Take Me To The River, Part 2, Schedule Elements–A Digital Inventory of Integrated Program Management Elements

Recent attendance at various forums to speak has interrupted the flow of this series on IPM elements. Among these venues I was engaged in discussions regarding this topic, as well as the effects of acquisition reform on the IT, program, and project management communities in the DoD and A&D marketplace.

For this post I will restrict the topic to what are often called schedule elements, though that is a nebulous term. Also, one should not draw a conclusion that because I am dealing with this topic following cost elements, that it is somehow inferior in importance to those elements. On the contrary, planning and scheduling are integral to applying resources and costs, in tracking cost performance, and in our systemic analysis its activities, artifacts, and elements are antecedent to cost element considerations.

The Relative Position of Schedule

But the takeaway here is this: under no circumstances should any program or project manager believe that cost and schedule systems represent a dichotomy, nor a hierarchy, of disciplines. They are interdependent and the behavior noted in one will be manifested in the other.

This is important to keep in mind, because the software industry, more than any other, has been responsible for reinforcing and solidifying this (erroneous) perspective. During the first generation of desktop application development, software solutions were built to automate the functions of traditional line and staff functions. This made a great deal of sense.

From a sales and revenue perspective, it is easier to sell a limited niche software “tool” to an established customer base that will ensure both quick acceptance and immediate realization of productivity and labor savings. The connection from the purchase to ROI was easily traceable in the time span and at the level of the person performing their workaday tasks.

Thus, solutions were built to satisfy the needs of cost analysts, schedule analysts, systems engineers, cost estimators, and others. Where specific solutions left gaps, such spreadsheet solutions such as Microsoft Excel were employed to fill them. It was in no one’s interest to go beyond their core competency. Once a dominant or set of dominant incumbents (a monoposony) inhabited a niche, they employed the usual strategies for “stickiness” to defend territory and raise barriers to new entries.

What was not anticipated by many organizations was the fact that once you automate a function that the nature of the system, if one is to implement the most effective organizational structure, is transformed to conform to the most efficient flow and use of data–and its resulting transformation into information and intelligence. Oftentimes the skill set to use the intelligence does not exist because the resulting insights and synergy involved in taking larger and more comprehensive datasets which themselves are more credible and accurate was not anticipated in adjusting the organizational structure.

This is changing and must change, because the old way of using limited sets of data in the age of big(ger) data that provide a more comprehensive view of business conditions is not tenable. At least, not if a company or organization wants to stay relevant or profitable.

Characteristics and Basic Elements of the Project Schedule

If one were to perform a Google search of project schedule while reading this post, you would find a number of definitions, some of which overlap. For example, the PMBOK defines a schedule as, quite simply, “the planned dates for performing activities and the planned dates for meeting milestones.”

Thus our elements include planned dates, activities, and milestones. But is that all? Under this definition, any kind of plan, from a minor household renovation or upgrade to building an aircraft carrier would contain only these elements.

I don’t think so.

For complex projects and programs, which is the focus on this blog, our definition of a project schedule is a bit more comprehensive. If you go to the aforementioned A Guide for DoD Program Managers mentioned in my last post, you will find even less specificity.

The reason for this is that what we define as a project schedule is part and parcel of the planning phase of a project, which is then further specified in the specific time-phased planning elements for execution of the project through its lifespan into production. It is the schedule that ties together all of the disciplines in putting together a project–acquisition, systems engineering, cost estimating, and project performance management.

In attending scheduled-focused conferences over the years and in talking to program management colleagues is the refrain that:

a. It is hard to find a good scheduler, and

b. Constructing a schedule is more of an art than a science.

I can only say that this cedes the field to a small cadre of personnel who perform an essential function, but who do so with few objective tests of effectiveness or accountability–until it is too late.

But the reality is quite different from the fuzzy perception of schedule that is often assumed. All critical path method (CPM) schedules describe the same phenomena, though the lexicon will vary based on the specific proprietary application employed.

In government-focused and large commercial projects, the schedule is heart of planning and execution. In the DoD world it is known as the Integrated Master Schedule (IMS), which utilize the inherent bottom-up relationships of elements to determine the critical path. The main sources regarding the IMS have a great deal of overlap, but tend to be either aspirational (and unfortunately not prescriptive in defining the basic characteristics of an IMS) or reflect the “art over science” approach. For those following along these are the DoD Integrated Master Plan and Integrated Master Schedule Preparation and Use Guide of 21 October 2005, the NAVAIR Integrated Master Schedule (IMS) Guidebook of February 2010, and the NDIA Planning and Scheduling Excellence Guide (PASEG) of 9 March 2016 (unfortunately no current direct link).

The key elements that comprise an IMS, in addition to what we identified under the PMBOK are that it is networked schedule consisting of specific durations that are assigned to specific work tasks that must be accomplished in discrete work packages. In most cases these durations will be derived by some kind of either fixed, manual method or through the inherent optimization algorithm being applied by the CPM application. More on this below. But these work packages are discrete, meaning that they represent the full scope of the work that must be accomplished to during the specified duration for the creation of an end product. Discrete work is distinguished from level of effort (LOE) work, the latter being effort that is always expended, such as administrative and management tasks, that are not directly tied to the accomplishment of an end product.

These work packages are tied together to illustrate antecedent and progressive work that show predecessor and successor relationships. Long term planning activities, which cannot be fleshed out until more immediate work is completed are set aside as placeholders called planning packages. Each of the elements that are tracked in the IMS are based on the presentation of established criteria that define completion, events, and specific accomplishments.

The most comprehensive IMSs consist of detailed planning that include resources and elements of cost.

Detailed Elements of the IMS

Given these general elements, the best source of identifying the key elements of detailed schedules is also found in Department of Defense documents. The core document in this case is the Data Item Description for the IMS numbered as DI-MGMT-81650. The latest one is dated March 30, 2005. There are a minimum of 32 data elements, some of these already mentioned and which I will not repeat in this post since they are pretty well listed and identified in the source document.

For those not familiar with these documents, Data Item Descriptions (or DiDs–gotta love acronyms) represent the detailed technical documents for artifacts involved in the management of DoD-related operations. Thus, this provides us with a pretty good inventory of elements to source. But there are others that are implied.

For example, the 81650 DiD identifies an element known as “methodology.” What this means is that each scheduling application has an optimization engine where the true differences in schedule construction and intellectual property reside. Elements that affect these calculations are time-based, duration-based, float, and slack, and those related to resources.

These time-based elements consist of early start, early finish, late start, late finish. Duration-based elements consist of shortest time, longest time, greatest rank weight. An additional element related to schedule float identifies minimum slack. Resources are further delineated by the greatest work content and the greatest cumulative resource content.

I would note that the NDIA PASEG adds some sub-elements to this list that are based on the algorithmic result of the schedule engines and, thus, tends to ignore the antecedent salient elements of validating the optimization engine found above. These additional sub-elements are total float, free float, soft constraints, hard constraints, and–also found in the aforementioned DiD–program, task, and resource calendars.

Normally, this is where a survey would end–with schedule-specific data elements focused on the details of the schedule. But we’re going to challenge our assumptions a bit more.

