Industrial engineering and information technology (IT) have been ideal complements to each other for many years. In recent years the greater competitiveness of the markets, the raising of the quality of the products and services offered by the companies and the need to respond in an agile and efficient way to the changes in the same, have increased this union.
Three main aspects have benefited from this: supply (or value) chains, the creation of “competency networks” and finally allowing the automation of productivity monitoring of employee jobs in manufacturing environments. In conclusion, this union has become for many companies a vital part of their business strategies.
INTRODUCTION
In recent years, the competition that companies face is increasingly strong in almost all industries.
Businesses constantly struggle to maintain and increase their sales, their customer base, and their market share. Manufacturing companies in particular have been embroiled in stiff competition. In order to stay in the market, companies have been forced to constantly reinvent their manufacturing processesand to review in detail the way they operate. This implies spending time to analyze the manufacturing processes, decide the best use of the available resources (workers, time, machinery, etc.) and ensure quality throughout the process. It is in this aspect that industrial engineering plays a predominant role, optimizing the production process and even going beyond the borders of the company itself, extending its benefits to customers and suppliers (optimizing the supply chain). Despite these benefits, its implementation in companies has taken time. (Kuman, 2001).
It is at this point that information technology (IT) has entered to promote industrial engineering, becoming its best ally. Currently there are many examples of the fusion between both elements in almost all companies, for example: resource planning systems ERP (Enterprise Resource Planning) in companies that help to integrate information in firms, assigning available resources, and decision making, quality control systems, design software for the layout of a manufacturing plant, inventory control, etc. (Kuman, 2001)
The production systems therefore have also undergone changes, to become modular manufacturing systems, ready to be reconfigured and start the production of new products in a short time. In turn, the way in which companies coordinate with their suppliers and customers (supply chain) and the way in which information flows between them has also changed. The use of industrial engineering techniques and technological advances have been two pillars of these changes. Inventories and their administration that tend to keep them to the minimum necessary, determining quantities to be manufactured, choosing the best transportation routes, allocating the best use of resources for the manufacture of a product, among other issues, are decisions that many of companies face and which require the use of information technologies and industrial engineering (among other aspects). (Kuman, 2001)
To describe the union process between information technologies and industrial engineering, it is necessary to review the cases in which companies have implemented or improved existing systems, as well as the consequences of these.
Starting from this, we will start with improvements implemented in the companies which include both the use of industrial engineering and information technologies.
METHODOLOGY
The present work was carried out based on an extensive bibliography search in the digital library of the Instituto Tecnológico y de Estudios Superiores de Monterrey. The databases consulted were:
- ACMEmeraldGarnet IntrawebIEEExploreProquest
From each of these databases, five articles related to the topics of industrial engineering and information technology (IT) were obtained. In all cases, articles published in recent years were searched to show the current reality in both topics. Based on this research, what is stated in this article is supported, this being referenced at the time of mentioning any idea of said works.
CHAPTER 1 "SUPPLY CHAIN AND INFORMATION TECHNOLOGIES (IT)"
Over the years there have been an innumerable number of cases studied about improvements brought about by the implementation of information technology (IT). One of the most benefited fields in this regard has been the supply chain (or value chain as it is currently known). Specifically, in a study of the role of technology in the supply chain (Kuman, 2001) it is concluded that the use of information and communication technologies ICT (Information Communication and Technology) is vital for the supply chain to add value and can create a significant cost reduction. In this study, it is also commented that the use of ICT at the beginning was very focused on trying to improve the estimates of demand, which is a step in the right direction, but it is certainly not enough.Highly competitive markets, with consumers increasingly sensitive to prices and the constant need to change or renew products, have required a much more agile and efficient supply chain. This implies the ability to respond to market changes on the spot, and an uninterrupted flow of updated information throughout the entire supply chain (from the most basic inputs until the product is purchased by the final consumer). In order to obtain such agility and efficiency, therefore, it is necessary to have the so-called “Advanced Planning Systems” APS (Advanced Planning Systems) (Kuman, 2001). These systems analyze transactional data that occur at the operational level throughout the supply chain, and serve as support for decision making. Such software,They include powerful algorithms for linear programming, forecasts, and time series among other techniques. These complex mathematical models require powerful computers, as well as a continuous flow of data, which must intercommunicate with various areas of the company such as: manufacturing, sales, marketing, etc. (Kuman, 2001) Trying to perform these operations manually would be extremely inefficient and would surely lead to serious errors. Figure 1 shows the functional areas of APS systems.(Kuman, 2001) Trying to perform these operations manually would be extremely inefficient and would surely lead to serious errors. Figure 1 shows the functional areas of APS systems.(Kuman, 2001) Trying to perform these operations manually would be extremely inefficient and would surely lead to serious errors. Figure 1 shows the functional areas of the APS systems.
Figure 1
Functional Domains of APS Systems (Kuman, 2001)
As can be seen, the APS supports the company in planning both in the short term (operational level) and in the long term in decision-making (strategic level). Computer systems are those that allow great flexibility and agility to respond to constant changes.
