The value of sharing planning information in supply chains

Purpose - The development of information technology has made it possible for companies to get access to information about their customers’ future demand. This paper outlines various approaches to utilize this kind of visibility when managing inventories of end products on an operative level. The purpose is to explain the consequences, for capital tied up in inventory, of sharing four different types of planning information (point-of-sales data, customer forecasts, stock-on-hand data, planned orders) when using re-order point (R,Q) inventory control methods in a distribution network. Design/methodology/approach - A simulation study based on randomly generated demand data with a compound Poisson type of distribution is conducted. Findings - The results show that the value of information sharing in operative inventory control varies widely depending on the type of information shared, and depending on whether the demand is stationary or not. Significantly higher value is achieved if the most appropriate types of information sharing are used, while other types of information sharing rather contribute to decreased value. Sharing stock-on-hand information is valuable with stationary demand. Customer forecast and planned order information are valuable with non-stationary demand. The value of information sharing increases when having fewer customers, and when the order quantities are large. Sharing point-of-sales data is not valuable, regardless of the demand type. Research limitations/implications - The use of simulation methodology is a limitation, because the study has to be limited to a specific model design, and because it is not based on primary empirical data. The study is especially limited to dyadic relationships in supply chains, and to distribution networks with a rather limited number of customers. Practical implications - Guidance is given about what type of information should be appropriate to share when different types of demand patterns and distribution networks, and how order batch sizes and lead times affect the value of information sharing when using re-order point (R,Q) methods. Originality/value - Very limited research providing specific assessments of potential inventory control consequences when sharing planning information in various contexts has been found in the literature. The findings and conclusions should also be valuable for the supply chain integration and collaborative planning literature

Cognate-aware morphological segmentation for multilingual neural translation

This article describes the Aalto University entry to the WMT18 News Translation Shared Task. We participate in the multilingual subtrack with a system trained under the constrained condition to translate from English to both Finnish and Estonian. The system is based on the Transformer model. We focus on improving the consistency of morphological segmentation for words that are similar orthographically, semantically, and distributionally; such words include etymological cognates, loan words, and proper names. For this, we introduce Cognate Morfessor, a multilingual variant of the Morfessor method. We show that our approach improves the translation quality particularly for Estonian, which has less resources for training the translation model.Comment: To appear in WMT1

Transfer learning and subword sampling for asymmetric-resource one-to-many neural translation

There are several approaches for improving neural machine translation for low-resource languages: monolingual data can be exploited via pretraining or data augmentation; parallel corpora on related language pairs can be used via parameter sharing or transfer learning in multilingual models; subword segmentation and regularization techniques can be applied to ensure high coverage of the vocabulary. We review these approaches in the context of an asymmetric-resource one-to-many translation task, in which the pair of target languages are related, with one being a very low-resource and the other a higher-resource language. We test various methods on three artificially restricted translation tasks—English to Estonian (low-resource) and Finnish (high-resource), English to Slovak and Czech, English to Danish and Swedish—and one real-world task, Norwegian to North Sámi and Finnish. The experiments show positive effects especially for scheduled multi-task learning, denoising autoencoder, and subword sampling.There are several approaches for improving neural machine translation for low-resource languages: monolingual data can be exploited via pretraining or data augmentation; parallel corpora on related language pairs can be used via parameter sharing or transfer learning in multilingual models; subword segmentation and regularization techniques can be applied to ensure high coverage of the vocabulary. We review these approaches in the context of an asymmetric-resource one-to-many translation task, in which the pair of target languages are related, with one being a very low-resource and the other a higher-resource language. We test various methods on three artificially restricted translation tasks-English to Estonian (low-resource) and Finnish (high-resource), English to Slovak and Czech, English to Danish and Swedish-and one real-world task, Norwegian to North Sami and Finnish. The experiments show positive effects especially for scheduled multi-task learning, denoising autoencoder, and subword sampling.Peer reviewe

Bruk av KPI i digital markedsføring

Denne masteravhandlingen ser på hvilke KPI'er som er i bruk blant et lite utvalg norske bedrifter som driver innenfor B2B. Den ser også på hva slags effekt bruk av disse KPI'ene har på bedriftenes digitale markedsføringsstrateg

Morfessor 2.0: Python Implementation and Extensions for Morfessor Baseline

Morfessor is a family of probabilistic machine learning methods that find morphological segmentations for words of a natural language, based solely on raw text data. After the release of the public implementations of the Morfessor Baseline and Categories-MAP methods in 2005, they have become popular as automatic tools for processing morphologically complex languages for applications such as speech recognition and machine translation. This report describes a new implementation of the Morfessor Baseline method. The new version not only fixes the main restrictions of the previous software, but also includes recent methodological extensions such as semi-supervised learning, which can make use of small amounts of manually segmented words. Experimental results for the various features of the implementation are reported for English and Finnish segmentation tasks

Morfessor 2.0: Toolkit for statistical morphological segmentation

Morfessor is a family of probabilistic machine learning methods forfinding the morphological segmentation from raw text data. Recentdevelopments include the development of semi-supervised methods forutilizing annotated data. Morfessor 2.0 is a rewrite of the original,widely-used Morfessor 1.0 software, with well documented command-linetools and library interface. It includes algorithmic improvements and new features such as semi-supervised learning, online training, and integrated evaluation code.Peer reviewe