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Key pitfalls and good practices related to system analysis are described in the next two sections. Some of the key pitfalls encountered in planning and performing system analysis are provided in Table 4. Processes for Engineering a System. Systems Engineering Handbook. Washington, D. Ring, J, H. Eisner, and M. Blanchard, B. Systems Engineering and Analysis , 5th ed. Jump to: navigation , search.

System Analysis. During engineering, assessments should be performed every time technical choices or decisions are made to determine compliance with system requirements. Categories : Part 3 Topic System Definition. Navigation menu Personal tools Log in. Namespaces Page Discussion. This page was last edited on 21 August , at Engineering, development tools equipment and software , project management, test-benches, mock-ups and prototypes, training, etc.

Raw materials and supplying, spare parts and stock assets, necessary resources to operation water, electricity power, etc. Expenses of structure subsidiaries, stores, workshops, distribution, information acquisition, etc. Taxes, installation customer , resources necessary to the operation of the product water, fuel, lubricants, etc. Field services, preventive maintenance, regulation controls, spare parts and stocks, cost of guarantee, etc.

Identifier; name; description; relative weight; scalar weight. Identifier; name; description; value. In the context of systems engineering, a cost is an amount expressed in a given currency related to the value of a system element, a physical interface, and a physical architecture. An event having a probability of occurrence and consequences related to the system mission or on other characteristics. Used for technical risk in engineering. A risk is the combination of vulnerability a danger or threat. Identifier; name description; status. Models containing statistics are included in this category.

Models by analogy also use former projects. The system element being studied is compared to an already existing system element with known characteristics cost, reliability, etc.

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Then these characteristics are adjusted based on the specialists' expertise. Learning curves allow foreseeing the evolution of a characteristic or a technology. One example of evolution: "Each time the number of produced units is multiplied by two, the cost of this unit is reduced with a certain percentage, generally constant. The theory of probability allows classifying the possible candidate solutions compared to consequences from a set of events as criteria. These models are applicable if the number of criteria is limited and the combination of the possible events is simple.

Take care that the sum of probabilities of all events is equal to one for each node. When the number of criteria is greater than ten, it is recommended that a multi-criteria decision model be established. This model is obtained through the following actions: Organize the criteria as a hierarchy or a decomposition tree. In the operations management field, operational practices like total quality management or just in time have been seen as a way to improve operational performance and ultimately financial performance.

Empirical support for this effect of operational practices in financial performance has been, however, limited due to research design and the inherent difficulties of using performance as a dependent variable. In this paper, we tested the relationship between selected operational practices quality management, just in time, ISO certification and services outsourcing in financial performance outcomes of profitability and growth.

Analysis using multiple regression explored the direct effect of practices and their interaction with industry dummies. Results did not support the existence of a positive relationship with financial performance. A negative relationship of outsourcing with both profitability and growth was found, supporting some critical views of the outsourcing practice. A weaker negative relationship between ISO certification and growth was also found. Some interactions between practices and industries were also significant, with mixed results, indicating that the effect of practices on performance might be context dependent.

Key words: operational practices; financial performance; operations strategy. The search for a recipe for superior performance using operational practices has been a frequent concern in management literature since the early days of the scientific management by Taylor Operations management has extensively explored the potential of the then successful Japanese management techniques when applied to western companies.

Despite its relevance to the field, a more rigorous and scientific evaluation of the impact of management practices in financial performance still shows mixed results as demonstrated in more detail in the literature review section of this paper.

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Several reasons can account for these mixed results. Second, some operational practices may deliver positive outcomes in some settings, but negative outcomes in others, and identifying these interactions is not simple. Outsourcing is one example, as indicated by Rossetti and Choi Third, identifying what constitutes a practice is also not simple. Powell showed that the soft, cultural aspects of quality management are the ones that can affect performance and not simply the adoption of practices. Fourth, imitation of successful practices continuously wears out financial benefits through competition, following the RBT logic.

