Fujitsu 2017 Future of Logistics Challenge
From the science and technology perspective, identify an innovative logistic opportunity and explain why, how and when it would deliver benefit across the Defence Support Network. Given the delegated Defence Operating Model, how would you ensure rapid and successful implementation?
Major Richard Sanders RLC is an accomplished writer, having won a previous Fujitsu Future of Logistic Challenge. He again produces a lucid paper that offers a good, clear articulation of many of the lines of development in the UK’s Defence Support Network Transformation (DSN(T) programme. This work is very well researched and written, and leads to the conclusion that if the planned transformation goes well, it could lead to logistic information superiority for UK Armed Forces.
Sanders has looked at some of the challenges associated with new technology, such as the legal, ethical and social risks associated with allowing advanced analytics to make contractual decisions but, given the enormous breadth of innovative technology he highlights, leaves it to the reader to assess and decide how to manage those risks. This essay provides a very good overview that demonstrates current DSN(T) programme options, many of which ACDS (Log Ops) and the wider MOD is already investigating. Major Sanders was equal third in the 9th Fujitsu Future of Logistics Challenge annual Essay Competition in 2017.
“A Transformed Defence Support Network that delivers globally-agile logistics support to the Joint Expeditionary Force and Joint Force 2025, maximising the freedom of action of operational commanders”. (Defence Logistics Vision 2016)
Technological innovation offers a vision of machines executing logistic processes cheaper, faster and better, freeing logisticians to focus on enabling the commander’s intent. In reality technology presents both opportunity and risk. Progressing from an imperfect present to an envisioned future requires strategic insight and leadership, and balanced trade-offs in disciplined execution of change programme management. Few technologies originate in the defence sector, the rate and direction of technological change cannot be controlled by defence, and it is often a combination of technologies that leads to real innovation[i]. Integrating sufficiently mature technologies across the Defence Support Network (DSN) will involve changes in processes, organisation and culture; requiring time and sustained effort to become securely embedded.
This essay will ultimately examine the opportunity for the DSN to gain information superiority through the integration and risk mitigation of five technologies, under governance from the delegated Defence Operating Model. The five technologies are: Artificial Intelligence (AI); the Internet of Things (IoT), Human Machine Interfaces (HMI), platform based architectures, and Distributed Ledger Technology (DLT).
This essay is in four parts:
- The opportunities of change – why technological innovation in the DSN is non-discretionary;
- How corporate governance will enable delegated implementation of this change;
- Innovative technologies, the opportunities and challenges they present to the DSN,
- Summary – bringing together technological, organisational and cultural change.
The Opportunities of Change
Why Change is Essential
The DSN is a complex, adaptive, socio-technical system of systems that generates data in such volume and diversity that it challenges human capacity to exploit it fully. These information exchanges often occur within constantly altering physical and informational networks, often also in austere environments. Information overload easily arises, impacting human cognition in making optimal and timely decisions. Time and effort is wasted in finding and validating information, issues of information security place strain on delivery and assurance systems. Humans are prone to error and bias, and largely blind to underlying patterns and trends in big data sets. This leaves them poorly equipped to anticipate and forestall log-jams, bottlenecks and other emergent phenomena, or to manage the resultant feedback that tips the system toward failure. This prompts emergency interventions which inevitably adds to systemic disorder. Information overload therefore impedes and adds cost to flows of materiel, services, information and money across all stakeholders and core functions of Defence. Buying out risk by efficiently stockpiling materiel or assets requires dynamic systemic understanding of how DSN architectures will behave under varying loads. Political imperatives of value for money, and potential adversaries’ assaying of UK sustainability provide the ultimate incentives to deliver value for defence.
