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.

Lean management has become a much-researched topic in operations management. Beyond

its technical aspects, nowadays the analysis of soft factors (corporate culture, organization,

management, human resource management, knowledge transfer practices) have come to the

fore. However, there are few sources available to the lean organization to find out what organizational

changes are taking place alongside the lean application, and what organizational

structures are being developed. In our study first we deal with the literature-based concepts

of lean organizational structure and with the international examples, and then through five

Hungarian corporate solutions and with help of the literature of organizational theories we

synthesize the lean organizational forms.

The main aim of this research is to compare the results of the study of demand’s plan and

standardized time based on three heuristic scheduling methods such as Campbell Dudek

Smith (CDS), Palmer, and Dannenbring. This paper minimizes the makespan under certain

and uncertain demand for domestic boxes at the leading glass company industry in Indonesia.

The investigation is run in a department called Preparation Box (later simply called PRP)

which experiences tardiness while meeting the requirement of domestic demand. The effect

of tardiness leads to unfulfilled domestic demand and hampers the production department

delivers goods to the customer on time. PRP needs to consider demand planning for the

next period under the certain and uncertain demand plot using the forecasting and Monte

Carlo simulation technique. This research also utilizes a work sampling method to calculate

the standardized time, which is calculated by considering the performance rating and

allowance factor. This paper contributes to showing a comparison between three heuristic

scheduling methods performances regarding a real-life problem. This paper concludes that

the Dannenbring method is suitable for large domestic boxes under certain demand while

Palmer and Dannenbring methods are suitable for large domestic boxes under uncertain

demand. The CDS method is suitable to prepare small domestic boxes for both certain and

uncertain demand.

In the article, the significance and essence of management of intelligent manufacturing in

the era of the fourth industrial revolution has been presented. The current revolution has

a large impact on the operation of the company. Through the changes resulting from the

application of modern technologies, production processes are also undergoing revolutions,

which results in changes in such indicators of business development. Management of intelligent

manufacturing is also a challenge for socially responsible activities; due to solutions of

Industry 4.0, enterprises directly and indirectly influence environmental protection, which

results in benefits for all mankind. In the article, the analysis and assessment of management

of intelligent manufacturing, using modern technologies during the production process,

has been carried out, with particular emphasis on the components of management such as:

monitoring, control, autonomy, optimization. Moreover, the impact of the above components

of management on changes in the following indicators (KPI – Key Performance Indictors)

has been evaluated, i.e. (1) quality, (2) rapidity of the production process implementation,

(3) performance and (4) productivity, (5) decrease in waste generated during the technological

process and (6) amount of consumed electricity. For the purposes of conducting the

research, a case study has been used, developed due to the information shared by the company

manufacturing machinery and equipment for the polymer processing industry, in which

intelligent solutions of Industry 4.0 are being applied. The presented article is a significant

contribution to the current development of knowledge in the field of implementing Industry

4.0 solutions for polymer processing. The article is a combination of theoretical and practical

knowledge in the field of management and practical industrial applications. It refers to the

most current research trends.