Supply chains, in turn, can be optimized through the use of simulation, rather than linear programming. In particular, when the optimization of the supply chain of a refinery is analyzed (Koo, Chen, Adhitya, Srinivasan and Karimi, 2006) it is concluded that this approach is more valid and seems more useful in these cases. The supply chains of refineries in general, are very complex networks, with independent entities and a high degree of complexity (and therefore variables to consider). In the study (Koo, et al. 2006) it is concluded that the simulation worked adequately to optimize supply chain policies, as well as to improve investment decisions. In this sense, the study (Koo, et al.2006) mentions that it was necessary to adopt a vision of a greater scope that covered the entire supply chain (and not just one part, such as planning, purchasing, or oil transportation). In these very complex systems, with an infinity of variables and factors to consider, simulation can be a valuable support tool, as long as it is used and interpreted correctly. Simulations necessarily require computers to run them, since it is necessary to perform hundreds or thousands of calculations to obtain results. On a modern computer, running the refinery simulation took an average of one day (Koo, et al. 2006). This time was too long, so the program was run again on more powerful computers (with multiprocessors),which resulted in reductions of the simulation time to 1 hour, this is a 95% saving. This again, clearly shows us the way in which industrial engineering (and its optimization algorithms, or simulations) have been complemented with information technologies to add value to supply chains and companies.
CHAPTER 2 "COMPETITION NETWORKS AND INFORMATION TECHNOLOGIES (IT)"
It has been seen how supply chains require the use of information technology to operate efficiently, as well as to optimize and evaluate performance. One area of particular benefit from IT has been the enhancement of supply chain sub-functions. For example, small and medium-sized enterprises (SMEs) have resorted (mainly in Germany) to the use of “competence networks”. These elements are short-term virtual cooperation networks between various SMEs, which are subdivided into their main competencies (core competence). For example, an SME company is recognized for its high quality of manufacturing, it can enter manufacturing competition networks, together with other SMEs specialized in manufacturing. At the same time,another SME recognized for its innovation and product development, can enter the competition networks of "Prototyping", etc. This allows SMEs greater flexibility and agility to respond to the needs of their clients, particularly due to the fact that they do not have large financial resources or infrastructure to face different market conditions on their own. (Berlak and Weber, 2004). In this way, virtual markets are created, which bring together various organizations to extract from them the best competencies of each one, coupled with an “e-business” strategy, the competitive advantages of SMEs are strengthened (Berlak and Weber, 2004). An example of the structure of a competency network is shown below in Figure 2.You can enter the competition networks of "Prototyping", etc. This allows SMEs greater flexibility and agility to respond to the needs of their clients, in particular due to the fact that they do not have large financial resources or infrastructure to face the different market conditions on their own. (Berlak and Weber, 2004). In this way, virtual markets are created, which bring together various organizations to extract from them the best competencies of each one, coupled with an “e-business” strategy, the competitive advantages of SMEs are strengthened (Berlak and Weber, 2004). An example of the structure of a competency network is shown below in Figure 2.You can enter the competition networks of “Prototyping”, etc. This allows SMEs greater flexibility and agility to respond to the needs of their clients, particularly due to the fact that they do not have large financial resources or infrastructure to face different market conditions on their own. (Berlak and Weber, 2004). In this way, virtual markets are created, which bring together various organizations to extract from them the best competencies of each one, coupled with an “e-business” strategy, the competitive advantages of SMEs are strengthened (Berlak and Weber, 2004). An example of the structure of a competency network is shown below in Figure 2.in particular due to the fact that they do not have large financial resources or infrastructure to face different market conditions on their own. (Berlak and Weber, 2004). In this way, virtual markets are created, which bring together various organizations to extract from them the best competencies of each one, coupled with an “e-business” strategy, the competitive advantages of SMEs are strengthened (Berlak and Weber, 2004). An example of the structure of a competency network is shown below in Figure 2.in particular due to the fact that they do not have large financial resources or infrastructure to face different market conditions on their own. (Berlak and Weber, 2004). In this way, virtual markets are created, which bring together various organizations to extract from them the best competencies of each one, coupled with an “e-business” strategy, the competitive advantages of SMEs are strengthened (Berlak and Weber, 2004). An example of the structure of a competency network is shown below in Figure 2.coupled with an “e-business” strategy, the competitive advantages of SMEs are strengthened (Berlak and Weber, 2004). An example of the structure of a competency network is shown below in Figure 2.coupled with an “e-business” strategy, the competitive advantages of SMEs are strengthened (Berlak and Weber, 2004). An example of the structure of a competency network is shown below in Figure 2.