Finally, given all the above points, large samples are necessary to have the power to identify relationships that may have been weakened or diluted by all of these factors. This paper contributes to the attempt of answering the question: do management practices lead to superior financial performance? In the next section we review the literature and previous studies that explore the impact of operational practices and performance. A methodology section describes the sample, operationalization of variables and method of analysis used. We then present and discuss the results and a conclusions section ends the paper.

According to Hayes and Pisano , firms are on performance curves based on the resources they use, but new manufacturing technologies, including management-related ones, such as JIT and TQM Total Quality Management , might place firms on new performance curves. Table 1 summarizes the references that we use in each operational practice. TQM can be defined as a management philosophy that integrates with a series of practices emphasizing continued improvement, meeting consumer expectations and needs, reducing re-work, long-term planning, redesigning processes, competitive benchmarking, teamwork, constant results measurement, and a close relationship with suppliers Ross, The results of several of the empirical studies on ties between quality practices and organizational performance are mixed.

Powell , for example, uses RBT to study the impact of some elements of TQM programs on the creation of competitive advantage. The results suggest that practices associated with TQM programs are not capable of generating sustainable competitive advantages, but some of the characteristics present in quality programs help form intangible and behavioral elements such as leadership, organizational skills and culture.

Kaynak contributed to the discussion with a comprehensive review of the literature. The author investigated the links between the different TQM practices, attempting, in particular, to determine how they affect organizational performance on three levels: operational, marketing and financial. The same practices also affect financial and marketing performance through the organization's operational performance. Cho and Pucik examined the relationship between quality, innovation, growth, profitability and the firm's market value.

The results of the structural equations model show that quality has different effects on profitability and growth. While the quality has a direct impact on profitability, its effect on growth is mediated by innovation. On the other hand, Mohrman et al. More recently, Sila tested the impact of TQM practices on certain organizational performance variables. The results show that a direct relationship exists between TQM practices and organizational efficiency, but no significant connection was found with either financial or market performance.

Only indirect effects of TQM made themselves felt on these two latter performance variables. Based on all of the above studies, we may say that some positive connection may be expected between quality and performance, but this relationship is not always direct, as suggested by some researchers. Furthermore, some results are difficult to compare and, sometimes, conflicting. Literally, JIT means producing goods and services exactly when they become needed, not before or after. The philosophy of JIT helps guide the actions of an organization's managers and is based on doing things well and simply, improving them constantly, and eliminating waste; all of this with the involvement of everyone in the organization.

JIT as a set of techniques and tools represents the means to attain the fundamentals the philosophy prescribes. Vuppalapati, Ahire and Gupta argue that firms that implement both philosophies jointly attain better performance than those that view and implement them in isolation. Several of the authors who empirically investigated the benefits of JIT, such as Bartezzaghi, Turco and Spina and Upton , focused their studies on the benefits relative to organizations' operating performance, including reduced lead time, production time and procurement batches, increased process flexibility, accelerated delivery, low cost and low cycle time, to name a few.

As a result, these authors found significantly improved operational efficiency. The authors also found a positive relationship between JIT and financial and efficiency metrics. They found that firms with a broader adoption of the JIT approach were able to attain better financial performance.

The authors were also unable to find a positive correlation between the Manufacturing JIT variable and profitability, and a negative correlation between Quality JIT and profitability. Finally, the authors show that no significant evidence exists that firms with JIT become more profitable over the years. Their results show that the best performance and greatest evolution were found with firms that had implemented TOC. JIT firms had no better performance than traditional manufacturers.

In addition, they showed no performance improvement after implementing JIT. We can see that here, too, no consensus exists among the various researchers as to whether JIT can truly improve an organization's financial performance. Even so, several studies showed improved operations performance. Another interesting view can be found in Fullerton et al.

Therefore, adoption of JIT is not supposed to bring about immediate return on investment. According to the authors, this partly explains the low validity and consistency of empirical surveys attempting to show a relationship between financial performance and JIT adoption. The ISO standards were first published in the late s and quickly became a benchmark for quality management. In fact, the causal link is firms with better financial performance are more likely to get ISO certified. The authors suggest some hypotheses as to why this might be.

