Alkaner analytics applications and ship recycling a road map

Selim Alkaner PhD, CEng, MRINA, PMP Ministry of Defence – (MOD-UK)*

Presented at the International Conference on Ship Recycling SHIPREC 2013 8 - 9 April 2013, WMU, Malmö, Sweden Author Disclaimer: “The analysis, opinions and conclusions expressed in this paper are those of the author and do not necessarily represent those of the Ministry of Defence - United Kingdom, or any other department of Her Britannic Majesty’s Government of the UK. Further, such views should not be considered as constituting an official endorsement of factual accuracy, opinion, or recommendation of the MOD UK, or any other department of Her Britannic Majesty’s Government of the UK.”

© Dr. S.Alkaner, WMU SHIPREC, Malmö, 2013

Outline of the Presentation  Description of the Problem  Analytics:  Background in Ship Recycling  Overview of Approaches  Challenges for Ship Recycling  Roadmap for Ship Recycling  Conclusions  Recommendations

© Dr. S.Alkaner, WMU SHIPREC, Malmö, 2013

About the presenter  Currently at “Portfolio Analytics” Section in Cost

Assurance and Analysis Services (CAAS), Defence Equipment & Support (DE&S), Ministry of Defence UK  EU-FP5/6/7 Project work: Ship dismantling, Ship production, Life-cycle Environmental Impact Analysis of ships  PhD, Naval Architecture: Simulation Modelling of Ship Production  Naval Architect & Marine Engineer (ITU)  MRINA, CEng  Project & Programme Manager, PMP, APMP © Dr. S.Alkaner, WMU SHIPREC, Malmö, 2013

Description of the Problem The level of complexity creates a number of problems that is being discussed by the stakeholder community and the industry. Some of the key problems discussed and addressed within the scope of this presentation are as follows: Lack of clear boundaries between systems Conflict of interests amongst stakeholders Decision making under subjective and multi-objective-multiple-criteria Non-uniform expertise of multiple decision makers on system design attributes  Lack of objective scientific data to support and enhance decision making process    

© Dr. S.Alkaner, WMU SHIPREC, Malmö, 2013

Ship Recycling – System-of-Systems Ship recycling, as a “system-of-systems”, is a collection of dedicated sub-systems that pool resources and capabilities, comprised of interdependent and concurrent components which are linked through operational and managerial interactions. These sub-systems are:      

12-a 2-b 345-

The ship recycling facility: Safety & Health (S&H) risks S&H risks related to hazardous materials on board Other types of S&H risks Accidents and incidents Human factors and organisational features Risks for the environment

© Dr. S.Alkaner, WMU SHIPREC, Malmö, 2013

© Dr. S.Alkaner, WMU SHIPREC, Malmö, 2013

Analytics Analytics: “the scientific process of transforming data into insight for making better decisions" Insights, within the context of decision making, are new information giving decision maker a set of actionable ideas to drive the business in their stated desired direction and provide competitive advantage.

Š Dr. S.Alkaner, WMU SHIPREC, MalmÜ, 2013

Analytics – Overview of Approaches Focus

Key question

How it is done?

DESCRIPTIVE

PREDICTIVE

PRESCRIPTIVE

Prepare and analyse Historical data

Future trends and relationships in data

New ways to operate by balancing constraints

“what happened?”

“what will happen next?”

“What’s the best outcome given a set of circumstances?”

• Dashboards • Charts • Key Performance Indicators

© Dr. S.Alkaner, WMU SHIPREC, Malmö, 2013

• Statistical methods • Data mining • Forecasting • Predictive modelling

• Simulation • Optimisation

Analytics in Ship Recycling Design Parameters/Variables & Operating Conditions v. Objectives

Ref: EU-FP7-DIVEST & EU-FP6- SHIPDISMANTL Projects

© Dr. S.Alkaner, WMU SHIPREC, Malmö, 2013

Analytics in Ship Recycling

Ref: EU-FP7-DIVEST (B L/R) & EU-FP6- SHIPDISMANTL (T L/R) Projects

© Dr. S.Alkaner, WMU SHIPREC, Malmö, 2013

Analytics – Hierarchy of Approaches Prescriptive Predictive

Applicable knowledge

Descriptive

Computational models

Descriptions / Explanations

Information (facts) Ref: Lundin M, et al., Uncertainty modelling for threat analysis, Proceedings of the14th ICCRTS, 2009, Washington D.C.

© Dr. S.Alkaner, WMU SHIPREC, Malmö, 2013

Analytics - Challenges

Ref: Thomas H. Davenport, The Rise of Analytical Performance Management, SAS Whitepaper

+ Challenges of pan-industry & collaborative work in Ship Recycling Competition Market Forces Ownership of Outputs Commercial confidentiality Intellectual Property Rights Š Dr. S.Alkaner, WMU SHIPREC, MalmÜ, 2013

Analytics Framework for Ship Recycling Strategy & Planning

Define Analytics Strategy Define problem, objectives & metrics

Data collection & preparation

Execution

Integrate Technology

Perform Analytics

Implement decisions

Report Results & insights

Performance Management

Monitor & follow-up

Š Dr. S.Alkaner, WMU SHIPREC, MalmÜ, 2013

Sustain Performance Improvement

Conclusions  Analytics is "the scientific process of transforming data into insight for     

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making better decisions" Actionable ideas are created as an output of Analytics such as Descriptive, Predictive and Prescriptive approaches Use of "Analytics" as an emerging approach that would help decision makers to increase the speed and quality of their actions Descriptive Analytics has already gained acceptance in the ship recycling community and includes contributions from all stakeholders The Community's need to answer more complex questions demands a move from Descriptive to Predictive Analytics Understanding the impact of decisions that would affect a number of systems concurrently is of critical importance in complex systems such as ship recycling Prescriptive Analytics, as a sustainable solution, is needed for connecting the knowledge gained at the ship's end-of-life with the design stage, as the industry’s reputation in safe, green, and cost-effective operations increases

© Dr. S.Alkaner, WMU SHIPREC, Malmö, 2013

Recommendations  A Framework for successfully implementing Analytics Capability is presented

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as 3-stage approach. 1) Strategy and Planning, 2) Execution of Analytics, 3) Performance Management Integration issues of data sources and technologies should also be considered as key enablers to Analytics Use of more complex and resource intensive Predictive and Prescriptive Analytics would bring stricter protection of outputs as market forces, competition, and confidentiality as well as IPR issues emerge Setting up an organisation such as " Centre for Analytical Excellence for Ship Life-cycle " would help to overcome roadblocks and lead to analytical maturity in the sector Support and funding of data-oriented work as well as establishing required key technology infrastructure for advanced Analytics studies by recognised and independent international maritime bodies would be welcomed

© Dr. S.Alkaner, WMU SHIPREC, Malmö, 2013

Contact: Dr. Selim Alkaner s.alkaner@btinternet.com © Dr. S.Alkaner, WMU SHIPREC, Malmö, 2013

© Selim Alkaner

© Selim Alkaner

Thank you