Our survey analyses the SWDs of the Valencian Region. In Spain the role of the SWDs is not easy, but in the case of Valencia's SWDs there is an additional factor: they are small and medium enterprises, working primarily as subcontractors or suppliers of conventional companies. This is why their goals focus on reducing costs and increasing productivity in order to provide a quick response to their customers within the stipulated quality parameters. To survive in a competitive market, this is the only way to achieve the main goal of the SWDs: to grow and promote the integration of a greater number of people with disabilities into quality jobs.
It is therefore necessary to develop and implement best practice in the strategies of the SWDs, that is, the manufacture of products with an excellent assembly quality and the lowest possible cost and with an efficient delivery time. To achieve this in a conventional business is not simple, but in the scenario under study, the high heterogeneity of the workers makes this task even more complicated, and therefore requires new management models and approaches.
In recent years, there has been an initial academic interest (Chi, 1999; Hasegawa and Katayama, 2000; Colella, 2001; Katayama, 2001; Katayama et al., 2001; Katayama and Hwang, 2008) in analysing the most suitable management techniques in environments with people with disabilities. In this sense, our research work analyses the OR/MS tools that have been shown to be most efficient and how these tools can help improve work accessibility in SWDs, based on the fulfilment of CSR; that is, efficient employment integration of disabled workers in SWDs or even in those companies that could operate as direct employers.
After almost a decade working with Valencia's SWDs, the authors have implemented a number of innovative proposals, based on intuition about which decision tools could be more efficient for this double purpose of enhancing productivity and improving accessibility to work, having obtained some good results (see Miralles et al., 2003a, 2005, 2007, 2008 or Canos and Miralles, 2007).
At this point of the research, we thought it would be appropriate to carry out a rigorous analysis of the current status of the Valencian SWDs, so as to gain an objective view of the real impact of the decision tools. The research covers most of Valencia's SWD centres, and analyses the level of implementation of OR/MS tools and their impact through conventional parameters relative to production efficiency and specific parameters relative to work accessibility. The main objectives can be summarized as follows:
- •To promote best practice in the management strategies used with employees, equipment and materials.
- •To reflect on the situation of the SWDs and possible ways of improvement.
- •To have a useful list of best practices for production management and human resources (HR) in SWDs.
- •To look for the correlations among the characteristics of the enterprises and the best use of the OR/MS tools, especially focusing on lean production tools.
The survey was conducted in the form of personal interviews, as there were some questions that required further clarification. This also allowed us to explain the importance of the research to the centre managers. At each SWD, the questionnaire was passed, at least, to either the manager or the production officer. In some cases, the questionnaire was answered by two managers, then taking the average value of the answers.
4.3.1. Sample and measures
In this study, we analysed 48 of Valencia's SWDs (>40% of the total SWD centres in the Valencian Region), including companies that work in different industrial sectors and deliver products and/or services. The fieldwork was carried out during the year 2007 using a questionnaire comprising 192 questions divided into four groups:
- 1Control variables: data about the company, its structure and its industrial sector.
- 2OR/MS tools and their level of implementation in the company.
- 3Competing priorities of the company.
- 4Results obtained through certain indicators.
The answers were then used to draw the most significant correlations and identify the type of OR/MS tool that best suits each company depending on the type of business, disability and/or priority. Because of space constraints, this paper will only describe the first two groups (control variables and tools), whereas the relationship between competing priorities and the results obtained by the SWDs will be analysed in future works.
4.3.2. Control variables
When analysing the relationship between the type of business (characterized by the so-called control variables) and the use of OR/MS tools, the companies had to be grouped into the smallest possible number of statistically valid sets. For this, the responses of the control variables were assigned values 0 or 1. Table 1 illustrates some examples.
