Project Analytics: A Learning based Approach (Revisited)

30 Pages·2016·0.45 MB·english

by  Sumit Chakraborty

Project Analytics:
A Learning based Approach (Revisited)
Sumit Chakraborty
Fellow, MIS (Indian Institute of Management Calcutta),
Bachelor of Electrical Engineering (Jadavpur University), India.
E-mail: [email protected], [email protected];Phone: [phone]

Abstract : Classical models of resource constrained project scheduling problems are not
adequate to solve real world problems due to increased complexities and uncertainties.
Intelligent project analytics are essential for complex, fuzzy, stochastic, multi-mode,
resource constrained project scheduling problems with multiple objectives. This work
explores how to apply the concept of intelligent deep analytics for project management.
Efficient project management requires coordination and integration among seven
elements associated with a project such as Scope (novelty, objectives, constraints),
System (technology), Structure (complexity), Staff, Skill (innovation, design, SCM, ERP)
&, Style (pace, leadership), Security (threat analysis, risk assessment and mitigation)
and Strategy (shared vision, communication). It is essential to define the scope of a
project correctly through feasibility study, priority and cost-benefit analysis. This work
presents an algorithmic Project Analytics Mechanism (PAM) in terms of agents, input,
output, strategic moves, case based planning algorithm, performance metrics,
revelation principle, verification protocols for security intelligence and payment
function. The intelligence of PAM is explored through a set of strategic moves such as
case based planning, collaborative, security and collective intelligence. The complexity
of the analytics is analyzed in terms of computational cost and security analysis.
Traditionally, the computational burden of project planning depends on the efficiency
of heuristic search algorithm to find out the critical path of a project. But, it may not
capture the uncertainties, risks and complexities involved in a real world project. The
computational complexity of PAM is associated with the efficiency of case based
reasoning (CBR) i.e. case retrieval and case adaptation algorithms. Case based planning
searches reference plan from a case base through efficient case retrieval and
adaptation mechanism. 100% matching in case retrieval is a NP hard problem.
Traditionally, many CBP algorithms have tried to find exact matching between the
graphs of resource and time constrained project networks. It may be practically
infeasible. The basic objective of K-Nearest Neighbor Search algorithm in PAM is to
search for approximate matching among the neighbors. The project analytics monitor
project performance and adjusts the reference plan. The revelation principle preserves
the privacy of contracts and payment function through signcryption. This work also
outlines the architecture of an intelligent project analytics in terms of computing,
communication, data, application and security schema. The concept of deep project
analytics and PAM has been applied to analyze three test cases – smart village project,
smart city project and software project management.
Keywords : Project analytics, Deep Analytics, Performance metrics, Case based
planning, Case retrieval, Case adaptation, Smart villages, Smart cities, Software project
management
Reference of document : Technical Report/PAM/15082016 v2.0 Page 1
1. Introduction