Framing Assumptions of Schedules and Programs

The essential document that provides a definition of the term “framing assumption” was published by RAND Corporation in 2014 entitled Identifying Acquisition Framing Assumptions Through Structured Deliberation by Mark V. Arena and Lauren A. Mayer.  The definition of a framing assumption is “any explicit or implicit assumption that is central is shaping cost, schedule, or performance expectations.”

As I have explored in my prior post, the use of the term “cost” is a fuzzy one. To some it means earned value management, which measures a small part of the costs of development and ownership of a system. To others it means total cost of ownership. Schedule is an implicit part of this definition, and then we have performance expectations, which I will deal with in a separate post.

But we can apply the concept of framing assumptions in two ways.

The first applies to the assumed purpose of the schedule. What do we construct one? This goes back to my earlier statement that “…the schedule…ties together all of the disciplines in putting together a project–acquisition, systems engineering, cost estimating, and project performance management.”

For the NDIA PASEG the IMS is a “tool, not just a report” that “provides an ever-changing window into the progress (or lack of it) of current work effort. The strategic mission of the schedule is to point out future risks and opportunities.”

For the NAVAIR IMS Guide the IMS “At a top level…contain(ing) the networked, detailed tasks necessary to ensure successful program execution…” that “capture(s) project tasks and task relationships”, “show(s) the magnitude and how long each task will take”, “show(s) resources, durations, and constraints for each task” and “show(s) the critical path.”

For the DiD 81650 “The Integrated Master Schedule (IMS) is an integrated schedule containing the networked, detailed tasks necessary to ensure successful program execution.”

But the most comprehensive definition that goes to the core of the purpose of an IMS can be found in paragraph 1.2 of the DoD Integrated Master Plan and Integrated Master Schedule Preparation and Use Guide (IMP/IMS Guide). The elements of this purpose is worth transcribing, because if we have a requirement and cannot ask the “So What?” question, that is, if we cannot effectively determine why something must be done, then it probably does not need to be done (or we need to apply rigor in the development our expertise).

For what the IMP/IMS Guide does is clearly tie the schedule to the programmatic framing assumptions (used in the context in which RAND meant it) from initial acquisition through planning. Thus, the Integrated Master Plan (IMP) is firmly established as an antecedent and intermediate planning process (not merely an artifact or tool), that results in the program R&D execution process.

Taken in whole these processes and the resulting artifacts of the processes provide:

a. Provides offerors and acquiring activities with detailed execution planning, organization, and scheduling information that sets realistic expectations for the resulting contract action.

b. Serves as the execution plan for how the supplier will meet the contract’s performance requirements within cost and schedule constraints.

c. Provides a basis for integrating all of the functions involved in development and deployment of the system being acquired and, after award, sets the framing assumptions of the program.

d. Provides the basis for determining and assessing progress, identifying risks, determining the basis for contractual award fees and penalties, assess progress on Key Performance Parameters (KPPs) and Technical Performance Measures (TPMs), determine alternative paths to project completion, and determine opportunities for innovation and new acquisitions not apparent at the time of the award.

What all of this means is that the Integrated Master Schedule is too important to be left to the master scheduler. Yes, the schedule is a “tool” to those at the most basic tactical level in work execution. Yes, it is also an artifact and record.

But, more importantly, it is the comprehensive notional representation of the project’s or program’s scope, effort, progress, and assessment.

Private and Government-focused Industry Practice

A word has to mentioned here about the difference in practice between purely private industry practice in managing large projects and programs, and the skewing in the posts that focus on those industries that focus on public sector acquisition.

In the listing of schedule elements listed earlier there is reference to resources and elements of cost, yet here is an area that standard practice diverges. In private industry the application of resource assignments to specific work is standard practice and found in the IMS.

In companies focused on the public sector and DoD, the practice is to establish a different set of data outside of the schedule to manage resources. Needless to say this creates problems of validation of data across disparate systems related to the lowest level of planning and execution of a project or program. The basis for it, I think, relates to viewing the schedule as a “tool” and not the basis for project execution. This “tool” mindset also allows for separate “earned value engines” that oftentimes do not synchronize with the execution of the schedule, not only undermining the practical value of both, but also creating systems complexity and inefficiency where none need exist.

Another gap found in many areas of public acquisition concerns the development of an integrated master plan antecedent to the integrated master schedule. The cause here, once again, I believe is viewing the discipline of systems engineering separate; one that is somehow walled off from the continuing assessment of program execution, though that assumption is not supported by program phasing and milestone planning and achievement.

From the perspective of Integrated Program/Project Management, these considerations cannot be ignored, and so our inventory of essential data elements must include elements from these practices.

But Wait! There’s More!

Most discussions at conferences and professional meetings will usually stop at this point–viewing cost and schedule integration as the essence of IPM–with “cost’ limited to EVM. Some will add some “oh by the ways” such as technical performance and risk. I will address these in the next post as well.

But there are also other systems and processes that are relevant to our inventory. But what I have covered thus far in this series should challenge you if you have been paying attention.

I tackled cost first because of the assumptions implicit in equating it with EVM, and then went on to demonstrate that there are other elements of cost that provide a more comprehensive view. This is not denigrate the value of EVM, since it is an essential process in project management, but to demonstrate that its analytics are not comprehensive and, as with any complex system, require the contribution of additional information, depending on the level and type of work performance and progress being recorded and assessed.

In this post I have tacked the IMS, and have demonstrated that it is not supplementary process, but central to all other processes and actions being taken in the execution of the project or program. Many times people enter the schedule from an assessment of cost performance–tracing cost drivers to specific schedule activities and then tasks. But this has it backwards, based on the best technology available sometime in the late 1990s.

It is the schedule that brings together all relevant information from our execution and control processes and systems. It seems to me that perhaps the first place one goes is the schedule, that the first element to trace are those related to schedule slippage and unexpected resource consumption, and then to trace these to contract cost impact.

But, of course, there is more–and these other elements may turn out to be of greater consequence than just cost and schedule considerations. More on these in my next post.

In Closing: Battle Rhythm and the Plans of the Day and Week

When I was on active duty in the Navy we planned our days and weeks around a Plan of the Day or Plan of the Week. This is a posted agenda so that the entire ship or command understands the major events that affect its operations. It establishes focus on the main events at hand and fosters communication both laterally and vertically within the chain of command.

As one rises in rank and responsibility it is important to understand the operational tempo of the unit or ship, its systems, and subsystems. This is important in avoiding crisis management.This is known as Battle Rhythm.

Baked into the schedule (assuming proper construction and effective integrated product teaming) are the major events, milestones, and expected achievement of the program or project. Thus, there are events that should be planned around and anticipation of these items on a daily, weekly, biweekly, monthly, quarterly, and major milestone basis.

Given an effective battle rhythm, a PM should never complain about performance and progress indicators “looking into the rear view mirror”. If that is the case then perhaps the PM should look at the effectiveness and timeliness of the underlying project and program systems.Thus, when a PMO complains of information and intelligence being too late to be actionable, it is actually describing a condition of ineffective, latent, and disjointed information and intelligence systems.

Thus, our next step in our next post is to identify more salient IPM elements that cut to the heart of the matter.

Don’t Stop Thinking About Tomorrow–Post-Workshop Blogging…and some Low Comedy

It’s been a while since I posted to my blog due to meetings and–well–day job, but some interesting things occurred during the latest Integrated Program Management (IPMD) of the National Defense Industrial Association (NDIA) meeting that I think are of interest. (You have to love acronyms to be part of this community).

