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The accurate measurement of demand variability amplification across different nodes in the supply chain, or “bullwhip effect,” is critical for firms to achieve more efficient inventory, production, and ordering planning processes. Building on recent analytical research that suggests that data aggregation tends to mask the bullwhip effect in the retail industry, the purpose of this paper is to empirically investigate whether different patterns of data aggregation influence its measurement.

Utilizing weekly, product-level order and sales data from three product categories of a consumer packaged goods manufacturer, the study uses hierarchical linear modeling to empirically test the effects of data aggregation on different measures of bullwhip.

The authors findings lend strong support to the masking effect of aggregating sales and order data along product-location and temporal dimensions, as well as the dampening effect of seasonality on the measurement of the bullwhip effect.

These findings indicate that inconsistencies found in the literature may be due to measurement aggregation and statistical techniques, both of which should be applied with care by academics and practitioners in order to preserve the fidelity of their analyses.

Using product-weekly level data that cover both seasonal and non-seasonal demand, this study is the first, to the author’s knowledge, to systematically aggregate data up to category and monthly levels to empirically examine the impact of data aggregation and seasonality on bullwhip measurement.

Agricultural aggregation schemes provide numerous farmer-facing benefits, including reduced transportation costs and improved access to higher-demand urban markets. However, whether aggregation schemes also have positive food security dimensions for consumers dependent on peri-urban and local markets in developing country contexts is currently unknown. This paper aims to narrow this knowledge gap by exploring the actors, governance structures and physical infrastructures of the horticultural value chain of Bihar, India, to identify barriers to using aggregation to improve the distribution of fruits and vegetables to more local market environments.

This study uses mixed methods. Quantitative analysis of market transaction data explores the development of aggregation supply pathways over space and time. In turn, semi-structured interviews with value chain actors uncover the interactions and decision-making processes with implications for equitable fruit and vegetable delivery.

Whilst aggregation successfully generates multiple producer-facing benefits, the supply pathways tend to cluster around urban export-oriented hubs, owing to the presence of high-capacity traders, large consumer bases and traditional power dynamics. Various barriers across the wider enabling environment must be overcome to unlock the potential for aggregation to increase local fruit and vegetable delivery, including informal governance structures, cold storage gaps and underdeveloped transport infrastructures.

To the best of the authors’ knowledge, this study is the first critical analysis of horticultural aggregation through a consumer-sensitive lens. The policy-relevant lessons are pertinent to the equitable and sustainable development of horticultural systems both in Bihar and in similar low- and middle-income settings.

Large/stochastic spatiotemporal demand data sets can prove intractable for location optimization problems, motivating the need for aggregation. However, demand aggregation induces errors. Significant theoretical research has been performed related to the modifiable areal unit problem and the zone definition problem. Minimal research has been accomplished related to the specific issues inherent to spatiotemporal demand data, such as search and rescue (SAR) data. This study provides a quantitative comparison of various aggregation methodologies and their relation to distance and volume based aggregation errors.

This paper introduces and applies a framework for comparing both deterministic and stochastic aggregation methods using distance- and volume-based aggregation error metrics. This paper additionally applies weighted versions of these metrics to account for the reality that demand events are nonhomogeneous. These metrics are applied to a large, highly variable, spatiotemporal demand data set of SAR events in the Pacific Ocean. Comparisons using these metrics are conducted between six quadrat aggregations of varying scales and two zonal distribution models using hierarchical clustering.

As quadrat fidelity increases the distance-based aggregation error decreases, while the two deliberate zonal approaches further reduce this error while using fewer zones. However, the higher fidelity aggregations detrimentally affect volume error. Additionally, by splitting the SAR data set into training and test sets this paper shows the stochastic zonal distribution aggregation method is effective at simulating actual future demands.

This study indicates no singular best aggregation method exists, by quantifying trade-offs in aggregation-induced errors practitioners can utilize the method that minimizes errors most relevant to their study. Study also quantifies the ability of a stochastic zonal distribution method to effectively simulate future demand data.

Multiple studies have illustrated that the needs of various users seeking descriptive bibliographic data for pop culture resources (e.g. manga, anime, video games) have not been properly met by cultural heritage institutions and traditional models. With a focus on manga as the central resource, the purpose of this paper is to address these issues to better meet user needs.

Based on an analysis of existing bibliographic metadata, this paper proposes a unique bibliographic hierarchy for manga that is also extendable to other pop culture sources. To better meet user requirements of descriptive data, an aggregation-based approach relying on the Object Reuse and Exchange-Open Archives Initiative (OAI-ORE) model utilized existing, fan-created data on the web.

The proposed hierarchy is better able to portray multiple entities of manga as they exist across data providers compared to existing models, while the utilization of OAI-ORE-based aggregation to build and provide bibliographic metadata for said hierarchy resulted in levels of description that more adequately meet user demands.

Though studies have proposed alternative models for resources like games or comics, manga has remained unexamined. As manga is a major component of many popular multimedia franchises, a focus here with the intention while building the model to support other resource types provides a foundation for future work seeking to incorporate these resources.