A project is associated with a network of organizations that satisfies the demand of
ultimate customer by producing values in the form of products and services. Project
management is a novel management paradigm; the basic objective is to fulfill ultimate
customer demands by integrating a network of organizational units through
systematic coordination of material, information and financial flows [11,15]. A project
chain includes all the stages involved directly or indirectly with a project like suppliers,
manufacturers, distributors, project service providers, consultants and customer. Each
stage performs different processes and interacts with other stages of the chain; there is
a flow of material, information and funds between different stages.
Integration of organizational units and coordination of flows of material, information
and funds are the challenges of today’s project management. A lack of coordination
occurs if information is distorted as it moves across the project chain or if different
stages of the chain focus on optimizing their local objectives. Efficient coordination and
integration depends on choice of partners or strategic alliance, inter-organizational
collaboration and leadership. Effective use of information and communication
technology, integration of project planning and enterprise resource planning system
and process orientation ensure improved coordination of different flows in project
management. Collaborative planning, forecasting and replenishment is a strategic tool
for comprehensive value chain management of a project. This is an initiative among all
the stakeholders of the project in order to improve their relationship through jointly
managed planning, process and shared information. The ultimate goal is to improve a
firm’s position in the competitive market and the optimization of its own value chain
in terms of optimal inventory, improved sales, higher precision of forecast, reduced cost
and improved reaction time to customer demands.
Collaborative intelligence highlights the importance of sharing appropriate strategic
data for greater transparency and accuracy of resource constrained, multi-objective
and stochastic project planning. Information technology allows project chain partners
to interconnect, but trust is also important. The interplay between trust and technology
encourages the commitment of collaboration among the organizations. The partners of
a project chain are often reluctant to share their private information. It is an
interesting option to explore how privacy can be ensured in exchange of strategic
information for collaborative intelligence. The trading agents must define points of
collaboration for information sharing in terms of DCOR, CCOR and SCOR plans. DCOR
plan analyzes project scope, requirements engineering, design, coding, configuration
and customization. CCOR plan analyzes the strategies of erection, testing,
commissioning, maintenance and performance optimization. SCOR plan analyzes
demand, inventory, production and capacity, sourcing, distribution, warehousing,
transportation, reverse logistics and evaluate supply chain performance in terms of
lead time, cost, quality and service. The agents also analyze the intelligence and
feasibility of various types of contracts such as service, sourcing, push-pull, revenue
sharing and buy back contract. The project analysts assess and mitigate risks of
uncertainties in delivery and capacity planning adaptively and resiliently; adjust
reference project plan against exceptions and compute revised plan (P’).

Reference of document : Technical Report/PAM/15082016 v2.0 Page 2
Information flow : Design documents

Material flow

C
1

Upstream direction Downstream direction

S
1

S C
2
2

S Erection Testing Commissi
n
oning

C
3

Suppliers Project Service Providers Consultants Customer

Figure 1 : Collaborative Intelligence in Project Management

The outcome of collaborative intelligence is a set of intelligent contracts. Collaborative
planning is a common approach for a group of decision-making agents (DMAs) to
reach mutually beneficial agreements. This is an important conflict management and
group decision-making approach for making an intelligent contract by a set of agents.
The agents exchange information in the form of offers, counter-offers and arguments
and search for a fair consensus. Efficient coordination mechanisms are essential to
achieve a set of rational structured plan. The coordination mechanisms get competitive
intelligence from a set of strategic business intelligence moves. The trading agents start
interaction with their initial plans; call a specific set of moves; share strategic
information but do not disclose private data; negotiate and finally settle the desired
output plans. The basic objective is to improve the financial and operational
performance of a project plan through systematic coordination of the flows of
information, resources and funds. The present work is organized as follows.
Section 1 defines the problem of project management. Section 2 presents project
analytics mechanism (PAM). Sections 2.1, 2.2, 2.3, 2.4 and 2.5 present ‘7-S’ deep project
analytics model, case retrieval, case adaptation, performance metrics and KPI and
Reference of document : Technical Report/PAM/15082016 v2.0 Page 3
portfolio rationalization respectively. Section 4 shows the complexity analysis. Section
5 concludes the work by depicting system architecture of project analytics.

