AHP and INNOVATION STRATEGY as PROJECT PORTFOLIO MANAGEMENT

AHP & Innovation Strategy as Project Portfolio Management © Sajjad Khaksari (2017)
AHP AND INNOVATION STRATEGY AS
PROJECT PORTFOLIO MANAGEMENT
Sajjad Khaksari, Politecnico di Torino, March 2017
ABSTRACT
The following technical report with some improvement describes the "chapter 9" of the
project called: “Strategic Management of Technological Innovation Concerning The
Battery Electric Vehicles (BEV)”.The "Chapter 9" [1] agglutinates all chapters of "case 4" in
which the BEV project introduces a real company case study (Workhorse Group Inc.).
Chapter 9 takes performances of Analytic Hierarchy Process (AHP) method which has
developed by professor Thomas L. Saaty in the 1970s. Also, the following report proposes
to contribute an optimizing decision-making framework for some authentic Multiple-
Criteria Decision-Making (MCDM) circumstances.
Furthermore, the "supplementary resources" are available in the attachment of the
Battery Electric Vehicles project. Remark that the “.sdmod” stands for the formats of
“SuperDecisions Software”. The ".sdmod" formats adopted in “part c” of the project con-
cerning the definition of AHP Tree and the related arguments regarding the Innovation
Strategy as Project Portfolio Management (PPM). Also, the “PPM-F.xls” file format in-
cludes the “Solver” and the results of “part a” and "part b".
PART A - CATEGORIZE THE R&D
The “part a” began with introducing the Workhorse case study and some questions base
on the available information. Questions require researchers to categorize the Workhorse
R&D segments and try to organize their innovation activities. Also, the section asks a
question concerning the dimension of the appropriate projects to set the differences
among those innovative projects. Besides, the “part a” requires an applied research and a
basic financial analysis among the mentioned projects portfolio. Finally, the “part a” of
the research ask to distinguish between the platforms, products, and customized projects.
However, the henceforth part of following technical report is based on the information
available on Workhorse Group Inc. website [2] and the FORM 10-K [3] of the UNITED
STATES, SECURITIES AND EXCHANGE COMMISSION Washington, D.C. 20549, December
2015. Also, in this part the market researchers try to categorize the Workhorse R&D and
its innovation activities (Fig. 9.1).
In addition, the Excel file (PPM-F.xls) available in attachment illustrates the Workhorse
Inc. financing condition and monitoring its core or implemented research approach,
between Workhorse Inc. project responsibilities (see Fig. 9.3 and Fig. 9.4).
PART B - NEXT GENERATION PROJECT
The “part b” works amid introducing the interest of the Amedeo family (fiction name) to
evaluate the Workhorse’s R&D projects, based on real data. In appropriate, they desire to
examine and assess the development activities of latest versions of two leading Work-
horse product the "lines A and B". The Fig. 9.2 outlines the project portfolio while the
projects A1, A2, A3, B1 and B2 are platform project, and they possess a “Next Generation
Project” (A#.1 or B#.2). Ordinarily, the 45 Full-Time Equivalent (FTE) resources are avail-
able for the R&D activities, and resources can be increased up to 10 % by using external
resources at an additional cost of approximately four (4) k€ per man/month.
Also, "part b" ponders on the project portfolio shown in Fig. 9.2, concerning the two
products A and B by analyzing and computing the Expected Commercial Value (ECV) and
the Productivity Index (PI) indicators for each project. Besides, it tries to find out a succinct
graphical representation for the portfolio such as bubble charts, pie charts, resource
workload charts by employing the appropriate variables. In addition, "part b" attempts to
optimize the Workhorse project portfolio with a math programming model utilizing the
Excel Solver. The Objective Functions might maximize the ECV of selected projects and
minimize the cost of overtime. Recognizing that the Resource Capacity Constraints re-
quirement includes the possibility of using overtime human resources. Furthermore, by
constraining the Next Generation Projects, those tasks can not be activated if in the
previous year of the corresponding new product has not been launched.
Another challenge occurs when the designers investigate to take out those constraints
and recount the optimal solutions. Do the results change? If yes, in what terms might be
considered a value of the new product development for the projects? Also, extra difficulty
transpires during the evaluation of new profitability regarding the immediate termina-
tion of the product A.The question is what the financial consequences would be?
PART C - AHP TREE AND CRITERIA STRATEGIC ALIGNMENT
The "part c" defines an AHP Tree to illustrate the Criteria Strategic Alignment into the
company objectives (see Fig. 9.5). In this recent part, the crucial challenge is that how the
circumstances might be modified if the enterprise business strategy distinguished as a
"Definitely Cost Oriented" strategy?
Page ! of 51© Sajjad Khaksari (2017) - Torino
WORKHORSE GROUP INC. R&D ORGANIZATION
Financing
Conditions
Workhorse Inc. as a
national Ohio-based
has a competitive
core competence
Workhorse Inc.
Project Portfolio
Workhorse R&D and
innovation activities
Fig. 9.1: Workhorse Group Inc. R&D Organization
Fig. 9.1: Workhorse Group Inc. R&D Organization
Fig. 9.2: Representation of the Project Portfolio
Fig. 9.3: A Screenshot of Excel File with Solver Parameters Window - LP Method
Fig. 9.4: A Screenshot of Excel File with Final Results - X* ECV

