A DoD Logistics-Chain Performance Model

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Since the emergence of civilization, successful war-fighting strategy depends upon the optimization of both a cost-effective and a wide-ranging ability to deliver supplies sustaining the warfighter.  Even still, modern military strategy seeks at once to preserve the robustness of one’s own logistics chain while disrupting that of the adversary.  As an extension of the national economy, the breadth of logistics support finds natural constraint, mirroring that of nation’s economic autonomy.  Given this threshold, the obligation of a government to provide global logistics support competes with an ever-pressing responsibility of being everywhere and at-once capable of responding powerfully to threat.  How, then, to maintain strategic advantage in a contracting economy?

Within the US Department of Defense (DoD), the need for integrated supply chain management is both a necessary objective and a perpetual obstacle to achieving optimal global-warfighting advantage.  Particularly in times of peace, the DoD must continue to grow its global capability—keeping pace with potential threats—while decreasing its national burden.  Assuming rates of taxation remains steady, Congress must distribute finite funds throughout competing government agencies, which each must operate at increased capacity despite its funding allocations.  This makes for an interesting problem: how can an agency know how and where to distribute its resources in order to best meet its obligatory purpose? Further, how can an agency better advocate its needs to Congress so that the burdens of fiscal constraint are balanced among agencies?

In 2016, then Secretary of Defense Ashton Carter released the 2017 Budget Defense Posture Statement, in which he described five evolving challenges driving the focus of DoD’s planning and budgeting: (1) Russia and (2) China – the great power competition; (3) North Korea and (4) Iran—regionally specific deterrence of nuclear aggression; (5) the regionally broad effort to counter terrorism, specifically ISIL.  To meet these challenges, and to act as a global presence the US defense forces must promise both size and capability not only to deter wars, but also to fight and win.  What more, these forces must remain agile in their ability to respond to emerging threats and future shifts in warfighting design.  To balance the competing demands between budget constraint and future defense capability, Carter recognized the inherent opportunities of strategic, organizational and technological innovation in recognizing, leveraging and commanding strategic advantages.  Thus, the management of innovation emerges as an essential process shaping future warfighting advantage.

The proposed concept model recommends a systems-level, top-down approach to defense logistics-chain management, which aims to support strategic decision-making both to improve supply-chain performance and to scope service-specific applications of innovative technologies and portfolio modernization efforts.  While this systems view contradicts the traditional bottom-up approach to organization management, supply-chain management and data analytics, the top-down demand for decision-support affects operational- and tactical-level alignment of planning and decision-making to strategic-level initiatives.  Particularly significant for organizations attempting to leverage data as an enterprise asset, the systems approach to performance improvement sends a direct and specific demand for data, information technologies, and systems portfolios, all oriented to a specific objective: support the war-fighter.  By considering the performance attributes of the joint logistics chain, the DoD can measure a baseline of overall performance, establish quantifiable strategic goals, monitor the efficacy of funding allocations across innovative organizational and technological initiatives, and streamline its ability to meet war-fighter requirements.  This approach lends itself as a powerful opportunity for the DoD to optimize its capacities and capabilities despite economic constraint, and to “lighten the load” of unproductive, unnecessary and cumbersome information silos.

Holistic Supply-Chain Management

With the rise of Big Data and the Internet of Things, coordinated supply-chain management emerges from theoretical potential into current reality.  Innovations in technologies, methodologies and analytic techniques revolutionize the linear treatment of the industrial supply-chain into a view of a globally integrated information network.  Whereas traditional approaches to supply-chain management focus on optimizing performance along functional areas and linear process flows, the modern, global supply network demands a more holistic approach.   Local optimums in all parts do not necessarily lead to optimization for a system; however, a systems approach to global optimization balances total sustainability for that system.  In a systems approach to supply-chain management, high-level strategic goals cascade requirements throughout lower-tier supporting structures, functions and objectives, thus increasing both the efficiency and the efficacy with which the total supply-chain satisfies its customer.

According to the US Marine Corps Integrated Logistics Capability Metrics Study (2002), most organizations do not collect or use metrics well for two reasons: functionally oriented metrics not only compete for resources, they also often fail to inform strategic planning decisions.  To support an organization’s decision of what data to collect and prioritize, there is a need to approach data from the perspective of a larger framework so that the collected data elements support and complement each other.  Metrics that align themselves to only one business-area function are ultimately ineffective and counterproductive. Further, there is a need for organizations to relate their metrics to strategic missions and goals.  A top-down, systems approach ensures that collected metrics are helping to propel the organization’s mission and goals.

