ARMING THE REVOLUTIONARY UNITED FRONT

On 7 July 1999, the government of Sierra Leone and the Revolutionary United Front (RUF) signed the Lomé Peace Agreement to end the civil war. A central component of this agreement called for the RUF to disarm. A year later, the RUF leader, Foday Sankoh, was in custody and the future of the peace accord in grave doubt. Far from disarming, all parties have been rearming at an alarming rate, in contravention of a 1997 UN arms embargo and despite a regional moratorium on the production, procurement and sale of small arms and light weapons. The political and security situation remains extremely fragile. This article explores the ease with which small arms and light weapons can be obtained, and questions the efficacy of existing armament and disarmament policies. Given the availability of arms, the weakness of the current government, the relative strength of the RUF, and the fluidity of alliances among the country's armed groups, the likelihood of continued conflict in Sierra Leone is great.     

Demand forecasting by temporal aggregation

Demand forecasting performance is subject to the uncertainty underlying the time series an organization is dealing with. There are many approaches that may be used to reduce uncertainty and thus to improve forecasting performance. One intuitively appealing such approach is to aggregate demand in lower‐frequency “time buckets.” The approach under concern is termed to as temporal aggregation, and in this article, we investigate its impact on forecasting performance. We assume that the nonaggregated demand follows either a moving average process of order one or a first‐order autoregressive process and a single exponential smoothing (SES) procedure is used to forecast demand. These demand processes are often encountered in practice and SES is one of the standard estimators used in industry. Theoretical mean‐squared error expressions are derived for the aggregated and nonaggregated demand to contrast the relevant forecasting performances. The theoretical analysis is supported by an extensive numerical investigation and experimentation with an empirical dataset. The results indicate that performance improvements achieved through the aggregation approach are a function of the aggregation level, the smoothing constant, and the process parameters. Valuable insights are offered to practitioners and the article closes with an agenda for further research in this area. © 2013 Wiley Periodicals, Inc. Naval Research Logistics 60: 479–498, 2013