Scheme of Competition Networks (Berlak and Weber, 2004)
As can be seen, competition networks bring together several organizations and group them according to their specialization to meet customer requirements in an agile and efficient manner. It is important to emphasize that there are different competition networks, with different objectives, such as:
- Strategic Network: With alliances between several companies, seeking a competitive advantage over the external ones of the competition network Red Compound: Alliances between two or more similar companies in order to carry out a task (generally in the long term), taking advantage of the synergies between instead of both working in isolation.Operational Network: SME alliances to give greater value to the client by efficiently taking advantage of the use of network resources.Virtual Enterprise: They are virtual companies, temporarily created to take advantage of market opportunities, contributing its "core competencies" to add value to its alliance to the network.
These different networks imply the use of virtual spaces, which would only be possible with the use of information technologies. The creation of these networks is not an easy task, but there are more and more indications that, when implemented correctly, it can generate higher benefits than what was invested in creating them (Berlak and Weber, 2004).
CHAPTER 3 “PRODUCTIVITY AND INFORMATION TECHNOLOGIES (IT)
The union between IT and industrial engineering is not only limited to the supply chain, but also to the production line within a company. One of the main objectives of industrial engineering is to constantly increase the productivity and quality of manufactured products. In principle, undesirable or unproductive situations must then be identified (waiting for material, waiting for the next assembly, machinery problems, etc.). Identifying these situations traditionally requires industrial engineers who "observe" the operators, identify inefficiencies, and through careful analysis reach the root causes and from there, corrective actions are initiated. All this process,Analyzing inefficiencies is extremely costly (particularly in developed countries) and slow to create notable improvements in productivity. If to this we add the fact that there are a large number of operators working in a plant, the task becomes complicated and takes considerable time. Hence the need to develop an automated system to identify inefficiencies and their root causes. Recently, studies have been carried out (Hattori, Itakura and Orihara, 2006) which show that computer systems may be able to analyze the behavior of operators on the production line and associate it with normal work situations, or with undesired situations. The system must, in turn, identify the root causes of inefficiencies in order to correct them on the spotor at least give all the information necessary to be able to deduce it (Hattori, et al. 2006).
In this way, through an automated system, it is possible to identify unproductive situations, in an automated way and on a large scale. This is achieved by associating the behavior of the operators (as well as other data: number of available parts, presence of other operators in the same work area, absence or presence of assembled parts together with the knowledge poured into the system by the industrial engineer) to “Basic situations” (Hattori, et al 2006). The basic situations therefore indicate to the system what type of inefficiency is being presented, and based on this the peripheral data collection system (through data mining) extracts all the relevant information. In this way, the industrial engineer reviews the situation and acts at the moment to remedy it.A diagram of such a system is shown in figure 3.
The schematic in Figure 3 represents how the automated system can identify a basic situation. As can be seen, it is necessary to integrate “knowledge” into the system, about the expected behaviors of the operators, and corroborate it with the peripheral information collection system, to arrive at the “basic situation” (Hattori, et al. 2006). For example, the presence of two or more operators in a work area (in which there should normally only be one), can be an indication of problems at that work station. In this case, the system checks with the help of the peripheral information system on the variables of the workstation (use of machinery, number of parts in stock, leaks, etc.) and determines if there is an inefficiency,as well as trying to collect all the information necessary to determine the root cause of this situation (or better yet propose a possible root cause and corrective action to follow, subject to verification by the person in charge of the production line).
This system, which is still under development, promises not only to bring higher productivity to manufacturing plants, but it would also cost a fraction of the total cost of implementing such optimizations in a traditional way. Additionally, the detection of undesirable situations, as well as the identification of the root cause, and its correction would be completed in a shorter time. (Hattori, et al. 2006) It is important, however, to clarify that although said system allows the identification of inefficiencies, and provides the support information for the identification of root causes, the final decision on what to do, continues to fall on the human factor.
CONCLUSIONS
Manufacturing companies, as has been seen throughout this work, have opted for the use of information technologies (IT) coupled with industrial engineering techniques. The different algorithms used by industrial engineering have been fully exploited through the use of information technologies (in particular the use of simulation and optimization of the supply chain). In short, there is a reciprocal benefit between industrial engineering and information technology. Industrial engineering techniques require the use of information technologies to be fully exploited and implemented in the complex real world.
Ultimately this translates into considerable cost savings for companies (by optimizing available resources: transport, machinery, deciding what to buy and what to manufacture, Just in Time, an increase in productivity, etc.). These improvements, however, to be fully exploited, must be combined with several changes in the organizational (or even managerial) processes, since only in this way can IT create added value to the company. Although information technologies can be imitable, the fact of using IT and shaping it to the specific needs of a company and its different organizational processes, make the system very difficult to imitate 100%, and little useful for its competitors, which each have a different reality. This can consequently resultin a short-term competitive advantage, and which when adding them can give in the end a long-term competitive advantage.
It is to be expected, therefore, that in the future new information technologies (together with industrial engineering) will continue to drive companies to higher levels of productivity and in many cases create competitive advantages. As global markets become more demanding and companies continue their constant search to reduce costs, lower manufacturing times and higher quality in their products; These techniques will continue to form a vital part of global business strategies in manufacturing companies.
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