The decision making in a complex environment is an extremely complex issue specially
when distress is genuine and mis-designing might generate financial collapses, human
resource opposes, and environmental damages. In that complex, knotty, and complicated
condition, the Analytic Hierarchy Process (AHP) [4] for Decision Making and the Analytic
Network Process (ANP) [5] might assist project managers to optimize their decisions. The
Analytic Network Process (ANP) is the most comprehensive framework for the analysis of
societal, governmental and corporate decisions that is available to the decision-maker.
AHP and ANP methods allow analyzers to include all the factors and criteria, tangible and
intangible that have bearing on making the best decision. In fact, Analytic Hierarchy
Process (AHP) is a decision-making framework used for Multiple-criteria decision-making
(MCDM) [6] which is a sub-discipline of operations research because explicitly estimates
multiple conflicting criteria in decision making.
Therefore, the AHP is a structured technique for organizing and analyzing complicated
decisions, based on mathematics and psychology. It was developed by Thomas L. Saaty in
the 1970s [7] and has been extensively studied and refined since then [8]. In addition,
Thomas L. Saaty supervised the Super Decisions software that implements the ANP for
decision making [9] in networks and makes it available free to researchers and educators
at www.superdecisions.com [10]. Also, in the section of "Sample Models", from the
SuperDecisions website, examples of some models developed with the SuperDecisions
software are available and organized by model type and by subjects. Extending into the
sub-subject of the "Automobile industry” [11] some interesting cases are developed
concerning the Ford Explorer Decision, Porsche SUV Decision, and Alternative Fuel.
However, in this technical report the Workhorse Inc. innovative projects, referred as a
Case Study and the AHP method with SuperDecisions Software served as a framework to
optimize analyzing phenomena [12]. The structure of case demonstrates the strategic
criteria concerning the Project Portfolio Management (PPM) deal with Amedeo's family
dilemmas. The following AHP structure provides the case judgement based on the basic
information, knowledge, pairwise comparison, and reasons to derive the position of
priorities to which the company aspire to allocate and spread their efforts and their
enterprise resources. Although the Amedeo's family as many other pioneer companies
are not as thrilled about the old technologies in vehicle industry because of their looking
forward customer’s needs and because of their market competency forecasting system.
They as other pathfinder companies, regularly face the remarkably complex problems
concerning the ordering schedules and prioritizing the various strategic or innovative
decisions (Multi-decisions) [13]. The critical various criteria such as Product Design,
Financial Factors (ECV, PI, NPV), Forecasting the future Marketshare,Technical or Financial
Risks, and after all the Resource Commitments. In that striking critical circumstances, the
AHP method might assists the companies to reduce significantly their frustration of lost
and help them to increase their profits (as a strategic goal). The AHP technique and the
SuperDecisions demands users to introduce three (3) fundamental CLUSTERS concerning
Goal, Criteria, and implemented Alternatives.
The following Fig. 9.6 illustrates the screenshot of Main Network window from the AHP
for Workhorse Inc. case study in environment of SuperDecisions V3.0 [14]. Also, Fig. 9.6
includes the three (3) mentioned above clusters and the complete implementation of the
project. As mentioned previously in abstract the supplementary resource file format
called "AHP Workhorse.sdmod" is available in the attachment of the BEV [15] project.
Notice that the ".sdmod" stands for the SuperDecisions file format. The Fig. 9.6 shown
the GOAL CLUSTER including its “1Goal Node” and its four (4) strategic criteria: RISK,
CONTRIBUTION, COSTS, and PROFIT (Summed up according to Fig. 9.5). The full criteria
for the projects simplified in these only four routs. However that is evident these criteria
have some sub-criteria such as Planned Costs, Sunk Costs, and Cost to Production con-
cerning COST node. Putting sub-criteria into a hierarchical model might improve its
accuracy, and it can improve the syntonization and sensitization of results.
Belatedly, the third CLUSTER includes all available alternative projects: A1,A1.1,A2,A2.1,
A3, B1, B1.1, B2 and B2.1 (according to Fig. 9.5). Once analyzer arranges the clusters,
and he/she obtained their associated nodes, that is time to “Select parent (from) node”s
and connect them to the “Child (to) cluster”s. These parents nodes and their child win-
dows are shown in left side of Fig. 9.6.Then by clicking on the "1Goal Node" analyzer can
select the criteria and by clicking on the Judgments tabs, that might be possible to enter
in assessments mode where making the pairwise comparisons is predicted. (see Fig. 9.7)
Then by performing the same assessment progress for other clusters and nodes, the AHP
network will be complete (see the Fig. 9.8). However, in this phase of the analyzing
process, the comparison network of the project optimization would be possible.
Nevertheless, besides of pairwise comparison questionnaire mode, there are other four
(4) possible modes for entering assessments: Graphical, Verbal, Matrix, and Direct. The
Fig. 9.9 illustrates the screenshot of the "Matrix Mode" concerning the "1Goal Node"
while the direction of the arrow indicates the criterion in which the mentioned criteria is
more important (e.g. PROFIT is further preferable than COST). Additionally, the change of
the dominant elements is possible by Double-click on their relative arrows.
Ultimately, by click and mark the judgments set as a "Completed Comparison", since the
nodes are connected, the concluding result of "Inconsistency" will be reliable (Fig. 9.10).
The Results revealed that the "CONTRIBUTION" or the "Competency" of the Project. Also,
results determined the profitability (ECV, NPV, and PI) of both distinguished preferable
criteria (in this case the PROFIT and the CONTRIBUTION).
Fig. 9.5: Project Features & Resources Spent Per Quarter
Fig. 9.6: Screenshot of Main Network Window in AHP - SuperDecisions V3.0
Fig. 9.7: Screenshot of the Judgments tab and its Assessments Mode
Fig. 9.8: Comparison Questionnaire Concerning The CONTRIBUTION And RISK Clusters