The DSN could benefit greatly from innovative change, if technological reach and organisational grasp could be well aligned. Whether the enterprise business driver be geopolitical strategy, or shareholder value and profit, value travels both ways across the private/public divide. The fundamental nature or ‘physics’ of logistics are common to both sectors, as recognised by MoD’s adoption of the DefSCOR model and Defence Logistic Framework (DLF), yet the DSN compares poorly against industry benchmarks, as shown by Table 1. The existing, largely ‘mandraulic’ systems and ways of working have probably squeezed out as much benefit as they ever will; Defence needs a logistical information force multiplier if it is to deliver order of magnitude improvements.
|Comparing MoD performance to industry benchmarks|
|Demand forecast accuracy||97.8%||47%|
|Raw Materials and Consumables inventory cover||15 days||10.3 years|
|Average lead time from dispatch from industry
to available for issue
|3 days||23 days|
|Deliveries on time||98%||70%|
|% of inventory sent for disposal||0.3%||8%|
Table 1 – MoD Supply Chain benchmarked performance (from EY Support Chain Improvement High Level Business Case dated 3 Jul 15)
Potential Benefits of Innovative Technologies
Innovative technologies may help reduce information overload and exploit information as a strategic asset across the DSN, reducing overheads and maximising outputs. The opportunities to do this are broadly characterised as follows:
- Reducing transaction time and process error end-to-end;
- Enhancing trust and security across a decentralised, federated and franchised support network through demonstrable assurance, provenance and distributed encryption;
- Improving ‘what-if’ and real-time inventory and asset demand/availability forecasting and spend and fulfilment options across concurrency sets and DSN configurations;
- Maintaining higher asset availability and fidelity through assured component acquisition, provenance and through-life management
- Contracting agilely across the Defence Industrial community, especially benefitting SMEs.
In an enterprise as complex as the MoD and wider DSN, how might change be planned, executed and controlled to deliver these benefits?
How corporate governance will enable implementation
Corporate Governance and the Delegated Defence Operating Model
Corporate governance is the way in which organisations are directed, controlled and led, defining relationships and the distribution of rights and responsibilities, rules and procedures for achieving objectives and managing performance. A strong corporate framework takes an evidence based approach to achieve outputs and hold those responsible to account.
The Defence Board and Head Office direct strategic capability requirements and priorities in accordance with which the FLCs (including JFC which includes ACDS (Log Ops) the logistics process owner) generate and develop capability through resource agreed with Financial and Military Capability Planning (FinMilCap). The ‘Operate’ function lies with Permanent Joint Headquarters (PJHQ), Joint Force Logistics Command (JFLogC) and a range of joint capabilities under Joint Forces Command (JFC). Delivering the ‘Enable’ core function to Front Line Command (FLC) requirements are Defence Infrastructure Organisation (DIO), Science and Technology (S&T) and Defence Business Services (DBS). Defence Equipment and Support (DE&S) acquires materiel from industry to fulfil FLCs requirements. Information Systems Services (ISS) (part of JFC) has a crucial cross-functional role across the DOM in that the Chief Defence Information Officer (CDIO) Directs IT policy across Defence and acts as skills champion in generation and development of information capability across all TLBs and FLCS. ISS both acquires information systems, acts as delivery agent for Joint enablers, and delivers day to day for customers in the ‘operate’ function. The CDIO owns the MoD Information Strategy (MODIS) which mandates Enterprise Architecture and Network Rules to be applied to all Information and Communications Technology (ICT) that will contribute to or consume Defence network resources. The MODIS is central to realising the technological opportunity for DSN and has 3 principal thrusts:
- Defence as a Platform (DaaP);
- Placing customers as the central ISS focus;
- Agile procurement in information systems and services.
Innovative technologies, the opportunities and challenges they present to the DSN
Challenges of Change
Change programmes must be in phase with the conceptual, future and funded capability planning horizons, in which technology maturity level is a major factor. Technologies must be capable of integration within future systems, whilst permissive of operating and migrating legacy systems and programmes. Evidence-based evaluation processes de-risk potentially promising but immature and doubtfully scalable technology solutions. Innovation must advance as fast as necessary – but no faster – maintaining coherence in governance and across Defence Lines of Development (DLoDs) is key.
