The implementation of digital technologies for operations management: a case study for manufacturing apps

Abstract

Manufacturing applications address business to business (B2B) with highly customised applications developed for specific requirements, offering highly specialised solution-oriented and service-based software components, systems, and digital tools that aim at a fast and accurate decision-making support system. The purpose of this paper is to describe the implementation of digital technologies for operations management using manufacturing or engineering apps (eApps), for product design and manufacturing processes. In particular, starting from the specific needs of two companies from mature European industries as automotive and food, this work depicts how this kind of solutions can support companies and improve their operations. In particular, related benefits and challenges faced for the full implementation of the developed tools are highlighted. Moreover a business model to exploit the manufacturing apps is also proposed. The business model proposed for the exploitation of the eApps supports the commercialisation of all the revenue streams offered by this rapidly growing sector taking into account the specific needs of the concerned stakeholders through a diversified value proposition.     

Model-based clustering of multivariate skew data with circular components and missing values

Motivated by classification issues that arise in marine studies, we propose a latent-class mixture model for the unsupervised classification of incomplete quadrivariate data with two linear and two circular components. The model integrates bivariate circular densities and bivariate skew normal densities to capture the association between toroidal clusters of bivariate circular observations and planar clusters of bivariate linear observations. Maximum-likelihood estimation of the model is facilitated by an expectation maximization (EM) algorithm that treats unknown class membership and missing values as different sources of incomplete information. The model is exploited on hourly observations of wind speed and direction and wave height and direction to identify a number of sea regimes, which represent specific distributional shapes that the data take under environmental latent conditions.     

A weighted polynomial regression method for local fitting of spatial data

Typically, parametric approaches to spatial problems require restrictive assumptions. On the other hand, in a wide variety of practical situations nonparametric bivariate smoothing techniques has been shown to be successfully employable for estimating small or large scale regularity factors, or even the signal content of spatial data taken as a whole.We propose a weighted local polynomial regression smoother suitable for fitting of spatial data. To account for spatial variability, we both insert a spatial contiguity index in the standard formulation, and construct a spatial-adaptive bandwidth selection rule. Our bandwidth selector depends on the Gearys local indicator of spatial association. As illustrative example, we provide a brief Monte Carlo study case on equally spaced data, the performances of our smoother and the standard polynomial regression procedure are compared.This note, though it is the result of a close collaboration, was specifically elaborated as follows: paragraphs 1 and 2 by T. Sclocco and the remainder by M. Di Marzio. The authors are grateful to the referees for constructive comments and suggestions.     

Bayesian networks for imputation

Summary.  Bayesian networks are particularly useful for dealing with high dimensional statistical problems. They allow a reduction in the complexity of the phenomenon under study by representing joint relationships between a set of variables through conditional relationships between subsets of these variables. Following Thibaudeau and Winkler we use Bayesian networks for imputing missing values. This method is introduced to deal with the problem of the consistency of imputed values: preservation of statistical relationships between variables ( statistical consistency ) and preservation of logical constraints in data ( logical consistency ). We perform some experiments on a subset of anonymous individual records from the 1991 UK population census.     

Parameter estimation of state space models for univariate observations

This paper contributes to the problem of estimation of state space model parameters by proposing estimators for the mean, the autoregressive parameters and the noise variances which, contrarily to maximum likelihood, may be calculated without assuming any specific distribution for the errors. The estimators suggested widen the scope of the application of the generalized method of moments to some heteroscedastic models, as in the case of state-space models with varying coefficients, and give sufficient conditions for their consistency. The paper includes a simulation study comparing the proposed estimators with maximum likelihood estimators. Finally, these methods are applied to the calibration of the meteorological radar and estimation of area rainfall.     

On disjoint (3t, 3, 1) cyclic difference families

We give a new and easier proof of the existence of a disjoint (3t  , 3, 1) cyclic difference family for every t>3$t>3$, first proved by Dinitz and Shalaby (2002). Our purely theoretical construction is still elementary but simpler and does not need to be checked by computer.     

Estimating rating transition probabilites with missing data

In this article we provide a rigorous treatment of one of the central statistical issues of credit risk management. GivenK-1 rating categories, the rating of a corporate bond over a certain horizon may either stay the same or change to one of the remainingK-2 categories; in addition, it is usually the case that the rating of some bonds is withdrawn during the time interval considered in the analysis. When estimating transition probabilities, we have thus to consider aK-th category, called withdrawal, which contains (partially) missing data. We show how maximum likelihood estimation can be performed in this setup; whereas in discrete time our solution gives rigorous support to a solution often used in applications, in continuous time the maximum likelihood estimator of the transition matrix computed by means of the EM algorithm represents a significant improvement over existing methods.     

Frugivory and habitat use by thrushes (Turdus spp.) in a suburban area in south Brazil

Thrushes (Turdus spp., Turdidae) are among the most common frugivorous birds in urban areas around the world, where they disperse the seeds of a variety of plant species. We studied the abundance, habitat use, foraging behavior and diet of four thrush species (Turdus rufiventris, T. amaurochalinus, T. leucomelas, and T. albicollis) in a suburban area in south Brazil. Abundance, habitat use and foraging behavior were based on birds surveyed along a 3,240 m transect crossing open (formed by lawns, streets, and buildings) and forested areas. Diet was based on fecal samples collected from mist-netted birds. Turdus rufiventris was the most abundant species, followed by T. amaurochalinus, T. leucomelas, and T. albicollis. All species used forest fragments more frequently than expected by chance. A total of 91.8% (n = 147) of the fecal samples contained fruit remains, while 42.2% contained only animal matter. Most of the foraging records were on the ground, where birds got mainly invertebrates. Fruits and invertebrates were eaten more frequently in open than in forested areas. A total of 25 seed morfospecies were found in the droppings, including five exotic plant species. Thrushes overlapped widely in the fruit composition of their diets. The high abundance and degree of frugivory, coupled with the frequent use of forest patches, indicate that thrushes are among the great bird contributors to the seed dispersal occurring in urban forest patches, potentially influencing the vegetation dynamics of such habitats so important for the maintenance of the biodiversity in urban areas.     

Neural networks in production planning and control

The purpose of this paper is to present a selection of the most significant ANN (artificial neural network) applications used to solve PPC (production planning and control) problems. Therefore, the paper itself is a reasoned classification rather than a simple survey. The PPC domain is divided into various areas, and for each area one or more examples of ANNs are discussed, together with the benefits and drawbacks of their application to that particular area. Finally, two case studies developed by the authors are reported and discussed to analyse the main implementation problems to be considered when using ANNs.