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Transforming Army’s Logistics Capabilities through Emerging Big Data Analytics – Challenges and Opportunities

Journal Edition
Volume 14, Number 2, Cyber-warfare Edition

‘The problem has never been that the issues relevant to logistics transformation have remained unknown. Rather the problem has been the manner and means by which change is implemented.’

- Lieutenant Colonel David Beaumont

Abstract

The key to a strong military organisation lies not only in its combat power, but also in its ability to generate logistic and battlefield intelligence, and effectively use it to make timely decisions. In the event of a war, soldiers with different equipment, vehicles, and communication systems are deployed and the battlefield situation is monitored using several information systems. The faster a military can analyse, interpret, and make decisions from the information, the faster it will be able to respond to the threats it faces. Big Data Analytics, commonly known as Big Data, has a potential to transform Army’s business, and Army needs to harness its benefits in order to bolster its Combat Service Support (CSS) capability.

Introduction

Big Data 1 is defined as a large, complicated volume of data, whether structured or unstructured, that inundates business operations on a daily basis. Big Data consists of data sets that are so huge and complex that the customary data processing applications would not adequately handle them. The concept of Big Data can be understood through the description of the ‘Four Vs.’ 2 First, Volume – whereby organisations collect large data from a variety of sources such as financial transactions, business dealings, and social media platforms. Second, Velocity – where data streams in at an unknown speed, which has to be handled in a timely fashion. Third, Variety – where data can be structured or unstructured, and come in many forms, such as numeric, audio, video, and even mail. Finally, Veracity – where data extracted from several sources needs its veracity to be verified.

The contemporary age of budget austerity exerts unrelenting pressures on key Defence decision makers. 3 One of the crucial elements to a successful military operation is the provision of superior logistics capability. Military logistics relies strongly on the flow of information, materiel management, and finance. This article will posit that the adoption of Big Data in conjunction with emerging and secure technologies is crucial for military logistics operations, but that in order to do so numerous challenges must be dealt with. This article will first describe Big Data analytics and its generic military applications. It will then identify and enumerate the major opportunities that could be used for military logistics operations including its use in contemporary Defence initiatives. Finally, it will identify major challenges associated with Big Data and the use of Big Data analytics within Defence and suggest potential solutions to overcome challenges.

Big Data Techniques

Exploration and exploitation (ie analytics) of Big Data involves acquisition, cleaning, and transformation of data, the extraction of understanding of the relationships that exist in the data, and finally delivery of value from the data.4 The first Big Data technique is ‘Association Rule Learning’ which is used for the discovery of interesting correlations between variables in large databases. The second technique is ‘Classification Tree Analysis’ which is used to identify the categories in which a new observation fits. The third technique is ‘Genetic Algorithms’ which are evolution functions and are used for the identification of inheritance, mutation, and natural selection data. The fourth technique is ‘Machine Learning’ which is used to

differentiate between the spam and non-spam emails while determining the best content for dealing with potential customers.5 Big Data encompasses the use of predictive analysis, user behaviour analytics, and other complex data analytics techniques for the extraction of value from data.6 Several commercial organisations utilise Big Data for consumer intelligence by using predictive analytics.7 The first type of Big Data that resides in military logistics is the structured type that is stored in the databases in an orderly manner. In military logistics, machines and humans are the two sources

of structured data, and examples include global positioning system (GPS) data, usage statistics of vehicles, ships and aircraft, and health care data. The second type is unstructured data that resides in traditional column databases and which have no clear format in storage. Examples include mobile communication and geospatial data.8

Applications

Big Data can be applied to bring together data from multi-domain military capabilities, for example, data from logistics, health informatics, intelligence, information warfare, financial management, human resources, and geospatial system and space management; however, Big Data cannot be used to generate new plans for predicting the future with a higher level of certainty.9 Big Data could enable military planners to use data subjects that revolve around equipment usage and inventory, maintenance of aircraft, ships and vehicles, configuration baseline management, technical directives, and supply cost to come up with a trend analysis for recurring exercises.

Furthermore, for Army, enterprise- wide visibility such as where their assets are, the resources expended, the number of hours the resources were used per day, and the number of resources used per day is pertinent. Big Data can be used to uncover improper maintenance of equipment, issues in the training sessions of soldiers or any component issue. Additionally, daily readiness reporting that involves a process for messages going out daily from aircraft, vehicles, ships, or unmanned aerial vehicles can be done using Big Data.

