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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.

Describes a model of a hierarchical planning system to provide a comprehensive approach to the complex production planning and scheduling problem. The model supplies a link between long‐term and short‐term planning; the three tiers of the hierarchy implement: long‐term inventory planning on a cost minimization basis; shorter‐term production planning; and daily sequencing. Emphasizes efficient processing and transmission of information.

Some hierarchical approaches for production planning in a batch type manufacturing environment are described. In a single stage production system, three aggregation levels exist: types, families and items. The performance of a hierarchical system model is largely dependent on the methods of disaggregation at different levels. This paper reports on a study of hierarchical methods at the family disaggregation level and incorporates a simple modification to improve upon a heuristic proposed by Winters. Results indicate that even a very simple hierarchical planning approach can give a significant reduction in backorders in a production shop having severe capacity restriction.

The impact of rapidly developing technologies on digitalization of production and planning has affected all sectors in a short period. This impact has led to both positive and negative outcomes. While it is helping the decision-makers to make better and more productive choices, it also can create a cluster of information and data that can put an undue burden on processes. Today, we call this Industry 4.0, which is defined as the use of digital technologies, based on data processing, the end-to-end connection of value chains, and ensuring data fluidity. Industry 4.0 consists of technologies such as cyber-physical systems, internet of things, cloud computing, big data and analysis, autonomous vehicles, and augmented reality. With the digitalization of the entire production and planning processes, data-based applications are made; thus it is important to protect data in this context, pointing to the critical importance of cybersecurity. Companies are constantly working on taking the necessary cybersecurity measures to prevent exposure to any cyberattacks. One of the biggest steps toward the development of production and planning is undoubtedly the integration and adaptation of blockchain technology. The use of blockchain technologies has been a major breakthrough for the sector in order to effectively meet customer needs, ensure information security, reduce costs, and achieve rapid growth. With digital transformation, production and planning must comply with the principle of transparency. In this context, integrating blockchain technologies into the production and planning ecology for data security will provide companies with a serious competitive advantage.

In recent years there has been an increasing interest in make-to-stock and make-to-order combined strategies in food manufacturing operations. However, most scholarly work to-date has neglected the role of hierarchical production planning (HPP) in guiding small- and medium-sized enterprise (SME) implementation of such strategies. The purpose of this paper is to address food SME manufacturers’ readiness to adopt such strategies, in terms of internal integration and their capability to adopt formalised planning approaches.

This study adopted an action research methodology to explore the potential impact of HPP in SME food manufacturers. Selected companies had identified product variety management as a challenge and also had recognised the need to enhance internal integration. Given this, the research team, from a theoretical perspective, proposed the use of HPP set within a broader decision-making conceptual framework to improve internal integration and planning.

This paper adopts the fundamental position that HPP provides a useful framework in the establishment of strategic and tactical level constraints and priorities which then act as specific guides at the operational level, and presents empirical evidence in a food SME manufacturing context. In the cases the authors studies, the cascading effect of this decision-making framework focused attention on key metrics, encouraged greater internal integration and delivered tangible, significant improvements in performance. This was greatly facilitated by the provision of new key data on the cost of certain managerial trade-offs which these firms faced.

SMEs are of a scale that requires a formalised planning approach; however production planning systems are typically designed for large scale enterprises. This paper addresses the need of SMEs in this regard. Well-established supply chain metrics were used to establish the benefits of both HPP and resulting improvement in internal integration and beyond, in terms of improvement in the quality of planning decisions.

The study attempts to develop a multi-product multi-period (MPMP) aggregate production plan (APP) to fulfill the customers' demand in terms of throughput and lead time for achieving market competence.

This research proposes an integrated Fuzzy analytical hierarchy process (FAHP), multi-objective linear programming (MOLP), and simulation approach. Initially, FAHP is used to select the essential objectives a firm desires to achieve. Adopting the MOLP, an APP is formulated for the firm under study. Later, the simulation model of a firm is created in a discrete-event simulation (DES) software Arena© to evaluate the applicability of the proposed APP. A comparative analysis of the manufacturing performance levels (namely throughput, lead time, and resource utilization) achieved through the implication of an existing production plan and proposed APP is conducted further.

