A variety of studies have responded to the issues surrounding productivity in the manufacturing environment, including the printing industry. With lean manufacturing’s quest for waste elimination approach, reduction of lead times; elimination of waste and improvement of business performance are realised ([7]; [1]). [19] argued that adopting lean manufacturing significantly reduce no-value adding activities. Lean manufacturing is a common practice in manufacturing in pursuit of competitive edge and being responsive to customer demands of high-quality printed materials ([26]). A variety of lean tools are used to enhance performance of manufacturing organisations as argued by [29], however, the lack of published material related to lean manufacturing in the South African printing systems remains a concern. Those lean tools include single-minute-exchange-of-die (SMED); 5S; cause and effect diagrams; value stream mapping (VSM); kaizen; total quality management (TQM) and total productive maintenance (TPM). Printing systems' survival and success are dependent on improved turnaround times which has a positive impact on overall cost. Through its focus on reducing change over time in a production process, SMED shorten lead times for printed materials ([12]). Reducing set up time enables meeting customer expectations and increasing machine availability as argued by [14]. The use of older conventional machines presents a disadvantage whenever a changeover takes place due to extended times taken to complete such a change over ([22]). It is for this reason that manufacturing organisations are adopting the SMED technique to reduce change over time. A variety of authors argue that 5S is a philosophy focusing on simplifying work environment; effective organisation of the workplace and reduction of waste, improving safety and quality ([3]; [37]). Through its five-step approach, 5S was implemented to make workplace tidy. 5S is used as a business improvement tool irrespective of the size and organisational sector where employee involvement is of significant importance to successfully implement this approach and prevent it being regarded as just a “spring cleaning” attempt ([5]). To achieve effective, efficient and organised work centres and improve productivity; reduce costs and workplace accidents; improve quality standards, printing departments could use 5S.

Formalising the application of the famous Five Why’s method resulted to root-cause-analysis (RCA), serving as a foundational paradigm of widely used and highly refined business processes with high process maturity ([2]). RCA is a systematic approach used to identify root causes of specific events ([18]; [4]). [15] suggested that several tools that have emerged from the literature as generic standards for identifying root causes include cause-and-effect diagram. Cause-and-effect diagram provides an orderly way of looking at effects and the causes that contribute literature effects ([38]). Listing all possible causes and effects on the diagram and then focusing on each cause per category until all of them are dealt with, define the origin of the problem such as low productivity. VSM has become one of the most popular and widespread development tools within the lean community as a system level tool that maps material and information flows within an organisation or through the value chain, analyse and eliminate waste in manufacturing to ensure a smooth flow of operational processes ([11]; [21]). The use of VSM has enabled lean manufacturing to contribute to the attainment of printing division’s objectives which include profitability; efficiency; customer satisfaction; high quality products and responsiveness. [41] further suggested that VSM assist printing systems address sustainability challenges that they are facing due to the growth in customers’ demands. In times of innovation, rapid economic development and desire for competitive edge, printing departments try to improve quality of printed materials, optimise printing processes and reduce costs associated with running a printing department. As a philosophy that seeks to continuously improve, kaizen could be used to solve challenges related to work through its support of employee involvement. Customer satisfaction is an important concept in marketing and consumer research and it is common opinion that if customers are satisfied with their printed materials, they are more likely to continue printing with the same printing department. Kaizen methodology has a number of related and often overlapping practices which include TPM; just-in-time; elimination of seven wastes; automation; plan-do-check-act (PDCA) cycle and poke-yoke ([42]). TQM is an organisational approach that began in the 1950’s and has consistently turned out to be better known following the mid 1980’s ([36]). TQM can be defined as an approach to managing organisations which emphasises continuous improvement of quality and customer satisfaction, entails the application of tools and approaches for managing organisational processes ([36]). Through the adoption of TQM, a printing department could achieve customer satisfaction; employee involvement and continuous improvement ([31]). Evolutions in the business world have made quality a priority for the individual manufacturing organisations, including printing departments, resilience and competitiveness. [17] argued that printing departments should undertake TQM as a journey rather than a destination in pursuit of customer satisfaction, enabling continuous improvement which will continuously influence customer satisfaction levels. Like other organisations that are enjoying TQM benefits, the printing department could also strengthen its performance and competitive advantage by adopting the philosophy ([25]). As a result of global competition and ever-changing customer demands, many printing departments have been driven to enhance their performance through effective management programs such as TPM. To improve their competitive position globally, more printing departments are turning to proactive solutions like TPM which plays a pivotal role in enhancing global competitiveness.