When a military organisation’s applications reside on its premises, it is responsible for satisfying the regulatory compliance requirements. The military organisation may move the applications and data to a cloud- storage capability but it cannot move the regulatory compliance and duty of care to protect privacy. The first major opportunity is to use cloud computing for Big Data analytics that allows military organisations to leverage the off- premises information communication technology (ICT) function and reduce the efforts required in fulfilling regulatory compliance requirements. Cloud computing helps in de-siloing quality and compliance management across production, which leads to effective supplier audit reliability and minimised compliance reporting costs.10 Cloud computing has also helped to reduce tooling costs. Using Big Data in cloud-based systems helps to minimise tooling time and costs considerably by saving previous configurations (eg for 3D printing of spare parts). Usually, military logistics management systems analyse the information offered by the supplier database when receiving a supply mission and then proceed to evaluate the probable support capacity provided by the suppliers to the military depots by use of cloud computing systems.

The second opportunity is to use Blockchain 11 as a less mature but secure system that can be embedded within contemporary logistics information systems.12 Maersk and IBM piloted the first Blockchain program which was focused on the creation of a single digitaldistributed ledger where numerous documents related to a shipment could be stored.13 Globally, Blockchain is being piloted by several companies. For example, T-Mining piloted Blockchain to provide clearance for personnel to pick up a load in order to avoid fabricated pickups. Kouvala Innovation is trialling carrier ‘mining’ applications that would bid for the right to move a shipment using a radio frequency identification tag. This involves awarding a contract to a carrier that is the best value for money, potentially using Ethereum.14 15 However, I argue the manifold increase in the power requirement and safeguarding infrastructure to run Blockchain data warehouses without interruption would require substantial investment. But the real value would only be realised if it is implemented.16

The third opportunity is the management of a real-time health usage and monitoring system (HUMS) for soldiers. Sensors have become ubiquitous in our daily lives and they generate a high rate and scale of data. For example, Equivital created a military training system called ‘Black Ghost’ that can be used to track a soldier’s location in real-time.17 Black Ghost is used with EQ02 LifeMonitor, which is a body sensor system incorporated with auxiliary data management software and a visualisation tool. It comprises a body- worn sensor device that monitors health conditions such as heart rate, respiration, activity, body temperature, and GPS data. The system can allow a commander to see if a soldier has violated a convoy or patrol order and warns the commander if any soldier shows signs of fatigue. The system also provides an alert feed which highlights incidents as they occur in the field. Commanders can log into a soldier’s activity feed and view event information linked to a specific type of order. This could be used to detect and analyse any deterioration in a soldier’s performance over a certain period. In a unit, each soldier is continually assessed to monitor contributions to the overall performance of the unit, as demonstrated by the US Army’s use of the system on soldiers operating in Iraq.18 However, I suggest that collection of real-time raw data only is insufficient to reduce levels of injuries in the field. If data from Black Ghost and LifeMonitor could also be analysed in real-time as part of the Army’s health information system, it may prevent heat injury or illness and be crucial to Army’s operations.19

The fourth opportunity is HUMS for land vehicle platforms, which promises increased vehicle usage, readiness and reliability, and reduced sustainment cost. This data is used to provide maximum vehicle availability and optimum rate of effort to allow for fleet management. In a bid to prove that the HUMS concept was viable, in early 2011,20 the Land Engineering Agency (LEA) instructed original equipment manufacturers (OEMs) to design and incorporate HUMS into four combat service platforms, namely M1A1, ASLAV, M113, and PMV. The trial was conducted on M113 and the result was promising. The trial report recommended enhancements in fleet management via automated data entry, improved operational statistics, and greater understanding of a mission. Aviation industry uses HUMS to generate diagnostic information that is required for optimum performance of aircraft. HUMS senses, monitors, and communicates maintenance needs of the critical aircraft components.21 LEA’s contemporary vehicle HUMS systems, known as ‘VHUMS’, present several benefits. I suggest, however, that if effective data management is not implemented (including automatic data collection and analytics) then the sustainment cost of land materiel remains a major challenge. Furthermore, VHUMS in its present form is perceived as a maintenance enhancement initiative under Plan Centaur.

If Big Data is incorporated then there is potential to gain insight on terrain analysis and operator behaviour, but if Big Data is not incorporated then the concern will remain as to whether Defence uses its vehicles safely 22 and to their best availability. Therefore, Big Data analytics could be crucial for an optimised and safe operation of Defence vehicles.

