number representing the number of departments in scope. And the more one goes into details, the higher the complexity. To move forward the parties need to compromise, and as no one wants to do that, the discussions are prolonged, the discussions get personal, issues are escalated, and probably more negative effects can be met.

Tensions can be rooted in politics, in the friction between different goals, in the need to prioritize requirements, postponing or leaving things out of scope, or by pushing an agenda other parties don't agree with. Besides the typical constraints of projects, there’s the complexity of performing a huge amount of work within a limited period, time during which the resources must be available, the quality must match the expectations, and there are so many aspects to be considered!

Of course, not all implementations are like this, though each such project is a real exam of maturity for the people involved in it. Sometimes, it’s better to have people who care about the decisions made. On the opposite side, there are organizations that go almost blindly with the solutions suggested to them, with all the effects resulting from this. Probably, the middle way between these two extremes is more indicated, though it’s hard to find such a path through all complexity.

An ERP implementation is highly dependent on the initial conditions under which the project has started, the commitment made by the various parties involved in the project, the way resources are made available, on what’s considered in plan, on the communication that takes place, the planning done and its enforcement, etc. Of course, some topics can be addressed also later, though delays tend to create more delays that can have a ripple effect through the project. Under normal circumstances the backlog and other aspects can be manageable, though it’s enough for a few issues to gather momentum so that their cumulative impact can have an exponential impact.

Certain sensitive project topics can easily lead to crises and abnormal behavior, though such situations are usually exceptions (until they are not). It’s important to have in place the processes and procedures that can be used to address this kind of situation, and, not less important, have them communicated to the team. Moreover, it’s not necessary to reinvent the wheel - the processes defined in IT and project methodologies can be used and adapted for this purpose.

It's important to have in place all the processes, procedures and checkpoints needed to support the project. The people participating in a project should have some hands-on experience with them, including the exceptions (e.g. escalation procedures). It’s useful to have a mentor or some experienced person who can help with advice and even attend meetings and provide constructive feedback. Just having some awareness sessions with no feedback can be as dangerous as not having any training at all! It’s suboptimal to use the implementation itself as an environment for learning though in extremis this approach may work as well.

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📉Graphical Representation: Extremes (Just the Quotes)

"Missing data values pose a particularly sticky problem for symbols. For instance, if the ray corresponding to a missing value is simply left off of a star symbol, the result will be almost indistinguishable from a minimum (i.e., an extreme) value. It may be better either (i) to impute a value, perhaps a median for that variable, or a fitted value from some regression on other variables, (ii) to indicate that the value is missing, possibly with a dashed line, or (iii) not to draw the symbol for a particular observation if any value is missing." (John M Chambers et al, "Graphical Methods for Data Analysis", 1983)

"Skewness is a measure of symmetry. For example, it's zero for the bell-shaped normal curve, which is perfectly symmetric about its mean. Kurtosis is a measure of the peakedness, or fat-tailedness, of a distribution. Thus, it measures the likelihood of extreme values." (John L Casti, "Reality Rules: Picturing the world in mathematics", 1992)

"If the underlying pattern of the data has gentle curvature with no local maxima and minima, then locally linear fitting is usually sufficient. But if there are local maxima or minima, then locally quadratic fitting typically does a better job of following the pattern of the data and maintaining local smoothness." (William S Cleveland, "Visualizing Data", 1993)

"Variance and its square root, the standard deviation, summarize the amount of spread around the mean, or how much a variable varies. Outliers influence these statistics too, even more than they influence the mean. On the other hand. the variance and standard deviation have important mathematical advantages that make them (together with the mean) the foundation of classical statistics. If a distribution appears reasonably symmetrical, with no extreme outliers, then the mean and standard deviation or variance are the summaries most analysts would use." (Lawrence C Hamilton, "Data Analysis for Social Scientists: A first course in applied statistics", 1995)

"Clearly, the mean is greatly influenced by extreme values, but it can be appropriate for many situations where extreme values do not arise. To avoid misuse, it is essential to know which summary measure best reflects the data and to use it carefully. Understanding the situation is necessary for making the right choice. Know the subject!" (Herbert F Spirer et al, "Misused Statistics" 2nd Ed, 1998)

"A feature shared by both the range and the interquartile range is that they are each calculated on the basis of just two values - the range uses the maximum and the minimum values, while the IQR uses the two quartiles. The standard deviation, on the other hand, has the distinction of using, directly, every value in the set as part of its calculation. In terms of representativeness, this is a great strength. But the chief drawback of the standard deviation is that, conceptually, it is harder to grasp than other more intuitive measures of spread." (Alan Graham, "Developing Thinking in Statistics", 2006)

"Many scientists who work not just with noise but with probability make a common mistake: They assume that a bell curve is automatically Gauss's bell curve. Empirical tests with real data can often show that such an assumption is false. The result can be a noise model that grossly misrepresents the real noise pattern. It also favors a limited view of what counts as normal versus non-normal or abnormal behavior. This assumption is especially troubling when applied to human behavior. It can also lead one to dismiss extreme data as error when in fact the data is part of a pattern." (Bart Kosko, "Noise", 2006)

"Standard quantile graphs offer certain advantages over cumulative percent frequency graphs. Among these advantages are ease of construction, actual data points are shown as opposed to summaries of class intervals, no decisions are required as to what the best size class interval might be, the same curve functions as a less-than and greater-than curve, and the actual maximum and minimum values are shown on the graph." (Robert L Harris, "Information Graphics: A Comprehensive Illustrated Reference", 1996)

"[…] an outlier is an observation that lies an 'abnormal' distance from other values in a batch of data. There are two possible explanations for the occurrence of an outlier. One is that this happens to be a rare but valid data item that is either extremely large or extremely small. The other is that it is a mistake - maybe due to a measuring or recording error." (Alan Graham, "Developing Thinking in Statistics", 2006)

"Plotting data is a useful first stage to any analysis and will show extreme observations together with any discernible patterns. In addition the relative sizes of categories are easier to see in a diagram" (bar chart or pie chart) than in a table. Graphs are useful as they can be assimilated quickly, and are particularly helpful when presenting information to an audience. Tables can be useful for displaying information about many variables at once, while graphs can be useful for showing multiple observations on groups or individuals. Although there are no hard and fast rules about when to use a graph and when to use a table, in the context of a report or a paper it is often best to use tables so that the reader can scrutinise the numbers directly." (Jenny Freeman et al, "How to Display Data", 2008)