Prediction Algorithm of Collaborative Innovation Capability of High-End Equipment Manufacturing Enterprises Based on Random Forest

This paper studies the competitiveness of listed companies in high-end equipment manufacturing industry by using random forest. Random forest is a supervised machine learning algorithm that is actually based on the regression and classification. It takes some important decisions that are always based upon the set of samples. It counts majority for the classification purposes while it takes an average for the regression. For empirical analysis, 88 listed companies are selected. It is found that there are great differences in comprehensive competitiveness among industries. Enterprise scale accounts for a high proportion in the comprehensive competitiveness, and its score often affects the comprehensive strength; and the gap between companies in the same industry is also obvious. The empirical evaluation results of this paper provide three enlightenments for enterprises to improve their comprehensive competitiveness, such as seizing the strategic opportunity to expand the market, expand the scale of enterprises, improve asset management, and narrow the industry gap.

Deploying Big Data Enablers to Strengthen Supply Chain Agility to Mitigate Bullwhip Effect: An Empirical Study of China’s Electronic Manufacturers

With the development of economic globalization, the uncertainty of supply chains is also increasing, and alleviating the bullwhip effect has become an important issue. From previous discussions on alleviating the bullwhip effect, there was no research on alleviating it by enhancing supply chain agility through improving big data. Moreover, it has not been found that quality function deployment is used to analyze the interdependence between big data and supply chain agility, as well as between supply chain agility and the bullwhip effect. In particular, the interaction of bullwhip effect factors are not considered. In this study, the multicriteria decision-making integrated framework is proposed and the largest relay manufacturer in China is taken to identify key big data enablers to enhance supply chain agility and mitigate the bullwhip effect, thus providing an effective method for electronic equipment manufacturing enterprises to develop a supply chain that can quickly respond to changes and uncertainties. These big data enablers can enhance supply chain agility and reduce the bullwhip effect. This framework provides an effective method for electronic manufacturers to formulate supply chain agility indicators and big data enablers to mitigate the bullwhip effect and also provides a reference for other manufacturing enterprises in supply chain management.

A Formalised Approach to Optimal Adoption of a Complex of Technical Means

The research relates to the field of automation of technological preparation of production in transport equipment manufacturing sector and proposes the concept of choosing the optimal design model for a complex of technical means within a control system in automated production. The factors characterising the dynamics of the processes have been studied, and the corresponding mathematical models have been drawn up, considering the cost characteristics and the economic feasibility of adopting integratedtechnical means.The main objective of this work is to propose consideration of all parameters that have weight, including the dynamics of technical and economic processes. It is shown that cost characteristics are direct indicators of economic efficiency of the implemented complex of technical means.Methodically, this work was carried out based on economic and mathematical analysis of adoption of a complex of technical means at a transport equipment manufacturing enterprise considering approaches previously suggested by researchers.Thus, for the most accurate determination of the final cost of a certain product option, it is necessary to consider all factors of design decisions. Based on these data, a model of cost characteristics of products is built, analysing which it is possible to select the optimal product design, optimal assemblies and units with specific components and specific quality indicators. This allows obtaining the optimal technological version of the design solution during manufacturing.A method of searching for an optimal production cycle when introducing an automated production system is proposed. It is proposed to consider the losses associated with temporary freezing of funds, including the need to perform a convolution of optimization criteria. To formalise the process of making optimal decisions, it is also proposed to harmonise the products and market needs. Besides, the search methodology should include the search for an optimal group of employees responsible for implementation of specific and narrowly focused tasks, which makes it possible to improve the quality indicators of automated production with an adopted complex of technical means (CTM).To minimise the time spent on entire commissioning of a product from the development stage to receipt of the finished product, it is required to speed up the development work. This can be done by increasing production capacity, as well as by reducing the time of partial cycles of the structure’s existence.Even though the model is becoming excessively redundant, we suppose that introduction of additional elements is necessary to consider all the nuances that help choosing the best solution regarding optimisation issues, which will allow determining of the full economic efficiency of the complex.

Challenges in the Automotive JIS/JIT Production of Steering Wheels Involving Traceability

The purpose of this article is to present the new challenges faced by the automotive industry and specifically by the steering wheel producers in terms of product design changes, process changes and the solution to solve them, the concept of TTM - Total Traceability Management. The article presents the traceability solution as a proved solution for managing JIS (Just in Sequency) & JIT (Just in Time) manufacturing concepts in terms of customer demand sequence attainment, process sequence attainment, and technical test verifications. The research is based on case studies deployed in production facilities, in the companies TRW and Key Safety Systems. Both entities are steering wheel producers with JIS&JIT conditioned production systems and are also using latest technology in synchronizing the traceability communication with various cars producers (Porsche, BMW, Daimler, etc.). This concept of manufacturing and delivery of the products to the famous automotive companies is crucial for the supplier including the base of business, because the entire work in this domain is completely automatic by robots. That means: all the final products (cars end others), including the components from different suppliers, also the technology of assembling, it need to be adapted in order to be performed by robots. The components as steering wheels, and many others, arrive at the assembling line at OEMs (Original Equipment Manufacturing) facilities and the robots work on directly the buffers with these components in order to ensure the continuous assembling process in the condition of total flexibility and without errors.

Simulation of Production Processes in Combination with Traceability

The TTM (Total Traceability Management) is becoming a mandatory process requested by all OEMs (Original Equipment Manufacturing) as a system to be deployed by the relevant suppliers. Process parameters from the machines are gathered by the TTM software and based on a defined algorithm the product is receiving new statuses, as physical and software characteristics. Given the high complexity of the current process flows, the highly automated lines and cells, the increased amount of process parameters, the necessity of synchronizing various production steps, it is needed that a simulation process to be done in advance of the full process deployment. The paper work reviews both the mathematical apparatus that underlies the simulation, and one of the various available simulation programs in academia. It is used one of the most usual software for simulation SIMQuick which is based on statistical methodologies. SIMQuick performs a virtual simulation of the process being used in various areas of activity including production in automotive, automation, supply chain processes and services, providing the valuable data for the behavior of the process and providing the premises for layout, flows, and resources improvements. The supply chain software ERP requirements with TTM technical inputs and outputs are the data pillars for the simulation which are presented as a result of the research.