Lean has established itself as the primordial approach to obtain operational excellence. Its simple and intuitive techniques focus on reducing lead time through continuous improvement, involving all levels of employees in the organization. However, the rate of successful implementations has remained low. This paper contributes to the understanding of continuous improvement in a Lean context, by analyzing a database of almost 10.000 improvement actions, from 85 companies, covering the time frame 2010–2018. It discusses categories of actions, their impact and cost, as well as key characteristics of the companies. It proposes an objective criterion to identify “success” and “failure” in Lean implementation and tries to link these to operational results. It is probably the first time an analysis of this magnitude on the subject has been performed.

The population structure, seasonal and diel changes in vertical distribution of two siphonophore species, Dimophyes arctica and Pyrostephos vanhoeffeni , in Croker Passage (Antarctic Peninsula) are examined, and compared with the results obtained by other au− thors in various oceanic areas. Zooplankton samples were taken at discrete depth intervals between 0 and 1200 m during day and night shifts, in both summer and winter seasons. Dimophyes arctica was present both in polygastric and eudoxid forms, with the latter being dominant throughout the entire study period. The results obtained demonstrate that Antarctic waters clearly enhance the reproductive ability of this species when compared with specimens from other oceanic regions. Maximum densities of Dimophyes arctica were recorded in December in the 200–400 m depth horizon. However, high concentrations of eudoxids were also recorded at deeper parts of the water column. Pyrostephos vanhoeffeni was, in contrast, most abundant in autumn and winter, and both species were found to proliferate and disperse or sink further down the water column during autumn and winter. Daily vertical migration was observed only during the summer period.

The paper discusses Bayesian productivity analysis of 27 EU Member States, USA, Japan and Switzerland. Bayesian Stochastic Frontier Analysis and a two-stage structural decomposition of output growth are used to trace sources of output growth. This allows us to separate the impacts of capital accumulation, labour growth, technical progress and technical efficiency change on economic development. Since estimates of the growth components are conditioned upon model parameterisation and the underlying assumptions, a number of possible specifications are considered. The best model for decomposing output growth is chosen based on the highest marginal data density, which is calculated using adjusted harmonic mean estimator.

The article contains considerations on possible actions directed at increasing productivity of hard

coal mining industry. It is necessary to improve the state of the industry, and ensure its survival. Basic

definitions and measures concerning productivity and management were presented to illustrate examples

referring to a mining enterprise. Then, basing on organizing, one of the management functions, the issue

of productivity in a mining enterprise and its influence on improving effectiveness of operational management,

was analysed. An assumption was presented that solutions concerning identification of sources

and volume of costs, hitherto existing in mining enterprises, ought to be complemented with the planning

function following process approach. It can be the starting point for decisions of economic feasibility of

given deposits, seams or parts of them, before mining operations start, and to control incurred costs in

process approach. The article is summed up with a process algorithm of cost management.

Spare parts are one of the important pillars in the after-sales service of automotive business.

Customers will satisfied and comfortable if the availability of spare parts is guaranteed.

Spare Part Center is one of function to support unit sales and as well as profit-oriented,

so the accuracy and speed of spare part acceptance by the customer is an important key

to winning the competition. Order Picking is one of the supply chain processes that play

a role in warehouse operations to meet customer needs. Order Picking is the most expensive

activity in warehousing and can reach 55% of the total cost of warehousing operations, so it

is considered a top priority in increasing productivity, even reaching 65% of total warehouse

operating costs. The purpose of this research is to increase productivity in the process

of picking order through reduction of processing time. Increased productivity is done by

improving the working method of the picking process. From the result the comparing, the

method by zone requires less total picking time (193.712 seconds) than by routing (249.559

seconds) decreased 55.85 second time, in other words, an increase of 22.38%. With the Visual

