Project Analytics : A Learning based Approach

25 Pages·2016·0.348 MB·English

by  Sumit Chakraborty

Project Analytics :
A Learning based Approach
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). 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
network. 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, PAM, Case based planning, Case
retrieval, Case adaptation, Smart villages, Smart cities, Software project management

Reference of document : Technical Report / PAM/ 15082016 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 [15,19]. 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 Page 2
Information flow : Design documents

Material flow

C
1

Upstream direction Downstream direction

S
1

S2 C
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 describes 7-S deep Project
analytics model. Section 3 presents project analytics mechanism (PAM). Section 4
performs the complexity analysis. Section 5 concludes the work by depicting system
architecture of project analytics.

Reference of document : Technical Report / PAM/ 15082016 Page 3
2. 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 [19,20,21].
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 [13,14,16,17]. 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. The fourth role, maintaining
forward momentum, is performed 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 [18].

Novelty Objectives Constraints Technology Complexity

Scope (S): System (S):
1 2
Application domain Computing, Data,
Direct scope Networking, Application

Indirect scope & Security schema

Security (S ): Structure (S):
Threa t analy6tics Strategy (S): Activities, No3des
7
Risks a ssessment Communication Ordering constraint
Risk mitigation Shared vision Time & resources

Skill & Style (S): Staff (S ):
5 4
Pace Technical staff
ERP, SCM,CRM Management staff
Leadership style Legal experts

Figure 2 : Deep Project Analytics ‘7-S’ model
Reference of document : Technical Report / PAM/ 15082016 Page 4
3. PROJECT ANALYTICS MECHANISM [ PAM]

Agents : Project chain partners /* Service providers (S), Client (C) or sevice 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 :
 Call deep analytics model ‘ 7-S’; /* refer figure 1*/
 case based planning through case retrieval, case adaptation, learning and case
base maintenance;
 multidimensional view of intelligent reasoning (logical, analytical, probabilistic,
uncertainty, perception, imaginative) to assess a project in terms of novelty,
technology, complexity and pace;
 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;
 explore computational intelligence of project analysis in terms of data
visualization checklist (dashboards, charts, alert), performance scorecard, time
series analysis, insights analysis, risk analysis, cause-effects analysis, prediction
and recommendation.
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 3.1 */
case adaptation based on q; estimate time, resources, activity and ordering
constraints; /* refer algorithm CAA, section 3.2*/
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:
 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:
Reference of document : Technical Report / PAM/ 15082016 Page 5
 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
audit fairness and correctness of project plan computation and
adjustment as per exceptions based on rationality;
o
monitor authentication, authorization, correct identification,
transparency and accountability in project planning, execution and
control;
o
verify project performance in terms of reliability, consistency, resiliency,
liveness, deadlock-freeness, reachability, synchronization and safety.
Output : Project intelligence (project plan, performance metrics).

3.1 Case Retrieval Algorithm [CRA]

Case Based Planning [1-9]: Case based reasoning (CBR) is a methodology for solving
problems by utilizing previous experience. 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 4.1.1]
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’.
Reference of document : Technical Report / PAM/ 15082016 Page 6
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
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 results 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.

3.1.1 Similarity Search Algorithm

Input : Training objects :D; Test object: Z (a vector of attribute values);
L : Set of classes to label the objects; output: c  L where c is the class of z;
z z
Algorithm:
for each object y  D do
compute d(z,y), the distance between y and z;
end
select ND, k closest training objects for z;
c ∑ I(v = class (c ));
 
z = argmax v L y N y
Reference of document : Technical Report / PAM/ 15082016 Page 7
where I(.) is an indicator function that returns the value 1 if its argument is true
and 0 otherwise.
d(x,y) = √∑n (x – y )2 i.e. eucledian distance;
k=1 k k
d(x,y) = √∑n |(x – y )| i.e. manhattan distance;
k=1 k k
x , y are the attributes of x and y respectively; w = 1/ d(y,z)2;
k k i
distance weighted voting : c = ∑ w . I (v =class (c ));
 
z argmax v L y N i y

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 and has an unknown class label. The algorithm
then assigns a class to z by taking the class of majority of neighboring objects. 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) set of labeled
training objects to be used for evaluating a test object’s class; (b) a distance of similarity
metric to compute the closeness of objects; (c) the value of k, number of nearest
neighbors and (d) the method of distance measurement. KNN is a specific case of
instance based learning such as CBR. It is particularly suited for multimodal classes as
well as applications in which an object can have many class labels. The 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.

