Management and Production Engineering Review

In order to assess the challenges and needs of Austrian companies with respect to current

business and technological developments, a regular well-researched compilation of empirical

data of the Austrian manufacturing industry is necessary. Hence, a panel of 104 decisionmakers

(owners, CEOs, managing directors and plant managers) from leading Austrian

industrial companies was assembled in form of an “industry panel” to investigate current

issues of production work in Austria by means of a survey.

In order to allow for a longitudinal study, it is planned to survey the same group of people

every year; hence the instrument of an annual panel-survey was chosen. To date the panel

consists of 104 leaders from different Austrian or international companies with at least one

factory location in Austria. The panel was assembled first in 2018/2019 and the administered

survey contained 23 questions. The actual questions comprise topics that concern the current

economic situation and future expectations, operational issues with respect to delivery

time, product variability and demand fluctuations, as well as questions relating to innovation,

automation and the application of current technological developments (i.e. assistance

systems, machine learning, etc.) in manufacturing. This paper presents the survey results

and conclusions of the 2019 panel on production work in Austria.

The realization of digitalization in production companies – currently also referred to as Industry

4.0 – aims for reduction of internal value creation costs as well as costs for intercompany

collaboration and plays a key role in their current strategy development. However, related

strategy research still lacks to provide operationalized digitalization methods and tools to

practitioners with scientific rigor as well as real-world relevance. To challenge this status

quo, we present a scientifically grounded 14-step procedure model including 11 practically

tested tools, developed specifically for real-world application. The model leads practitioners

from their first contact with industrial digitalization, through the maturity assessment of

143 digitalization items, until the implementation of a KPI-monitoring system and a continuous

improvement process. We applied and re-worked the procedure model during three

years of application. Validation and Feedback from practitioners and scholars indicate, that

the model drives strategy development towards objective and data-based decision making

and increases stakeholder engagement in organizations considerably.

The spread of digital technologies dramatically changes production processes. The fourth

industrial revolution opens up new opportunities for the introduction of technologies, having

a significant impact on the production cycle, starting with highly automated production lines

and ending with the large-scale implementation of technological solutions designed to improve

productivity, optimize costs, quality and reliability. Defining digital transformations,

primarily in the manufacturing industry, as a strategic imperative for the entire economy

based on opinions and intentions of entrepreneurs (short and medium-term), key aspects of

the digitalization process in Russian medium, high-tech and low-tech manufacturing industries

are revealed. A set of tendencies in the development of digital technologies by their main

types is presented, the level of industry participation in digital transformation is shown, as

well as many other important digital transformation processes in enterprises that are not

measured by quantitative statistics.

The application of churn prevention represents an important step for mobile communication

companies aiming at increasing customer loyalty. In a machine learning perspective,

Customer Value Management departments require automated methods and processes to

create marketing campaigns able to identify the most appropriate churn prevention approach.

Moving towards a big data-driven environment, a deeper understanding of data

provided by churn processes and client operations is needed. In this context, a procedure

aiming at reducing the number of churners by planning a customized marketing campaign

is deployed through a data-driven approach. Decision Tree methodology is applied to drow

up a list of clients with churn propensity: in this way, customer analysis is detailed, as well

as the development of a marketing campaign, integrating the individual churn model with

viral churn perspective. The first step of the proposed procedure requires the evaluation of

churn probability for each customer, based on the influence of his social links. Then, the

customer profiling is performed considering (a) individual variables, (b) variables describing

customer-company interactions, (c) external variables. The main contribution of this work

is the development of a versatile procedure for viral churn prevention, applying Decision

Tree techniques in the telecommunication sector, and integrating a direct campaign from

the Customer Value Management marketing department to each customer with significant

churn risk. A case study of a mobile communication company is also presented to explain

the proposed procedure, as well as to analyze its real performance and results.

Due to fast-paced technical development, companies are forced to modernise and update

their equipment, as well as production planning methods. In the ordering process, the customer

is interested not only in product specifications, but also in the manufacturing lead

time by which the product will be completed. Therefore, companies strive towards setting

an appealing but attainable manufacturing lead date.

Manufacturing lead time depends on many different factors; therefore, it is difficult to predict.

Estimation of manufacturing lead time is usually based on previous experience. In the

following research, manufacturing lead time for tools for aluminium extrusion was estimated

with Artificial Intelligence, more precisely, with Neural Networks.

The research is based on the following input data; number of cavities, tool type, tool category,

order type, number of orders in the last 3 days and tool diameter; while the only output

data are the number of working days that are needed to manufacture the tool. An Artificial

Neural Network (feed-forward neural network) was noted as a sufficiently accurate method

and, therefore, appropriate for implementation in the company.

The study on cognitive workload is a field of research of high interest in the digital society.

The implementation of ‘Industry 4.0’ paradigm asks the smart operators in the digital factory

to accomplish more ‘cognitive-oriented’ than ‘physical-oriented’ tasks. The Authors propose

an analytical model in the information theory framework to estimate the cognitive workload

of operators. In the model, subjective and physiological measures are adopted to measure

the work load. The former refers to NASA-TLX test expressing subjective perceived work

load. The latter adopts Heart Rate Variability (HRV) of individuals as an objective indirect

measure of the work load. Subjective and physiological measures have been obtained by

experiments on a sample subjects. Subjects were asked to accomplish standardized tasks

with different cognitive loads according to the ‘n-back’ test procedure defined in literature.

Results obtained showed potentialities and limits of the analytical model proposed as well as

of the experimental subjective and physiological measures adopted. Research findings pave

the way for future developments.

Additive manufacturing in recent years has become one of the fastest growing technologies.