Furthermore, the snap "Inconsistency Report" would be available by clicking on the
"Computing" tab where the Fig. 9.11 illustrates the results concerning the criteria nodes.
The second cell of Fig. 9.11, COST versus PROFIT (in red color), has a "Current Value" of
8.000000. It means that the "PROFIT"s are more important than "COST"s [see the Fig. 9.9
as the previous view of Matrix Mode, where the red arrow indicates the PROFIT].
By the way, this important topic resembles to be reasonable, since the incubators are
usually eager to financially invest in R&D activity of the new products (or new technology)
whenever those innovative outcomes might increase their profit. However, the Best Value
of 3.626568 (in red color as well) indicates the optimization because of the other criteria
judgments. For example, the "COST" should be a bit less preferable than the "PROFIT",
and if analyzers revise the corresponding judgment to a red judgment of 3.626568, the
"New Inconsistency" value would be 0.053830. This is a suggestion of SuperDecisions
software that seems reasonable as well because practically when the Planned Costs, Sunk
Costs, and Cost of Production in an innovative project increase so much, the probability of
distantly obtaining a fruitful and profitable project will decrease dramatically. After all,
the desirable and feasible results might be illustrated by clicking and by ordering to
synthesize the whole network. Fig. 9.12 revealed the overall synthesized priorities for the
alternatives of AHP networks concerning the Workhorse Inc. projects.As respects, only the
weight of B1 and B2.1 criterion is swayed the half of the entire evaluation [16]. Also, due
to the fact that, the goal cluster was to increase the Profit and decrease the failure, the
product line B has considered as the top ranked alternative.
Before the conclusion of this paper, it is imperative to mention that the SuperDecisions
software is puissant enough to offer a "Graphical Sensitivity” [17] of the “AHP network”.
The software would help users to create a graphical sensitivity. Also, users are able to
select the “Computations” and then “Sensitivity” command. Moreover, by selecting the
“Edit“ pop-up window, and by determining the Independent Variable (that is possible to
take to the Sensitivity input selector box), and users might change the “Independent
Variable” into their Goals. In fact, Fig. 9.13 illustrates the Sensitivity Analysis of the Work-
horse Inc.AHP model for the main network.
Indeed, in Sensitivity Analysis’ output, the products from line B (B1 orange color, and
B2.1 pink color) are graphically communicated as two top-rated project alternatives. The
graphical representation of Sensitivity is available in other four different modes such as Plot,
Piechart,Horizontal Bar-chart,and Bar-chart (Fig.9.14,Fig.9.15,Fig.9.16,and Fig.9.17).
FURTHER READING AND BWM
Recollect that for further research and for reading more concerning the classifications and
relative computations, the ".sdmod" SuperDecisions file and the Excel file regarding this
article is available in BEV attachments. Also for additional reading, besides the AHP as a
multi-criteria decision-making (MCDM) problem solver, the author recommends re-
searchers those are interested in MCDM investigations to read the Jafar Rezaei (2015),
Best-worst multi-criteria decision-making method, Elsevier B.V. ScienceDirect, Omega,
Volume 53, June 2015 [18] [19]. The Best-Worst Method (BWM) is a new proposed
method to solve the multi-criteria decision-making (MCDM) problems. The BWM aims to
solve MCDM problems according to the best (e.g. most desirable, most important) [20]
and the worst (e.g. least desirable, least important) criteria those initially identified by the
decision-maker. Furthermore, pairwise comparisons are then conducted between each of
those two criteria (Best and Worst) and the other criteria while a maximin problem is
expressed and solved to determine the weights of different criteria.
Fig. 9.9: Matrix Mode Comparison
Fig. 9.10: Oriented Results
Fig. 9.11: Inconsistency Report
Fig. 9.12: Overall Synthesized Priorities For The Alternatives & Ideals, Normals,And Raw
Fig. 9.13: Sensitivity Analysis