Program Management and Integrated Program Management

First off is the initiative by the Program Management Working Group to gain greater participation by program managers with an eye to more clearly define what constitutes integrated program management. As readers of this blog know, this is a topic that I’ve recently written about.

The Systems Engineering discipline is holding their 21st Annual Systems Engineering Conference in Tampa this year from October 22nd to the 25th. IPMD will collaborate and will be giving a track dedicated to program management. The organizations have issued a call for papers and topics of interest. (Full disclosure: I volunteered this past week to participate as a member of the PM Working Group).

My interest in this topic is based on my belief from my years of wide-ranging experience in duties from having served as a warranted government contracting officer, program manager, business manager, CIO, staff officer, and logistics officer that there is much more to the equation in defining IPM that transcends doing so through the prism of any particular discipline. Furthermore, doing so will require collaboration and cooperation among a number of project management disciplines.

This is a big topic where, I believe, no one group or individual has all of the answers. I’m excited to see where this work goes.

Integrated Digital Environment

Another area of interest that I’ve written about in the past involved two different–but related–initiatives on the part of the Department of Defense to collect information from their suppliers that is necessary in their oversight role not only to ensure accountability of public expenditures, but also to assist in project cost and schedule control, risk management, and assist in cost estimation, particularly as it relates to risk sharing cost-type R&D contracted project efforts.

Two major staffs in the Offices of the Undersecretary of Defense have decided to go with a JSON-type schema for, on the one hand, cost estimating data, and on the other, integrated cost performance, schedule, and risk data. Each initiative seeks to replace the existing schemas in place.

Both have been wrapped around the axle on getting industry to move from form-based reporting and data sharing to a data-agnostic solution that meet the goals of reducing redundancy in data transmission, reducing the number of submissions and data streams, and moving toward one version of truth that allows for SMEs on both sides of the table to concentrate on data analysis and interpretation in jointly working toward the goal of successful project completion and end-item deployment.

As with the first item, I am not a disinterested individual in this topic. Back when I wore a uniform I helped to construct DoD policy to create an integrated digital environment. I’ve written about this experience previously in this blog, so I won’t bore with details, but the need for data sharing on cost-type efforts acknowledges the reality of the linkage between our defense economic and industrial base and the art of the possible in deploying defense-related end items. The same relationship exists for civilian federal agencies with the non-defense portion of the U.S. economy. Needless to say, a good many commercial firms unrelated to defense are going the same way.

The issue here is two-fold, I think, from speaking with individuals working these issues.

The first is, I think, that too much deference is being given to solution providers and some industry stakeholders, influenced by those providers, in “working the refs” through the data. The effect of doing so not only slows down the train and protects entrenched interests, it also gets in the way of innovation, allowing the slowest among the group to hold up the train in favor of–to put it bluntly–learning their jobs on the job at the expense of efficiency and effectiveness. As I expressed in a side conversion with an industry leader, all too often companies–who, after all, are the customer–have allowed themselves to view the possible by the limitations and inflexibility of their solution providers. At some point that dysfunctional relationship must end–and in the case of comments clearly identified as working the refs–they should be ignored. Put your stake in the ground and let innovation and market competition sort it out.

Secondly, cost estimating, which is closely tied to accounting and financial management, is new and considered tangential to other, more mature, performance management systems. My own firm is involved in producing a solution in support of this process, collecting data related to these reports (known collectively in DoD as the 1921 reports), and even after working to place that data in a common data lake, exploring with organizations what it tells us, since we are only now learning what it tells us. This is classical KDD–Knowledge Discovery in Data–and a worthwhile exercise.

I’ve also advocated going one step further in favor of the collection of financial performance data (known as the Contract Funds Status Report), which is an essential reporting requirement, but am frustrated to find no one willing to take ownership of the guidance regarding data collection. The tragedy here is that cost performance, known broadly as Earned Value Management, is a technique related to the value of work performance against other financial and project planning measures (a baseline and actuals). But in a business (or any enterprise), the fuel that drives the engine are finance-related, and two essential measures are margin and cash-flow. The CFSR is a report of program cash-flow and financial execution. It is an early measure of whether a program will execute its work in any given time-frame, and provides a reality check on the statistical measures of performance against baseline. It is also a necessary logic check for comptrollers and other budget decision-makers.

Thus, as it relates to data, there has been some push-back against a settled schema, where the government accepts flat files and converts the data to the appropriate format. I see this as an acceptable transient solution, but not an ultimate one. It is essential to collect both cost estimating and contract funds status information to perform any number of operations that relate to “actionable” intelligence: having the right executable money at the right time, a reality check against statistical and predictive measures, value analysis, and measures of ROI in development, just to name a few.

I look forward to continuing this conversation.

To Be or Not to Be Agile

The Section 809 Panel, which is the latest iteration of acquisition reform panels, has recommended that performance management using earned value not be mandated for efforts using Agile. It goes on, however, to assert that program executive “should approve appropriate project monitoring and control methods, which may include EVM, that provide faith in the quality of data and, at a minimum, track schedule, cost, and estimate at completion.”

Okay…the panel is then mute on what those monitoring and control measure will be. Significantly, if only subtly, the #NoEstimates crowd took a hit since the panel recommends and specifies data quality, schedule, cost and EAC. Sounds a lot like a form of EVM to me.

I must admit to be a skeptic when it comes to swallowing the Agile doctrine whole. Its micro-economic foundations are weak and much of it sounds like ideology–bad ideology at best and disproved ideology at worst (specifically related to the woo-woo about self-organization…think of the last speculative bubble and resulting financial crisis and depression along these lines).

When it comes to named methodologies I am somewhat from Missouri. I apply (and have in previous efforts in the Dark Ages back when I wore a uniform) applied Kanban, teaming, adaptive development (enhanced greatly today by using modern low-code technology), and short sprints that result in releasable modules. But keep in mind that these things were out there long before they were grouped under a common heading.

Perhaps Agile is now a convenient catch-all for best practices. But if that is the case then software development projects using this redefined version of Agile deserve no special dispensation. But I was schooled a bit by an Agile program manager during a side conversation and am always open to understanding things better and revising my perspectives. It’s just that there was never a Waterfall/Agile dichotomy just as there never really was a Spiral/Waterfall dichotomy. These were simply convenient development models to describe a process that were geared to the technology of the moment.

There are very good people on the job exploring these issues on the Agile Working Group in the IPMD and I look forward to seeing what they continue to come up with.

Rip Van Winkle Speaks!

The only disappointing presentation occurred on the second and last day of the meeting. It seemed we were treated by a voice from somewhere around the year 2003 that, in what can only be described as performance art involving free association, talked about wandering the desert, achieving certification for a piece of software (which virtually all of the software providers in the room have successfully navigated at one time or another), discovering that cost and schedule performance data can be integrated (ignoring the work of the last ten years on the part of, well, a good many people in the room), that there was this process known as the Integrated Baseline Review (which, again, a good many people in the room had collaborated on to both define and make workable), and–lo and behold–the software industry uses schemas and APIs to capture data (known in Software Development 101 as ETL). He then topped off his meander by an unethical excursion into product endorsement, selected through an opaque process.