2. Project Analytics Mechanism [ PAM]

Agents : Project chain partners /* Service providers (S), Client (C) or service consumer,
Supply chain partners, Mediator */;
Objectives :
 search an approximate efficient plan for a multi-objective, multi-mode,
stochastic, time and resource constrained project;
 manage uncertainty through improved coordination and integration;
Input : demand plan of C, service plan of S, project parameters, project case base;
Strategic moves (S):
7
 call deep analytics model ‘ 7-S’; /* refer section 2.1, figure 1*/
 case based planning through case retrieval, case adaptation, learning and case
base maintenance;
 define project intelligence p = f(C , C’ , S , B , M ); C : collective intelligence, C’ :
i p p p p p p p
collaborative intelligence, S : security intelligence, B : business intelligence; M :
p p p
machine intelligence, f: secure verification function.
 fix intelligent contracts in terms of price, discount, payment terms, mode,
incentives and penalty clauses through negotiation;
 Project portfolio rationalization through linear / proportional / priority based
selective capital allocation.
 Uncertainty management strategies :
o
multidimensional view of intelligent reasoning (logical, analytical,
probabilistic, perception, imaginative) to assess a project in terms of
novelty, technology, complexity and pace;
o
Estimate critical path based on S,S and S;
3 4 5
o
Contingency plan
o
Alternative solutions and parallel paths for resiliency
o
Business case
o
Prototype
o
Simulation game
Algorithm :
define project scope (q: aspiration point, reservation point, preferential thresholds)
based on S,S and S;
1 2 3
do case based planning;
case retrieval from case base through similarity search based on q; /* refer
algorithm CRA, section 2.2 */
case adaptation based on q; estimate time, resources, activity and ordering
constraints; /* refer algorithm CAA, section 2.3*/
set reference project plan  P;
call analytics (A) during project execution  verify project intelligence p = f(C , C’ , S ,
i p p p
B , M ) based on S ,S,S and S;
p p 4 5 6 7
adjust P  P’ based on p ;
i
case evaluation and case base maintenance;
Payment function:
Reference of document : Technical Report/PAM/15082016 v2.0 Page 4
 fix intelligent contracts (payment terms, payment mode, penalty clause, reward)
through multi-party negotiation;
 audit business intelligence in terms of incentives received by corrupted agents
and adversaries. The honest agents compute penalty function and charge the
corrupted agents.
Revelation principle:
 S and C preserve privacy and confidentiality of signcrypted contracts and
payment function; also ensure non-repudiation and data integrity.
 verify security intelligence (S ) of the project analytics.
6
o
call threat analytics and assess risks of single or multiple attacks on the
analytics such as false data injection and privacy attack, sense exception
and give alerts.
 Identify what is corrupted or compromised: agents, communication
/ data / application / computing schema?
 time : what occurred? what is occuring? what will occur? assess
probability of occurrence and impact.
 insights : how and why did it occur? do cause-effect analysis.
 recommend : what is the next best action?
 predict : what is the best or worst that can happen?
o
Measure and monitor optimal number of critical project performance
metrics (refer section 2.4) :
 Operations : scope creep, project completion stage, flexibility,
quality, cost, time, inventory, customer satisfaction;
 Finance : revenue growth rate, cost reduction, profitability, ROI,
payback period, NPV;
 Human Resources (HR) : performance, productivity, capacity
utilization, skill;
 audit fairness and correctness of project plan computation and
adjustment as per exceptions based on rationality;
 monitor authentication, authorization, correct identification,
transparency and accountability in project planning, execution
and control;
 verify project performance in terms of reliability, consistency,
resiliency, liveness, deadlock-freeness, reachability,
synchronization and safety.
Output : data visualization checklist (dashboards, charts, alert), performance scorecard,
time series analysis, insights analysis, risk analysis, cause-effects analysis, prediction
and recommendation.

2.1 Deep Project Analytics Model : 7-S

This section presents a model of deep analytics for project management. It is basically
an integrated framework which is a perfect combination or fit of seven factors or
dimensions. Many projects fail due to the inability of the project managers to
recognize the importance of the fit and their tendency to concentrate only on a few of
these factors and ignore the others. These factors must be integrated, coordinated and
synchronized for effective project management [15,16,17].
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Traditional approaches to project management focus on long-term planning and
stability to mitigate various risks. But, complex project management requires a mix of
traditional and agile approach to cope with uncertainties [8,10,12,13]. The intension
driven role develops collaboration. The event driven role integrates planning and
review with learning. The other important roles of the project managers are to prevent
major disruptions and maintaining forward momentum continuously. They must
acknowledge the emergence of a problem and then try to minimize the frequency and
negative impact of unexpected events in a dynamic environment. They must be people
oriented, information oriented and action oriented [14].