APPENDIX I
Fig. 9.17: Sensitivity Analysis Bar-Chart
Fig. 9.14: Sensitivity Analysis Plot
Fig. 9.16: Horizontal Bar-Chart
Fig. 9.15: Sensitivity Analysis Piechart

REFERENCES
[1] Marco Cantamessa, Francesca Montagna (2016), Management of Innovation and Product
Development, Integrating Business and Technological Perspectives, Springer-Verlag, London.
pp.163-188,Chap.9:InnovationStrategyasPPM.ISBN978-1-4471-6723-5
[2] Workhorse Group Inc. website: http://workhorse.com - Workhorse | Electric Trucks,
Delivery Drones,Telematics.
[3] SEC - US SECURITYAND EXCHANGE COMMISSION,Workhorse Group Inc.
[4] The analytic hierarchy process (AHP)
[5] The Analytic Network Process (ANP)
[6] Multiple-criteria decision-making (MCDM)
[7] Thomas L. Saaty - Wiki
[8] Thomas L. Saaty - University of Pittsburgh Katz Graduate School of Business.
[9] Thomas L. Saaty (1987), Decision making, new information, ranking and structure,
MathematicalModeling8:125-132·December1987;DOI:10.1016/0270-0255(87)90555-0
[10] Thomas L. Saaty - The Super Decisions: www.superdecisions.com
[11] Thomas L. Saaty (2017),Automobile industry - Supper Decision Cases.
[12] Thomas L. Saaty (1979), Optimization by the Analytic Hierarchy Process,
University of Pennsylvania.
[13] Thomas L. Saaty, "Multi-decisions Decision-Making: In addition to wheeling and
dealing our national politics needs a formal approach to prioritization for better decision-
making. How?", University of Pittsburgh.
[14] SuperDecisions V3.0 - Windows 3.0 Beta
[15] Sajjad Khaksari, Shabir Ahmad, Mirko Tartarotti (2017), Strategic Management of
Technological Innovation Concerning The Battery Electric Vehicles (BEV),Torino, Italy.
[16] B1 criterion + B2.1 criterion = 0.196706 + 0.279488 = 0.476194
[17] Graphical Sensitivity - “Sensitivity analysis is used to analyze the effects of variations
in judgments on the stability of the final outcome.” (1):3 - “In model building using the
AHP, sensitivity analysis is a crucial step in determining if the solution is implementable
and robust.” (15):345.
[18] Dr.J. (Jafar) Rezaei - TU Delft,Transport and Logistics Group.
[19] Jafar Rezaei (2015), "Best-worst multi-criteria decision-making method", Elsevier
B.V. ScienceDirect, Omega, Volume 53, June 2015, Pages 49–57. http://dx.doi.org/
10.1016/j.omega.2014.11.009
[20] Best-Worst Method (BWM) - http://bestworstmethod.com
[21] Madjid Tavanaa,b, Morteza Yazdanic and Debora Di Caprio (2017), An application of
an integrated ANP–QFD framework for sustainable supplier selection, INTERNATIONAL
JOURNAL OF LOGISTICS: RESEARCH AND APPLICATIONS, 2017 VOL. 20, NO. 3, 254–275
http://dx.doi.org/10.1080/13675567.2016.1219702
[22] Levine HA (2006), Project portfolio management: a practical guide to selecting
projects.Wiley, San Francisco.
[23] R. Srinivasan, "Himalaya: head-to-heel herbal healthcare", Emerald Emerging
Markets Case Studies, (2016) , https://doi.org/10.1108/EEMCS-06-2015-0137
[24] Gyan Prakash, Sangeeta Sahney, Abhinav Vohra, "The voice of the customer in the
design of organized retail stores", Emerald Emerging Markets Case Studies, (2015) ,
https://doi.org/10.1108/EEMCS-10-2014-0261