For this last, the speaker was either unaware or didn’t care (usually called tone-deafness) that the event’s expenses were sponsored by a software solution provider (not mine). But it is also as if the individual speaking was completely unaware of the work behind the various many topics that I’ve listed above this subsection, ignoring and undermining the hard work of the other stakeholders that make up our community.

On the whole an entertaining bit of poppycock, which leads me to…

A Word about the Role of Professional Organizations (Somewhat Inside Baseball)

In this blog, and in my interactions with other professionals at–well–professional conferences–I check my self-interest in at the door and publicly take a non-commercial stance. It is a position that is expected and, I think, appreciated. For those who follow me on social networking like LinkedIn, posts from my WordPress blog originate from a separate source from the commercial announcements that are linked to my page that originate from my company.

If there are exhibitor areas, as some conferences and workshops do have, that is one thing. That’s where we compete and play; and in private side conversations customers and strategic partners will sometimes use the opportunity as a convenience to discuss future plans and specific issues that are clearly business-related. But these are the exceptions to the general rule, and there are a couple of reasons for this, especially at this venue.

One is because, given that while it is a large market, it is a small community, and virtually everyone at the regular meetings and conferences I attend already know that I am the CEO and owner of a small software company. But the IPMD is neutral ground. It is a place where government and industry stakeholders, who in other roles and circumstances are in a contractual or competing relationship, come to work out the best way of hashing out processes and procedures that will hopefully improve the discipline of program and project management. It is also a place of discovery, where policies, new ideas, and technologies can be vetted in an environment of collaboration.

Another reason for taking a neutral stance is simply because it is both the most ethical and productive one. Twenty years ago–and even in some of the intervening years–self-serving behavior was acceptable at the IPMD meetings where both leadership and membership used the venue as a basis for advancing personal agendas or those of their friends, often involving backbiting and character assassination. Some of those people, few in number, still attend these meetings.

I am not unfamiliar with the last–having been a target at one point by a couple of them but, at the end of the day, such assertions turned out to be without merit, undermining the credibility of the individuals involved, rightfully calling into question the quality of their character. Such actions cannot help but undermine the credibility and pollute the atmosphere of the organization in which they associate, as well.

Finally, the companies and organizations that sponsor these meetings–which are not cheap to organize, which I know from having done so in the past–deserve to have the benefit of acknowledgment. It’s just good manners to play nice when someone else is footing the bill–you gotta dance with those that brung you. I know my competitors and respect them (with perhaps one or two exceptions). We even occasionally socialize with each other and continue long-term friendships and friendly associations. Burning bridges is just not my thing.

On the whole, however, the NDIA IPMD meetings–and this one, in particular–was a productive and positive one, focused on the future and in professional development. That’s where, I think, that as a community we need to be and need to stay. I always learn something new and get my dose of reality from a broad-based perspective. In getting here the leadership of the organization (and the vast majority of the membership) is to be commended, as well as the recent past and current members of the Department of Defense, especially since the formation of the Performance Assessments and Root Cause Analysis (PARCA) office.

In closing, there were other items of note discussed, along with what can only be described as the best pair of keynote addresses that I’ve heard in one meeting. I’ll have more to say about some of the concepts and ideas that were presented there in future posts.

Friday Hot Washup: Daddy Stovepipe sings the Blues, and Net Neutrality brought to you by Burger King

Daddy Stovepipe sings the Blues — Line and Staff Organizations (and how they undermine organizational effectiveness)

In my daily readings across the web I came upon this very well written blog post by Glen Alleman at his Herding Cat’s blog. The eternal debate in project management surrounds when done is actually done–and what is the best measurement of progress toward the completion of the end item application?

Glen rightly points to the specialization among SMEs in the PM discipline, and the differences between their methods of assessment. These centers of expertise are still aligned along traditional line and staff organizations that separate scheduling, earned value, system engineering, financial management, product engineering, and other specializations.

I’ve written about this issue where information also follows these stove-piped pathways–multiple data streams with overlapping information, but which resists effective optimization and synergy because of the barriers between them. These barriers may be social or perceptual, which then impose themselves upon the information systems that are constructed to support them.

The manner in which we face and interpret the world is the core basis of epistemology. When we develop information systems and analytical methodologies, whether we are consciously aware of it or not, we delve into the difference between justified belief and knowledge. I see the confusion of these positions in daily life and in almost all professions and disciplines. In fact, most of us find ourselves jumping from belief to knowledge effortlessly without being aware of this internal contradiction–and the corresponding reduction in our ability to accurately perceive reality.

The ability to overcome our self-imposed constraints is the key but, I think, our PM organizational structures must be adjusted to allow for the establishment of a learning environment in relation to data. The first step in this evolution must be the mentoring and education of a discipline that combines these domains. What this proposes is that no one individual need know everything about EVM, scheduling, systems engineering, and financial management. But the business environment today is such, if the business or organization wishes to be prepared for the world ahead, to train transition personnel toward a multi-disciplinary project management competency.

I would posit, contrary to Glen’s recommendation, that no one discipline claim to be the basis for cross-functional integration, only because it may be a self-defeating one. In the book Networks, Crowds, and Markets: Reasoning about a Highly Connected World by David Easley and Jon Kleinberg of Cornell, our social systems are composed of complex networks, but where negative perceptions develop when the network is no longer considered in balance. This subtle and complex interplay of perceptions drive our ability to work together.

It also affects whether we will stay safe the comfort zone of having our information systems tell us what we need to analyze, or whether we apply a more expansive view of leveraging new information systems that are able to integrate ever expanding sets of relevant data to give us a more complete picture of what constitutes “done.”

Hold the Pickle, Hold the Lettuce, Special Orders Don’t Upset Us: Burger King explains Net Neutrality

The original purpose of the internet has been the free exchange of ideas and knowledge. Initially, under ARPANET, Lawrence Roberts and later Bob Kahn, the focus was on linking academic and research institutions so that knowledge could be shared resulting in collaboration that would overcome geographical barriers. Later the Department of Defense, NASA, and other government organizations highly dependent on R&D were brought into the new internet community.

To some extent there still are pathways within what is now broadly called the Web, to find and share such relevant information with these organizations. With the introduction of commercialization in the early 1990s, however, it has been increasingly hard to perform serious research.

For with the expansion of the internet to the larger world, the larger world’s dysfunctions and destructive influences also entered. Thus, the internet has transitioned from a robust First Amendment free speech machine to a place that also harbors state-sponsored psy-ops and propaganda. It has gone form a safe space for academic freedom and research to a place of organized sabotage, intrusion, theft, and espionage. It has transitioned from a highly organized professional community that hewed to ethical and civil discourse, to one that harbors trolls, prejudice, hostility, bullying, and other forms of human dysfunction. Finally and most significantly, it has become dominated by commercial activity, dominated by high tech giants that stifle innovation, and social networking sites that also allow, applying an extreme Laissez-faire attitude, magnify and spread the more dysfunctional activities found in the web as a whole.

At least for those who still looked to the very positive effects of the internet there was net neutrality. The realization that blogs like this one and the many others that I read on a regular basis, including mainstream news, and scientific journals still were available without being “dollarized” in the words of the naturalist John Muir.

Unfortunately this is no longer the case, or will no longer be the case, perhaps, when the legal dust settles. Burger King has placed it marker down and it is a relevant and funny one. Please enjoy and have a great weekend.