Novelty Objectives Constraints Technology Complexity

Priority

Scope (S): System (S):
1 2
Application, Primary & Computing, Data,
secondary scope, Networking, Application

Portfolio rationalization & Security schema

Security (S ): Structure (S):
6 3
Threat analytics Strategy (S): Activities, Nodes
7
Risks a ssessment Shared vision Ordering constraint
Risk m itigation Strategic alliance Time & resources
Performance metrics

Skill & Style (S): Staff-Resources (S ):
5 4
Pace, Planning, Execution 5M – Machine, Material,
ERP, SCM, CRM, Support, Method, Money, Man
Leadership style (Technical, Mgmt., Legal)

Figure 2 : Deep Project Analytics ‘7-S’ model

Traditional project management approach follows four steps such as definition,
planning, execution and termination. But, no projects are so linear. Once project
execution starts, reality may demand exception management i.e. the adjustment and
amendment in the planning or definition phases. Each industry has a different profile
of risk. Deep analytics is applicable to both adaptive and linear project management
approaches. Many projects fail due to conventional approach which may not adapt to
a dynamic business environment. Deep analytics is essential to understand the nature
of a project and identify the gaps between as-is and to-be capabilities in a systematic
and compelling way. The 7-S model uses seven factors to analyze a project. The first
dimension is scope. At first, it is very crucial to identify the scope of a project
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rationally through feasibility study and cost-benefit analysis. It is essential to identify
the primary and secondary scopes through portfolio rationalization and analysis of
priority, novelty, objectives and constraints of a set of projects. Perception based
emotional and readymade thoughts may affect the correctness of scope analysis. Scope
creep is a serious concern in project management. The second dimension is system; it is
essential to decide appropriate technology. For instance, an information technology
project requires sound computing, networking, data, application and security schema.
The third dimension is structure of a project which is explored through complexity
analysis of a graphical project network in terms of nodes, activities, estimation of time
and resources and ordering constraints (e.g. sequential, parallel). The fourth dimension
is staff-resources which estimates resource allocation plan in terms of man i.e. human
resources (e.g. technical, management and legal staff), machine, material, method and
money (fund or capital). The fifth dimension is skill and style; an efficient project
management approach demands multiple skills of the stakeholders such as pace or time
management, resource planning, supply chain and customer relationship management,
group dynamics and leadership style. The sixth dimension is security which is focused
on threat analytics, risk assessment and mitigation techniques. Finally, the seventh
dimension is strategy which defines shared vision, communication protocol, strategic
alliance, collaboration and performance measurement in terms of KPIs. PAM
highlights a set of intelligent strategic moves to tackle uncertainties and complexities
in time and resource constrained project management.
Novelty indicates how intensely new innovations are crucial aspects of a project. A
project should be assessed on the scale of sophistication of technology, which may be
low, medium or high. Another critical factor is the complexity of project in terms of
product, service and process. Pace indicates the urgency of a project – normal, fast,
time critical or blitz. Different projects have varying degrees of newness or novelty. A
derivative product development project may have low risk and few future concerns. A
new version of an existing product needs detailed analysis and market research.
Breakthrough product development projects face high risks. Each project is unique, but
not in every respect and may have some common features. The uncertainty in a project
is a measure of the mix of new and mature technology and existing knowledge base; it
may arise from technological aspects, new service offering or new market segments.
High technology projects are subject to time delays, cost overruns and risks of product
failure. The complexity base measures three different types of complications within a
project such as assembly (low), system (medium) and array (high). High complexity
requires efficient coordination and integration among various phases and systems of a
project. Pace indicates a sense of urgency and time sensitivity. The failure of time
critical projects results from the violation of milestone deadlines and related
opportunity loss; blitz projects are crisis projects with extremely urgent timing. There
are various strategies for optimal pace management such as contingency plans,
alternative solutions in parallel, resilient approach and business cases to manage
emergency and to overcome uncertainties and unexpected surprises.
A project may be delivered on time and budget through the efforts, skill and
professionalism of the project managers. But, it may not meet the needs of the customer
due to uncertainty and misunderstanding. The basic objective of the deep analytics is
to figure out the actual structure of a project as compared with the existing
capabilities, the gap and the measure of project success in terms of efficiency, impact on
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the customer, impact on the team, business success and preparation for the future. It is
rational to take both short and long term view of a project plan since success may
change during the life-cycle of a project with the change of environmental parameters
and information. Does anything change from a future point of view? Does a project
have sufficient flexibility to adapt to new requirements of a dynamic business
environment? Are the incentives aligned properly with customer satisfaction, system
performance, deadline and budget requirements? The deep analytics is useful to find
the gaps between as-is and to-be requirements of a project, efficient resource planning,
uncertainty and risk management. Correct use of deep analytics clearly highlights low-
medium-high benefit opportunity and low-medium-high risk difficulty.