The Five Innovative Technologies
Innovative technologies are best tested in the crucible of commercial competition – the deployed space is no place for immature, ‘bleeding edge’ technologies. What technologies might enable the opportunity of logistics information superiority?
Artificial Intelligence (AI)
Artificial Intelligence is an overarching term for computer programmes that take a group of examples, work out a pattern that explains the examples, then uses these patterns to make predictions about new examples. The programme can thus find significant trends and patterns across big data sets and so aid timely and better human decisions. Min (2010)[ii] argues that AI in a logistics context has several sub-fields demonstrably suited to supply chain management, four of which are summarised below:
- Machine learning provides computers with the ability to learn without being explicitly programmed. The computer acquires knowledge directly from data and learns to solve problems. A DSN platform application using machine learning could enable data from equipment usage, component resupply and platform repair to be conflated probabilistically and update the enterprise reference data warehouse. Such contextualised information would inform future support chain spend, configuration and operations management decisions for planned or actual circumstances at the strategic, operational and tactical levels.
- Expert systems emulate human cognitive skills for a defined context using rules and heuristics provided by human experts. A human-machine interface (HMI) makes process workings comprehensible and therefore trusted by the user. Expert systems have been used to solve multiple echelon inventory control problems, scheduling and inventory optimisation in manufacturing, and vehicle repair and maintenance scheduling.
- Genetic Algorithms (GA) are stochastic techniques that search for solutions in ways that mimic natural evolution. They have been successfully applied to problems such as vehicle routing and scheduling, delivery and pickup, transportation network optimisation, and location-allocation problems.
- Agent-based systems sub-divide and then solve a decision problem using independent programme entities called agents which can compete and cooperate with other agents whilst pursuing their own goals. They have been used in supply chain management to augment or partially replace human decision making in demand planning and forecasting, production planning, supply chain coordination, strategic e-procurement.
The Internet of Things (IoT)
This refers to the potential connectivity of any item to be connected to the internet with unique identifiers or codes that can transfer any operating data collected (such as by Health and Usage Monitoring Systems) over a network, without human involvement. With the appropriate architectures in place, and enabled by AI (e.g. ability to interpret data such as excessive vibration or heat), devices perform on the edge of the enterprise, co-ordinated from centralised or decentralised hubs. The systems interrelations deliver synergistic benefits such as smarter deployed operational infrastructure, maintenance, materiel and repair management, or transportation.
Platform based enterprise architectures
An Enterprise Architecture (EA) is a structured approach to the capture of information about the business in order to support business improvement. In Defence, EA spans all IT systems under development and in use across the delegated Defence Operating Model. The platform based approach is a new aspect of information architecture. In this context ‘Platform’ means a group of technologies that are used as a base upon which other applications, processes or technologies are developed or used, with enablers that turn technology platforms into a coherent ecosystem including third party developers[iii].
Congruent with the Government as a Platform (GaaP) initiative, DaaP aims to deliver Defence ICT services with shared sets of components and infrastructure (e.g. compute, network, storage) supporting all mission and enabling services/applications (e.g. logistics). Each platform has fully integrated sets of corporate and specialised ICT services that FLC and Top Level Budget (TLB) customers can procure easily through an Information Systems Services (ISS) service catalogue. DaaP will also provide a platform for customers to build and operate their own Line of Business applications and ICT capabilities, integrated and interoperable with other platform services.