 

Money for Nothing — Project Performance Data and Efficiencies in Timeliness

I operate in a well regulated industry focused on project management. What this means practically is that there are data streams that flow from the R&D activities, recording planning and progress, via control and analytical systems to both management and customer. The contract type in most cases is Cost Plus, with cost and schedule risk often flowing to the customer in the form of cost overruns and schedule slippages.

Among the methodologies used to determine progress and project eventual outcomes is earned value management (EVM). Of course, this is not the only type of data that flows in performance management streams, but oftentimes EVM is used as shorthand to describe all of the data captured and submitted to customers in performance management. Other planning and performance management data includes time-phased scheduling of tasks and activities, cost and schedule risk assessments, and technical performance.

Previously in my critique regarding the differences between project monitoring and project management (before Hurricane Irma created some minor rearranging of my priorities), I pointed out that “looking in the rear view mirror” was often used as an excuse for by-passing unwelcome business intelligence. I followed this up with an intro to the synergistic economics of properly integrated data. In the first case I answered the critique demonstrating that it is based on an old concept that no longer applies. In the second case I surveyed the economics of data that drives efficiencies. In both cases, new technology is key to understanding the art of the possible.

As I have visited sites in both government and private industry, I find that old ways of doing things still persist. The reason for this is multivariate. First, technology is developing so quickly that there is fear that one’s job will be eliminated with the introduction of technology. Second, the methodology of change agents in introducing new technology often lacks proper socialization across the various centers of power that inevitably exist in any organization. Third, the proper foundation to clearly articulate the need for change is not made. This last is particularly important when stakeholders perform a non-rational assessment in their minds of cost-benefit. They see many downsides and cannot accept the benefits, even when they are obvious. For more on this and insight into other socioeconomic phenomena I strongly recommend Daniel Kahneman’s Thinking Fast and Slow. There are other reasons as well, but these are the ones that are most obvious when I speak with individuals in the field.

The Past is Prologue

For now I will restrict myself to the one benefit of new technology that addresses the “looking in the rear window” critique. It is important to do so because the critique is correct in application (for purposes that I will outline) if incorrect in its cause-and-effect. It is also important to focus on it because the critique is so ubiquitous.

As I indicated above, there are many sources of data in project management. They derive from the following systems (in brief):

a. The planning and scheduling applications, which measure performance through time in the form of discrete activities and events. In the most sophisticated implementations, these applications will include the assignment of resources, which requires the integration of these systems with resource management. Sometimes simple costs are also assigned and tracked through time as well.

b. The cost performance (earned value) applications, which ideally are aligned with the planning and scheduling applications, providing cross-integration with WBS and OBS structures, but focused on work accomplishment defined by the value of work completed against a baseline plan. These performance figures are tied to work accomplishment through expended effort collected by and, ideally, integrated with the financial management system. It involves the proper application of labor rates and resource expenditures in the accomplishment of the work to not only provide an statistical assessment of performance to date, but a projection of likely cost performance outcomes at completion of the effort.

c. Risk assessment applications which, depending of their sophistication and ease of use, provide analysis of possible cost and schedule outcomes, identify the sensitivity of particular activities and tasks, provide an assessment of alternative driving and critical paths, and apply different models of baseline performance to predict future outcomes.

d. Systems engineering applications that provide an assessment of technical performance to date and the likely achievement of technical parameters within the scope of the effort.

e. The financial management applications that provide an accounting of funds allocation, cash-flow, and expenditure, including planning information regarding expenditures under contract and planned expenditures in the future.

These are the core systems of record upon which performance information is derived. There are others as well, depending on the maturity of the project such as ERP systems and MRP systems. But for purposes of this post, we will bound the discussion to these standard sources of data.

In the near past, our ability to understand the significance of the data derived from these systems required manual processing. I am not referring to the sophistication of human computers of 1960s and before, dramatized to great effect in the uplifting movie Hidden Figures. Since we are dealing with business systems, these methodologies were based on simple business metrics and other statistical methods, including those that extended the concept of earned value management.

With the introduction of PCs in the workplace in the 1980s, desktop spreadsheet applications allowed this data to be entered, usually from printed reports. Each analyst not only used standard methods common in the discipline, but also developed their own methods to process and derive importance from the data, transforming it into information and useful intelligence.

Shortly after this development simple analytical applications were introduced to the market that allowed for pairing back the amount of data deriving from some of these systems and performing basic standard calculations, rendering redundant calculations unnecessary. Thus, for example, instead of a person having to calculate multiple estimates to complete, the application could perform those calculations as part of its functionality and deliver them to the analyst for use in, hopefully, their own more extensive assessments.

But even in this case, the data flow was limited to the EVM silo. The data streams relating to schedule, risk, SE, and FM were left to their own devices, oftentimes requiring manual methods or, in the best of cases, cut-and-paste, to incorporate data from reports derived from these systems. In the most extreme cases, for project oversight organizations, this caused analysts to acquire a multiplicity of individual applications (with the concomitant overhead and complexity of understanding differing lexicons and software application idiosyncrasies) in order to read proprietary data types from the various sources just to perform simple assessments of the data before even considering integrating it properly into the context of all of the other project performance data that was being collected.

The bottom line of outlining these processes is to note that, given a combination of manual and basic automated tools, that putting together and reporting on this data takes time, and time, as Mr. Benjamin Franklin noted, is money.

By itself the critique that “looking in the rear view mirror” has no value and attributing it to one particular type of information (EVM) is specious. After all, one must know where one has been and presently is before you can figure out where you need to go and how to get there and EVM is just one dimension of a multidimensional space.

But there is a utility value associated with the timing and locality of intelligence and that is the issue.

Contributors to time

Time when expended to produce something is a form of entropy. For purposes of this discussion at this level of existence, I am defining entropy as availability of the energy in a system to do work. The work in this case is the processing and transformation of data into information, and the further transformation of information into usable intelligence.

There are different levels and sub-levels when evaluating the data stream related to project management. These are:

a. Within the supplier/developer/manufacturer

(1) First tier personnel such as Control Account Managers, Schedulers (if separate), Systems Engineers, Financial Managers, and Procurement personnel among other actually recording and verifying the work accomplishment;

(2) Second tier personnel that includes various levels of management, either across teams or in typical line-and-staff organizations.

b. Within customer and oversight organizations

(1) Reporting and oversight personnel tasks with evaluating the fidelity of specific business systems;

(2) Counterpart project or program officer personnel tasked with evaluating progress, risk, and any factors related to scope execution;

(3) Staff organizations designed to supplement and organize the individual project teams, providing a portfolio perspective to project management issues that may be affected by other factors outside of the individual project ecosystem;

(4) Senior management at various levels of the organization.

Given the multiplicity of data streams it appears that the issue of economies is vast until it is understood that the data that underlies the consumers of the information is highly structured and specific to each of the domains and sub-domains. Thus there are several opportunities for economies.

For example, cost performance and scheduling data have a direct correlation and are closely tied. Thus, these separate streams in the A&D industry were combined under a common schema, first using the UN/CEFACT XML, and now transitioning to a more streamlined JSON schema. Financial management has gone through a similar transition. Risk and SE data are partially incorporated into project performance schemas, but the data is also highly structured and possesses commonalities to be directly accessed using technologies that effectively leverage APIs.