[25] Polymeros Chrysochou, Athanasios Krystallis, Ana Mocanu, Rachel Leigh Lewis,
(2012) "Generation Y preferences for wine: An exploratory study of the US market
applying the best-worst scaling", British Food Journal, Vol. 114 Issue: 4, pp.516-528,
https://doi.org/10.1108/00070701211219531
[26] Sanjaykumar R Gangurde, (2016) "Benchmark the best factory data collection
system (FDC) using AHP-GRA method", Benchmarking: An International Journal, Vol. 23
Issue: 2, pp.359-370, https://doi.org/10.1108/BIJ-03-2014-0023
[27] Chris Voss, Nikos Tsikriktsis, Mark Frohlich, (2002) "Case research in operations
management", International Journal of Operations & Production Management, Vol. 22
Issue: 2, pp.195-219, https://doi.org/10.1108/01443570210414329
[28] Thomas L. Saaty, Luis G. Vargas auth. (2013), "Decision Making with the Analytic
Network Process; Economic, Political, Social and Technological Applications with Benefits,
Opportunities, Costs and Risks", Springer, New York.
[29] Saaty TL (1980) The analytic hierarchy process. McGraw-Hill, New York.
[30] Belton V, Steward TJ (2002) Multiple criteria decision analysis, an integration
approach. Kluwer Academic Publishers, Norwell.
[31] Vesela Veleva, Maureen Hart, Tim Greiner, Cathy Crumbley, (2003) "Indicators for
measuring environmental sustainability: A case study of the pharmaceutical industry",
Benchmarking: An International Journal,Vol. 10 Issue: 2, pp.107-119.
[32] Oliver Gassmann (Editor), Hans Meixner (Editor), (2006) "Sensors Applications,
Volume 2, Sensors in Intelligent Buildings", Sensor Review, Vol. 26 Issue: 3, pp.252-252,
https://doi.org/10.1108/sr.2006.26.3.252.1
[33] Stephan Winterhalter,Tobias Weiblen, Christoph H. Wecht, Oliver Gassmann, (2017)
"Business model innovation processes in large corporations: insights from BASF",
Journal of Business Strategy,Vol. 38 Issue: 2, pp.62-75.
[34] Lockett G, Hetherington B, Yallup P, Stratford M, Cox B (1986) Modeling a research
portfolio using AHP: a group decision process. R&D Manage 16:151–160
[35] Chan, F. T. S., & Chung, S. H. (2004a). Multi-criteria genetic optimization for
distribution network problems. International Journal of Advanced Manufacturing
Technology, 24, 517–532.
[36] Carolina Machado, J. Paulo Davim (2013), Management and Engineering Innovation,
Wiley-ISTE.ISBN: 978-1-84821-554-2
[37] Thomas L. Saaty (1986), Absolute and relative measurement with the AHP. The most
livable cities in the United States, Socio-Economic Planning Sciences · February 1986.
DOI: 10.1016/0038-0121(86)90043-1
[38] Thomas L. Saaty (1980),The Analytic Hierarchy Process, University of Pittsburgh.
[39] William Ho, Ali Emrouznejad (2009), Multi-criteria logistics distribution network
design using SAS/OR, Expert Systems with Applications 36(3):7288-7298 ·
[40] Chan, F. T. S., & Chung, S. H. (2005). Multicriterion genetic optimization for due date
assigned distribution network problems. Decision Support Systems, 39, 661–675.
[41] Ali Emrouznejad & Marianna Marra (2017): The state of the art development of AHP
(1979–2017): a literature review with a social network analysis, International Journal of