Stream Mapping (VSM) in this research can reduce to travel time, it means that the total

distance traveled is small than the current method. The impact from VSM approach will

eliminate time for preparation of 1.960 seconds, and take empty trolley of 200 seconds. In

this case some of traveling non-value

This study demonstrates application of Lean techniques to improve working process in

a sewing machine factory, focusing on the raw material picking process. The value stream

mapping and flow process chart techniques were utilized to identify the value added activities,

non-value activities and necessary but non-value added activities in the current

process. The ECRS (Eliminate, Combine, Rearrange and Simplify) in waste reduction was

subsequently applied to improve the working process by (i) adjusting the raw material picking

procedures and pre-packing raw material as per demand, (ii) adding symbols onto the

containers to reduce time spent in picking material based on visual control principle, and

(iii) developing and zoning storage area, identifying level location for each row and also

applying algorithms generated from a solver program and linear programming to appropriately

define the location of raw material storage. Improvement in the raw material picking

process was realized, cutting down six out of 11 procedures in material picking or by 55%,

reducing material picking time from 24 to 4 min or by 83%. The distance to handle material

in the warehouse can be shortened by 120 m per time or 2,400 m per day, equal to 86%

reduction. Lean techniques

The activities of the organisation concentrate mainly on meeting customers’ requirements. For this purpose, various activities are being conducted for customer satisfaction surveys. In this context, it is important to predict the quality of the product and the changes in the cost of the purchase product. The purpose of this study is to propose a method for predicting the quality level of a product and change the cost of the product considering current customers’ requirements for a combination of product feature states and pro-quality changes. The method includes the calculation of the quality level of the product using the punctationformalised method, where the level depends on a combination of values of states (parameters) attributes of the product, that is, current and modified. The method was tested as an example of a household vacuum cleaner for which 20 attributes were determined. According to the Pareto rule (20/80), the four product attributes important for customers were selected. Thereafter, for important attributes, possible combinations of the values of these attributes were determined. In addition, an algorithm for determining the possible combinations of product attribute states in the MATLAB program was developed. Additionally, the change in the current cost of the product considering the change in the quality level was estimated. The product cost changes were determined based on the actual cost of the product and the current product quality level. The method allows the determination of all combinations of values of state attributes of the product, such that it is possible to take appropriate improvement actions both in terms of quality and cost. The results from the method allow the prediction of product satisfaction for customers and they are favourable in terms of production cost. Therefore, it is possible to design the product in advance and support the producer in preparatory activities.

This paper presents an empirical analysis of economic growth in respect of its components, namely input change, technological progress and changes in efficiency. In this work the Bayesian Stochastic Frontier method as well as the output change decomposition procedure, are used in order to evaluate their influence on economic growth. The use of panel data in the study allows for a detailed analysis of economic growth in a given economy and enables the search for general patterns that govern the process. The study is carried using a set of sixteen countries over the period 1995‒2005.

Finite fossil fuel resources, as well as the instability of renewable energy production, make the sustainable management of energy production and consumption some of the key challenges of the 21st century. It also involves threats to the state of the natural environment, among others due to the negative impact of energy on the climate. In such a situation, one of the methods of improving the efficiency of energy management – both on the micro (dispersed energy) and macro (power system) scale, may be innovative technological solutions that enable energy storage. Their effective implementation will allow it to be collected during periods of overproduction and to be used in situations of scarcity. These challenges cannot be overestimated - modern science has a challenge to solve various types of problems related to storage, including the technology used or the control/ /management of energy storage. Heat storage technologies, on which research works are carried out regarding both storage based on a medium such as water, as well as storage using thermochemical transformations or phase-change materials. They give a wide range of applications and improve the efficiency of energy systems on both the macro and micro scale. Of course, the technological properties and economic parameters have an impact on the application of the chosen technology. The article presents a comparison of storage parameters or heat storage methods based on different materials with specification of their work parameters or operating costs.

