310 likes | 475 Views
Laying the foundations A paper for ISMOR 20. 26 th August 2003 Glenn Richards. Contents. 1 Introduction 2 Battlefield Infrastructure Studies 3 Method 4 Data 5 Conclusions 6 Questions. Introduction. Section 1. Introduction. What is Battlefield Infrastructure (BfI)?
E N D
Laying the foundations A paper for ISMOR 20 26th August 2003 Glenn Richards
Contents 1 Introduction 2 Battlefield Infrastructure Studies 3 Method 4 Data 5 Conclusions 6 Questions
Introduction Section 1
Introduction • What is Battlefield Infrastructure (BfI)? • fuel, water, power and accommodation • Little previous study in the UK • availability of data has been the key • This presentation will • examine the studies • discuss relative merits of 2 OR methods and • discuss data requirements, types, problems etc
Battlefield Infrastructure Studies Section 2
BfI Overarching Study 1 • Aim • understand the provision of BfI • identify potential choke points in the systems • examine possible technologies to improve BfI • find possible links between the components of BfI • prioritise and focus future research
BfI Overarching Study 2 • Soft analysis - problem elicitation • Method • literature search • capture of current concepts of operation • obtain baseline data • interviews with stakeholders • study day • identification of possible areas suitable for technology research • analysis of findings • hard issues • soft issues
BfI Overarching Study 3 • Results • baseline statement of capability to support a deployed op force • interactions between the four components of BFI • directions for future research and analysis identified • e.g. use of pipelines for water and fuel distribution • Most importantly... • recommend more studies where required!
Follow on studies • Following the scoping study, requests for three follow-on studies: • Deployed Fuel Handling Equipment Support Studies • Deployed Water Handling Equipment Support Studies • UK Forces Deployed Operations Electric Power
Method Section 3
General method • Quantitative studies of BfI are ORBAT driven • based on the amount of men and equipment deployed to an operation • Use agreed scenarios for modeling • For water and fuel studies • existent doctrine used (eg 25 litres/man/day) • solutions based on achievement of policy norms • Different from a large amount of military OR
‘Top-down’ vs. ‘Bottom-up’ • Two approaches to solving military OR problems • What’s the difference? • ‘bottom-up’, from performance to capability • many studies - Engr to Arty • ‘top-down’, from ORBAT to required quantities • DFHE • Bottom-up establishes need, top-down accepts it
‘Bottom-up’ studies • In a particular scenario or vignette • define/postulate a number of tasks that have to be achieved in a certain time • use the time in which a single equipment could conduct defined tasks • aggregate up to derive number of equipments required for whole scenario • Or • using equipment with defined performance • assess the capability of forces of different composition in combat simulation • quantities from performance
Advantages of ‘Bottom-up’ approach • Applicable for many types of study from Arty to Engr eqpt • Gets buy in from immediate stakeholders • i.e. those at MJPs • Can be good to examine particular scenario reqts, as examining each one by a MJP • Customers used to approach capabilities • Easy to examine different equipment • Better feeling for scenario chronology
Disadvantages of ‘Bottom-up’ approach • Often based on limited ops within a campaign • Problems capturing data: initial task list, task time etc • Data often superseded with arrival of new stakeholders • Problems amalgamating reqts from different vignettes especially for vehicles that perform more than one function • Results require interpretation to • relate them to the entire campaign • allow for military structural issues • Large amount of preparation for MJPs • Specialised military knowledge requirement
‘Top-down’ example: DFHE RDS • Obtain agreed ORBATS • Obtain agreed policy norms • fuel quantities, storage reqts, nodes, etc • Give battlefield locations, nodes • Using policy norms work out what’s stored where, moved where, support modules reqts, etc • Simple sums • Capability reqts • Info on current & future kit • Equipment reqts
Typical supply network 7 days RSG FSG SPOD Divisional Rear Boundary BSA Move 2 FCUs a day MRA 14 FCUs Cdo LoC
‘Top-down’ Policy + doctrine
Advantages of ‘Top-down’ approach • Simple, quicker • normally can be done by adding and dividing • May require less military input • good if military scarce • Avoid the problems of aggregation to campaign level • Can be used to examine: • achievement of policy norms (eg water supply) • equipment needed to meet accepted requirement (eg power supply) • Less hassle from changing stakeholders • guaranteed audit trail policy + agreed ORBATs
Disadvantages of ‘Top-down’ approach • Works best with agreed policy & doctrine • useful as a ‘what if’ vis a vis strawman policy • ORBATs • always disagreements • Rigidly adheres to policy statements • Can become independent of physical data within scenario • Not applicable to everything: bridges etc • Need to physically get policy docs • Simple • NOT HEADLINE MAKING OR!
Data Section 5
Definition of Data • “Factual information, especially information organised for analysis or used to reason or make decisions. ” • In terms of OR studies what exactly constitutes data? • is anything that is input into a study considered to be data? • something that has been measured is data, • but what about estimates or mil judgement? • are the hard-wired assumptions imbedded in a model data? • Definition of data can be a complicated issue • means different things to different people (programmer, analyst, customer, military stakeholder etc) • In this paper all inputs into an OR study
Why are data important? • Data is … Data are • after much debate data are plural! • OR used to inform decisions e.g. procurement etc. Why? • to apply scientific rigour and method to them • OR can be ignored unless it gains the ‘buy in’ of stakeholders • input data also subject to the same rigour of scrutiny? • GARBAGE IN = GARBAGE OUT • Quality of data not always appreciated • often delivery of results takes priority over input data
Types of data • Several classifications of data can be proposed, eg • high /low level (e.g Govt BoI vs mobility of a land platform • low level feed into high? • hard/soft, objective/subjective etc • However, in practice distinctions fuzzy • Soft data • schemes of manoeuvre, future doctrine, threat data etc • Hard data • platform data, policy statements, ORBATs etc
Problems with data collection • Time • hard to get, large amounts • up to 75% of study spent collecting data • Why hard? • often unvalidated/anecdotal • knowledge is power • data management not sexy subject • often subject to ‘fads’ • expensive and time consuming, leading to poorly maintained sources or gaps • data just not known • imbedded within models: self perpetuating • data from previous studies are often used at the customer’s request • rotation of military staff
Problems with multiple data sources • First glance multiple sources better than none • Closer inspection problems become apparent • different data sources give different values • design v use • performance on a range v performance in the field v performance in a model • current v future • centralised v distributed • historical v predicted • objective v subjective • Each source of data may be the ‘correct’ one • arbiter: the customer and stakeholder community
Methods used for obtaining data • Despite problems all is not lost • Methods for obtaining data • communication • undoubtedly the best • use of military personnel • involve the customer at an early stage • use of existing data • industry and other technical experts • historical data • strawman data • sensitivity analysis
Data Conclusions • Data vital for any study • the quality of data and stakeholder buy in important • Where data not available strawman and sensitivity useful • Time should be spent ensuring data fit for purpose • If time spent collecting data reduced • more time for analysis • more cost efficient studies • More effort required managing data • More knowledge sharing and communication are required!
Conclusions Section 6
Conclusions • Top down and bottom up approaches both have advantages and disadvantages • horses for courses • Data are important • many problems • but that’s why they pay us to do it • many solutions • some outlined in paper • I’d like to know yours