Production Research, DOI: 10.1080/00207543.2017.1334976
[42] Altuzarra, A., P. Gargallo, J. Moreno-Jiménez, and M. Salvador. 2013. “Influence,
Relevance and Discordance of Criteria in AHP-global Bayesian Prioritization.”
International Journal of Information Technology & Decision Making 12: 837–861.
[43] Enrico Briano, Roberto Nicola Mosca, Roberto Revetria, Alessandro Testa (2011), A
Simplified Human Cognitive Approach for Supporting Crowd Modeling in Tunnel Fires
Emergency Simulation, 24th International Conference on Industrial Engineering and
Other Applications of Applied Intelligent Systems, IEA/AIE 2011, Syracuse, NY, USA.
[44] Siavash Khajavi, Jan Holmström, Jouni Partanen (2013), Additive manufacturing in
the spare parts supply chain, Computers in Industry 65(1) ·
[45] Boris Vladimirovich Sokolov, Dmitry Ivanov, Evgeniy M Zaychik (2006), The
Formalization And Investigation Of Processes For Structure-Dynamics Control Models
Adaptation Of Complex Business Systems, May 2006. DOI: 10.7148/2006-0292
[46] Gonçalo Homem de Almeida Correia (2017), The Urban Dynamics Educational
Simulator (UDES): tutorial for a tool to teach agent-based and Land Use and Transport
(LUT) Interactions Modeling (Model Version 1.3), Delft University of Technology, Version:
1, State: In Progress, DOI: 10.13140/RG.2.2.25661.26080/2.
[47] Johannes Müller, Gonçalo Homem de Almeida Correia, Klaus Bogenberger (2017),
An Explanatory Model Approach for the Spatial Distribution of Free-Floating Carsharing
Bookings: A Case-Study of German Cities, Sustainability 9(7):1290 · July 2017.
[48] Ali Emrouznejad, Madjid Tavana (2014), Performance Measurement with Fuzzy Data
Envelopment Analysis, Springer-Verlag Berlin Heidelberg. ISBN 978-3-642-41372-8
[49] Alem, S.M., Jolai, F., Nazari-Shirkoohi, IS.: An integrated fuzzy DEA-fuzzy AHP
approach: a new model for ranking decision-making units. Int.J. Operat. Res.
[50] Franziska Schmalfuß, Kristin Muehl, Josef Krems (2017), Direct experience with
battery electric vehicles (BEVs) matters when evaluating vehicle attributes, attitude and
purchase intention, Transportation Research Part F Traffic Psychology and Behaviour
46:47-69. DOI: 10.1016/j.trf.2017.01.004
[51] Alexander, R. (2017) Using the Analytical Hierarchy Process Model in the
Prioritization of Information Assurance Defense In-Depth Measures? A Quantitative Study.
Journal of Information Security, 8, 166-173.
[52] Mohammad Taleai, Ali Mansourian, Mohammad Ali Sharifi (2009), Surveying
general prospects and challenges of GIS implementation in developing countries: A
SWOT-AHP approach,Journal of Geographical Systems 11(3):291-310 ·
Page 5 of 5© Sajjad Khaksari (2017) - Torino

AHP and INNOVATION STRATEGY as PROJECT PORTFOLIO MANAGEMENT

Recommended

Recommended

More Related Content

Similar to AHP and INNOVATION STRATEGY as PROJECT PORTFOLIO MANAGEMENT

Similar to AHP and INNOVATION STRATEGY as PROJECT PORTFOLIO MANAGEMENT (20)

More from Sajjad Khaksari

More from Sajjad Khaksari (11)

Recently uploaded

Recently uploaded (20)

AHP and INNOVATION STRATEGY as PROJECT PORTFOLIO MANAGEMENT