Back to the Future

The current state, despite advances in the data formats that allow for easy rationalization and normalization of data that breaks through propriety barriers, still largely is based a slightly modified model of using a combination of manual processing augmented by domain-specific analytical tools. (Actually sub-domain analytical tools that support sub-optimization of data that are a barrier to incorporation of cross-domain integration necessary to create credible project intelligence).

Thus, it is not unusual at the customer level to see project teams still accepting a combination of proprietary files, hard copy reports, and standard schema reports. Usually the data in these sources is manually entered into Excel spreadsheets or a combination of Excel and some domain-specific analytical tool (and oftentimes several sub-specialty analytical tools). After processing, the data is oftentimes exported or built in PowerPoint in the form of graphs or standard reporting formats. This is information management by Excel and PowerPoint.

In sum, in all too many cases the project management domain, in terms of data and business intelligence, continues to party like it is 1995. This condition also fosters and reinforces insular organizational domains, as if the project team is disconnected from and can possess goals antithetical and/or in opposition to the efficient operation of the larger organization.

A typical timeline goes like this:

a. Supplier provides project performance data 15-30 days after the close of a period. (Some contract clauses give more time). Let’s say the period closed at the end of July. We are now effectively in late August or early September.

b. Analysts incorporate stove-piped domain data into their Excel spreadsheets and other systems another week or so after submittal.

c. Analysts complete processing and analyzing data and submit in standard reporting formats (Excel and PowerPoint) for program review four to six weeks after incorporation of the data.

Items a through c now put a typical project office at project review for July information at the end of September or beginning of October. Furthermore, this information is focused on individual domains, and given the lack of cross-domain knowledge, can be contradictory.

This system is broken.

Even suppliers who have direct access to systems of record all too often rely on domain-specific solutions to be able to derive significance from the processing of project management data. The larger suppliers seem to have recognized this problem and have been moving to address it, requiring greater integration across solutions. But the existence of a 15-30 day reconciliation period after the end of a period, and formalized in contract clauses, is indicative of an opportunity for greater efficiency in that process as well.

The Way Forward

But there is another way.

The opportunities for economy in the form of improvements in time and effort are in the following areas, given the application of the right technology:

  1. In the submission of data, especially by finding data commonalities and combining previously separate domain data streams to satisfy multiple customers;
  2. In retrieving all data so that it is easily accessible to the organization at the level of detailed required by the task at hand;
  3. In processing this data so that it can converted by the analyst into usable intelligence;
  4. In properly accessing, displaying, and reporting properly integrated data across domains, as appropriate, to each level of the organization regardless of originating data stream.

Furthermore, there opportunities to realizing business value by improving these processes:

  1. By extending expertise beyond a limited number of people who tend to monopolize innovations;
  2. By improving organizational knowledge by incorporating innovation into the common system;
  3. By gaining greater insight into more reliable predictors of project performance across domains instead of the “traditional” domain-specific indices that have marginal utility;
  4. By developing a project focused organization that breaks down domain-centric thinking;
  5. By developing a culture that ties cross-domain project knowledge to larger picture metrics that will determine the health of the overarching organization.

It is interesting that when I visit the field how often it is asserted that “the technology doesn’t matter, it’s process that matters”.

Wrong. Technology defines the art of the possible. There is no doubt that in an ideal world we would optimize our systems prior to the introduction of new technology. But that assumes that the most effective organization (MEO) is achievable without technological improvements to drive the change. If one cannot efficiently integrate all submitted cross-domain information effectively and efficiently using Excel in any scenario (after all, it’s a lot of data), then the key is the introduction of new technology that can do that very thing.

So what technologies will achieve efficiency in the use of this data? Let’s go through the usual suspects:

a. Will more effective use of PowerPoint reduce these timelines? No.

b. Will a more robust set of Excel workbooks reduce these timelines? No.

c. Will an updated form of a domain-specific analytical tool reduce these timelines? No.

d. Will a NoSQL solution reduce these timelines? Yes, given that we can afford the customization.

e. Will a COTS BI application that accepts a combination of common schemas and APIs reduce these timelines? Yes.

The technological solution must be fitted to its purpose and time. Technology matters because we cannot avoid the expenditure of time or energy (entropy) in the processing of information. We can perform these operations using a large amount of energy in the form of time and effort, or we can conserve time and effort by substituting the power of computing and information processing. While we will never get to the point where we completely eliminate entropy, our application of appropriate technology makes it seem as if effort in the form of time is significantly reduced. It’s not quite money for nothing, but it’s as close as we can come and is an obvious area of improvement that can be made for a relatively small investment.

Synergy — The Economics of Integrated Project Management

The hot topic lately in meetings and the odd conference on Integrated Project Management (IPM) often focuses on the mechanics of achieving that state, bound by the implied definition of current regulation, which has also become–not surprisingly–practice. I think this is a laudable goal, particularly given both the casual resistance to change (which always there by definition to some extent) and in the most extreme cases a kind of apathy.

I addressed the latter condition in my last post by an appeal to professionalism, particularly on the part of those in public administration. But there is a more elemental issue here than the concerns of project analysts, systems engineers, and the associated information managers. While this level of expertise is essential in the development of innovation, relying too heavily on this level in the organization creates an internal organizational conflict that creates the risk that the innovation is transient and rests on a slender thread. Association with any one manager also leaves innovation vulnerable due to the “not invented here” tact taken by many new managers in viewing the initiatives of a predecessor. In business this (usually self-defeating) approach becomes more extreme the higher one goes in the chain of command (the recent Sears business model anyone?).

The key, of course, is to engage senior managers and project/program managers in participating in the development of this important part of business intelligence. A few suggestions on how to do this follow, but the bottom line is this: money and economics makes the implementation of IPM an essential component of business intelligence.

Data, Information, and Intelligence – Analysis vs. Reporting

Many years ago using manual techniques, I was employed in activities that required that I seek and document data from disparate sources, seemingly unconnected, and find the appropriate connections. The initial connection was made with a key. It could be a key word, topic, individual, technology, or government. The key, however, wasn’t the end of the process. The validity of the relationship needed to be verified as more than mere coincidence. This is a process well known in the community specializing in such processes, and two good sources to understand how this was done can be found here and here.

It is a well trod path to distinguish between the elements that eventually make up intelligence so I will not abuse the reader in going over it. Needless to say that a bit of data is the smallest element of the process, with information following. For project management what is often (mis)tagged as predictive analytics and analysis is really merely information. Thus, when project managers and decision makers look at the various charts and graphs employed by their analysts they are usually greeted with a collective yawn. Raw projections of cost variance, cost to complete, schedule variance, schedule slippage, baseline execution, Monte Carlo risk, etc. are all building blocks to employing business intelligence. But in and of themselves they are not intelligence because these indicators require analysis, weighting, logic testing, and, in the end, an assessment that is directly tied to the purpose of the organization.

The role and application of digitization is to make what was labor intensive less so. In most cases this allows us to apply digital technology to its strength–calculation and processing of large amounts of data to create information. Furthermore, digitization now allows for effective lateral integration among datasets given a common key, even if there are multiple keys that act in a chain from dataset to dataset.