Examples of control variables aggregated to 0–1
|Technology used||0||Less advanced||Decision making structure||0||Decentralized|
|Demand variability||0||Unpredictable||Workers salary||0||SWD agreement|
|1||Predictable||1||Higher than agreement|
|Productive capacity use||0||Low||Number of workers with a fixed contract||0||Low|
|Products innovations frequency||0||Low||Intensity of competitive rivalry||0||Low|
4.3.3. Analysis of tools
For the tools group, the answers relative to the same topic were aggregated into a single variable. In such a way, more reliable information on the degree of knowledge and implementation of each tool is obtained. A total of 18 tools were analysed (Table 2).
|Design for manufacturing||JIDOKA||Knowledge management|
|Operations technology||TPM – total productive maintenance||Human resources in manufacturing|
|Kanban||Standardized operations||Management of the supply chain|
|SMED||Five S||Salary (variable)|
|Work cells||Visual factory||Staff training|
4.4. Results of the survey: control variables vs. OR/MS tools
This section presents the most relevant findings about the relation between the control variables and the OR/MS tools obtained from the ANOVA analysis. The use of the tools is rated on a scale from 0 (not used) to 5 (widely used), while the control variables use a 0–1 scale. This allows detecting more easily whether the item analysed relates to a greater or a lesser use of the OR/MS tool.
4.4.1. Production scheduling
From the 48 companies surveyed, 28 have a production scheduling tool for determining the allocation of resources and quantity of goods to produce. The use of techniques associated with production scheduling has a value of 2.607 out of 5. This means that, in general, these tools are known and moderately used, although there is still room for improvement. For example, the companies that programme their production do it on a regular basis and on all products (3.661 out of 5). However, production levelling is less widely used (1.732 out of 5), with a huge potential for improvement.
There are two general statistically significant conclusions obtained from the ANOVA analysis related to the number of workers and the type of disability of the employees (Table 3).
Results of ANOVA for “production scheduling”
The first conclusion is logical because the higher the number of workers, the more complex the management tasks and, therefore, the more necessary the use of standard programming tools. Regarding the type of major disability of the employees, the only possible explanation is that the companies with workers with mental disabilities are mostly devoted to repetitive jobs. For this reason, Production Scheduling is more widely used than in other cases.
4.4.2. DFM – design for manufacturing
Only 10 companies from all SWDs surveyed design the products or services they offer. The efficiency of the design process in these companies has been valued 2.263 out of 5. Only the following correlations are significant (see Table 4).
Results of ANOVA for “DFM”
Larger companies obtain better design values than smaller ones. This may be due to company size, as large companies require increased efficiency in all procedures to avoid management problems, while small companies tend to be more informal, which do not affect them in their daily work.
Generally, companies that design and also manufacture obtain better results, probably due to a better knowledge of the products. Also, young firms or companies that frequently change the products they offer obtain worse results in product design. This may be related to the lack of experience in some cases and to the typical rush process in others.
4.4.3. Operations technology
The use of technology in production is particularly low in Valencia's SWDs, with an average of 0.652 points out of 5. In addition, the use of technology is not particularly high in any specific area: production, storage or management (ERP or MRP). This is mainly due to the fact that most SWDs tend to have manual rather than automated tasks that require many workers, and where investing in technology sometimes means reducing human resources, which is not desirable.
In fact, this assumption is reinforced by the only significant correlation in this section: the percentage of workers with fixed contracts is lower in those companies that use more technology.
4.4.4. Manufacturing control through Kanban
Kanban cards are hardly used in Valencia's SWDs. It has obtained an average score of 0.891 out of 5. A deeper analysis shows that the managers of these companies are aware of the benefits of flexible manufacturing (2.172 points). However, the technique of Kanban cards is unknown by most SWD managers as a method of controlling production and materials.
4.4.5. Preparation of machines: single minute exchange of die (SMED) techniques
Among the companies that manufacture or assemble, the concern to reduce setup times is considerable (2.422 points out of 5). Nevertheless, they do not know and do not apply the SMED techniques (1.266). The most significant relationships from the ANOVA analysis are shown in Table 5. Companies that manufacture standard products, and do not offer services, and that schedule production, are those where SMED techniques are more often applied.
Results of ANOVA for “SMED”
4.4.6. Work cells
The cluster of machines by products in order to reduce the displacement of materials is a very general action in Valencia's SWDs (3.234 points out of 5), indicating a single meaningful relationship: the companies with the largest number of product sets are more concerned with the grouping of machinery into work cells.
4.4.7. Line balancing
The concern to balance well the workload among those SWDs dedicated to manufacturing/assembling is high, because the average is 2.586 points. Furthermore, according to the ANOVA analysis, this concern is particularly significant in firms with a certificate of quality. Furthermore, as shown in Table 6, line balancing is more often used in those companies with many employees (more concerned about having balanced work loads), which do not offer services (and therefore are focused on production), and that schedule their production.
