How to Catch and Manage Innovative Practices

Innovation happens at the boundaries of disciplines
Most innovation happens at the boundaries between disciplines or specializations. It is when people meet across the boundaries that new knowledge is generated or integrated and new innovations comes up. We know this, but we also know the relative complexity to manage innovative processes at a given boundary. The researcher Carlile has spent a lot of time in many papers to explore and investigate this complexity. We argue that by understanding this complexity we might be able to better manage these processes.
Carlile proposes that we should shed light on three different properties of knowledge at boundaries; difference, dependence and novelty (Carlile and Rebentisch 2003, Carlile 2004). Differences in knowledge refer to a difference in the amount of accumulated knowledge. And this is a dilemma. Creating a complex service (i.e. innovation), for example, often requires differences in the amount and type of knowledge. At the same time, practically, it means that different actors have different experiences, different terminologies, different incentives, etc. Furthermore, every actor has to re-learn. This might have a negative impact of the willingness of an actor to participate in an innovative process. Nevertheless, these processes need to be overcome.
The second relational property of knowledge at a boundary is dependence. To be able to manage innovative processes we need to take into account how different actors and their activities are dependent on each other. As Carlile (2004: 556) points out - “Without dependence, difference is of no consequence”. Dependence can, for example, be described in political terms, i.e. are actors willing to participate in innovative processes because of situated dependence? Furthermore, how will innovative processes change dependence between actors or processes?

The third relational property of knowledge at a boundary is how novel the circumstances are. As Carlile writes; “… the most challenging aspect of the relational nature of knowledge at a boundary is that for each actor there is novelty to share with others and novelty to assess from others.” To be able to manage innovative processes we must be aware of that when novelty arises there is often a lack of common knowledge to adequately share an assess knowledge at a boundary. An innovative thought might, for example, be regarded with suspicion and insecurity, not because the idea is not of great value, but that there is a lack of language to catch the innovation with.
Our approach and analysis may indeed be regarded as complex and abstract. However, by illuminating what happens at the boundaries between disciplines and specializations may help us to understand and develop an innovative climate in our organizations. By focusing on the three properties of boundaries we may open a window to further understanding about how the flux and flow of organizational processes can be arrested in concepts and translated into pragmatic use. What could be more instrumentally usable?
Recommended reading
Carlile, P., and E. Rebentisch (2003) Into the black box: The Knowledge transformation cycle, Management Science. Vol. 49, pp 1180-1195.
Carlile, P. (2004), Transferring, translating, and transforming: An integrative framework for managing knowledge across boundaries, Organization Science Vol. 15 No. 5, pp. 555-568.
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How to become Better Qualified for career in Project Management?

Project Management has evolved into a well defined discipline which requires the application of knowledge, skills, tools, and techniques to project activities to meet project requirements. To be successful as a Project Manager, one must have very good understanding of all the Knowledge Areas*** related to Projects namely :

• Project Integration Management
• Project Scope Management
• Project Time Management
• Project Cost Management
• Project Quality Management
• Project Human Resource Management
• Project Communications Management
• Project Risk Management
• Project Procurement Management

Understanding the best practices in each of these knowledge areas and being well versed in the Project Management Context and Processes is essential for being a successful Project Manager.

It is highly recommended that every Project Manager should read the PMBOK^® 2008 (publication of PMI^®) which provides an excellent overview of the concepts mentioned above.

Taking the PMP^® Certification Exam is also highly recommended because PMP^® certification is the profession's most globally recognized and respected certification credential. The PMP^® designation following your name tells current and potential employers that you have a solid foundation of project management knowledge that can be readily applied in the workplace.

*** : As defined by PMI^®

Six Sigma SQC ControlCharts

A control chart is a popular statistical tool for monitoring and improving quality. Originated by Walter Shewhart in 1924 for the manufacturing environment, it was later extended by W. Edward Deming to the quality improvement in all areas of an organization (a philosophy known as Total Quality Management, or TQM).

Try our control chart calculator for attributes (discrete data) and control chart calculator for variables (continuous data).

The purpose of control charts

The success of Shewhart's approach is based on the idea that no matter how well the process is designed, there exists a certain amount of nature variability in output measurements.

When the variation in process quality is due to random causes alone, the process is said to be in-control. If the process variation includes both random and special causes of variation, the process is said to be out-of-control.

The control chart is supposed to detect the presence of special causes of variation.

