With the increasing demand of customisation and high-quality products, it is necessary for

the industries to digitize the processes. Introduction of computers and Internet of things

(IoT) devices, the processes are getting evolved and real time monitoring is got easier.

With better monitoring of the processes, accurate results are being produced and accurate

losses are being identified which in turn helps increasing the productivity. This introduction

of computers and interaction as machines and computers is the latest industrial revolution

known as Industry 4.0, where the organisation has the total control over the entire value chain

of the life cycle of products. But it still remains a mere idea but an achievable one where IoT,

big data, smart manufacturing and cloud-based manufacturing plays an important role. The

difference between 3rd industrial revolution and 4th industrial revolution is that, Industry

4.0 also integrates human in the manufacturing process. The paper discusses about the

different ways to implement the concept and the tools to be used to do the same.

Rescheduling is a frequently used reactive strategy in order to limit the effects of disruptions

on throughput times in multi-stage production processes. However, organizational deficits

often cause delays in the information on disruptions, so rescheduling cannot limit disruption

effects on throughput times optimally. Our approach strives for an investigation of

possible performance improvements in multi-stage production processes enabled by realtime

rescheduling in the event of disruptions. We developed a methodology whereby we

could measure these possible performance improvements. For this purpose, we created and

implemented a simulation model of a multi-stage production process. We defined system

parameters and varied factors according to our experiment design, such as information delay,

lot sizes and disruption durations. The simulation results were plotted and evaluated

using DoE methodology. Dependent on the factor settings, we were able to prove large improvements

by real-time rescheduling regarding the absorption of disruption effects in our

experiments.

The article presents tools, methods and systems used in mechanical engineering that in

combination with information technologies create the grounds of Industry 4.0. The authors

emphasize that mechanical engineering has always been the foundation of industrial activity,

while information technology, the essential part of Industry 4.0, is its main source of innovation.

The article discusses issues concerning product design, machining tools, machine tools

and measurement systems.

The application of the 5S methodology to warehouse management represents an important

step for all manufacturing companies, especially for managing products that consist of

a large number of components. Moreover, from a lean production point of view, inventory

management requires a reduction in inventory wastes in terms of costs, quantities and time

of non-added value tasks. Moving towards an Industry 4.0 environment, a deeper understanding

of data provided by production processes and supply chain operations is needed:

the application of Data Mining techniques can provide valuable support in such an objective.

In this context, a procedure aiming at reducing the number and the duration of picking

processes in an Automated Storage and Retrieval System. Association Rule Mining is applied

for reducing time wasted during the storage and retrieval activities of components

and finished products, pursuing the space and material management philosophy expressed

by the 5S methodology. The first step of the proposed procedure requires the evaluation

of the picking frequency for each component. Historical data are analyzed to extract the

association rules describing the sets of components frequently belonging to the same order.

Then, the allocation of items in the Automated Storage and Retrieval System is performed

considering (a) the association degree, i.e., the confidence of the rule, between the components

under analysis and (b) the spatial availability. The main contribution of this work is

the development of a versatile procedure for eliminating time waste in the picking processes

from an AS/RS. A real-life example of a manufacturing company is also presented to explain

the proposed procedure, as well as further research development worthy of investigation.

Maritime freight transport represents an effective solution, allowing to ensure a low-impact

service both under an economic and a sustainable perspective. As a consequence, in the last

ten years, an increasing trend of goods transported by sea has been observed. In order to

improve the terminal containers’ performance, recently published scientific studies shown

the applicability of the ‘lean logistic’ concept as a strategic key for ensuring a continuous

improvement of the logistic chain for inter-/intra terminal containers’ activities. According

to this approach, the adoption of a dry port can positively affect terminal containers’ performance,

but this requires resources and investments due to inter-terminal activities (e.g.

transport of the container from port to dry port and vice versa). The purpose of the study is

to develop a mathematical programming optimization model to support the decision making

in identifying the best containers’ handling strategy for intermodal facilities, according to

lean and green perspectives. Numerical experiments shown the effectiveness of the model in

identifying efficient material handling strategies under lean and green perspective.

The scientific goal of this article was to confirm the thesis that efficient complaint management

can be one the company’s competitive advantage elements of in the sphere of logistic

customer service. The theoretical part of the article presents basic foundations related to

complaint management process as an important element of post-trade sales process in customer

service. The research part presents an example of the implementation of efficient

assumptions of the complaint management process on the example of a construction industry

manufacturing company. Guidelines for the design and implementation of an effective

and efficient complaint handling process are presented. An example of process analysis is

done using appropriate quality tools.