Ben Courage describes himself as “a passionate and enthusiastic” senior logistics manager working for the Defence Equipment and Support (DE&S) Headquarters in Bristol. Ben completed a Ministry of Defence Engineering apprenticeship with the Army Based Repair Organisation (ABRO) and has 12 years of through-life support experience which includes in-depth logistics and engineering knowledge. His wealth of in-service support expertise has been imperative for delivering tangible efficiencies and financial savings within the Defence sector. Ben is in his final year of studying for a Systems Engineering MSc that is primarily focused around the UK MOD environment.
This entry captured the eye owing to its original thought and the foundation of evidence and observation upon which it was developed. At the heart of the submission was a programme of work and a model designed to exploit AI and other technology for more effective logistics activity and resource forecasting, enabling benefit across the domain and a spectrum of incremental gains adding up to a collectively significant improvement. The passion of the author was evident yet was balanced against the detail required by the question, producing an idea that the markers felt was genuinely implementable. The qualities and experience of this “passionate and enthusiastic senior logistics manager” were evident in this high-quality essay.
The content and any views expressed in this essay are a personal perspective, reflect the personal views of the author, and may not necessarily reflect that of UK Defence.
As the question is based on becoming the head of Defence logistics, I am going to propose a selection of pioneering initiatives that would rationalise the way Defence forecasts logistics.
The term initiative is “an act or strategy intended to improve a situation; a fresh approach to something”. This definition has formed the basis and suggesting a “fresh approach” for forecasting methodologies whilst including Artificial Intelligence (AI). To date, there are a small number of methods embedded with the Front Line Commands (FLCs) that contributes to the ability to forecast logistic support within Defence. This paper has been written to present a compelling case for the implementation of a logistics forecasting selection tool and technique; whilst incorporating industry procedures that are currently being utilised within the private sector. As stated by Steve Banker (Forbes, 2017), whilst machine learning and AI “have been used in supply chain applications for some time, there is an ongoing ‘arms race’ to more effectively leverage both machine learning and AI in Demand and Supply planning”. Steve continues to define AI as any tool that “can perceive its environment and take actions that maximise the chance of success”.
This paper is designed to explore the concepts of AI within Defence logistics and suggest a way of consolidating into an overarching programme of work with a Forecast Options Matrix (FOM). The FOM is intended to drive innovation, increase efficiency and allow tangible financial savings to be realised. This paper is also answer the questions that the author feels would be asked if the stated initiatives were initiated and came to fruition.
The problem situation
A forecasting method has been initiated within the Reducing Logistic Need (RLN Programme Brief, 2018) programme. This bespoke and independent forecasting initiative has matured over the last 18 months and is aspiring to be a methodology that provides the ability to forecast logistics requirements. From research, it is assumed that the service provider, Defence Equipment and Support (DE&S), can influence the contractual logistic support arrangements based on the outputs from any forecasting technique. From personal experience, the outputs from conducting embryonic forecasting holds merit but fails to consider the realities of contractual constraints.
How industry forecasts – Sales and Operational Planning
From research within the private sector, Industry forecasts logistics based on the Sales and Operations Planning (S&OP) methodology. To define, S&OP (The balance, 2018a) “is a decision-making process that makes certain that tactical plans in every business area are in line with the overall view”. This gives an understanding that S&OP is a cohesive and joined-up approach to delivering business. Utopia for the S&OP process is a “single operating plan that identifies the allocation of company resources, including time, money and employees” (The balance, 2018a). To summarise, private logistics companies that utilise the S&OP process realise several “benefits such as a greater visibility of the demand and supply across the enterprise, improved inventory management… increased accuracy in budget forecasting, and an improved product lifecycle management process” (The balance, 2018a). From the above the following initiatives have stemmed from the possibility of adopting Industry techniques within the Defence environment.