Descriptions of what the core platforms do (DaaP version 2.0 hence slightly different from shown in Figure 1) are shown at Table 2.
|Connectivity Platform||Enables users to connect systems and services and to third party networks through a range of bearers connecting “battlespace”, “barracks”, “business” and beyond.|
|Workplace Platform||Enables users to produce, share and consume “common” information services such as productivity, messaging, collaboration, voice, video or web browsing.|
|Hosting Platform||Hosts data, applications and/or services within DaaP with cloud based and private storage and computing options. Expected to evolve from infrastructure services to a Platform as a Service (PaaS), exploiting Service Oriented Architectures. (Author’s note: may extend to specialised focus (e.g. logistics, medical ) applications that may evolve into community of interest platforms – ‘logistics as a platform’).|
|Analytics Platform||Provides contextual analysed data out of all the stored information including services to store and manage the data. Through specialised analysis queries, it will mine data stored in the Hosting Platform and processes.|
|Provisioning Platform||Facilitates development of systems and services to connect to or integrate within DaaP.|
|Enterprise Defence Platform||Enables MOD Information Systems to be monitored and defended against attacks.|
|Enterprise Management Platform||A platform that enables the services offered in other DaaP platforms, and other services, to be managed and continually optimised.|
Table 2 – Description of DaaP 2.0 core platforms (MoD ISS 2016)
These technologies will find their expression in the DaaP ‘New Style of IT’ (NSOIT) (Base) and NSOIT (Deployed). Technology maturity risk means that DaaP (Base) architectures should first be established before ‘Logistics as a Platform’ begins to emerge in the deployed space. Concurrently, the People and Training Doctrine DLoDs would need to produce logisticians culturally attuned, indoctrinated and trained to help develop and exploit the new services.
Towards Logistics as a Platform
The DSN (Transformation) Information Services Spiral 1 Programme (Figure 5) manages migration from legacy logs IS to new Services In the timeframe 2017-2021. The following services will be hosted on DaaP (Future) open architectures:
- An Integrated Shared Service Layer enabling transformation through a ‘plug and play’ approach to the integration of legacy and new services.
- A stable Enterprise Data Warehouse (EDW) holding data from transactional systems, operational data stores and external sources suitably aggregated for data analysis and business reporting.
- Master Data Management (MDM) processes and tools defining and managing non- transactional data) in consistent format and of assured provenance.
Any horizon scanning by ISS for new IT solutions must accord with technology maturity policy and scrutiny by the Chief Scientist’s S&T department. Piloting and experimentation includes such initiatives as the Innovation Accelerator (administered by Defence Science and Technology Laboratory (Dstl)) which provides opportunity for small and medium industry players to prepare technology demonstrators. Evidence based on realistic and robust use cases confirm proof of concept sufficient to take the capability to initial gate and gaining funding for the assessment phase of the acquisition cycle, including questions of scalability, integration and cross-DLoD capability development.
Distributed Ledger Technology (DLT) and smart contracts
Highly centralised systems represent a single point of failure and are more vulnerable to attack than distributed systems. Distributed ledgers have the potential advantage of moving much of the security processes into the background, making systems easier and cheaper to use on cloud-hosted, platform based architectures.
In non-financial contexts it is possible for such distributed ledgers – and their encryption mechanisms – to be partially private or ‘permissioned’, such as for an accredited stakeholders to the DSN, and their networks of suppliers, as shown at Figure 7. Sometimes described as ‘Block Chain’, this technology was developed to underpin the Bitcoin virtual currency hosted on a fully public ledger.
Figure 7 – Permissionless and Permissioned DLT Systems and possible DSN scope (modified from CSA, 2016)
A ‘blockchain’ is a distributed and encrypted consensus system that maintains a permanent and immutable record of transactions on the web, incapable of being falsified after the event and not requiring third party validation. A block chain is a list of blocks, each one referring back cryptographically to the one that went before. From the starting block being verified by multiple computers distributed around the network, successive transactions add to the block creating simultaneous multiple copies creating an indestructible record of an accounting ledger alterable only by falsifying every single copy of the distributed ledger. Each party throughout the chain has access to the complete and non-falsifiable transactional record going back to its origin. Another potential benefit of such technology is that information assurance can be largely automated, allowing the audit and inspection regime more focused human attention.