Of the numerous Supply Chain Management frameworks, the American Production and inventory Control Society’s (APICS’s) Supply Chain Operational Reference (SCOR) analytic methodology alone sets the industry standard.  The first tier of the SCOR method introduces the following strategic-level performance metrics, which subsequent tiers decompose to the diagnostic level: (1) Reliability, (2) Responsiveness, (3) Flexibility, (4) Asset Management Efficiency, and (5) Total Cost.  Reliability assesses supply-chain performance in terms of its ability to correctly deliver the right product to the right place, at the right time, in the right quantity, in the right condition, with the right documentation.  Responsiveness assesses the rate of speed at which a supply chain provides its products to the end-user.  Flexibility assesses the supply-chain’s capacity to respond to sudden, non-forecasted shifts in customer demand.  Asset Management Efficiency reflects how effectively the organization manages assets supporting demand satisfaction.  Total Cost assesses the performance of the supply-chain in terms of operational expenses.

Joint Logistics-chain Performance

Using these performance attributes as a point of departure, the proposed model borrows from the US Marine Core Metrics Study, and reduces the SCOR-recommended Level-1 metrics to only one performance metric for each attribute (Table 1).  Additionally, the DoD Logistics-chain Performance Model adds an integral measure of DoD performance: Readiness.  Distinct from Reliability and Responsiveness, Defense Readiness is a uniquely-military term describing the ability of both equipment and personnel to meet the unanticipated challenges of combat.  For the purpose of measuring logistics-chain performance, the Total Operational Availability of equipment lends itself as an essential measure of logistics-chain performance.  In breaking each performance attribute down by metrics and supporting metrics, this model helps decision-makers to distinguish what data need to be collected from each service. Thus, this model not only satisfies a need for defining joint-service data interoperability requirements, it also implies underlying operational- and tactical-level metric requirements for each service, branch, and unit.

Table 1: DoD Logistics-Chain Performance Indicators


In assessing Logistics-chain performance along these key indicators, the DoD itself emerges as the visionary leader, advocating for optimal performance in supporting the Combatant Commander.  The systems view of a total defense supply-chain performance not only establishes a descriptive baseline for its joint logistics-chain, it sets a precedent for tracking this performance over time, thus enabling the maturation of a future predictive and prescriptive analytic capability across the DoD.  In the face of increased budget constraint, quantifiable goals trickle-down from the strategic-level throughout the entire joint enterprise, ensuring the better alignment of resources to the combatant commanders’ needs.  This top-down demand for data provides a powerful standard by which each service may assess organization-level decisions to maintain, consolidate, or archive legacy information systems.  Likewise, local decisions to integrate emerging technological innovations may be assessed quantitatively against the performance needs of the joint supply chain.  Anxieties about the potential value of investing in sensor-collected data, for example, may thus find assurance.  What more, organization-level information systems and data analytic-enabling projects are more likely to find fiscal support through adequate demonstration of alignment to strategic-level performance attributes.


Innovation, as with Big Data, often falls victim to the “pro-innovation bias” that assumes it is always good.  Bandwagon pressures irrationally influence organizations who must needs justify either their legitimacy, or their competitive advantage (Abrahamson & Rosenkopf, 1993).  The more ambiguous the benefit of a particular innovation, the greater the pressure to conform to collective decisions to adoption; the greater the number who adopt, the greater the bandwagon pressure.  Within the service branches, leaders recognize the need to innovate.  However, for want of quantifiable national-strategic guidance, many service-level initiatives approach technological modernization of the logistics-chain and its supporting processes in a disjointed effort.  All recognize the need to support the entire DoD with quality data; all focus on their own optimizations.  By defining the required metrics supporting a holistic view of the joint services as an integrated logistics-chain, the Secretary of Defense provides both a unified vision and the quantitative incentives, which help to mitigate the irrational pressures to innovate.  Instead, the decision to adopt or adapt new technologies becomes one of how well it supports performance of the defense system first, and then that of the service.


Abrahamson, E., & Rosenkopf, L. (1993). Institutional and Competitive Bandwagons: Using Mathematical Modeling as a Tool to Explore Innovation Diffusion. The Academy of Management Review, 18(3), 487-517. Retrieved from http://www.jstor.org/stable/258906

Carter, A. (2016, February). 2017 Budget defense posture statement: Taking the long view, investing for the future (Rep.). Retrieved October 4, 2017, from Department of Defense website: https://www.defense.gov/Portals/1/Documents/pubs/2017DODPOSTURE_FINAL_MAR17UpdatePage4_WEB.PDF

Marine Corps logistics operational architecture: US Marine Corps integrated logistics capability metrics study (Rep.). (2002). Washington, DC: Headquarters, United States Marine Corps.

SCOR Framework. (n.d.). Retrieved October 4, 2017, from http://www.apics.org/apics-for-business/products-and-services/apics-scc-frameworks/scor