The initiatives to invoke – The Forecast Options Matrix (FOM)
The Forecast Options Matrix (FOM) can be found at Figure 1. The FOM has been designed by the author to allow any piece of Defence equipment to be plotted to identify the optimum logistic forecasting technique. The categories within the FOM are the ability to ‘forecast’ the equipment usage and the ability to ‘influence’ the logistic ‘support levers’. The logistic ‘support levers’ are defined by the Support Solutions Envelope (SSE, 2018) as; inventory, man-power, infrastructure, Special Tools and Test Equipment (STTE) and Technical Documentation (TD) etc…
Many of the logistic ‘support levers’ may not be within the total control of the Authority (DE&S) in the ability to influence the activities conducted. This could be due to contractual mechanisms and constraints in place. The ability to forecast the equipment usage requires the FLCs to provide equipment usage profiles for each major platform. This would be via a Unit of Measure such as Equipment Availability Days, kilometres travelled on wheels or tracks, rounds fired etc… Measuring the accuracy of the defined Activity Forecast would be key to understanding the accuracy of it. As with any equipment usage profile, the “forecast is not static and should be reviewed by management on a regular basis” (The balance, 2018b). This helps to ensure that information on future trends and the internal or external environment is incorporated. Having a robust and timely Activity Forecast for a Defence equipment would be a requirement placed upon the FLCs (the equipment users) for which they could utilise the FOM.
The contents of the FOM – Activity and Resource Planning (A&RP)
A&RP is an initiative within the RLN programme. The purpose of the Activity and Resource Planning (A&RP) process is to “integrate activity forecasting and demand planning” (A&RP Handbook, 2015). This will allow the FLCs to better manage their resources and communicate changes to DE&S (who would then supply or procure equipment, spares and commodities) on a frequent basis to an agreed battle rhythm. A&RP is designed to enable the FLCs to generate a single, shared, integrated, agreed and affordable plan for the provision of resources in support of delivering equipment availability. The A&RP process follows the similar five stage process that is comparable to S&OP which is utilised in Industry. The stages are;
Stage 1 – Activity Forecasting. Defining the equipment usage to be undertaken by the customer (the FLC).
Stage 2 – Demand Planning. Translating the Activity Forecast into a scheduled, quantified and costed materiel requirement.
Stage 3 – Supply Planning. Planning the activities that need to take place to support the level of Defence equipment activity required.
Stage 4 – Integrated Reconciliation. Integrating and Reconciling all plans, to align them to create a single operating plan for the organisation.
Stage 5 – Senior Management Review. Providing a holistic overview of the A&RP process to the Availability Working Group (AWG).
From interviews conducted, the author has determined that an ‘emerging property’ of A&RP is an Inventory ‘buy-plan’. The ‘buy-plan’ will identify the Equipment Support Materiel (Known in wider industry as the Bill of Materials (BoM) required to undertake routine maintenance or repair) required to deliver the defined Activity Forecast. This would then allow tangible savings to be realised by optimising the BoM to the Activity Forecast and would be achieved by eradicating poor forecasting that results in DE&S procuring material that the FLCs does not need and, conversely, improve procurement of what really is needed.
The contents of the FOM – Forecast and Resource Planning (F&RP)
The Forecast and Resource Planning (F&RP) process within UK Defence is an initiative designed by the Equipment Support Continuous Improvement Team (ESCIT) to consolidate the total Integrated Logistic Support (ILS) suite of activities. F&RP will allow the optimised support solution to be identified to deliver the Activity Forecast at the optimal cost. The ‘support levers’ that allow the optimisation to be identified can be related to any Defence equipment and is balanced to ensure the optimum solution is identified. As stated by Steve Glass, the Support Enablers Operating Centre (SEOC) are aspiring to embed the “Forecast and Resource Planning methodology as standard support practice” (DESider, 2017). F&RP is situated at the top of the FOM, the reasoning is that due to F&RP encompassing the total support elements, there is a need to be able to have control of all the logistic ‘support levers’.
The contents of the FOM – Activity and Resource Planning ‘Light-Touch’ (A&RP LT)
This is a new initiative that has been designed by the author. A&RP LT allows an opportunity for Defence that would realise tangible financial savings. The premise of A&RP LT is to conduct A&RP at the embryonic stages of the equipment lifecycle or if the equipment logistic ‘support levers’ are being delivered via a 3rd party contractor, i.e. Contracting for Availability. This forecasting method bridges the gap by delivering the A&RP cycles at a level of granularity to attribute a cost to the defined Activity Forecast. The outputs of A&RP LT could be deemed as “30,000 feet view” of forecasting the logistic need that is required to support the defined FLC Activity Forecast. A&RP LT does not provide the level of granularity that is experienced from the outputs of A&RP, but rather an overarching estimated ‘cost of activity’.
How the FOM works
The FOM is very simple to utilise and can be applied to any type of Defence equipment. The FOM works by plotting a piece of Defence equipment on the X axis based on the ability to forecast the equipment usage, and with that, the accuracy of it. The ‘100%’ end of the graph represents complete confidence and ability to acquire an Activity Forecast. The Y axis denotes the confidence and ability to influence the ‘support levers’ in the same manner as the X axis. Current thinking is that as Defence equipment matures, so could the optimised forecasting logistics methodology.