In the era of the fight against global warming and in light of the search for energy with the least possible impact on the environment, interest in hydrogen has become a natural direction of development. Striving for a zero-emission Europe by 2050, the EU promotes low-emission and ultimately emission-free hydrogen for the widest possible use in the economy. Poland has developed a strategic document specifying the necessary activities for the use of hydrogen in the economy, which should at the same time maintain its competitiveness. Poland is currently the third producer of hydrogen in the European Union, which enables strategic thinking about maintaining Poland as a leading player on the hydrogen market in the long term. Currently, hydrogen in Poland is produced by (usually large) state-owned enterprises for their own needs with only a small margin of its resale. This is conventional hydrogen that is mainly obtained from natural gas. Therefore, it is difficult to talk about the hydrogen market, which must develop so that this raw material can be widely used in many branches of the modern economy. However, this requires taking a number of legislative, research and development and investment activities, as well as directing the national energy transformation to renewable energy sources, which may ultimately reduce the costs of pure hydrogen production. A number of actions have been taken, but the delay in legislative actions is slowing down the creation of the hydrogen market and is limiting the interest of private businesses in engaging in transformation activities.

The article is to present the application of genetic algorithm in production scheduling in a production company. In the research work the assumptions of the methodology were described and the operation of the proposed genetic algorithm was presented in details. Genetic algorithms are useful in complex large scale combinatorial optimisation tasks and in the engineering tasks with numerous limitations in the production engineering. Moreover, they are more reliable than the existing direct search algorithms. The research is focused on the effectivity improvement and on the methodology of scheduling of a manufacturing cell work. The genetic algorithm used in the work appeared to be robust and fast in finding accurate solutions. It was shown by experiment that using this method enables obtaining schedules suitable for a model. It

We propose a Bayesian approach to estimating productive capital stocks and depreciation rates within the production function framework, using annual data on output, employment and investment only. Productive capital stock is a concept related to the input of capital services to production, in contrast to the more common net capital stock estimates, representing market value of fixed assets. We formulate a full Bayesian model and employ it in a series of illustrative empirical examples. We find that parameters of our model, from which the time-path of capital is derived, are weakly identified with the data at hand. Nevertheless, estimation is feasible with the use of prior information on the production function parameters and the characteristics of productivity growth. We show how precision of the estimates can be improved by augmenting the model with an equation for the rate of return.

The article presents a methodology for analysing historical gas production data and determining the gas reserves and the petrophysical parameters of a reservoir-aquifer system. These parameters are obtained from a fitting algorithm using production data sets. A forecast of the future field gas production can be created on the calibrated mathematical model basis. The developed method is based on the material balance assumptions and the widely used Fetkovich and van Everdingen-Hurst equations for calculating water influx. To conduct the calculations and analyse production data, the computer application was developed using Python programming language. A user-friendly graphical interface makes the proposed application convenient and intuitive to use. The software was calibrated based on the literature data from the gas field of known parameters and then validated using five case studies of the actual gas fields in the Polish Carpathian Foredeep. From the tests, very high compatibility between the computed and the real field values were obtained. An additional comparison with the commercial program MatBal confirmed the proper functioning of the application.

The growth of the global population, urbanization as well as economic and industrial development, affect the continuously increasing demand for mineral aggregates. The current assessed global production of mineral aggregates amounts to 50 billion Mg/year, which statistically approximates 6.5 Mg per an inhabitant of the globe. In terms of consumption volume, water is the only raw material ahead of aggregates. Despite such a great scale, in many countries and regions the extraction and production of aggregates belong to the least regulated sector of human activity. This refers particularly to the countries of A sia, A frica, and North A merica, where both the resources and the extraction of aggregates, particularly of sand and gravels, are either not monitored and registered. It significantly increases the negative impact on the natural environment, due to the destruction of riverbeds and oxbows, coastal erosion, drying up cultivation areas, etc. In the reports, local terminology of aggregates often functions, which makes it difficult to compare them and prepare appropriate balances. In order to regulate the unfavorable situation, one of the main conclusions of the Report (UNEP 2019) is the need of implementing a common requirement to plan and monitor the process of extraction of natural resources. The paper presents the possibility of forecasting the extraction and producing aggregates based on the consumption of cement, i.e. the basic building material. A lthough the analyzed coefficient of mineral aggregate production per unit of cement consumption (production) varies, its advantage is the fact that the production of cement is identified and taken into account in balances of industrial production of the majority of countries, whereas such identification for mineral aggregate production are still lacking.