On the back of DLT, smart contracts represent computer programs that automatically validate and execute conditions that have to be realised to complete a transaction. These would be auditable records that should fit well with DaaP and could apply to physical goods and services as well as financial transactions. The technical code embedded in the contract obviates the need for a third party and assures compliance with the conditions of the contract, or will not execute all activities if all stipulations are not met. Smart contracts employed in DSN services could, for example, govern the manufacture, regulatory and licensing provenance of assets or their components, from raw material to major sub-assemblies and finished product. The item’s life cycle, through-life properties such as configuration and upgrade, its progress through the supply chain – all could be recorded as per Figure 8 whilst updating the data warehouses and Master data registers of the DSN.
Figure 8 – The Blockchain and IoT enabled supply chain
As a result the usage data captured on the analytics platform can be assured as to its currency, accuracy and veracity, engendering trust and simplifying the assurance process, as well as performance and usage information, with trends detected and options offered to the human decision maker through AI embedded in workplace and analytics platforms and applications. The same principle could apply to any other good or service based contract, enabled by IoT connectivity, executed by AI assisted applications and hosted on platform based services and architectures. Smart contracts may make transactional or low value contracting, fulfilment and payment for goods and services more agile and readily accessible, to the benefit of taxpayers and the wider economy, especially the SME sector. Other foreseen benefits of DLT and smart contracts include transparency and traceability in how UK money obtains value for defence from contractors in support to operations, Host Nation Support or to and from alliance partners through strengthened interoperability. Estonia is a lead nation in applying DLT across the private and public sector, and would be a good place to begin scoping the feasibility and scalability of any such approach. The NATO Communications and Information Agency is evaluating areas of blockchain technology relating to military logistics as part of the NATO 2016 Innovation Challenge, and the results are as yet awaited.
On the risk side, the application of contract law alongside technical code would not be straightforward to implement if legal, ethical and social complications are to be minimised. The rigour of technical code would need to be at least as good as for legislative regulations. The ‘people’ questions of governance, doctrine and training surrounding implementation would be as complex as the technological aspects. The scalability of the technology with an alliance context would need extensive piloting and experimentation before its technological, regulatory, cultural and operational risks became acceptable.
People – the Human Machine Interface
For the human logistician to understand enablement of the supported commander’s intent and management of logistics risks, the basic ‘physics’ of the DSN must be understood, in terms of its EA and desired ‘ends’, enabling ‘ways’ and ‘means’. Also understood must be the taxonomy of decisions which should be fully automated, partially delegated with options for human decision , and those political/policy aims and values which are inherently human and cannot be left to machines. The more AI systems learn, and the more humans learn to trust them, the higher up the decision hierarchy AI will climb. We do not yet know if or when the machines will join humans at the top of this particular ladder.
In fashioning the informational views needed to support human decisions, logisticians will need to reflect on their logistics Command and Control, problem structuring, and decision making methodologies. The translation of commander’s intent via a logistics concept to an automated and recognisable logistics picture will require collaboration between enterprise architects, logisticians, data scientists and computing specialists. The machine-human information flows are only as useful as the capacity of human cognition to understand and act upon them. Professional logisticians will need to reflect and imagine what logistic informational views will be needed by decision makers across the enterprise. One cannot manage what one does not understand.
Summary and Conclusions
The combination of emerging technologies may, in time, add benefit if properly integrated. Such integration requires S&T-led and evaluated pilot studies, such as the UK Innovation Accelerator and NATO Innovation Challenge programmes. The scalability of a given technology would need to be systematically evaluated; what works in a small or medium enterprise may not scale up to alliance level. Technical and organisational de-risking (such as processes and culture change) would have to be assessed on the basis of lessons learned from established practice in industry and the home base before being extended into the deployed space.