A selection of scenarios has been developed to interrogate the FOM. Identifying such a novel and contentious initiative will attract questions and require considered, evidenced answers to senior management in Defence. To resolve any potential questions, the selection of ‘scenarios’ below may pre-empt possible questions and include mitigating responses.
Scenario 1 – A change in logistic support contractual arrangements.
If a piece of Defence equipment was to undergo a change in contractual support arrangements; i.e. moving from the ‘traditional’ support arrangements to ‘Contracting for Availability’ (CfA), the necessity to conduct the A&RP method would not be required. This would be due to the in-service support of the Defence equipment being delivered by the CfA provider. With that in mind, there still would be the necessity to provide a robust Activity Forecast which would still allow the Defence equipment to be positioned on the FOM. Moving into the ‘A&RP LT’ area of the FOM would allow a ‘cost of activity’ at a high level to be generated for Defence financial and budgetary planning. This would also allow the customer to question the financial costs of the CfA arrangement. A&RP LT would also be ‘predicting’ future financial expenses and could provide evidence for financial funding bids etc…
Scenario 2 – Defence equipment ‘in-service’ support maturity increases.
There could be the scenario where support for a Defence equipment evolves during the in-service phase of the CADMID cycle. This evolution could have the Original Equipment Manufacturer (OEM) providing a selection of in-service support activities (rather than a full CfA solution) during the initial fielding of the equipment. The logistics ranging and scaling of the Defence equipment for those systems being supported by the OEM would probably have been undertaken by them. As the support system matures through life with operational experience, the initial option to monitor contracted support through A&RP LT may see a progression into the A&RP zone within the FOM. This could be driven by the customers increasing ability to measure the accuracy of the Activity Forecast – allowing them to better influence the OEM’s support performance to mutual benefit (perhaps on a shared-benefit basis). This progression within the FOM would also cover any in-service support logistics activities being transferred back to the Authority, and with that, the customer’s ability to influence the ‘support levers’. The diagonal line on the graph visually represents this change on the FOM.
Scenario 3 – The Activity Forecast matures over time.
The author feels that this scenario is the direction of progress within the FOM that most Defence equipment would experience. This scenario is aimed at the ‘steady-state’ in-service support arrangements for Defence equipment. When the initial A&RP LT forecasting methodology is conducted, an emerging property would be to acquire an Activity Forecast from the customer which would then be compared against the actual equipment usage. An outcome of this analysis would be to allow a comparison of the “Activity Forecast Vs. Actual Usage”. This could be used to inform and influence future Activity Forecasts. The requirement to then direct Defence logisticians to conduct the A&RP process would be deemed a logical progression in the FOM – as shown in the horizontal straight line.
The three scenarios outlined above demonstrate the opportunities that can be realised from implementing the FOM, and its ability to manage forecasting as support systems mature in service. Examples or case studies from the scenarios could also be used to maintain senior management confidence that the optimum support solutions were (or were not) being used. To verify and validate the hypotheses proposed in the FOM initiative, the next stage is to question the likely outputs.
Questioning the outputs of the FOM.
Just simply plotting the Defence equipment on the FOM may not identify the ‘optimum’ logistic forecasting method required. To explain my rationale, if a Defence equipment has a defined Activity Forecast and greater than 80% of the ‘support levers’ are in the control of the MOD, it would be a reasonable approach to present a compelling case for F&RP to be the preferred logistic forecasting methodology. That said, the next step should be to understand the potential benefits that could be realised from conducting F&RP over and above utilising the A&RP logistic forecasting method.
As previously discussed, as A&RP forecasts the BoM element of Defence equipment support activity. The author believes that if the BoM consumes a large percentage (%) of the “total cost of support”, the optimum Return on Investment (RoI) may be to conduct A&RP. Current thinking is that if the BoM is greater than 75% of the “total cost of support”, A&RP would be deemed the preferred logistic forecasting method. Figure 2 (below) visually represents this belief.
The FOM could therefore be used to identify the best logistical forecast method, but a subsequent investigation activity of conducting the “total cost of support” analysis would be required if F&RP was identified as the best option.