The article presents the possibility of using the Cobb-Douglas production function for planning in a turbulent environment. A case study was carried out – the Cobb-Douglas function was used to examine the condition of the Polish hard coal mining industry and the progress which has been made after undertaking certain activities aimed at increasing the competitiveness of coal companies over recent years. Only the correct and confirmed identification of the causes of irregularities in the production process can allow for the introduction of effective remedies. The effectiveness of the solutions proposed by the author has been confirmed thanks to the simulation during which the impact of the proposed production strategy on the parameters of the CD function was examined. Three variants of production functions models were created and production productivity rates and marginal substitution rates were determined. The results enabled the verification of the progress of restructuring as well as identification of the origin of the observed problems and comparison of the current state with the results of analyses carried out in previous years. Scenarios of possible trend developments for the factors introduced into the function model in order to present remedial measures that could improve the process of hard coal extraction were created. The scenarios were created using the ARIMA class models. Which scenario is the most favourable was determined. A computer program, created by the author, for optimising the level and use of labor resources at the level of the entire coal company has been presented.

The main pathogen that deteriorates the quality of rose flowers during the postharvest stage belongs to the fungal genus Botrytis. The chemical products used to control the disease caused by this pathogen have been losing effectiveness due to the appearance of resistance. The present study describes the in vitro and in vivo fungicidal activity of Pelargonium graveolens essential oil and its chemical composition. The essential oil was obtained by hydrodistillation, and the in vitro fungicidal activity was determined by agar diffusion assays, showing 100% of fungal growth inhibition at 250 ppm. In vivo assays were performed on Rosa grandiflora flowers treated with 250 ppm of P. graveolens essential oil, using distillate water as a positive control and the commercial fungicide carbendazim as a negative one. No significant differences were obtained between the treatment with the essential oil and the treatment with the commercial fungicide. The chemical profile of the essential oil was determined by GC-MS. The main compounds detected were geraniol (24.89%), citronellol (19.50%), linalool (10.92%) and γ-eudesmol (8.93%). These results encourage the possible use of P. graveolens essential oil for the control of B. cinerea in rose flowers.

The publication reflects the current situation concerning the possibilities of using augmented reality (AR) technology in the field of production technologies with the main intention of creating a tool to increase production efficiency. It is a set of individual steps that respond in a targeted manner to the possible need for assisted service intervention on a specific device. The publication chronologically describes the procedure required for the preparation and processing of a CAD model. For this preparatory process, the PTC software package is used which meets the requirements for each of the individual operations. The first step is the routine preparation of CAD models and assemblies. These are prepared based on real models located on the device, and their shape and dimensions correlate with the dimensions of the model on the device. The second phase is the creation and timing of the disassembly sequence. This will provide the model with complete vector data, which is then paired with the CAD models in AR. This phase is one of the most important. It determines the location of the model concerning its relative position on the device, provides information on the relocation of parts of the model after the sequence is started, and essentially serves as a template for the interactive part of the sequence. The last two phases are used to connect CAD models with vector data, determine their position for the position mark, and prepare the user interface displayed on the output device. The result of this procedure is a functional disassembly sequence, used for assisted service intervention of a worker in the spindle drive of the Emco Mill 55 device.

Traditionally the aggregate production plan helps in determining the inventory, production, and work-force, based on the demand forecasts without considering the productivity loss at a tactical level in supply chain planning. In this paper, we include the productivity loss into traditional aggregate production plan and the prescriptive analytics technique, linear programming, is used to solve this problem of practical interest in the domain of multifarious businesses and industries. In this study, we discussed two model variations of the aggregate production planning problem with and without productivity loss, i) fixed work-force, and ii) variable Work Force. The mathematical models were designated to be solved by using an open-source python pulp package in order to evaluate the impacts of the productivity loss on both the models. PuLP is an open-source modeling framework provided by the COIN-OR Foundation (Computational Infrastructure for Operations Research) for linear and integer Programing problems written in Python. The computational results indicate that the productivity loss has direct impact on the workforce hiring and firing.

