Abstract

Abstract

Abstract

Abstract
Forecasting future sales is one of the most important issues that is beyond all strategic and planning decisions in effective operations of retail businesses. For profitable retail businesses, accurate demand forecasting is crucial in organizing and planning production, purchasing, transportation and labor force. Retail sales series belong to a special type of time series that typically contain trend and seasonal patterns, presenting challenges in developing effective forecasting models. This work compares the forecasting performance of state space models and ARIMA models. The forecasting performance is demonstrated through a case study of retail sales of five different categories of women footwear: Boots, Booties, Flats, Sandals and Shoes. On both methodologies the model with the minimum value of Akaike's Information Criteria for the in-sample period was selected from all admissible models for further evaluation in the out-of-sample. Both one-step and multiple-step forecasts were produced. The results show that when an automatic algorithm the overall out-of-sample forecasting performance of state space and ARIMA models evaluated via RMSE, MAE and MAPE is quite similar on both one-step and multi-step forecasts. We also conclude that state space and ARIMA produce coverage probabilities that are close to the nominal rates for both one-step and multi-step forecasts.

Abstract
Project-based organizations have characteristics that raise additional barriers to information management, knowledge sharing, and to organizational learning. The main causes of this are inadequate information architectures and governance, poor collaborative culture, and lack of organization-wide information management strategies. This chapter presents a comprehensive basis to understand the information and knowledge-sharing practices in PBO, as well as the methods and tools that information professionals and project managers should have in mind when performing their tasks. For that, literatures are reviewed focusing on the explanation of the processes of knowledge creation and sharing leading to organizational learning. The main conclusion is that a knowledge-sharing strategy in a PBO should include a set of mechanisms that address a customized mix of the codification and personalization dimensions and that strategies for collaborative information management should be used as enablers for embedding knowledge sharing within the organizational practices and culture.

Abstract
Real-Time For the Masses (RTFM) is a set of languages and tools being developed to facilitate embedded software development and provide highly efficient implementations geared to static verification. The RTFM-kernel is an architecture designed to provide highly efficient and predicable Stack Resource Policy based scheduling, targeting bare metal (singlecore) platforms. We contribute beyond prior work by introducing a platform independent timer abstraction that relies on existing RTFM-kernel primitives. We develop two alternative implementations for the ARM Cortex-M family of MCUs: a generic implementation, using the ARM defined SysTick- /DWT hardware; and a target specific implementation, using the match compare/free running timers. While sacrificing generality, the latter is more exible and may reduce overall overhead. Invariants for correctness are presented, and methods to static and run-time verification are discussed. Overhead is bound and characterized. In both cases the critical section from release time to dispatch is less than 2us on a 100MHz MCU. Queue and timer mechanisms are directly implemented in the RTFM-core language and can be included in system-wide scheduling analysis.