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The purpose of this paper is to identify cyclical patterns in the prices and production of UK pigmeat, 1989‐2008.

The approach takes the form of spectral analysis.

Results show evidence of both annual and seasonal cycles, but there is little evidence of a three‐to‐four‐year cycle. The likely cause of the latter's absence is increasing imports and a changing market structure.

Conventional wisdom is that the traditional pig cycle is of three‐to‐four years' duration and the UK's pig policy is based partly on its existence and that the design and implementation of UK pig policy are predicated on the basis of a traditional cycle is misplaced.

No previous or recent empirical investigation of the UK pig cycle exists, perhaps because its existence is considered to be axiomatic.

In this paper, a maintenance strategy based on improved imperfect maintenance actions with stochastic repair times for multiperiod randomly failing equipment is developed. The main objective is to minimize the total maintenance cost by jointly finding the optimal preventive maintenance (PM) cycle and planning horizon.

A model based on the mathematical theory of reliability is developed to minimize the total maintenance cost by jointly finding the optimal couple: PM cycle T* and planning horizon H*. The proposed model aims to characterize the evolutionary impact of imperfect PM actions on the equipment failure rate and the resulting mean number of failures. The conventional threshold accepting (TA) algorithm is implemented to solve the proposed model. A numerical example for a given set of input parameters is presented in order to show the usefulness of the proposed model. A sensitivity analysis of some of the key parameters is performed to demonstrate the coherence of the developed maintenance policy.

The obtained results showed a sensitive trade-off between PM frequency and the total maintenance cost. Performing PM actions more frequently helps significantly to reduce the expected number of corrective maintenance actions and the corresponding total cost. It has also been found that improving the efficiency of the PM actions allows for maintaining the equipment less frequently by increasing the time between successive PM actions.

Given the complexity of the objective function to be minimized and the stochastic nature of the model's parameters, the authors limited this study to equally cyclic production periods over the planning horizon.

The present model aims to provide an integrated maintenance/production comprehensive framework to assist planners in establishing maintenance schedules considering multiperiod randomly failing production systems and the evolutionary impact of imperfect PM actions on the equipment failure rate.

Contrary to the majority of existing works in the literature dealing with maintenance strategies, the authors consider that repair times are stochastic to provide a more realistic framework. In addition, the developed model considers the impact of imperfect maintenance on the equipment's mean time to failure. Thus, the evolutionary impact of imperfect PM actions on the equipment failure rate and the resulting mean number of failures is characterized. Simultaneously, the production planning horizon along with the length of each PM cycle is optimized in order to minimize the total maintenance cost over the planning horizon.

The purpose of this paper is to construct a task assignment model for U-shaped production lines with collaborative task, which is optimized by minimizing the number of workers and balancing the workload of the operators. The ultimate goal is to increase productivity by increasing the U-line balance and balancing the load on the operators.

First, task selection and update mechanism are analyzed and the task selection mechanism suitable for collaborative task is proposed. Second, M-COMOSAL is obtained by improving the original COMOSAL. Finally, The M-COMOSAL algorithm and the COMAOSAL algorithm are used to perform job assignment on the double-acting clutch U-shaped assembly line.

According to the allocation scheme obtained by M-COMSOAL, the beat can be adjusted according to the change of order demand. The final allocation scheme is superior to the COMSOAL algorithm in terms of number of workers, working time, production tempo and balance rate. In particular, compared with the old scheme, the new scheme showed a decrease of 16.7% in the number of employees and a 18.8% increase in the production line balance rate. Thus, the method is helpful to reduce the number of operators and balance the workload.

The new algorithm proposed in this paper for the assignment of collaborative task can minimize the number of workers and balance the load of operators, which is of great significance for improving the balance rate of U-shaped production lines and the utilization of personnel or equipment.

The purpose of this paper is to propose an integrated approach to process integration, automation, and optimization through enhanced business process models.

The approach is based on a framework of process integration for functional applications, automation for business workflows, and additional functionalities for process optimization. The proposed approach is illustrated using enhanced process models over business integration, automation, and optimization with data elements, structures, and organizational elements. The standard sales order process cycle, quotation approval process, and production order cycle are chosen for illustrating process integration, automation, and optimization, respectively.

The proposed approach combines applications and workflows using integrated process/data models and forms a foundation for business process optimization. It is shown that the integrated approach can improve existing business processes in enterprise resource planning (ERP), beyond business process re‐engineering (BPR) principles, once enhanced business process models are implemented. This approach eliminates need for a hierarchical representation of business processes and highlights the flexibility and visibility of business process implementation in ERP system environment.

Although process integration, automation, and optimization are illustrated using selected business process examples, it requires generalization of these enhancements over entire business blueprint of ERP system. Thus, one key limitation of this research is that it is not generalized for the entire business blueprint of ERP. This also requires changes to data structures beyond current relational data in many ERP systems.

This research provides an integrated approach to business process modeling beyond traditional functional and workflow applications by eliminating hierarchical nature of process and data elements.