The increasing availability of 3D printing devices means that every year more and more

devices of this type are found in the homes of ordinary people. Unfortunately, air pollution is

formed during the process. Their main types include Ultra Fine Particles (UFP) and Volatile

Compounds (VOC). In the event of air flow restriction, these substances can accumulate in

the room and then enter the organisms of people staying there. The article presents the

main substances that have been identified in various studies available in literature. Health

aspects and potential threats related to inhalation of substances contained in dusts and gases

generated during the process are shown, taking into account the division into individual types

of printing materials. The article also presents the differences between the research results

for 3d printing from individual plastics among different authors and describes possible causes

of discrepancies.

The main aim of the article is to develop a simulation model of flexible manufacturing

system with applying the ontology on flexibility. Designing manufacturing systems matching

both production and market requirements becomes more and more challenging due to the

variability of demand for a large number of products made in many variants and short

lead times. Manufacturing flexibility is widely recognised as a proven solution to achieve

and maintain both the strategical and operational goals of the companies exposed to global

competition. Generic simulation model of flexible manufacturing system was developed using

FlexSimr 3D software, then the example data were used to demonstrate the developed model

applicability. “The Ontology on Flexibility” was applied for evaluation of achieved flexibility

of manufacturing system.

This paper addresses supply chain transparency improvement in a triadic manufacturersupplier-

supplier relationship. It investigates the problem of improving transparency using

a set of interviews; then, a detailed problematization and a simulation model is formulated

based on the results. The interview results show that there are two key issues to be considered:

information systems issues related directly to transparency and capability issues related

to utilizing transparency. The simulation results support developing capabilities by illustrating

the effects of different options for coordinating material flow. The results of the study

also indicate that while solutions to improve transparency can be relatively straightforward

to implement, developing the capability to benefit from it can be more challenging, even in

a well-established close partnership. In addition, suppliers may be hesitant to collaborate

without active manufacturer involvement.

The Decision Makers in the production organizations, which produce multiple different products

at the same time, set the priorities for what the organization desires to produce. This

priority is sorting the products in order to schedule the production based on these priorities.

The production organizations receive a huge number of orders from different customers, each

order contains many products with close delivery dates. The organization aims to produce

multiple different products at the same time, in order to satisfy all customers by delivering

all orders at the right time. This study will propose a method to prioritize the production

to produce a multiple different products at the same time, the production lines will produce

multiple different products. This method will prioritize the products using Multi Criteria

Decision Making technique, and prioritize the production operations using a new algorithm

called Algorithm for Prioritization of Production Operations. In addition, the study will provide

an algorithm for production scheduling using the production priority calculated based

on the proposed method. The study will also compare the scheduling based on the priority

rules and based on the proposed method through total production time and the variety of

products produced.

A concern about the current state of relations between industry and the environment is

often neglected. However, it is important to underline that industry and sustainability are

not mutually exclusive. There are many industrial processes to blame when analyzing the

negative impact on current socio-ecological environment. The emerging question is whether

companies nowadays are ready to face challenges in the name of sustainability, the future

of the planet and generations to come. In addition, an assessment of industrial processes

may be very time-consuming and costly in financial terms. This fact allows developing sustainability

assessment approach and its measures for keeping track on to evaluate scale of

environmental, social and economic changes. The goal of the paper is to develop a multicriteria

decision-making approach for sustainability assessment of renewable energy technology.

A sustainability assessment approach combines life cycle-based methods integrated with

multi-criteria decision-making method based on analytical hierarchy process. The resulting

assessment method allows finding a compromise between industry and the environment and

identify potential intervention points for further research. As a result of decision-making

process, string ribbon technology was considered as the most sustainable. The applicability

of the proposed method is assessed based on photovoltaic panels.

Higher education institutions (HEIs) typically generate income from two main sources; student

fees and research income. In contrast, the predominant waste streams in HEIs tend

to include; (1) assignment/examination mark submission process, (2) photocopying process

and (3) the funding application process. Unintended internal process complexities and barriers

typically aggravate the challenges already inherent in the research grant application

process. Although Lean Six Sigma (LSS) has been adopted by a number of HEIs in Ireland,

very few have adopted an integrated LSS approach for waste reduction in the research grant

application process. To identify barriers and waste in the research grant application process

within an Irish HEI in an EU environment, the authors used an online survey deployed to

240 academics and researchers. The survey response rate was 13%. The participating HEI

in this pilot study generated an annual income (including student fees and research income)

exceeding e240 million for the academic year 2017/2018. Using an LSS lens, this paper identified

the primary waste in the research grant application process from an academic and

researcher perspective to be; editing and revising applications, liaising and communicating

with collaborators and waiting for information. Organised thematically, the main barriers

were strategic thinking, collaborator identification and co-ordination, eligibility, process,

time and support & mentoring. The results from this study can be used to inform the next

stage of the research where empirical studies will be carried out in other HEIs to develop a

practical roadmap for the implementation of LSS as an operational excellence improvement

methodology in the research grant application process.

The aim of this paper is to identify lean management instruments used to implement strategic

objectives related to the creation and retention of value in the area of value networks while

redefining the business model of service enterprises on the example of hotels. In relation

to the objective, a survey was conducted using the questionnaire method with the use

of Computer Assisted Web Interview technique, using a self-developed questionnaire. The

survey was carried out between February and May 2020 among 421 representatives of hotel

service companies operating in the three, four and five-star standard. In order to verify

the assumptions between the surveyed features, statistical inferences were used using the

Statistica programme. The research results may provide inspiration for the implementation

of lean management concept in the area of redefining business models conducive to value

creation. The issues presented in the paper are an attempt to fill the gap indicating practical

experience related to the use of lean management instruments in the hotel services sector

and their effectiveness in the process of redefining business models and value creation.