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This paper aims to address how perceived social media agility can promulgate co-creation processes, such as co-production and value-in-use, and how it impacts brand-related outcomes. This study also addresses calls for marketing scholars to investigate the types of personality traits that affect these potential relationships by accounting for the impact of technology reflectiveness.

This paper conducted an online survey with 321 adult subjects. The direct, indirect and conditional (moderation) effects were assessed using multivariate regression, various PROCESS models and the Johnson–Neyman technique (to probe the interaction terms). Additional supplemental analyses were conducted via PROCESS models.

The results show that perceived social media agility directly and indirectly (through co-production and value-in-use) positively influences brand attachment and that the order of these two processes matters (co-production followed by value-in-use). Results also show that the positive impact of perceived social media agility on co-production and value-in-use deviates for customers high in technology reflectiveness but can be manipulated according to which process comes first.

This paper expounds on the new construct of perceived social media agility by uniquely linking perceived social media agility to two distinct value co-creation processes (co-production and value-in-use) and brand-related outcomes while highlighting how consumer-specific traits can affect this relationship in a social media setting.

The textile industry holds immense significance in the economy of any nation, particularly in the production of synthetic yarn and fabrics. Consequently, the pursuit of acquiring high-quality products at a reduced cost has become a significant concern for countries. The primary objective of this research is to leverage data mining and data intelligence techniques to enhance and refine the production process of texturized yarn by developing an intelligent operating guide that enables the adjustment of production process parameters in the texturized yarn manufacturing process, based on the specifications of raw materials.

This research undertook a systematic literature review to explore the various factors that influence yarn quality. Data mining techniques, including deep learning, K-nearest neighbor (KNN), decision tree, Naïve Bayes, support vector machine and VOTE, were employed to identify the most crucial factors. Subsequently, an executive and dynamic guide was developed utilizing data intelligence tools such as Power BI (Business Intelligence). The proposed model was then applied to the production process of a textile company in Iran 2020 to 2021.

The results of this research highlight that the production process parameters exert a more significant influence on texturized yarn quality than the characteristics of raw materials. The executive production guide was designed by selecting the optimal combination of production process parameters, namely draw ratio, D/Y and primary temperature, with the incorporation of limiting indexes derived from the raw material characteristics to predict tenacity and elongation.

This paper contributes by introducing a novel method for creating a dynamic guide. An intelligent and dynamic guide for tenacity and elongation in texturized yarn production was proposed, boasting an approximate accuracy rate of 80%. This developed guide is dynamic and seamlessly integrated with the production database. It undergoes regular updates every three months, incorporating the selected features of the process and raw materials, their respective thresholds, and the predicted levels of elongation and tenacity.

Presents a linear goal programming (LGP) model, which can be used to derive an optimal daily production schedule for JIT production systems. Also provides an explanation of how a detailed post‐optimal LGP analysis can enable a decision maker to examine the effects of production scheduling in a JIT “mixed‐model” production environment. To illustrate the informational efficacy of the proposed JIT‐based LGP model, presents an illustrative example.

Presents a study which explored a relationship between production process focus and performance at the business unit level using the profit impact of marketing strategies (PIMS) database. The relationship between production process focus and financial performance for business units was partially supported using return‐on‐sales (ROS), and was not supported with return‐on‐assets and return‐on‐income. Indicates that the degree of production process focus is not directly related to a business unit′s performance. The implication is that the degree of production process focus must be recognized as part of a manufacturing strategy that is consistent with an overall business strategy.

An analysis of the productions and rules in the way they are used in artificial intelligence systems is presented. The proposed new definition for productions refers to a large number of types of productions which may be found in the literature on AI systems. This definition emphasizes in the most general way those production components which are important both for theory and for practice and which for some reasons remained unnoticed by many researchers. These components are implemented in a theoretical formalism which concludes the paper.

A systems framework for food production systems is posited in order to enable comparisons to be made with production operations outside the catering industry. By comparing “Cook Chill” and “Fast Food” systems it is seen that cellular production has been adopted. Other operations management techniques can similarly be applied.

Production is defined as the mission of creating wealth (economic goods and services) from a variety of resources (human and non‐ human) by adding values (intrinsic and extrinsic) through transformation (physical and conceptual) so as to derive utilities (form, place, time, economic, non‐economic). This mission is organised through a system. Basically, what a production system looks like is as Fig.1. It is basically the flow of various resources that defines the nature and characteristic of the production system.

Develops a linear programming model for integrated production planning based on the practice of a major Canadian steel making company. Considers the entire planning activity in the company as an integrated process involving a number of closely related sub‐functions, such as raw material purchasing, semi‐finished product purchasing and production, and capacity allocation, as well as finished product production and distribution. The mathematical programming model takes into account production costs, product throughput rates, customer demands, sales prices and facility capacities for optimal production planning. Presents a numerical example based on realistic system structure and practical planning data to illustrate the model. Computation results and analysis show that the integrated methodology is a feasible and practical approach for steel production planning.

An entrepreneur needs a tool on an enterprise level to determine opportunities and threats in a global perspective. The production‐allocation approach described in this article could be such a tool in developing a firm′s international manufacturing strategy. The approach is built around the production and cost function for the relevant manufacturing systems in the integral production chain for a specific product. The main question then is to determine the optimum location of the various manufacturing systems. In this article, the optimum location is the one which minimises the sum of manufacturing and transport costs. A case study based upon research for a European producer of foodstuffs is presented to illustrate the concepts of the production‐allocation approach.

The main objective of a 1986 survey of production and operations managers throughout New Zealand industry was to determine the extent of computer use. The survey explored the use of microcomputers, mini computers and mainframes by production managers. Job title and responsibilities of the manager responsible for the majority of production management functions were investigated. It appeared that information and decision making in production management was fragmented in most organisations. The survey examined the main uses of computer information and control systems in production planning and inventory control, the perceived benefits and problems. The main computer brands in use and types of software were also analysed. Comparisons were made with surveys of British companies and some work in the USA.