Develops a framework which provides a step towards better planning of production, scheming inspection and preventive maintenance. Studies the effects of an imperfect production process on the optimal production cycle time. The system is assumed to deteriorate during the production process and produce some proportion of defective items. Extends to the cases where the proportion of defective items and the cost of process restoration are not constant. Provides a comparative study of two monitoring policies where the preventive maintenance setting is used and not used in the deteriorating production process. These models are directly relevant to the management of the quality and reliability of the production process. When scheming inspection is adopted, it is shown that the optimal inspection intervals are equally spaced in the imperfect production process under the different policies, respectively. Provides a numerical example to illustrate the derivation of the optimal production cycle time in the models.

The purpose of this paper is to analyse the production cycle of glazed porcelain stoneware, from the extraction of raw materials to the packaging of the finished product, with the aim of verifying the effects of integrating an environmental impact assessment into the decision-making process for managing the life cycle, to make it economically and ecologically sustainable, in a holistic approach along the supply-chain.

The research is performed using the life cycle assessment and life cycle costing methodologies, to identify environmental impacts and costs, that occur during extraction of raw materials, transportation, ceramic tiles production, material handling, distribution and end-of-life stages within a cradle to grave perspective.

Through the use of a comprehensive analysis of the environmental impact assessment and related externalities, three possible strategic options to improve the environmental performance and costs of ceramic tile production were formulated, leveraging sustainability as a competitive advantage.

This exploratory research opens future lines of investigation, the first of which is to confirm the technological feasibility and market responsiveness to the three strategic solutions hypothesised thanks to the use of an innovative eco-design technique.

The research has allowed testing and validating the tools of environmental impact assessment (life cycle assessment) and economic impact assessment (life cycle costing as structured methodologies in a life cycle management framework, to help companies implement competitive strategies based on sustainability.

Studies the problem of producing a product range with high customer service in the implosive industries. In the implosive industries a wide range of end products are manufactured from a limited number of standard materials. The problem of producing a product range with high customer service is studied within the framework of product range, capacity and customer acceptable lead time. In the existing just‐in‐time literature, strong emphasis is put on set‐up and through‐put time reduction for achieving high product availability with low cost. However, the decision regarding the product range and its implications on production control is not adequately considered. It is proposed that the relevant time scope to consider demand and capacity is the product range cycle. Three cases from the food and paper industry are studied. The cases indicate constraints for achieving quick response capability.

Due to uncertainty in supply chains caused by the coronavirus disease 2019 (COVID-19), organizations are adjusting their supply chain design to address challenges faced during the pandemic. To safeguard their operations against disruption in order quantities, supply chain members have been looking for alternate suppliers. This paper considers a two-level supply chain consisting of a manufacturer and two suppliers of a certain type of components required for the production of a finished product. The primary supplier (supplier A) is unreliable, in the sense that the quantity delivered is usually less than the ordered quantity. The proportion of the ordered quantity delivered by supplier A is a random variable with a known probability distribution. The secondary supplier (supplier B) always delivers the order in its entirety at a higher cost and can respond instantaneously. In order for supplier B to respond instantaneously, the manufacturer is required to reserve a certain quantity at an additional cost. Once the quantity received from the main supplier is observed, the manufacturer may place an order not exceeding the reserved quantity.

A mathematical model describing the production/inventory situation of the supply chain is formulated. The model allows the determination of the manufacturer's optimal ordering policy.

An expression for the expected total cost per unit time function is derived. The optimal solution is determined by solving a system of nonlinear equations obtained by minimizing the expected total cost function.

The proposed model can be used by supply chain managers aiming at identifying various ways of handling the uncertainty in the flow of supplies across the chain.

This proposed model addresses a gap in the production/inventory literature.

The purpose of this paper is to determine the stochastic net present value (NPV) of a model smallholder poultry operation in Rwanda under production and market uncertainty.

A discounted cash flow calculator was used to determine the NPV of operator investments and operating cash flows, including time, materials and capital. Broiler production data, market prices and variable input costs were collected from 125 smallholder operations in the Musanze District, Rwanda. These data were combined with a historical price index tracking the inflation rate of Rwanda’s currency. Policies including overstocking, technical support repayment scheduling, selling broilers at a spot market price, using marketing contracts and selling poultry manure were compared using non-parametric paired comparisons and stochastic dominance.

Risk-neutral and risk-averse producers would prefer overstocking, delaying repayment of technical support services and selling manure to status quo operational policy. No differences were observed between the option to sell birds at spot market prices or through contracts.

This analysis demonstrates how individual managerial or an intervention in smallholder broiler production affects financial performance.

To mitigate risk associated with this novel enterprise, producers should consider overstocking birds. If local markets for manure were developed, the risks faced by new or beginning poultry operators could be mitigated.

A stochastic, discounted cash flow model calculator was used to determine the NPV and discounted payback period of operator investments and operating cash flows, including time, materials and capital.

Learning behaviors related to quality improvement in manufacturing systems (i.e. reduction of defectiveness over production cycles) are widely investigated. Many different approaches have been introduced to describe the link between the learning mechanism and quality performance of a plant. In a previous study by the same authors, a set of learning “composition laws” for two basic structures were defined to provide a tool to forecast the behavior of complex manufacturing systems composed by a network of elementary processes. This paper presents an empirical investigation about these learning composition laws on a real case in the field of automotive exhaust‐systems manufacturing.