The delegated Defence Operating Model as a meta-enterprise architecture ensuring coherence between functions, will enable Defence TLBs and FLCs to advance together, as quickly as is possible and desirable. The Defence IS strategy and ISS cross-functional role are central to the coherent development of DaaP, upon which innovative technologies will be hosted to meet customer requirements. The secure connectivity of devices through IoT requires robust bearer platforms and security and assurance regimes. Until the DaaP architectures in place, IoT connectivity and AI incorporated into applications and services, smart contracts and DLT cannot be connected through distributed and decentralised computing, leaving an infeasible computational and infrastructure burden. The DaaP and the DSN IT programmes are subject to resource constraints, as are all Defence programmes, and implementation of DSN(T) may well slip beyond current timeframes.
The parallel migration from legacy architectures and services, and the cultural, doctrinal, procedural and organisational changes attending, are at least as challenging as the technological problem. They also present an opportunity, whereby practitioners may reflect on the information needs for decision making, and the manner in which this information needs to be presented to enable commanders’ understanding of what is logistically feasible. Professional and technologically aware logisticians, collaborating with data scientists, human science specialists and computing developers, will – in time – enable IoT generated, blockchain assured, AI enabled logistical decision making. In so doing they will deliver information superiority to the DSN.
UK Government and MoD Sources
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HM Government Office for Science (2014) ‘The Internet of Things: making the most of the Second Digital Revolution’ A report by the UK Government Chief Scientific Adviser
MOD Defence Information Strategy 2016 (DCIO)
MOD JSP 604 – Defence Architecture Principles
MOD ISS (2016) – ‘CIO Note on the Enterprise Architecture to deliver Defence as a Platform’ (unpublished draft) accessed via ISS MOSS Site
MOD ISS ‘Designing for the future: Defence as a Platform (DaaP)’ presentation dated 4 Jun 2015
MOD (2016) ACDS (Log Ops) Defence Logistics Vision 2016 – Presentation
MOD ACDS (Log Ops) Logistics Network Enabled Capability Strategic Direction Jul 2011
MOD JFC DSN(T) IS Presentation dated 20 Jun 2016
MOD (2015) ‘How Defence Works’ Version 4.2
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Financial Times CapGemini ‘What blockchain can do for government’ accessed 15/04 2017
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MAinelli M and Smith M (2015) Sharing ledgers for sharing economies: an exploration of mutual distributed ledgers (aka blockchain technology) Global Financial Services Institute Winter 2015 Volume 3 – Issue 3
Min, Hokey (2010) ‘Artificial intelligence in supply chain management: theory and applications’, International Journal of Logistics Research and Applications, 13: 1, 13-39, First published on : 24 March 2009 (iFirst)
Badzar A (2016) Blockchain for securing Sustainable Transport Contracts and Supply Chain Transparency MSC Thesis Lund University
Abeyratne, S.A. and Monfared, R.P. (2016) Blockchain ready manufacturing supply chain using distributed ledger. International Journal of Research in Engineering and Technology , 05(09) pp 1-10
 The Defence Support Network is an evolution of the UK MOD’s Joint Supply Chain and recognises the increasing complexity of logistics, information, and enabling activities for defence on operations, and preparing force elements for operations. It includes Regular and Reserve Forces, other government departments, allies and industrial partners.
 The Defence Operating Model has its origins in Lord Levene’s 2010 Defence Reform Review, and became fully operational on 1 April 2014. While Defence will continue to refine parts of the operating model, this is now the long-term basis on which Defence business is managed. The operating model is based on: simple structures; fair and transparent delegation of responsibility to those best able to deliver strong organisational leadership, coupled with practical business skills; a culture of innovation and efficiency, removing needless process and flushing out bureaucracy; and joined-up corporate thinking and behaviour, placing the best interests of Defence at the heart of business.
 Under the Defence Operating Model the six core functions are: Direct; Acquire; Generate and Develop; Enable; Operate; and Account.
 DefSCOR is the enterprise methodology for DSN(T), a Defence-focused adaptation of the industry standard Supply Chain Operations Reference Model. The Defence Logistic Framework (DLF) is a digital codification of the Support Chain processes formerly covered by JSP 886 (The Defence Support Chain Manual).