The findings from the study depict that the proposed MOLP-based APP can satisfy the customers' requirement (namely throughput and lead time) and improve the level of resource utilization compared with the firm's existing production plan.

The proposed research facilitates researchers and practitioners to understand the process of developing MOLP-based MPMP APP and analyzing its applicability through simulation technique to be utilized for developing APP at their firm.

An integrated FAHP-MOLP-simulation framework is the novel contribution to the literature on production planning. It can be extended to solve strategic, tactical, and operational problems in different domains like service, healthcare, supply chain, logistics, and project management.

Significant transitions in firms (e.g. outsourcing) may impact the relative importance of production and inventory assets, affecting the hierarchical separation of planning decisions. The purpose of this paper is to contribute to planning literature by investigating how the production system and the planning environment influence the performance difference between hierarchical and monolithic planning. Further, it seeks to reduce the prevailing theory-practice gap in tactical planning.

Through an action research study, a monolithic model integrating tactical production planning decisions, subject to upstream supply chain constraints, with strategic investments decisions was developed, tested and implemented in a global OEM. Using the developed model and a measure of the capital cost of production assets relative to the cost of holding inventory, it is numerically examined how the production system and planning environment influence the performance of hierarchical and monolithic planning.

The research demonstrates the potential of integrating decisions and reveals significant performance differences between hierarchical and monolithic planning for firms with low capital cost relative to inventory holding cost.

The findings suggest a fit between planning processes, the production system and planning environment. Future research should empirically validate the findings and propositions.

The paper combine capital investments and production planning decisions, which usually transpire at different hierarchical levels and on different time-horizons, and investigates the consequences of hierarchical separation through a real-life validated case and numerical analysis.

The purpose of this paper is to develop a model for the production planning decision of a dairy plant in a multi-product setting under supply disruption risk and demand uncertainty while determining the optimal product-mix and material planning requirement.

A mixed-integer nonlinear programming model is proposed to determine the optimal product-mix that maximizes the expected profit of a dairy. The data are collected through visits to the dairy site, conducting brainstorming sessions with the plant manager and marketing head at the corporate office. Disruption data are collected from the India Meteorological Department, Odisha.

From the analysis, it is recommended that the dairy should not produce curd during the planning period. Moreover, turnover from toned, double toned and baby food is maximum than that of the curd and these products are produced in the planning period. The expected profit increases from its present value when an optimal product-mix is followed. Sensitivity analysis is performed to analyze the effect of demand uncertainty, supply disruption and production quota. The expected profit decreases as the supply failure probability increases.

The model is implemented in a dairy plant under Orissa State Cooperative Milk Producers Federation, Odisha, India. The proposed methodology has not been validated, theoretically. The concerned dairy is based on the Indian context, but the authors believe that the study is highly relevant to other dairies as well.

This study provides a methodology for dairy plant managers to plan production effectively under supply disruption risk with demand uncertainty. It also suggests material requirement planning at different factories of the dairy plant.

This paper develops a mathematical model for the production planning decision of a dairy plant that determines the optimal product-mix, which maximizes the expected profit of a dairy under disruption risk and demand uncertainty (in the Indian context).

Overview All organisations are, in one sense or another, involved in operations; an activity implying transformation or transfer. The major portion of the body of knowledge concerning operations relates to production in manufacturing industry but, increasingly, similar problems are to be found confronting managers in service industry. It is only in the last decade or so that new technology, involving, in particular, the computer, has encouraged an integrated view to be taken of the total business. This has led to greater recognition being given to the strategic potential of the operations function. In order to provide greater insight into operations a number of classifications have been proposed. One of these, which places operations into categories termed factory, job shop, mass service and professional service, is examined. The elements of operations management are introduced under the headings of product, plant, process, procedures and people.

The librarian and researcher have to be able to uncover specific articles in their areas of interest. This Bibliography is designed to help. Volume IV, like Volume III, contains features to help the reader to retrieve relevant literature from MCB University Press' considerable output. Each entry within has been indexed according to author(s) and the Fifth Edition of the SCIMP/SCAMP Thesaurus. The latter thus provides a full subject index to facilitate rapid retrieval. Each article or book is assigned its own unique number and this is used in both the subject and author index. This Volume indexes 29 journals indicating the depth, coverage and expansion of MCB's portfolio.