The printing department has a variety of machines needed to complete printed products which include desktop publishing; printing plate-making machine; printing machines; finishing machines and dynamic packaging machines. These machines require good care through proper maintenance so as to improve the integrity of production and quality systems. As a lean tool, TPM has a positive impact on the printing processes ([8]). For it is implemented by all employees, management and operators on the shop floor are involved and allowed to contribute towards the printing department. Total preventative maintenance focuses on the participation of all employees to implement a comprehensive maintenance program for all equipment throughout their life ([10]). [35] suggest that creating strong partnership for change between maintenance and production teams by analysing and removing unnecessary maintenance procedures; developing standard counter measures to common problems; reducing the time to repair as well as engaging operators in the asset care could have a lasting positive impact on productivity of the printing department. TPM could further increase productivity level; decrease defects and clients claims; decrease labour and maintenance costs; reduce inventory levels; eliminate environmental safety violations and increase employee involvement. Eliminating 70% - 75% of downtime associated with equipment failure help increase productivity by 20% - 25% ([28]; [9]). The opportunity cost of lost production; extension of fixed costs; cost of maintaining an increased number of spares as a result of the failure and the cost of maintaining redundant equipment to mitigate the effects of possible lost production represent economic consequences of a printing system failure.

Table 1 presents that 38% of the participants hold a matric or below followed by 31% of the participants who hold a diploma. Results further showed that 13% of the participants hold a university degree with another 13% holding a postgraduate qualification. Participants were spread across a variety of occupations including senior management service (SMS) accounting for 2.1%; middle management accounting for 4.2%; junior management accounting for 2.1%; artisan foreman (Team Leaders) accounting for 8.3%; artisans accounting for 32.3%; printers’ assistants accounting for 46.9%; material planner accounting for 2.1% and printing planner accounting for 2.1%.

Table 1. Demographics-level of education and occupation.

Furthermore, the results revealed that 51 participants have 11 or more years of work experience while 26 participants have 6 - 10 years of work experience as presented in Table 2. Participants with 4 - 5 years of work experience and 2 - 3 years of work experience accounted for 18% and 5% respectively. More participants of the study work within the Branch: Manufacturing accounting for 72.2% with Branch: Operations accounting for 20.6%. The most contributing division was Origination with participants accounting for only 4% of the total study participants as presented in Table 2.

Table 2. Demographics work experience, branch and division within the department.

A maximum of 83% of the participants defined productivity as the ratio of output to input.17% of the participants did not know productivity by definition as presented by Table 3. 75.8% of the participants believe that the department measured productivity with 24.2% suggesting the department doesn’t measure productivity. From the 75 participants that believed the department measured productivity, 45.3% rated the quality of measurement “good” and 32% of those participants rated the quality of measurement as “fair” as presented in Table 3 below. Responses of 34%, 28.9%, 10.7%, 17.6% and 6.3% of the respondents suggested that the department selected suppliers based on the price, quality, lead time, dependability and flexibility, respectively, denoting that price of service is perceived the decider in choosing service providers. 36% of the participants postulated that the department sometimes participates in productivity improvement programmes while 29% of the participants believed the department rarely participates in productivity improvement programmes as presented in Table 3.

Table 3. Productivity knowledge.

Only 54% of the participants suggested that machines were running at an optimum level as presented in Table 4. Customers and suppliers play a vital role in enhancing the department’s productivity. 58% of the participants indicated that customers understand the printing process, while 43.4% of the participants imply that “sometimes” suppliers deliver raw materials on time with only 16% suggesting raw materials were “always” delivered on time. 10% of the participants indicated that customer orders were “always” completed on time. 43.4% of the participants suggested customers amended their orders during production. 61% of the participants believe the department has sufficient capacity to improve productivity.

Table 4. Operational capability.