In summary, if forecasting 80% of the “total cost of support” can be provided from A&RP, the author believes it would be an unnecessary resource burden to provide the ability to influence the remaining 20%. Based on this, the author has ‘capped’ the Inventory at 75%. Therefore, if the BoM value is greater than 75% of the “total cost of support”, there isn’t the justification for conducting F&RP.
The Current Tool – VERITAS.
As the FOM contains a selection of processes that allow the ability to forecast logistics, sight must not be lost of the tool that would deliver the outputs. The current tool, VERITAS exploits “Defence data and how data can be presented in a meaningful way to enable the MOD to make more informed decisions” (Techmodal, VERITAS providers, 2018).
VERITAS is being utilised throughout DE&S and the FLCs with key functionalities including; Activity Forecasting, Costing and Modelling; Inventory, Repair and Ammunition Stockpile Management; In–Year Financial Management; Fleet and Performance Management.
Whilst briefly discussing the tool that has the ability and functionality to deliver all the processes within the FOM; to complete the People, Process, Tools model found at Figure 3, Logisticians would need to be consulted and trained in the process and the tool. This will be expanded later in this paper.
Why would I invoke the initiative?
Using the FOM, with the option of using A&RP LT would complement other Defence initiatives and allow better management of CfA solutions. Introducing the initiatives discussed in this paper would provide a more robust framework that could encompass different forecasting initiatives under the same work-package. The FOM and A&RP LT offers more opportunities for Defence Logisticians to select and utilise optimum logistic forecast methodologies and, based on the categories that form the FOM, provides evidence to inform strategic decision making when selecting support solutions in Defence.
Utopia would be to realise and re-invest tangible financial savings. This would become possible when the FOM is applied to a portfolio of Defence equipment (e.g. Protected Mobility Vehicles, Unmanned Air Systems, etc). This would enable consideration of expanding the best or optimum current in-service support arrangements across ranges to simplify processes and reduce logistics support costs.
FOM Implementation strategy
It is unwise to try and force any new methodology into established systems within Defence, even if they are likely to result in potential efficiencies. Every existing process is already rooted into the organisation and for the new forecasting methodologies to have a high success rate, they must be introduced in a manner that demonstrates and proves the benefits to both practitioners and end customers – from the end user and all the way up to the Defence Board – realising organisational objectives through efficiencies and improvements in Defence capability. To achieve this, steps need to be taken that include evaluating a preferred Unit of Measure for the Activity Forecasts from the FLC; conducting any required training for users of the processes and tools (VERITAS); and to rectify any operational issues, ensure security, integrity and accuracy of the data being utilised. There is also a need for an implementation strategy that summarises the benefits and, most importantly, sharing of Learning from Experience (LfE).
Identifying tangible savings from the initiatives.
As with any initiative, the RoI is imperative and understanding the possible financial savings and benefits that can be realised would provide solid foundations for any decision to adopt the initiative. The author has included a selection of high-level inventory statistics at Figure 4. These statistics give the view of the MOD Supply Chain from 2015 and would not include any of the initiatives discussed in this paper. Therefore, encompassing the FOM and A&RP LT would, in the mind of the author, provide financial savings to Defence. To add, and from self-improvement pursuits, applying the ‘marginal gains’ technique to Defence Logistics is possible. Marginal gains are “all about small incremental improvements in any process adding up to a significant improvement when they are all added together” (BBC News, 2015). This technique, if applied across Defence could deliver similar benefits to those experienced in the sporting world.
|Comparing MOD inventory performance to industry|
|Raw Materials and Consumables (RMC) inventory cover||15 days||10.5 Yrs|
|Average lead time (days) from dispatch from industry to available to issue||3||23|
|Deliveries on time||98%||70%|
|% of inventory sent for disposal||0.3%||8%|
Figure 4: MOD Supply Chain benchmarked performance (from EY Support Chain Improvement High Level Business Case dated 3 Jul 15)
To conclude, the author has researched, analysed, questioned and presented an overarching approach for logistics (the FOM) and a new logistics forecasting method (A&RP Light Touch). The initiatives stated, along with current working practices and aspirations of Defence could ultimately deliver tangible financial savings. Adopting the FOM within Defence would, as a minimum, provide a framework to guide Defence Logisticians to optimise their logistic support strategy. In addition, as time goes by and Defence equipment matures, so may the FOM. The flexibility of the FOM allows it to be moulded to the requirements of the customer and utilised across the Armed Forces.
