When to commit in a serial supply chain with forecast updating

Negotiations between an end product manufacturer and a parts supplier often revolve around two main issues: the supplier's price and the length of time the manufacturer is contractually held to its order quantity, commonly termed the “commitment time frame.” Because actual demand is unknown, the specification of the commitment time frame determines how the demand risk is shared among the members of the supply chain. Casual observation indicates that most manufacturers prefer to delay commitments as long as possible while suppliers prefer early commitments. In this paper, we investigate whether these goals are always in the firm's best interest. In particular, we find that the manufacturer may sometimes be better off with a contract that requires an early commitment to its order quantity, before the supplier commits resources and the supplier may sometimes be better off with a delayed commitment. We also find that the preferred commitment time frame depends upon which member of the supply chain has the power to set their exchange price. © 2003 Wiley Periodicals, Inc. Naval Research Logistics, 2003     

When is model complexity too much? Illustrating the benefits of simple models with Hughes' salvo equations

The simulations that many defense analysts rely upon in their studies continue to grow in size and complexity. This paper contrasts the guidance that the authors have received—from some of the giants of military operations research—with the current practice. In particular, the analytic utility of Hughes' simple salvo equations is compared with that of the complex Joint Warfighting System (JWARS), with respect to JWARS' key performance parameters. The comparison suggests that a family of analytic tools supports the best analyses. It follows that smaller, more agile, and transparent models, such as Hughes' salvo equations, are underutilized in defense analyses. We believe that these models should receive more attention, use, and funding. To illustrate this point, this paper uses two very simple models (by modern standards) to rapidly generate insights on the value of information relative to force strength. © 2003 Wiley Periodicals, Inc. Naval Research Logistics, 2003     

Strategic capacity decision‐making in a stochastic manufacturing environment using real‐time approximate dynamic programming

In this study, we illustrate a real‐time approximate dynamic programming (RTADP) method for solving multistage capacity decision problems in a stochastic manufacturing environment, by using an exemplary three‐stage manufacturing system with recycle. The system is a moderate size queuing network, which experiences stochastic variations in demand and product yield. The dynamic capacity decision problem is formulated as a Markov decision process (MDP). The proposed RTADP method starts with a set of heuristics and learns a superior quality solution by interacting with the stochastic system via simulation. The curse‐of‐dimensionality associated with DP methods is alleviated by the adoption of several notions including “evolving set of relevant states,” for which the value function table is built and updated, “adaptive action set” for keeping track of attractive action candidates, and “nonparametric k nearest neighbor averager” for value function approximation. The performance of the learned solution is evaluated against (1) an “ideal” solution derived using a mixed integer programming (MIP) formulation, which assumes full knowledge of future realized values of the stochastic variables (2) a myopic heuristic solution, and (3) a sample path based rolling horizon MIP solution. The policy learned through the RTADP method turned out to be superior to polices of 2 and 3. © 2010 Wiley Periodicals, Inc. Naval Research Logistics 2010     

How and why Boko Haram blossomed: examining the fatal consequences of treating a purposive terrorist organisation as less so

Boko Haram (BH) is an insurgent group that operates mainly in northeastern Nigeria. Its stated aim is to establish an Islamic state, and it employs terrorism as its strategy. Earlier interests of security analysts centred on the drivers of BH uprising and the possibility of its internationalisation. Today's concerns relate more to why the rebellion has lasted this long. In toeing the same line, this article demonstrates that BH is a purposive terror group against which the government has evolved no viable strategy. It examines some gains of BH over the past six years and how it benefitted from the government's underestimation of its capacity and determination. It concludes that to effectively engage BH, the Nigerian government must revaluate its threat and sincerely pursue an expanded strategy beyond the current military-centric approach. To be effective, government's response must be packaged in such a way as to enhance human security in the region.     

Artificial intelligence & future warfare: implications for international security

Recent developments in artificial intelligence (AI) suggest that this emerging technology will have a deterministic and potentially transformative influence on military power, strategic competition, and world politics more broadly. After the initial surge of broad speculation in the literature related to AI this article provides some much needed specificity to the debate. It argues that left unchecked the uncertainties and vulnerabilities created by the rapid proliferation and diffusion of AI could become a major potential source of instability and great power strategic rivalry. The article identifies several AI-related innovations and technological developments that will likely have genuine consequences for military applications from a tactical battlefield perspective to the strategic level.