Results of ANOVA for “line balancing”
Concerning the assembly lines, job rotation in SWD is almost mandatory so that workers learn different skills progressively. But in this heterogeneous environment job rotation is more complex as each job change generates an imbalance. In this sense, approaches like that reported in Costa and Miralles (2009) can be very useful.
4.4.8. Automatic defect JIDOKA
Detecting errors as they occur is one of the approaches less widely used in the studied SWDs (1.469 points out of 5), and this fact should be improved. Note that only the larger companies that assemble, but do not export, sometimes use these techniques.
4.4.9. TPM – total productive maintenance
Machine maintenance is generally contracted outside in Valencia's SWDs. The global score for this variable was 2.083, which is not very high. Maintenance is never delegated to the operators who handle them, independent of the type of disability.
This fact, together with those described in section 4.4.14, confirms the perception that there is a lack of confidence in the disabled workers, and it is not usual to delegate complex tasks to them.
4.4.10. Standardized operations
There is a moderate concern on standardized operations. From the three questions related to this issue, only the one related to the participation of operators in the standardization process (1.958 points) gets a low score.
The ANOVA analysis indicates certain relationships: work is more standardized in companies that schedule the production and have a high number of employees with fixed contracts, and obviously in companies that offer standard products (Table 7).
Results of ANOVA for the Tool “standardized operations”
4.4.11. “Five S” and visual factory
The importance of tidiness and cleanliness is very high (3.740 out of 5). This confirms the evidences that the authors found in some previous experiences with Valencia's SWDs (Miralles et al., 2003b). In a more detailed analysis, 5S is more frequently used in companies assembling products, not exporting, and with customized products.
However, Visual Factory techniques, very similar to 5S, are hardly applied, with an average score of 0.561 points. In fact, most SWD managers have never heard of these techniques.
4.4.12. TQM – total quality management
The questions about “Total Quality Management” were divided into three areas: involvement of the management staff; involvement of the operators; and use of statistical control for processes. The scores in the first two areas are high: 3.563 and 3.349 points, respectively. However, the use of statistical control is low. This may be due to the lack of statistical knowledge and training of most SWD managers. In terms of significant interactions, TQM is more widely used in those companies that have certificates of quality, those offering standard products, dealing with unpredictable demand, with many employees, assembling products and scheduling production. Most of these direct relationships are logical.
4.4.13. Knowledge management
Knowledge management techniques are hardly ever used in SWDs (1.302 average out of 5). In SWDs with mentally disabled employees, knowledge management approaches were not possible due to their intellectual limitations. The size of most Valencian SWDs could be another reason for these low figures.
4.4.14. Human resources in manufacturing
The answers given to questions related to human resources management in the area of manufacturing have been globally high, with 2.659 points out of 5. In particular, the questions related to direct communication between management and employees have better scores (4.594 points and 4.0625 points).
By contrast, those questions evaluating the responsibility delegated to employees have lower scores (1.490, 1.510 and 0.667). Therefore, we can conclude that there is good communication but the decisions follow a top-down approach in a typically hierarchical atmosphere.
4.4.15. Supply chain management
The answers to questions related to the supply chain have an average of 2.597 out of 5. But when the question refers to whether they establish long-term relationships with customers, the score is 4.219. This is because many of Valencia's SWDs depend on one company, which is often their only customer with a strong dependence.
In this area, the ANOVA showed that companies offering customized products, with mentally handicapped workers, that schedule the production, and those that were founded a long time ago, have a closer relationship with customers and suppliers.
4.4.16. Variable salary
Valencia's SWDs do not usually employ variable salary approaches to reward and motivate their employees. The average score is 1.313 points out of 5.
4.4.17. Staff training
The SWD managers have shown concern for employee and team leader training. The result has been an average of 2.932 points out of 5, those companies with advanced technology being more involved (which is logical, since to handle this technology more qualified and advanced training is required and renewed periodically), and those that produce on demand (employees should be better trained to meet the challenges of the market) (Table 8).
Results of ANOVA for “Staff training”
In addition, like with other tools, larger companies better know and apply the theoretical knowledge on lean production tools, mainly because they have more funding for training courses.