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The purpose of the paper is to introduce the Jensen–inaccuracy measure and examine its properties. Furthermore, some results on the connections between the inaccuracy and Jensen–inaccuracy measures and some other well-known information measures are provided. Moreover, in three different optimization problems, the arithmetic mixture distribution provides optimal information based on the inaccuracy information measure. Finally, two real examples from image processing are studied and some numerical results in terms of the inaccuracy and Jensen–inaccuracy information measures are obtained.

PurposeEnsuring high on-shelf availability at low inventory costs remains an important challenge in retailing. Inaccurate inventory records, i.e. discrepancies between the stock records displayed in the inventory system and the stock quantity actually found in the retail store, have been identified as one of the most important drivers of retail stockouts in the past. The purpose of this work is to investigate the causes of positive inventory discrepancies in retailing, i.e. where there is more inventory on-hand than identified by the inventory system.Design/methodology/approachBased on input from retailers, the authors develop a simulation model of a retail store that considers various error-prone processes and study in a full factorial test design how the different operational errors may drive inventory discrepancies, paying special attention to the sources of positive inventory record inaccuracies.FindingsThis makes it possible to gain insights into the process parameters retailers need to adjust to avoid inventory records becoming inaccurate. In addition, the authors analyze how positive inventory discrepancies relate to stockouts to further our understanding of the role so-called phantom products may play in a retailing context.Originality/valueWhile negative inventory discrepancies (where the stock that is available in the store is less than what the system displays) and their sources (theft, shrinkage, etc.) have been discussed quite frequently in the literature, the causes of positive inventory discrepancies (where the available inventory exceeds the system inventory) have received much less attention.

Concerning the notorious Sleeping Beauty problem, philosophers have debated whether 1/2 or 1/3 is rational as Beauty’s credence in (H) the coin’s landing heads. According to Kierland and Monton, the answer depends on whether her goal is to minimize average or total inaccuracy because, while the expected average inaccuracy of Halfing (i.e., assigning 1/2 to H) is smaller than that of Thirding (i.e., assigning 1/3 to H), the expected total inaccuracy of Thirding is lower than that of Halfing. In this paper, I argue that Halfing is average accuracy dominated but Thirding is not; and that each of the standard forms of Halfing and Thirding regards a different credence assignment as better than itself in terms of total accuracy. Therefore, Halfing is irrational, and Thirding is likely to be rational, for the goal of minimizing average inaccuracy; but both Halfing and Thirding, at least in their standard forms, are irrational for the goal of minimizing total inaccuracy.

This paper proposes an analysis of surprise formulated in terms of proximity to the truth, to replace the probabilistic account of surprise. It is common to link surprise to the low (prior) probability of the outcome. The idea seems sensible because an outcome with a low probability is unexpected, and an unexpected outcome often surprises us. However, the link between surprise and low probability is known to break down in some cases. There have been some attempts to modify the probabilistic account to deal with these cases, but as we shall see, they are still faced with problems. The new analysis of surprise I propose turns to accuracy (proximity to the truth) and identifies an unexpected degree of inaccuracy as reason for surprise. The shift from probability to proximity allows us to solve puzzles that strain the probabilistic account of surprise.

Wire electric discharge machining (WEDM) is an important non-traditional manufacturing technique for industries processing hard-to-machine materials. It can produce complex shapes with high dimensional accuracy and surface finish. Ti6Al4V is frequently used in biomedical applications such as surgical implants, dentistry, and orthopedic wires. All these applications require machining complex profiles with high accuracy in terms of dimensions and surface properties. Multi-pass machining is a proven technique for minimizing the damage on the machined surface but increasing the number of passes lowers the productivity. Hence, careful selection of wire offset value for trim cutting is crucial to maintain process efficiency and keep the number of passes minimum. The objective of this study is to investigate the effect of wire offset in multi-pass machining on surface integrity, dimensional accuracy, and cutting speed in complex WEDM of Ti6Al4V and limit the number of trim passes to one. In addition, effect of electrode composition on machining responses is studied for three different types of wires (uncoated brass, Broncocut-W, and Topas Plus X). Experimental results indicate that a single trim cut at an offset value of 0.11mmprovides better surface finish and minimum recast layer. Surface roughness of 1.31 µm is obtained using brass wire: 16.5% and 18.6% less than for Broncocut-W and Topas plus X, respectively. Similarly, recast layer of 8.183 µm attained by brass wire is smaller than 8.98 µm, and 10.041 µm produced by the other wires. The uncoated brass wire has proved to be the best electrode for surface finish, recast layer thickness, and dimensional accuracy of the machined profile. However, Bronococut-W wire has performed better in terms of cutting speed.