3.2 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.
Reference of document : Technical Report / PAM/ 15082016 Page 8
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?

Here, we consider a specific interactive search procedure called Light Beam Search
(LBS) method (Jaszkiewicz et al.,1999). The idea of light beam search is analogous to
projecting a focused beam of light from the aspiration point onto the nondominated
frontier. The lighted part of the frontier changes if the aspiration point or the point of
interest in the nondominated set is changed. As already mentioned above, any
interactive search (including LBS) occurs between a DMA and the MA. The mediator
asks the DMA to specify its preference in the form of aspiration and reservation point
and various types of preferential thresholds. At each iteration of LBS procedure, MA
generates a sample of nondominated points using this preferential information. The
sample is composed of a middle point and a set of non-dominated points from its
neighborhood. MA shows these points to the decision-making agent. Appendix 1 has
defined several parameters related to light beam search such as aspiration point (P ),
A
reservation point (P ), indifferent threshold (I ), strong preference threshold (S ), weak
R th th
preference threshold (W ), veto threshold (V ), middle point (MP) and characteristic
th th
neighbors.

Case Adaptation Algorithm [CAA]
Agents : A decision-making agent (DMA) and the mediator agent (MA).
Input : The mediator holds the deterministic problem; The DMA holds its aspiration
point, reservation point, indifferent threshold, strong and weak preference threshold
and veto threshold.
Output: DMA knows a set of solutions; MA can not know the output.

1. MA requests the DMA to specify its preferential parameters ( P ,P ,I , P , S , W , V
A R th th th th th
).
Reference of document : Technical Report / PAM/ 15082016 Page 9
2.The DMA generates (n-1) random set of preferential parameters and appends its
desired set of preferential parameters at a random position. The DMA sends to MA the
list H = (H,…,H ) where for a secret index 1jn, H = ( P , P , I , P , S , W , V ).
1 n j A R th th th th th
3. Repeat until the DMA is satisfied with a solution or concludes that no compromise
point exists for the present constraints
a. MA computes a middle point (MP) alongwith characteristic neighbors for each set of
preferential parameters.
b. The DMA gets back the results of k middle points alongwith characteristic neighbors
using k-out-of-n oblivious transfer protocol where kn.; DMA scans the inner area of
the current neighborhood and stores its preferred solutions in a private list L; it stores
1
the invalid middle points in a private list L
2.
c. Case
(i) The DMA wants to define a new aspiration and/or reservation point and/or
updates preferential thresholds :
-
The DMA adds a set of new aspiration and/or reservation points and/or new
preferential thresholds to the list H and sends H to MA.
-
MA projects the aspiration points onto the non-dominated set and generates middle
points with characteristic neighborhood.
-
The DMA gets back the result of desired middle point alongwith characteristics
neighbors using 1-out-of-n oblivious transfer protocol.
(ii) The DMA wants a point from the current neighborhood to be the new middle
point or wants to return to one of the stored points of L:
1
- The DMA adds the desired middle point to the list L and sends L to MA;
2 2
- MA generates neighborhood of the middle points.
- The DMA gets back the result of desired middle point alongwith characteristics
neighbors using 1-out-of- n oblivious transfer protocol.

The aforesaid algorithm preserves the privacy of individual preferential parameters of
the decision making agents such as aspiration point (P ), reservation point (P ),
A R
indifferent threshold (I ), strong preference threshold (S ), weak preference threshold
th th
(W ), veto threshold (V ), middle point (MP) and preferred solutions resulted from the
th th
search process. The mediator agent preserves the privacy of the deterministic multi-
objective optimization problem.

4. COMPLEXITY ANALYSIS OF PAM

Theorem 1: PAM verifies project intelligence in terms of collective, machine, security,
collaborative and business intelligence through multi-dimensional view on intelligent
reasoning.

The proposed mechanism (PAM) is defined by a set of elements : system, a group of
agents, a finite set of inputs of each agent, a finite set of outcomes as defined by output
function, a set of objective functions and constraints, payment function, an optimal set
of moves, revelation principle and model checking or system verification protocol. The
mechanism follows a set of moves. The basic building block of the mechanism is an
analytics having multidimensional view of intelligent reasoning. Reasoning has
Reference of document : Technical Report / PAM/ 15082016 Page 10