The purpose of this paper is to study assembling parameters in dye-sensitised solar cells (DSSCs) performance. For this end, 3a,7a-dihydroxy-5ß-cholanic acid (cheno) are selected as anti-aggregation agent and two solutions, namely, tetrabutyl ammonium iodide and (PMII)IL used as electrolyte.

A series of organic dyes were selected using N-substituents carbazole as electron donor group and acrylic acid and cyanoacrylic acid as electron acceptor groups. Absorption properties of purified dyes were studied in solution and on photoelectrode substrate. DSSCs were prepared in the presence of anti-aggregation agent and different electrolyte to determine the photovoltaic performance of each dyes.

The results showed that all organic dyes form J-aggregation on the photoanode substrate in the absence of anti-aggregation agent and the amounts of aggregation were reduced in the presence of anti-aggregation agent. DSSCs were fabricated in the presence of anti-aggregation agent. The photovoltaic properties were improved using tetrabutyl ammonium iodide as electrolyte. The maximum power conversion efficiency was achieved for D12 in the presence of cheno and tetrabutyl ammonium iodide as anti-aggregation agent and electrolyte, respectively.

Organic dye attracts more and more attention due to low cost, facile route synthesis and less hazardous.

The effect of anti-aggregation agent and electrolyte on DSSCs performance was investigated for the first time.

A flexible and realistic linguistic assessment approach is developed to provide a mathematical tool for synthesis and evaluation analysis of nuclear safeguards indicator information. This symbolic approach, which acts by the direct computation on linguistic terms, is established based on fuzzy set theory. More specifically, a lattice‐valued linguistic algebra model, which is based on a logical algebraic structure of the lattice implication algebra, is applied to represent imprecise information and to deal with both comparable and incomparable linguistic terms (i.e. non‐ordered linguistic values). Within this framework, some weighted aggregation functions introduced by Yager are analyzed and extended to treat these kinds of lattice‐value linguistic information. The application of these linguistic aggregation operators for managing nuclear safeguards indicator information is successfully demonstrated.

The act of consolidating multiple parcels of land to form a single larger parcel is known as land assembly. Laboratory experiments have enabled researchers to explore how various factors, environments, and institutions hinder or assist the aggregation process. This chapter surveys the experimental literature and highlights the experimental design used in those studies, as well as their main findings.

In a manufacturing system, time performances are measures of system response speed to external influences. This speed depends on the resource allocation process (materials, equipment, labour) which is driven by finished‐product forecasts. Describes two essential steps, in order to develop a model for evaluating time performances which is able to detect crucial resources. The first step is represented by analysing forecast characteristics; the second step is expressed by a definition of the environment of manufacturing resources. The model, depicted in its structure and in its relationships with the most common business tools, has been tested in a number of manufacturing firms and the results are also shown.

The purpose of this paper is to aggregate different preference information in group decision‐making process such as interval preference order, interval utility value, interval number reciprocal comparison matrix, and interval number complementary comparison matrix.

First, the consistency definitions of four kinds of uncertain preference information are defined. Then, the upper‐ and low errors are introduced to solve the inconsistent decision‐making case. Following that, the weight model for each uncertain preference is proposed, respectively.

The aggregation approach based on minimal group deviation errors is suggested in order to obtain the utmost consistent opinion. In addition, the consistency judgment level and consistency extent are defined owing to the aggregation result.

The calculation scale is large, if many decision makers will attend group decision‐making process.

A very useful approach for aggregation of the different preference in group decision‐making case.

Because of differences in knowledge structure, judgment level, and individual preference, decision makers express their judgment preferences via differently structured decision‐making processes. Owing to the complexity and uncertainty of decision‐making problems and the fuzziness of human thought, it is unrealistic to depict complex problems in the certain preference style. For decision‐making preference structures, group decision‐making aggregation approaches include the aggregation on the same kind of preference structure and the different kinds of preference structures. The study on the aggregation of the same kind of preference structure has received a deal of attention, but study into the aggregation of the different kinds of uncertainty preference structures is still a new field.

This study aims to provide a secured data aggregation with reduced energy consumption in WSN. Data aggregation is the process of reducing communication overhead in wireless sensor networks (WSNs). Presently, securing data aggregation is an important research issue in WSNs due to two facts: sensor nodes deployed in the sensitive and open environment are easily targeted by adversaries, and the leakage of aggregated data causes damage in the networks, and these data cannot be retrieved in a short span of time. Most of the traditional cryptographic algorithms provide security for data aggregation, but they do not reduce energy consumption.

Nowadays, the homomorphic cryptosystem is used widely to provide security with low energy consumption, as the aggregation is performed on the ciphertext without decryption at the cluster head. In the present paper, the Paillier additive homomorphic cryptosystem and Boneh et al.’s aggregate signature method are used to encrypt and to verify aggregate data at the base station.

The combination of the two algorithms reduces computation time and energy consumption when compared with the state-of-the-art techniques.

The secured data aggregation is useful in health-related applications, military applications, etc.

The new combination of encryption and signature methods provides confidentiality and integrity. In addition, it consumes less computation time and energy consumption than existing methods.