According to them, the systems certification requirements generate high implementation and maintenance costs, which make highly profitable firms more likely to implement than others. In the same sense, larger firms could better dilute those costs across their operations, which smaller firms lack such a choice. Finally, more profitable and bigger firms tend to compete internationally. Because global players make efforts to become world-class firms based on their quality management systems, certification becomes a seal of assurance that helps entering and operating in certain international target markets.

More broadly, Rao and Holt show that the economic performance of ISO adopting firms is not only associated with internal processes, but also with their suppliers and customers. The most remarkable result of Rao and Holt is that firms must integrate their suppliers into the adopting of environmentally correct practices, as this is crucial to successfully reducing production-process waste and residue. As for competitive gains the study shows that supply chains with integrated environmental practices green chains not only achieve substantial cost reductions, but can also expand sales and market share and even exploit new commercial opportunities.

As seen in this section, not many works investigate the relationship between ISO standards and performance and, in particular, ISO standards and financial performance. In most studies, this assessment is more qualitative than quantitative. Lei and Hitt define outsourcing as reliance on a certain outside source of manufactured components or value-added activities. Gilley and Rasheed attempt to clarify the concept of outsourcing, defining it as the purchase of a good or service that was originally produced internally, or might have been produced internally, but was in fact produced by a supplier.

To study outsourcing, two theories make a valuable contribution McIvor, : transaction cost economics TCE and the resource-based theory RBT of the firm. TCE specifies the conditions under which an organization should manage an economic exchange internally within its boundaries, and the conditions suitable for outsourcing - managing an economic exchange externally Williamson, , The level of transaction specific investment in the economic exchange is the principal determinant of whether an activity should be internalized or not.

An alternative theory to understanding the outsourcing decision is the RBT, which views the firm as a bundle of assets and resources that, if employed in distinctive ways, can create competitive advantage Barney, ; Peteraf, The RBT is important to the study of outsourcing, as superior performance achieved in organizational activities relative to competitors would explain why such activities are internalized within the organization. Prahalad and Hamel were pioneers in the idea that firms should focus on their core competences to become more competitive, thereby avoiding the waste of efforts on secondary activities.

Jiang and Qureshi say that most studies focused on understanding the determinants of the decisions to outsource and on control over the outsourcing process, and few empirical, results-focused studies are to be found from the literature. Londsdale suggests that only 5 percent of the surveyed firms attained significant benefits from outsourcing. Barrar, Wood, Jones and Vedovato used DEA Data Envelopment Analysis to compare the efficiency of firms that outsource their accounting services and of those that do not.

The survey shows that outsourcing accounting services is more efficient only in terms of productivity. Hays, Hunton and Reck investigate the impact of outsourcing on share prices. Hays et al. More recently, Jiang, Frazier and Prater did a broader empirical research with a focus on performance.

The authors assessed the results of outsourcing in terms of three distinct performance variables: cost, productivity and profitability. The study showed evidence that outsourcing may improve a firm's costs, but failed to find that it can improve an organization's profitability and productivity. This is broad-based and full of administrative, financial and operational information, based on a census survey in companies over 30 employees. The survey occurs every five years and our database is composed of two editions: 10, companies and 11, companies.

Our research comprises the industrial sector and has only companies that participated in both surveys, a total of 3, companies. Additionally, this research chose to use only single-site enterprises, i. This is due to the fact that PAEP provides aggregate organization data, that is, data on an organization's various manufacturing units.

Using information on single-location firms increases data reliability, as responses to the questionnaires always concerned that particular unit. The main implication of this choice was removal from the sample of a significant share of larger firms that normally have more than one manufacturing unit.

The purpose was to group together the largest possible number of firms with similar characteristics. Because some three-digit industries were left with a reduced number of firms, and to facilitate the analysis, we chose to work only with industries with 50 or more firms. Table 1 shows the industries and the number of firms. The sample is large enough to be regarded as a contribution to business administration knowledge in the specific area of operations management. In spite of this, the sample is not probabilistic and, therefore, the results have no outside validity and the conclusions cannot be generalized.