At the end of the line what we are left with is a strong correlation of data integrated across a number of domains that contribute to a picture of how an effort is performing. Still, even given the most powerful heuristics, a person–the consumer–must validate the data to determine if the results possess validity and fidelity. For project management this process is not as challenging as, say, someone using raw social networking data. Project management data, since it is derived from underlying systems that through their processing mimic highly structured processes and procedures, tends to be “small”, even when it can be considered Big Data form the shear perspective of size. It is small Big Data.

Once data has been accumulated, however, it must be assessed so as to ensure that the parts cohere. This is done by assessing the significance and materiality of those parts. Once this is accomplished the overall assessment must then be constructed so that it follows logically from the data. That is what constitutes “actionable intelligence”: analysis of present condition, projected probable outcomes, recommended actions with alternatives. The elements of this analysis–charts, graphs, etc., are essential in reporting, but reporting these indices is not the purpose of the process. The added value of an analyst lies in the expertise one possesses. Without this dimension a machine could do the work. The takeaway from this point, however, isn’t to substitute the work with software. It is to develop analytical expertise.

What is Integrated Project Management?

In my last post I summed up what IPM is, but some elaboration and refinement is necessary.

I propose that Integrated Project Management is defined as that information necessary to derive actionable intelligence from all of the relevant cross-domain information involved in the project organization. This includes cost performance, schedule performance, financial performance and execution, contract implementation, milestone achievement, resource management, and technical performance. Actionable intelligence in this context, as indicated above, is that information that is relevant to the project decision-making authority which effectively identifies specific probable qualitative and quantitative risks, risk impact, and risk handling necessary to make project trade-offs, project re-baselining or re-scope, cost-as-an-independent variable (CAIV), or project cancellation decisions. Underlying all of this are feedback loop systems assessments to ensure that there is integrity and fidelity in our business systems–both human and digital.

The data upon which IPM is derived comes from a finite number of sources. Thus, project management data lends itself to solutions that break down proprietary syntax and terminology. This is really the key to achieving IPM and one that has garnered some discussion when discussing the process of data normalization and rationalization with other IT professionals. The path can be a long one: using APIs to perform data-mining directly against existing tables or against a data repository (or warehouse or lake), or pre-normalizing the data in a schema (given both the finite nature of the data and the finite–and structured–elements of the processes being documented in data).

Achieving normalization and rationalization in this case is not a notional discussion–in my vocation I provide solutions that achieve this goal. In order to do so one must expand their notion of the architecture of the appropriate software solution. The mindset of “tools” is at the core of what tends to hold back progress in integration, that is, the concept of a “tool” is one that is really based on an archaic approach to computing. It assumes that a particular piece of software must limit itself to performing limited operations focused on a particular domain. In business this is known as sub-optimization.

Oftentimes this view is supported by the organization itself where the project management team is widely dispersed and domains hoard information. The rice bowl mentality has long been a bane of organizational effectiveness. Organizations have long attempted to break through these barriers using various techniques: cross-domain teams, integrated product teams, and others.

No doubt some operations of a business must be firewalled in such a way. The financial management of the enterprise comes to mind. But when it comes to business operations, the tools and rice bowl mindset is a self-limiting one. This is why many in IT push the concept of a solution–and the analogue is this: a tool can perform a particular operation (turn a screw, hammer a nail, crimp a wire, etc.); a solution achieves a goal of the system that consists of a series of operations, which are often complex (build the wall, install the wiring, etc.). Software can be a tool or a solution. Software built as a solution contains the elements of many tools.

Given a solution that supports IPM, a pathway is put in place that facilitates breaking down the barriers that currently block effective communication between and within project teams.

The necessity of IPM

An oft-cited aphorism in business is that purpose drives profit. For those in public administration purpose drives success. What this means is that in order to become successful in any endeavor that the organization must define itself. It is the nature of the project–a planned set of interrelated tasks separately organized and financed from the larger enterprise, which is given a finite time and budget specifically to achieve a goal of research, development, production, or end state–that defines an organization’s purpose: building aircraft, dams, ships, software, roads, bridges, etc.

A small business is not so different from a project organization in a larger enterprise. Small events can have oversized effects. What this means in very real terms is that the core rules of economics will come to bear with great weight on the activities of project management. In the world in which we operate, the economics underlying both enterprises and projects punishes inefficiency. Software “tools” that support sub-optimization are inefficient and the organizations that employ them bear unnecessary risk.

The information and technology sectors have changed what is considered to be inefficient in terms of economics. At its core, information has changed the way we view and leverage information. Back in 1997 economists Brad DeLong and Michael Froomkin identified the nature of information and its impact on economics. Their concepts and observations have had incredible staying power if, for no other reason, because what they predicted has come to pass. The economic elements of excludability, rivalry, transparency have transformed how the enterprise achieves optimization.

An enterprise that is willfully ignorant of its condition is one that is at risk. Given that many projects will determine the success of the enterprise, a project that is willfully ignorant of its condition threatens the financial health and purpose of the larger organization. Businesses and public sector agencies can no longer afford not to have cohesive and actionable intelligence built on all of the elements that contribute to determining that condition. In this way IPM becomes not only essential but its deployment necessary.

In the end the reason for doing this comes down to profit on the one hand, and success on the other. Given the increasing transparency of information and the continued existence of rivalry, the trend in the economy will be to reward those that harness the potentials for information integration that have real consequences in the management of the enterprise, and to punish those who do not.

Ground Control from Major Tom — Breaking Radio Silence: New Perspectives on Project Management

Since I began this blog I have used it as a means of testing out and sharing ideas about project management, information systems, as well to cover occasional thoughts about music, the arts, and the meaning of wisdom.

My latest hiatus from writing was due to the fact that I was otherwise engaged in a different sort of writing–tech writing–and in exploring some mathematical explorations related to my chosen vocation, aside from running a business and–you know–living life.  There are only so many hours in the day.  Furthermore, when one writes over time about any one topic it seems that one tends to repeat oneself.  I needed to break that cycle so that I could concentrate on bringing something new to the table.  After all, it is not as if this blog attracts a massive audience–and purposely so.  The topics on which I write are highly specialized and the members of the community that tend to follow this blog and send comments tend to be specialized as well.  I air out thoughts here that are sometimes only vaguely conceived so that they can be further refined.

Now that that is out of the way, radio silence is ending until, well, the next contemplation or massive workload that turns into radio silence.

