Production companies face the challenge of choosing a suitable process optimization method
from a variety of methods, even though their effect on operational processes is uncertain.
This study shows, using a statistical hypothesis test, the impact of the methods Kanban
and Standard Worksheet on an autonomous team in comparison to a team that applies
these methods. For this purpose, 44 companies – of different size and operating in various
industries – across Germany completed a business game and generated data regarding the
KPIs adherence to delivery date, number of reworks and inventory costs. Based on these
data, the team’s performance could be ascertained and compared with each other.
Digitalization and sustainability are important topics for manufacturing industries as they
are affecting all parts of the production chain. Various initiatives and approaches are set
up to help companies adopt the principles of the fourth industrial revolution with respect
sustainability. Within these actions the use of modern maintenance approaches such as
Maintenance 4.0 is highlighted as one of the prevailing smart & sustainable manufacturing
topics. The goal of this paper is to describe the latest trends within the area of maintenance
management from the perspective of the challenges of the fourth industrial revolution and
the economic, environmental and social challenges of sustainable development. In this work,
intelligent and sustainable maintenance was considered in three perspectives. The first perspective
is the historical perspective, in relation to which evolution has been presented in the
approach to maintenance in accordance with the development of production engineering. The
next perspective is the development perspective, which presents historical perspectives on
maintenance data and data-driven maintenance technology. The third perspective, presents
maintenance in the context of the dimensions of sustainable development and potential opportunities
for including data-driven maintenance technology in the implementation of the
economic, environmental and social challenges of sustainable production.
Today, the changes in market requirements and the technological advancements are influencing
the product development process. Customers demand a product of high quality and fast
delivery at a low price, while simultaneously expecting that the product meet their individual
needs and requirements. For companies characterized by a highly customized production, it
is essential to reduce the trial-and-errors cycles to design new products and process. In such
situation most of the company’s knowledge relies on the lessons learnt by operators in years
of work experience, and their ability to reuse this knowledge to face new problems. In order
to develop unique product and complex processes in short time, it is mandatory to reuse
the acquired information in the most efficient way. Several commercial software applications
are already available for product lifecycle management (PLM) and manufacturing execution
system (MES). However, these two applications are scarcely integrated, thus preventing an
efficient and pervasive collection of data and the consequent creation of useful information.
The aim of this paper is to develop a framework able to structure and relate information
from design and execution of processes, especially the ones related to anomalies and critical
situations occurring at the shop floor, in order to reduce the time for finalizing a new product.
The framework has been developed by exploiting open source systems, such as ARAS
PLM and PostgreSQL. A case study has been developed for a car prototyping company to
illustrate the potentiality of the proposed solution.
The current industrial constraints on production systems, especially availability problems
are complicating maintenance managers’ mission and making longer and further performance
improvement process. Dealing with these problems in a wiser managerial vision respecting
sustainability dimensions would be more efficient to optimize all resources. In this paper, and
after addressing the lean/sustainability challenge in a the literature to define main research
orientations and critical points in manufacturing and then maintenance specific context, two
case studies have been conducted in two production systems in Morocco and Canada, within
the objective to set a clearer scene of the lean philosophy implementation in maintenance
and within the sustainability scope from an empirical perspective. To activate the social dimension
being often non-integrated in the lean/sustainability initiatives, the article authors
reveal an original research direction assigning maintenance logistics as the leading part of our
approach to cover all sustainability dimensions. Furthermore, its management is discussed
for the first time in a sustainable framework, where the authors propose a new model considering
the lean/sustainable perspective and inspired by the rich Human-Machine interaction
memory to solve daily maintenance problems exploiting the operators’ experience feedback.
A project scheduling problem investigates a set of activities that have to be scheduled
due to precedence priority and resource constraints in order to optimize project-related
objective functions. This paper focuses on the multi-mode project scheduling problem concerning
resource constraints (MRCPSP). Resource allocation and leveling, renewable and
non-renewable resources, and time-cost trade-off are some essential characteristics which are
considered in the proposed multi-objective scheduling problem. In this paper, a novel hybrid
algorithm is proposed based on non-dominated sorting ant colony optimization and genetic
algorithm (NSACO-GA). It uses the genetic algorithm as a local search strategy in order to
improve the efficiency of the ant colony algorithm. The test problems are generated based on
the project scheduling problem library (PSPLIB) to compare the efficiency of the proposed
algorithm with the non-dominated sorting genetic algorithm (NSGA-II). The numerical result
verifies the efficiency of the proposed hybrid algorithm in comparison to the NSGA-II
algorithm.