Because logistic forecasting should always have been an essential component of effective planning at the Defence strategic level, decisions on strategy and the Armed Forces commitments continue to depend upon it. Utilising the FOM will contribute to giving strategic decision makers freedom of choice by understanding the availability of support resources. The key to ensuring Defence equipment availability to the end user is not procuring a mindless amount of materiel, but the timely delivery of sufficient materiel when and where required.
Whatever the way forward for forecasting for Defence logistics, having this opportunity to express initiatives in the logistics sector that could be implemented is an opportunity not to be missed.
This essay was edited for publication by Chris Markey FCILT, Chairman CILT Defence Forum.
BBC News (2014) Should we all be looking for marginal gains? Available at: http://www.bbc.co.uk/news/magazine-34247629 (Accessed: 12 April 2018).
Forbes (2017) Machine Learning And Artificial Intelligence In Demand Planning. Available at: https://www.forbes.com/sites/stevebanker/2017/12/08/machine-learning-and-artifical-intelligence-in-demand-planning/#58df7d994e83 (Accessed: 25 March 2018).
RLN Programme Brief (2018) Reducing Logistic Need Programme Brief Overview. Available at: http://cui1-uk.diif.r.mil.uk/r/87/DSupportSubStrategy/RLN/20180207-RLN_Programme-on-a-Page_Brief.pptx (Accessed: 04 April 2018).
The balance (2018a) Sales and Operations Planning – What is S&OP? Available at: https://www.thebalance.com/sales-and-operations-planning-2221398 (Accessed: 24 March 2018).
The balance (2018b) Strategic Forecasting In The Supply Chain Available at: https://www.thebalancesmb.com/forecasting-in-the-supply-chain-2221207 (Accessed: 24 March 2018).
A&RP Handbook (2015) Activity & Resource Planning Handbook: Version 1.5. Available at: MOD Intranet (Accessed: 20 March 2018).
DESider (2017) the magazine for the defence equipment and support. Available at: https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/656703/Desider_112-Nov2017.pdf (Accessed: 09 March 2018).
Techmodal (2018) Projects. Available at: http://www.techmodal.com/index#projects (Accessed: 10 April 2018).
SSE (2018) About the Support Solutions Envelope (SSE). Available at: https://www.aof.mod.uk/aofcontent/tactical/sse/content/intro/about.htm (Accessed: 14 April 2018).
 In 2015, the MOD introduced a new operating model giving the Royal Navy, Army, RAF and Joint Forces Command (the ‘Front Line Commands’) greater accountability and control over budgets, equipment and staffing. In line with Lord Levene’s report, and as part of the reorganisation of the top tiers of the UK Ministry of Defence (MOD), the Chiefs of Staff have been given many of the levers they need to generate and develop their respective Service to deliver military capability. The Front Line Commands (FLCs) have been made accountable, through the Service Chiefs, for planned and in-service equipment and support across all years and will now set the detailed equipment and support requirements for their own Service’s equipment. As such, they act as the customer for Chief of Defence Materiel (CDM) – noting that this post no longer exists within MoD. In this case, JFC is considered separately as a TLB; however, they are often considered, incorrectly, to be one of the FLCs.
 RLN is an opportunity that seeks to increase Capability whilst reducing demand and cost.
 The Support Solution Envelope (SSE) consists of signposts to policy and a tool providing advice and guidance on how to develop a support solution.
 Activity & Resource Planning Handbook – Version 1.5 Issued April 2017.
 Known in Defence as Equipment Support Material (ES Mat) this includes spares, consumables, tools, test equipment and any other specialist facilities required to undertake the support activity.
 Editor’s note: This is a bespoke F&RP solution developed within DE&S. Other Commercial Off The Shelf (COTS) solutions are used globally across industry and commerce.
 Director Support Enablers (DSE), DE&S.
 Editor’s Note: A sort of ‘sanity check’ on outsourced provision that would flag up contentious or unexpected results for management by exception.
A Defence approach to through life management covering Concept, Assessment, Demonstration, Manufacture, In-Service, Disposal.
 VERITAS is an Oracle-based Enterprise Resource Planning (ERP) system developed by Techmodal. Editor’s note: Other ERP, F&RP and wider Supply Planning Tools are, of course, available and more widely used in commerce and industry.
 Available at: http://blog.prometil.com/2016/07/safe-chez-les-clients/ (2018).
 In cycling, Team Sky has built its success on the aggregation of marginal gains. The idea is that if you can improve lots of things by a small amount, the net result can make the difference between winning and losing.