Over the past couple of months I’ve done quite a bit of traveling, and so have some new perspectives that and trends that I noted and would like to share, and which will be the basis (in all likelihood) of future, more in depth posts.  But here is a list that I have compiled:

a.  The time of niche analytical “tools” as acceptable solutions among forward-leaning businesses and enterprises is quickly drawing to a close.  Instead, more comprehensive solutions that integrate data across domains are taking the market and disrupting even large players that have not adapted to this new reality.  The economics are too strong to stay with the status quo.  In the past the barrier to integration of more diverse and larger sets of data was the high cost of traditional BI with its armies of data engineers and analysts providing marginal value that did not always square with the cost.  Now virtually any data can be accessed and visualized.  The best solutions, providing pre-built domain knowledge for targeted verticals, are the best and will lead and win the day.

b.  Along these same lines, apps and services designed around the bureaucratic end-of-month chart submission process are running into the new paradigm among project management leaders that this cycle is inadequate, inefficient, and ineffective.  The incentives are changing to reward actual project management in lieu of project administration.  The core fallacy of apps that provide standard charts based solely on user’s perceptions of looking at data is that they assume that the PM domain knows what it needs to see.  The new paradigm is instead to provide a range of options based on the knowledge that can be derived from data.  Thus, while the options in the new solutions provide the standard charts and reports that have always informed management, KDD (knowledge discovery in database) principles are opening up new perspectives in understanding project dynamics and behavior.

c.  Earned value is *not* the nexus of Integrated Project Management (IPM).  I’m sure many of my colleagues in the community will find this statement to be provocative, only because it is what they are thinking but have been hesitant to voice.  A big part of their hesitation is that the methodology is always under attack by those who wish to avoid accountability for program performance.  Thus, let me make a point about Earned Value Management (EVM) for clarity–it is an essential methodology in assessing project performance and the probability of meeting the constraints of the project budget.  It also contributes data essential to project predictive analytics.  What the data shows from a series of DoD studies (currently sadly unpublished), however, is that it is planning (via a Integrated Master Plan) and scheduling (via an Integrated Master Schedule) that first ties together the essential elements of the project, and will record the baking in of risk within the project.  Risk manifested in poorly tying contract requirements, technical performance measures, and milestones to the plan, and then manifested in poor execution will first be recorded in schedule (time-based) performance.  This is especially true for firms that apply resource-loading in their schedules.  By the time this risk translates and is recorded in EVM metrics, the project management team is performing risk handling and mitigation to blunt the impact on the performance management baseline (the money).  So this still raises the question: what is IPM?  I have a few ideas and will share those in other posts.

d.  Along these lines, there is a need for a Schedule (IMS) Gold Card that provides the essential basis of measurement of programmatic risk during project execution.  I am currently constructing one with collaboration and will put out a few ideas.

e.  Finally, there is still room for a lot of improvement in project management.  For all of the gurus, methodologies, consultants, body shops, and tools that are out there, according to PMI, more than a third of projects fail to meet project goals, almost half to meet budget expectations, less than half finished on time, and almost half experienced scope creep, which, I suspect, probably caused “failure” to be redefined and under-reported in their figures.  The assessment for IT projects is also consistent with this report, with CIO.com reporting that more than half of IT projects fail in terms of meeting performance, cost, and schedule goals.  From my own experience and those of my colleagues, the need to solve the standard 20-30% slippage in schedule and similar overrun in costs is an old refrain.  So too is the frustration that it need take 23 years to deploy a new aircraft.  A .5 CPI and SPI (to use EVM terminology) is not an indicator of success.  What this indicates, instead, is that there need to be some adjustments and improvements in how we do business.  The first would be to adjust incentives to encourage and reward the identification of risk in project performance.  The second is to deploy solutions that effectively access and provide information to the project team that enable them to address risk.  As with all of the points noted in this post, I have some other ideas in this area that I will share in future posts.

Onward and upward.

Rear View Mirror — Correcting a Project Management Fallacy

“The past is never dead. It’s not even past.” —  William Faulkner, Requiem for a Nun

Over the years I and others have briefed project managers on project performance using KPPs, earned value management, schedule analysis, business analytics, and what we now call predictive analytics. Oftentimes, some set of figures will be critiqued as being ineffective or unhelpful; that the analytics “only look in the rear view mirror” and that they “tell me what I already know.”

In approaching this critique, it is useful to understand Faulkner’s oft-cited quote above.  When we walk down a street, let us say it is a busy city street in any community of good size, we are walking in the past.  The moment we experience something it is in the past.  If we note the present condition of our city street we will see that for every building, park, sidewalk, and individual that we pass on that sidewalk, each has a history.  These structures and the people are as much driven by their pasts as their expectations for the future.

Now let us take a snapshot of our street.  In doing so we can determine population density, ethnic demographics, property values, crime rate, and numerous other indices and parameters regarding what is there.  No doubt, if we stop here we are just “looking in the rear view mirror” and noting what we may or may not know, however certain our anecdotal filter.

Now, let us say that we have an affinity for this street and may want to live there.  We will take the present indices and parameters that noted above, which describe our geographical environment, and trend it.  We may find that housing pricing are rising or falling, that crime is rising or falling, etc.  If we delve into the street’s ownership history we may find that one individual or family possesses more than one structure, or that there is a great deal of diversity.  We may find that a Superfund site is not too far away.  We may find that economic demographics are pointing to stagnation of the local economy, or that the neighborhood is becoming gentrified.  Just by time-phasing and delving into history–by mapping out the trends and noting the significant historical background–provides us with enough information to inform us about whether our affinity is grounded in reality or practicality.

But let us say that, despite negatives, we feel that this is the next up-and-coming neighborhood.  We would need signs to make that determination.  For example, what kinds of businesses have moved into the neighborhood and what is their number?  What demographic do they target?  There are many other questions that can be asked to see if our economic analysis is valid–and that analysis would need to be informed by risk.

The fact of the matter is that we are always living with the past: the cumulative effect of the past actions of numerous individuals, including our own, and organizations, groups of individuals, and institutions; not to mention larger economic forces well beyond our control.  Any desired change in the trajectory of the system being evaluated must identify those elements that can be impacted or influenced, and an analysis of the effort that must be expended to bring about the change, is also essential.

This is a scientific fact, proven countless times by physics, biology, and other disciplines.  A deterministic universe, which provides for some uncertainty at any given point at our level of existence, drives the possible within very small limits of possibility and even smaller limits of probability.  What this means in plain language is that the future is usually a function of the past.

Any one number or index, no doubt, does not necessarily tell us something important.  But it could if it is relevant, material, and prompts further inquiry essential to project performance.

For example, let us look at an integrated master schedule that underlies a typical medium-sized project.

 

We will select a couple of metrics that indicates project schedule performance.  In the case below we are looking at task hits and misses and Baseline Execution Index, a popular index that determines efficiency in meeting baseline schedule planning.

Note that the chart above plots the performance over time.  What will it take to improve our efficiency?  So as a quick logic check on realism, let’s take a look at the work to date with all of the late starts and finishes.

Our bow waves track the cumulative effort to date.  As we work to clear missed starts or missed finishes in a project we also must devote resources to the accomplishment of current work that is still in line with the baseline.  What this means is that additional resources may need to be devoted to particular areas of work accomplishment or risk handling.

This is not, of course, the limit to our analysis that should be undertaken.  The point here is that at every point in history in every system we stand at a point of the cumulative efforts, risk, failure, success, and actions of everyone who came before us.  At the microeconomic level this is also true within our project management systems.  There are also external constraints and influences that will define the framing assumptions and range of possibilities and probabilities involved in project outcomes.

The shear magnitude of the bow waves that we face in all endeavors will often be too great to fully overcome.  As an analogy, a bow wave in complex systems is more akin to a tsunami as opposed to the tidal waves that crash along our shores.  All of the force of all of the collective actions that have preceded present time will drive our trajectory.

This is known as inertia.

Identifying and understanding the contributors to the inertia that is driving our performance is important to knowing what to do.  Thus, looking in the rear view mirror is important and not a valid argument for ignoring an inconvenient metric that may only require additional context.  Furthermore, knowing where we sit is important and not insignificant.  Knowing the factors that put us where we are–and the effort that it will take to influence our destiny–will guide what is possible and not possible in our future actions.

Note:  All charted data is notional and is not from an actual project.