2.2 Case Retrieval Algorithm [CRA]

Case Based Planning [1-6]: Case based reasoning (CBR) is a methodology for solving
problems by utilizing previous experience and saves time and effort in project
planning. It involves retaining a memory of previous project problems and their
solutions and solving new problems by referencing the past cases. An expert presents a
new query case to CBR system. The system searches its memory of past cases stored in
case base and attempts to find a case that has the same problem specification of the
current case. If the system does not find an identical case in its case base, it will
attempt to find the case or cases that match most closely to the current query case.
There are two different types of search such as similarity search and neighborhood
search. In case of similarity search, the solution of the retrieved case is directly used for
the current problem. The system adapts the retrieved cases if the retrieved case is not
identical to the current case. In a complex search, the system requires the access of
multiple case bases which are located at various locations.

Case Retrieval Algorithm [CRA]
Agents : C, S;
Input: query (q);
Protocol:
Retrieve the most similar cases (c1,…,ck)  k nearest neighbors w.r.t. q from the
case base; /* Refer similarity search algorithm, section 2.2.1 */
Adapt the proposed solutions to a solution s(q)  compute s(q) by combining the
solutions sj of the cases cj. sj is weighted as per the differences between cj and q;
Learn after applying s(q) to q in reality  Store the new solution in the case base
for solving q’.
Evaluate performance: Rejection ratio = no. of unanswered queries / total no. of
queries.
Output: Recommended solution;

CBR is selected for resource and time constrained project planning due to various
reasons. The domain has an underlying model, the process is not random and the
factors leading to the success or failure of a solution can be captured in a structured
way. Cases recur in the domain though there may be exceptions and novel cases. The
solutions can be improved through case retrieval and case adaptation. Relevant
healthcare cases are available at different healthcare institutes; it is possible to obtain
Reference of document : Technical Report/PAM/15082016 v2.0 Page 8
right data. Case retrieval is the process of finding within the case base those cases that
are the closest to the current case. There must be criteria that determine how a case is
evaluated to be appropriate for retrieval and a mechanism to control how the case base
is searched. Most often, an entire case is searched. But, partial search is also possible if
no full case exists.
A case is a record of a previous experience or problem in terms of problem definition,
project scope in terms of application domain, size, cost, novelty, complexity, technology,
pace and risks, solution methodology, project network having initial and goat state,
activities, time and resource estimation, edges and constraints (e.g. ordering, time,
resource, data) and project plan. A case base also stores global best practices and
project management standards. All these information must be coded. 100% matching in
case retrieval is a NP hard problem. Data is stored based on domain knowledge and
objectives of the reasoning system. The cases should be stored in a structured way to
facilitate the retrieval of appropriate case when queried. It can be a flat or
hierarchical structure. Case indexing assign indices to the cases for retrieval and
comparisons. There are different approaches of case retrieval. In case of nearest
neighbor search, the case retrieved is chosen when the weighted sum of the features
that match the query case is greater than the other cases in the case base. A case that
matches the query case on n number of features is retrieved rather than a case which
matches on k number of features where k < n; different features may be assigned with
different weights. Inductive approach is driven by a reduced search space and requires
reduced search time. This result reduced search time for the queries. Knowledge based
approaches select an optimal set of features of case by using domain knowledge. The
complexity of case retrieval depends on multiple factors: (a) number of cases to be
searched, (b) domain knowledge, (c) estimation of the weights for different features and
(d) case indexing strategy.