The fifth opportunity is in the lean supply-chain management. This involves a set of organisations, for instance Defence and Defence Industries, which are directly linked with upstream and downstream production flows, services, finances, and information that collaborates to minimise costs and eliminate waste in order to add value.23 I would argue that to meet military logistics’ demands requires substantial information management capability and it has to be flexible enough to be readily deployed in order to complete a mission. However, a lean supply chain needs to be designed in such a way that it responds by effectively extracting knowledge from collected data to meet the fluctuations in operational demands effectively. If the lean supply chain is implemented then OEMs will be required to implement a ‘pull system’ for raw materials to support manufacturing processes.24 The key is to have all supply chain partners implement lean principles to truly squeeze all logistics costs out of the system.25 Consequently, principles of Big Data analytics could contribute to the secure management of information flow between Defence and Defence Industry in support of military operations.

Contemporary Defence initiatives could also harness the power of Big Data. For example, in the terrestrial communications project JP2047, within the scope of the project, Big Data could be used to form intelligence on a trusted insider,26 members’ social media behaviour pattern,27 and so on. Another example is the use of Big Data for the Enterprise Resource Planning (ERP) implementation based on SAP – the German software company that specialises in data processing applications and software – where all current systems are planned to be combined into a single system (excluding the human resource system that is based on Oracle). If incorporated, it could result in a substantial collection of data. If Big Data is not incorporated then the risk to the fundamental inputs to capability (FIC) will substantially increase.28 Big Data could be used for projects Land 121 and Land 400 to realise a logistic common operating picture (LCOP) for the logistic control network (LCN) using real-time data extraction from a vehicle communication system. This could be accomplished by employing sense and respond logistics on all land vehicles, thus weaving multiple CSS networks into a single capability.29 This has the potential to result in the elimination of the second and third line support that could create an end-to-end distribution network.30 Any possible savings from this elimination can be redirected towards acquiring superior materiel in order to reduce capability gaps.

Challenges

The opportunities presented above are crucial and significant to a military operation, however, benefits from these opportunities are not easy to achieve and several challenges remain which require careful consideration. First, a fundamental challenge is that people have difficulties in understanding the concept of Big Data 31 and then making a decision from collected data.32 There are few people with the required qualifications, skills, and experience to effectively work with Big Data. This is due to its complexity and intricacies presenting a challenge to new trainees as Big Data educators are scarce. Military personnel who would handle the Big Data require individual and customised training, a set of essential skills in order to handle the Big Data, and resources for continuous professional development. For example, one MQ-9 Reaper sortie collects the data equivalent of up to twenty laptops. Therefore, it is not surprising that much of this information can only be analysed retrospectively, rather than fully exploited in real-time. In the past few years, the number of intelligence analysts in the US military has soared in order to manage the information deluge. Moreover, one US Army retired intelligence officer claimed that ninety five percent of battlefield video data is never viewed by analysts, let alone assessed.33 The training exercises are intensive and the trainees are often put in situations where they need to simulate analysis and management of huge spreadsheets that involve hundreds of columns and tens of thousands of rows; a task that is even difficult for the trainers due to the complexity of the material. This leads to a talent gap.34 However, to overcome this challenge, stronger partnerships are required among educational institutions, Defence Industry, Research and Development organisations, and government agencies.

The second challenge is the risk of data corruption or theft through cyber- attacks which may present a devastating problem for military operations. Cyber-attacks are intended to control weapons and infrastructure to adversely influence daily operations. Cyber-attacks can be sudden, unforeseen, and their probability of occurrence can build up over time in the absence of applicable policy technology or management responses to contain an attack. Cyber-attacks on the military logistics infrastructure or ecosystem can lead to major delays, breakdowns, disruptions, and losses in the services and operations, threatening the security of a whole nation. For example, the Iranian authorities carried out a successful cyber-attack on the US Defence system (in particular, the RQ-170 Drone), and managed to control it.35 The use of Big Data for military logistics thus presents the risk of a cyber-attack that can damage military infrastructure leading to denial, or in the worst case, loss of infrastructure and/or corruption of data. However, if Blockchain technology is not incorporated in the logistical systems, even embedded within the Advanced Data Communications Units that make up the interface between human and machine, then the damage from cyber- attacks would be far more devastating.36

The third challenge involves the operational management of a contracted data warehouse and manual data extraction. A real-time ground surveillance system developed by DARPA – the US’ Defense Advanced Research Program Agency – known as ‘ARGUS’ collects up to 40 GB of information per second. 37 Therefore, hardware infrastructure and energy consumption that hosts Big Data is very costly, even with the availability of cloud computing.38 Recently, incidents of security breaches and fraudulent conduct by government 39 40 and Defence 41 contractors have been on the rise. If we are not conscientious about who can be trusted and with whom we do business, then it would seriously jeopardise military operations.42 In addition, contracted cloud computing warehouses come with their own challenges like operational transparency, lock-in contracts, higher bandwidth cost, multi-vendor systems integration, and loss of operational control.