 Defence Planning Assumptions are based on a series of operational scenarios, often comprising ‘concurrency sets’ of varying sizes and lengths of deployed operational tasks.
 Defence Enterprise Architecture and Network Rules are covered by JSP 604 .
 Government as a Platform(GaaP) is the vision for digital government; a common core infrastructure of shared digital systems, technology and processes on which to build customer centric government services; Defence as a Platform (DaaP), provides a set of common corporate and differentiated ICT services and is a significant contributor to GaaP. DaaP responds to the GaaP initiative by updating the Defence architecture to provide a universal user experience, and to ensure all systems and applications are ‘evergreen’ and deliver solutions that avoid vendor lock-in. The implementation of DaaP involves 3 interconnected strategic themes:
Defining and managing the ICT architecture for defence.
Continuing the transformation of ISS into an ‘ICT as a service’ organisation.
Developing a platform ecosystem to guide innovation to the benefit of both MOD customers and ICT suppliers.
 The 8 Defence Lines of (Capability) Development are considered under the headings: Training; Equipment; People; Information; Doctrine and concepts; Organisation; Infrastructure; and Logistics.
 Min(2010) identifies five machine learning approaches: 1. inductively based concept learning; 2. decision tree learning; 3. perceptron learning (an algorithm for deciding whether an input belongs to some specific class or not); 4. Bayesian learning based on increased probability of an inference as more data becomes available); 5. reinforcement learning that trains the computer to perform at high levels by giving constant rewarding feedback.
 Heuristics are mental shortcuts acquired through experiential learning in a given context.
 A stochastic process (such as the Monte Carlo technique) uses randomly generated instances within a given mathematical distribution to provide a probabilistic range of likely outcomes for a given input. This contrasts with a deterministic process which provides a given numerical output for a given input.
 Connected through technology such as Radio Frequency Identification (RFID).
 The structure or EA in UK Defence is the NATO Architecture Framework, supported by The Open Group Architecture Framework (TOGAF).
 The Top Level Budget (TLB) holder organisations within the MOD are:
Air Command; Central TLB; Joint Forces Command; Defence Equipment and Support; Land Forces; Navy Command
 It is anticipated that there will be a need for additional specialised applications which may evolve into community of interest owned platforms.
 Spiral 2 (not yet funded) is an engineering through life support capability.
[i] Dstl presentation to DELIVERING INNOVATIVE DEFENCE RESEARCH AND TECHNOLOGY Dr Bryan Wells Defence Science and Technology, UKMOD Chair, EDA R&T Steering Board Amsterdam, 25 April 2016
[ii] Min, Hokey (2010) ‘Artificial intelligence in supply chain management: theory and applications’, International Journal of Logistics Research and Applications, 13: 1, 13-39, First published on : 24 March 2009 (iFirst) Available from: https://www.researchgate.net/publication/247523024_Artificial_intelligence_in_supply_chain_management_Theory_and_applications [accessed Apr 30, 2017].
[iii] MOD ISS: (2016 ) Draft ISS paper ‘Defining the Enterprise Architecture to deliver Defence as a Platform’ Author: Wallis, K. V0.1
 Dstl presentation to DELIVERING INNOVATIVE DEFENCE RESEARCH AND TECHNOLOGY Dr Bryan Wells Defence Science and Technology, UKMOD Chair, EDA R&T Steering Board Amsterdam, 25 April 2016
 Min, Hokey (2010) ‘Artificial intelligence in supply chain management: theory and applications’, International Journal of Logistics Research and Applications, 13: 1, 13-39, First published on : 24 March 2009 (iFirst) Available from: https://www.researchgate.net/publication/247523024_Artificial_intelligence_in_supply_chain_management_Theory_and_applications [accessed Apr 30, 2017].
 MOD ISS: (2016 ) Draft ISS paper ‘Defining the Enterprise Architecture to deliver Defence as a Platform’ Author: Wallis, K. V0.1