2.2.1 Similarity Search Algorithm

input : Training objects : D; Test object: Z (a vector of attribute values);
output: k nearest neighbors of Z;
Algorithm:
for each object y  D do
compute d(z,y), the distance between y and z;
option 1 : eucledian distance d(x,y) = √∑n (x – y )2 ;
k=1 k k
option 2 : manhattan distance d(x,y) = √∑n |(x – y )|;
k=1 k k
sort d(z,y);
end;

The algorithm computes the distance or similarity between z and all the training
objects to determine nearest neighbor list for given training set D and test object z
which is a vector of attribute values. The storage complexity of KNN algorithm is o(n)
where n is the training objects. The time complexity is also o(n) since the distance needs
to be computed between the target and each training object. There are several key
elements of this approach : (a) a distance of similarity metric to compute the closeness
of objects; (b) the value of k, number of nearest neighbors and (c) the method of distance
measurement. KNN is a specific case of instance based learning such as CBR. The
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performance of KNN algorithm depends on the choice of k, an estimate of the best
value for k that can be obtained by cross validation. If k is very small, the results can
be sensitive to the noise points. If k is too large, then the neighborhood may include too
many points from the classes.

2.3 Case Adaptation Algorithm

Case adaptation is the process of translating the retrieved solution appropriate for the
current problem; it adds intelligence to the recommendation process. There are various
approaches of case adaptation. The retrieved case can be directly used as a solution to
the current problem without any modification. Otherwise, the retrieved solution should
be modified according to the current problem. The steps or processes of the previous
solution can be reused or modified. The solution of the current case can be derived by
combining knowledge of multiple retrieved cases. Case adaptation is a complex decision
making task, it considers multiple factors: how close is the retrieved case to the query
case? How many parameters are different between the retrieved and the query case?
DMAs can apply common sense or a set of rules or heuristics for case adaptation.
Making sense of the information found during an investigational search is a complex
task of case based reasoning. Sense making is to find meaning in a situation; it is the
cognitive act of understanding information. The system should support collaborative
information search by providing several rich and interactive views of the search
activities of a group. One of the problems facing HCI research today is the design of
computer interfaces to enable sense making of the processed information. Sense making
is not only important for individuals, but also for groups to achieve shared goals.
Traditional sense making tools focus on data mining, provide better information
representation, visualization and organization of search results. But, it is also required
to support the collaboration and communication that occurs among the investigators
when they make sense of information together.

Interactive search: The basic steps of interactive search algorithm which operates
between a DMA and the MA are as follows:
1. MA computes an initial feasible solution.
2. MA interacts with the DMA.
3. MA obtains a (or a set of) new solution. If the new solution or one of the previous
solutions is acceptable to the DMA, stop. Otherwise, go to step 2.

Here, the MA has the option of running interactive search session with the DMAs in
sequence or in parallel depending on its resources available. Either case, the result is
same, since the interaction with each DMA occurs independently of interaction with
others. The design of interactive search methods depends on various issues:
 The form through which the DMA gives information
 The approach by which the multi-objective problem is transformed into a single
objective optimization problem
 The type of data used for interaction with DMA
 Number of non-dominated points to be presented to the DMA (a single point or a
sample of points) and
 How the DMA evaluates a set of alternatives?
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