The fourth challenge is the data processing system.43 In conventional systems, at least some degree of human intervention is required to extract valuable information to present to decision makers. This leads to the dependency on the manual collection and extraction of data, resulting in the potential introduction of human error and inefficiency,44 but an automated system with intensive computing helps to ensure conformity of data while allowing the data managers to focus more on data management rather than research and evaluation. For example, Defence’s use of several commonly used and trusted logistics applications such as, ROMAN, ACMS, SPMS, VIPA, MILIS, MEMS, and MUIR are developed by Defence Industries which do not incorporate Big Data. As such, their accuracy, interoperability, and reliability are often challenged. Lessons learned from Linfox’s 45 Big Data implementation suggested that if data extraction and integration is carried out properly (adhering to a single coding standard) then the problems which have occurred can be explained and future issues can be predicted and avoided. Therefore, the use of Big Data combined with emerging technologies could provide Army with a centralised and streamlined way of managing Army’s resources.

Conclusions

We live in a digital era and military operations increasingly benefit from digitisation with an ever-greater dependence on accurate intelligence. Big Data analytics provides actionable insights that would enable Army planners to address the problems proactively before they happen, an initiative for which traditional reporting systems do not allow. Big Data can enable logistics systems to get smarter, faster, more secure, and more agile in order to support accurate and timely decisions. Also, the real-time management and monitoring of health data from soldiers could potentially prevent injury and illness. The combination of HUMS and Big Data analytics can facilitate the optimal and safe use of Defence and military vehicles. Big Data has a potential to optimise supply chain management by creating Army’s multiple CSS networks into a single capability that has a potential to eliminate the second and third line support, resulting in an effective use of the Army’s budget. This presents opportunities to accelerate, but also requires greater emphasis on the effective lean management of a huge volume of data and its security. Once these few challenges have been mitigated as suggested, I have no doubt that the use of Big Data in military operations is imminent. Therefore, I strongly argue that the use of Big Data with secure technologies is crucial for military logistics operations and critical for Army’s sustainment budget.

Big Data is the new oil and its breach is the new oil spill.

- Anonymous

Endnotes

  1. Statistical Analysis System, 2018, ‘Big Data: What it is and why it matters’, at: http:// www.sas.com, accessed 20 Oct 2017
  2. Ernst and Young, 2014, ‘Big Data – Changing the way businesses compete and operate’, Insights on governance, risks and compliance, at: https://www.ey.com/gl/en/ services/advisory/ey-big-data-big-opportunities-big- challenges, p 2
  3. D Woolner, 2000, ‘Pressures on Defence Policy: The Defence Budget Crisis, Research Paper 20’, Foreign Affairs, Defence and Trade Group, Commonwealth of Australia, at: https://www.aph.gov.au/About_Parliament/Parliamentary_Departments/Parli… y_Library/pubs/rp/rp9900/2000RP20, accessed 3 Nov 2017
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  6. Waller, 2013
  7. Y Wang and VJ Wiebe, 2014, ‘Big Data Analytics on the characteristic equilibrium of collective opinions in social networks’, International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), Vol 8, Issue 3, pp 29-44
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  9. Sagiroglu, 2013
  10. E Qi, J Shen and R Dou, 2013, The 19th International Conference on Industrial Engineering and Engineering Management, Springer
  11. Blockchain is a single, universally accessible digital ledger completed by the transformation of Bitcoin since 2009. It’s called a chain due to changes can be made
  12. ME Peak and SK Moore, 2017, Blossoming of the blockchain, IEEE Spectrum, Vol 54, Issue 10, p 25
  13. S Banker, 2017, ‘Blockchain In The Supply Chain: Too Much Hype’, Forbes, at: https:// www.forbes.com/sites/stevebanker/2017/09/01/blockchain-in-the-supply- chain-too- much-hype/#1a19c5e1198c, accessed 30 Oct 2017
  14. A public blockchain designed to store and execute smart contracts and other complex software applications. The first version was released in 2014
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  19. CA’s first safety concern is “Heat injury and illness”, Army Safety Day, presented on 31 Oct 2017
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  41. Sky News, 2017, ‘Defence Contractor’s Cyber Security Security Breached’, at: http:// www.skynews.com.au/news/top-stories/2017/10/12/defence-contractor-s- cyber- security-breached.html, accessed 3 Nov 2017
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  44. Davenport, 2013
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