A variety of lean tools could be adopted to improve productivity of the department. The study identified SMED as the lean tool with a significant relationship with printing machines operating level as supported by a significance level of 0.013 depicted in the Chi-Square test result in Table 5 ([30]).

Table 5. Chi-Square test results.

Exploratory factor analysis is defined as a data screening tool used to reduce a data set to smaller variables and to establish interrelationships between a set of variables ([30]). To establish construct validity and determine factors affecting productivity, a principle components analysis was carried out. Exploratory factor analysis was conducted by means of Kaiser-Meyer-Olkin (KMO) and Bartlett’s test of Sphericity. The retention of components was decided upon using the Kaiser’s eigenvalue of greater than 1 criterion which specifies that all components with eigenvalue above 1 are retained in the solution. Exploratory factor analysis was conducted on lean tools; lean tools’ usefulness and lean activities a company can perform. The KMO measure of sampling adequacy is 0.802 which is considered “very good” for proceeding with factor analysis ([39]). Bartlett’s Test of Sphericity shows Chi-Square value of 4341.554 with 861 degrees of freedom and significance level of 0.000. To indicate that the correlation matrix is not an identity matrix and that there are significant correlations among the variables, the p-value is less than 0.05 as presented in Table 6.

Table 6. KMO and Bartlett’s test.

The amount of variance in each variable that is accounted for by the extracted factors is described by the communalities. Initial communalities were set at 1.000 for each variable, indicating that all variables’ variances were assumed to be accounted for by the initial solution. To indicate how much the variance of each variable can be explained by the factors extracted in the analysis, the extraction communalities vary. Values ranged from 0.406 for Q28_14 suggesting that less than half of the variance in this variable was explained by the extracted factors to 0.909 for CC4 demonstrating a high degree of explained variance. Indicating that more of the variance was captured by the model, reflecting better suitability of the data for factor analysis, higher values were preferred. Furthermore, total variance explained by each component was derived from a principal component analysis. Total variance that each component explains before extractions represented by the eigenvalues. A clearer understanding of the variance accounted for by each component after rotation is provided by the rotation sums of squared loadings which help interpret components. The first component at 27.37% accounts for the largest share of variance with subsequent components accounting for progressively less variance percentage as presented in the scree plot below (Figure 1).

Figure 1. Scree plot.

The rotated component matrix from a principal component analysis with varimax rotation detailed how various items load on the 5 identified components. By presenting the relationship between each variable and prospective underlying factor they represent, rotated component matrix interpreted the factors. Component1 dominated by variables related to lean tools with high loadings ranging from 0.782 to 0.917, indicating a strong relationship of these variables with this component. Component 2 dominated by items related to lean tools’ level of usefulness with loadings ranging from 0.674 to 0.934 suggesting a strong contribution to the explanation of usefulness of these lean tools. Component 3 linked with lean activities a company could perform to improve on its performance where variables like Q28_12 have a loading of o.827 representing a strong correlation with machine performance related lean activities the department can perform to improve productivity. Component 4 with loadings ranging from 0.630 to 0.788 represent significant impact of lean activities on productivity improvement. Finally, component where variable like “investing in skills development” have the highest loading at 0.784 indicating a strong correlation with lean activities the department can perform to improve productivity. By making the loadings more distinct across components, the varimax rotation enhanced the interpretability enabling a clearer distinction between what each component represents. This study used construct validity to ascertain the degree of correlation between the decision variables with other measures that were theoretically predicted. Cronbach’s Alpha was used to measure decision variables’ reliability ([30]). The 3 coefficients extracted under the lean manufacturing scale, which were lean tools’ usefulness; lean tools and lean activities, had acceptable alpha values of 0.949, 0.965 and 0.948 respectively, indicating sufficient reliability and suggesting a strong relationship between the variables used in the security documents printing. Logistic regression analysis was conducted to examine the influence of lean tools on the probability of productivity improving. Results from the analysed data suggested that lean activities significantly impact productivity, either negatively if not applied or positively if applied. With Chi-Square equalling 38.926, df equalling to 8 and p < 0.001, presented by Omnibus test of model coefficients table below, the model is statistically significant.