The study uses two variables to measure firm performance: a profitability P estimate and the revenue growth rate RGR , as seen in the equations 1 and 2. The operating practices variables are the study's main independent variables. They were construed using indicators available in the database. The rationale and detailed operationalization are described in sequence. The bigger the number of methods and techniques a firm uses, the greater the variable for the firm.

The survey measures the adoption of eight quality practices: total preventive maintenance, kaizen, use of mini-plants, total quality management, quality auditing, statistical process control, quality indicators, and final inspection. We created a variable that counts the number of practices said to be adopted by each company. The variable ranges from zero to eight, depending on the number of practices each firm adopts. Strictly speaking, this is a categorical variable, but may be regarded as ordinal, assuming that the larger the number of practices, the more intense the use of quality management practices in general.

In its use as an independent variable in regressions, however, it is treated as an interval variable, which is a brave approximation at the very least. This treatment assumes that differences in an additional practice are equivalent across the scale, which is unlikely to have conceptual grounds. A dummy variable was created with the value of zero if the company used neither internal nor external JIT and with the value of one when the company practiced either one of them. A similar approach to JIT was used.

A dummy variable was created with the value zero for companies that used neither ISO nor ISO and with the value 1 if the company used either one. We therefore assume the level of outsourcing to be the level of spending on the purchase of services from third parties. The variable was calculated as follows, as seen in equations 3. Beside operating variables, two control variables were used. The control variables used here were selected based on their possible influence on dependent variables and the study's other independent variables. The following control variables are used:.


Represents firm size, under the assumption that bigger firms will have bigger revenues;. In order to compare the results of firms in different industries, we chose to standardize certain variables based on the industry's mean and standard deviation. Standardization transformed the mentioned variables as being a certain number of standard deviations above or below the industrial mean, thereby minimizing industries' effects on the analysis. A second treatment was also needed for the variables Revenue and Revenue Growth Rate.

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To answer the study's questions we use the statistical tools of Multiple Regression. Regression analysis goes beyond correlation analysis insofar as it estimates the parameters of the systematic behavior across them, besides measuring the association between output variable Y and a set of independent variables X 1 , X 2 , Two regressions were run for each dependent variable: one using production variables only, indistinctly for the various manufacturing industries, as the dependent variables are standardized by industry; and another with a set of dummy independent variables by industry.

Interaction between dummy industries and other independent variables were also tested. Because industry-effect was already addressed through variable standardization, the idea was to explore different industry-related links between other independent variables and the dependent variable. The purpose of this second regression is to investigate whether production variables may exert different influences in different industries. Regressions used the stepwise variable-selection method.

The operational practices are used more intensively in some industries and less in others, which may indicate the sector's level of competitiveness. This is the main conclusion of descriptive analysis in Table 4. Moreover, this sector has companies with the highest average age and larger average size, in terms of revenue, among the sectors analyzed. On the other hand, the level of practices adoption is small in the sectors Garments tailoring , Manufacturing, footwear and Manufacturing, ceramic products and, for the chosen sample, these sectors are represented by the smaller companies.

The first regression model presented used the dependent variable Profitability. The results of this model can be seen in Table 5.

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The model explained only 6. Size positively affected profitability, while outsourcing had a negative effect on it. The other variables, representing the various operating practices, had no significant effect on the dependent variable to be selected by means of the stepwise method.

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The second regression, including dummy industry variables, and interactions of these dummies with the other independent variables explained 8. The variables that most contributed to the explanation are listed in Table 6. Note that the size variable affected industries forging, stamping, powder metallurgy and metal-treatment services and manufacturing, furniture differently, that is, with negative impact on industry and positive for Likewise, the variable Age StA helped explain Profitability change in industry Manufacturing, plastic goods with its negative effect on firm Profitability.