In its basic form, the control chart is a plot of some function of process measurements against time. The points that are plotted on the graph are compared to a pair of control limits. A point that exceeds the control limits signals an alarm.

An alarm signaled by a control chart may indicate that special causes of variation are present, and some action should be taken, ranging from taking a re-check sample to the stopping of a production line in order to trace and eliminate these causes. On the other hand, an alarm may be a false one, when in practice no change has occurred in the process. The design of control charts is a compromise between the risks of not detecting real changes and of false alarms.

Assumptions underlying Control Charts

The two important assumptions are:

1. The measurement-function (e.g. the mean), that is used to monitor the process parameter, is distributed according to a normal distribution. In practice, if your data seem very far from meeting this assumption, try to transform them.
2. Measurements are independent of each other.

Constructing a 3-sigma ("Shewhart-type") control chart

During a stable stage of the process:

1. Determine the process parameter that you want to monitor (such as the process mean, or spread).
2. Create the centerline of the plot, according to the target value of your monitored parameter.
3. Group the process measurements into subgroups (samples) by time period. The points to be plotted on the plot, are some function of the process measurements within each subgroup, which estimate the target value.
   For example, if you are monitoring your process mean, then the points on the plot should be the sample-means, computed at regular intervals. Denote the point at time t as X_t
4. Create upper and lower control limits (UCL,LCL) according to the following formula:
   UCL = CL + 3 s
   LCL = CL - 3 s
   where s is the standard deviation of X_t.
   For the example above, X_t may be daily means of process measurements. If each daily sample comprises of n measurements, then the standard deviation of X_t is equal to the process standard deviation divided by the root of n
   
   
   After the control limits have been set, continue to plot the points on the graph, as a function of time. When a point exceeds the control limits, it indicates that the process is out of control, and action should be taken (of course, there is a slight chance that is is a false alarm).

An Example

To give you a feel of this statistical terminology, imagine a process that produces soap bars. The production manager wants to monitor the mean weight of soap bars produced on the line. The target value of a the weight of a single soap bar is 100 gm. It is also known that an estimate of the weight standard-deviation for a single soap bar, is 5 gm.

Daily samples of 10 bars are taken, during a stable period of the process. For each sample, the weights are recorded, and their mean/average is computed. The sample means are estimates of the process mean.

1. The monitored parameter is the process mean.
2. The center line in this case will be equal to 100 gm (the target).
3. The points on the plot will be the sample means (where each sample consists of 10 measurements).
4. The control limits are given by 100 ± 3 · 5 / root(10)

Sensitizing rules for control charts

The American Standard is based on "three-sigma" control limits (corresponding to 0.27% of false alarms), while the British Standard uses "3.09 sigma" limits (corresponding to 0.2% of false alarms). In both cases it is assumed that a normal distribution underlies the relevant estimators.

It has been shown that Shewhart-type charts are efficient in detecting medium to large shifts, but are insensitive to small shifts. One attempt to increase the power of Shewhart-type charts is by adding supplementary stopping rules based on runs. The most popular stopping rules were suggested by the "Western Electric Company" ("WECO"). These rules supplement the ordinary rule: "One point exceeds the control limits". Here are the most popular Western Electric rules:

• 2 of 3 consecutive points fall outside warning (2-sigma) limits, but within control (3-sigma) limits.
• 4 of 5 consecutive points fall beyond 1-sigma limits, but within control limits.
• 8 consecutive points fall on one side of the centerline.

An online calculator for the Western Electric Rules is available here.

Scope Creep

Typically Scope Creep affects all kinds of projects.

This can be solved in many ways.

For every project, the basic clarity should be there in the requirements. Written and well documented.

Such strong conviction, of not going overboard on required, will cut down cost over runs.

Any creep of requirements ( customers will always try to add new requirements, after signing the project dev. ) should be well documented and charged. Certain companies I have worked with have the policy of charging ( change management ) only if there is excess of effort > 10% expended, as there is always a buffer of 10% added in the initial quote.

***

I was reading an article now Managing Project Profitability and some points are well written, that can be used in any typical IT project implementation ( & management ).

--
Regards
Vijayashankar

SCAMPI LA

Hi All

I am looking for an opportunity and if you know any best fit, in the field of Delivery / Project Management, do refer me.

I have closer to 2 decades of experience in the IT industry.

Thanks.

Here is my profile.

Regards
Vijayashankar