Previous research provides conflicting results regarding how R&D expenditures impact market value. Given that financial analysts are the primary intermediaries between companies and investors, our study focused on the impact of R&D-related uncertainty, growth, and information asymmetry associated on analysts’ earnings forecasts. Based on 19,834 firm-year observations in the European market between 2005 and 2020, our results show that R&D activities lead to higher absolute forecast error and negative forecast error, indicating higher forecast inaccuracy with an optimistic bias. Additionally, these investments contribute to higher forecast dispersion, indicating disagreement among financial analysts. The comparison between 17 industries revealed that these effects are more pronounced in R&D-intensive industries than in non-R&D industries, uncovering the varied relationship between R&D investments and analyst forecasts across sectors.

In this article, we present a novel approach to measuring inaccuracy, introducing a two-parametric weighted generalized inaccuracy measure of order and type , along with its residual version. Our proposed measure depends on the proportional hazard rate model (PHRM) to uniquely determine the survival function, and we have derived a characterization result for this measure. Through our analysis under the PHRM framework, we have studied various properties of the proposed measure and their interrelationships.

We assessed provider directory accuracy and timely access in Maryland's Medicaid managed care program, using annual surveys from the annual random and representative provider surveys conducted on behalf of the Maryland Department of Health for 2018 and 2019. Based on 3,262 calls to 2,002 providers in 2018 and 2,739 calls to 2,033 providers in 2019, we found that provider directories are highly inaccurate. Insurance coverage could only be verified for 46% of the listed providers in 2018 and 56% of the listed providers in 2019. Among providers whose insurance participation was verified, beneficiaries were able to schedule timely general care appointments in 90% of verified providers in 2018 and 85% of verified providers in 2019; slightly more than 70% of appointments were scheduled on the first call. The success rate for urgent care appointments was lower but improved substantially once alternative providers were accounted for. Even for verified providers, timely access standards were often not met, particularly for general care. We also note the substantial variation across managed care organizations and across years. Our findings raise concerns from both an enrollee as well as a broader policy perspective. More oversight and enforcement are necessary to guarantee access to care.

In modular structures, inaccuracies of the modules superimpose over the entire structure. Depending on the placement of the modules, these inaccuracies have (different) effects on stresses and total deformations. Especially for structures with many individual modules, it is favorable to place them according to their influence. To cover structural diversity, column-, beam-, and wall-like modular structures made from plane modules are investigated. In numerical simulation, geometric inaccuracies are applied via an equivalent temperature approach, which allows almost arbitrary nodal deviations of the modules. With the elementary effects method, the sensitivities of the modules’ inaccuracies regarding their structural impact can be determined with minimal computational effort. On a predefined control node, the overall structural inaccuracies are examined in a simplified manner. Column-like structures experience higher deformations due to inclination than beam-like or wall-like structures. For column-like, the bottommost modules are decisive for the overall inaccuracy, as they contribute significantly to the inclination. By contrast, modules at the supports are identified as particularly sensitive for beam- and wall-like structures. Controlling module placement towards their mean absolute influence, the deformation at the control node is mathematically reduced by at least 43% compared to random placement. Thereby, even modules that do not comply with tolerance requirements for structural components can be used in areas of low influence for a sustainable and low-waste design.

Purpose The combination of the latest advancements in information and communication technologies with the latest developments in AutoID technologies, especially radio frequency identification (RFID), brings the possibility of high-resolution, item-level visibility of the entire supply chain. In the particular case of retail, visibility of both the stock count and item location in the shop floor is crucial not only for an effective management of the retail supply chain but also for physical retail stores to compete with online retailers. The purpose of this paper is to propose an autonomous robot that can perform stock-taking using RFID for item-level identification much more accurately and efficiently than the traditional method of using human operators with RFID handheld readers. Design/methodology/approach This work follows the design science research methodology. The paper highlights a required improvement for an RFID inventory robot. The design hypothesis leads to a novel algorithm. Then the cycle of development and evaluation is iterated several times. Finally, conclusions are derived and a new basis for further development is provided. Findings An autonomous robot for stock-taking is proven feasible. By applying a proper navigation strategy, coupled to the stream of identifications, the accuracy, precision, consistency and time to complete stock-taking are significantly better than doing the same task manually. Research limitations/implications The main limitation of this work is the unavailability of data to analyze the actual impact on the correction of inventory record inaccuracy and its subsequent implications for the supply chain management. Nonetheless, it is shown that figures of actual stock-tacking procedures can be significantly improved. Originality/value This paper discloses the potential of deploying an inventory robot in the supply chain. The robot is called to be a key source of inventory data conforming supply chain management 4.0 and omnichannel retail.