The automotive industry is characterized by a high degree of uncertainty. Companies are facing the challenge of producing different systems simultaneously. Additionally, the global quantity of electric vehicles is also expected to increase significantly. This results in the following capability to remain competitive: Effective and efficient adaptions of production systems to model variations and volume increases. While flexible production is identified as the most promising concept, defining the actual flexibility level of included production resources is essential for its proper realization. A literature review on existing flexibility assessment approaches revealed their emphasis on high-level enablers and limited practical applicability in the automotive industry. In contrast, focusing the assessment on single workstations supports the selection of appropriate production resources. Therefore, a simple and structured standard procedure for a production resource flexibility assessment was developed. This theoretical construct was subsequently complemented with practical insights through its application on two real-life case studies within one automotive engineering company. Summarizing and discussing the findings in combination with a conclusion completed this paper.

Production development has for decades concentrated on incremental improvements by exploiting existing manufacturing knowledge to improve existing production systems or adapt them for new product developments. Building up an “ambidextrous innovation” ability, and more specifically in increasing focus on explorative production innovation, is important to balance production development efforts and obtain sustainable development of production. This paper aims to provide a conceptual framework for “ambidextrous production innovation” that conceptualizes and highlights phenomenon characteristics from exploitative and explorative perspectives. The conceptual framework describes “production innovation” as the process of either increasing or developing a new production capability, enabling opportunities for new product designs. This process can be either “product-driven” or “production-driven” depending on the primary objective of the development.

In this article we have described a multiproduct model of economical dynamics of Gale type, in which the changes in production technology (the dynamics of Gale type production spaces) depend upon the scale of targeted investments. Under such assumptions we have proved a so-called “weak” version of a multilane turnpike theorem in the Gale type economy with varying technology which converges to a certain limit technology. It states that in the long periods of time, regardless of the initial state of the economy, the optimal growth processes almost always lie close to the family of steady growth paths with maximum growth rate called the multilane turnpike.

Underground mining development is directly related to face drilling rig performance. Reducing operating costs and improving productivity are current and crucial topics for mining projects around the world within the development phase. Unlike past approaches, this article is based on variations of equipment availability and utilisation, and their impact on development plans success and costs decrease. To assess the influence of these parameters, daily field data were collected to identify major downtimes in normal cycles and apply adequate corrective measures to mitigate them. Additionally, this article presents the reader with a graphic illustration of the correlation between utilisation and development, including historical data. This paper was developed from October 2017 to March 2018. The result of this study seeks to identify when projects generate profits by comparing four situations with constant productivity, but variables such as the possession rate, maintenance fee, production and utilisation. Finally, it is demonstrated that success in mining projects, related to equipment, is proportional to the utilisation of the fleet, with the correct management of productivities.

The aim of the present study was to investigate inline lactate dehydrogenase (LDH) dynamic changes based on different cow factors – different number and stages of lactation, milk yield, and the status of reproduction in clinically healthy dairy cows.

In the Herd Navigator system, LDH activity levels (μmol/min per litre) were measured using dry-stick technology. A total of 378 cows were selected. According to their reproductive status, the cows were classified as belonging to the following groups: Fresh (1 – 44 days after calving); Open (45 – 65 days after calving); Inseminated (1 – 35 days after insemination); Pregnant (35 – 60 days after insemination and pregnant). According to their productivity, the cows were classified into the following groups: <15 kg/day, 15 – 25 kg/day, 25 – 35 kg/day and >35 kg/day. The cows were milked with a DeLaval milking robot (DeLaval Inc. Tumba Sweden) in combination with a Herd Navigator analyser (Lattec I/S. Hillerød Denmark).

In conclusion inline dynamic changes in the milk LDH concentration may increase together with the rise in the lactation period frequency. The highest LDH level determinated in the group of the fresh cows ranged from 5 to 10 DIM, while the highest LDH concentration level was found in the fresh cow milk. Thus, there was a positive relationship between the milk concentration of LDH and the milk yield.