PurposeThe purpose of this paper is to design a research model and analyze the relationship between open innovation and cleaner production. The paper maps and characterizes the conditions of open innovation against cleaner production in Indonesian batik small and medium enterprise (SME), particularly in Java and Madura. The mapping process is executed by classifying the batik SME into four quadrants. The diagram is a quadrant in which there are four parts to distinguish each of the ability of batik SMEs in understanding and achieving cleaner production through open innovation. This research will obtain a new method or model that can be applied by organizations to achieve cleaner production through an open innovation. The data is obtained from 182 batik SMEs located in Laweyan, Madura and Lasem (in Java Island, Indonesia).Design/methodology/approachOne of the problems in batik SME is the waste management from the dyeing and wax removal process. In the first stages of this research, a number of initial models were elaborated as a reference, then the results of the elaboration became a new research model. The research model that has been produced is then tested using data from respondents. Based on the test results, the model can be stated valid or not. In this study, the model is valid after testing data from 182 respondents, because all outer loading for all indicators is above 0.7. The composite reliability and AVE values of all constructs were above 0.7 and 0.5. Based on the validated research model, the data is statistically processed by using the Structural Equation Modeling (SEM). By using the SEM method and statistical software SMART PLS 3.0this research can be supported to achieve the research objectives.FindingsBased on data testing and processing, open innovation climate could predict a sustained relationship to open innovation with an accuracy rate of 0.466 and influence rate of 0.427, whereas open innovation could predict a sustained relationship to cleaner production with an accuracy rate of 0.183 and influence rate of 0.324. The relationships between open innovation climate and open innovation; including open innovation toward cleaner production, are statistically significant because all prediction values and accuracy in the model have met the criteria for measurement parameters based on the value of R2, p value and T-statistics to be stated as a significant relationship.Research limitations/implicationsThis research provides an overview of the influence and importance of open innovation in creating an environmentally friendly production process in the context of cleaner production. Cleaner production on batik SMEs can be achieved through open innovation, both for inbound open innovation and outbound open innovation. Open innovation comprehensively provides support for batik SMEs in achieving cleaner production. Open innovation can be run well and optimally if it gets support from a conducive climate open innovation. Furthermore, the implementation of cleaner production could be a guideline for the owner to minimize the waste from batik SME production, both for natural and synthetic dyes. Some limitations in these study include the absence of influence from the existing stakeholders on batik SMEs on the implementation process of open innovation; the use of the cross-sectional approach that results in the unavailability of further analysis regarding the dynamics or improvements that occur in attaining cleaner production through open innovation; and finally providing no analysis of the differences in characteristics at each location of batik SMEs.Originality/valueThe implementation of cleaner production model is considered as one of the new methods and references in conjunction with reducing the negative impact of waste toward the environment, particularly in the traditional textile industry which is limited in waste management capability.

Purpose The purpose of this paper is to develop and to empirically test a model that explains how managing differences between an information technology (IT) provider and an overseas client influences partnership quality and ultimately affects the continuity of the relationship. Design/methodology/approach A field survey by distributing questionnaires to Indonesian IT providers was conducted over four months, yielding 78 completed responses. These empirical data were analyzed by the partial least squares–structural equation modeling technique to examine the measurement and structural models. Findings Managing differences, i.e. cultural, temporal and standards differences, has a positive impact on partnership quality through inter-firm interaction, i.e. information exchange, coordination and participation. Partnership quality, consisting of the dimensions of commitment, trust and integration, has a substantial positive impact on the continuity of the relationship. Research limitations/implications This study was limited by the use of a limited number of samples, reducing the precision of the results. Practical implications This study suggests that if the IT provider is able to manage the cultural, temporal and standards differences with the overseas client, it increases information exchange, coordination and participation between both parties, which are necessary for establishing a high-quality partnership. Originality/value This study is the first empirical examination of how the management of differences between an IT provider and an overseas client influences the continuity of their relationship through interaction and partnership quality.

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All production processes produce variance around the desired target value of quality characteristic. This variance affects the product quality level. Accordingly variance reduction needs to be done as the main goal of quality improvement programs. However effort to improve quality of each product unit must take into account to improvement costs. This paper proposes an optimization model for quality improvement in multi-stage processes using a non linear programming model by selecting alternatives process and determining unit of production of each stage to maximize profit as the difference between total income and total relevant cost. Total cost includes manufacturing cost, quality loss cost, rework and scrap cost, and quality improvement implementation cost. This optimization model is implemented in make-to-order manufacturer that produces crimper (a parts of joining plastic packages in packaging machine) which consist of five main stage manufacturing processes. Sensitivity analysis shows that the optimal solution is not sensitive if little changes occur in the constraints scenario. Thus, adding the value constraint on the quality specification, stage capacity, and quality improvement budget will not improve the objective function.

Abstract. Standard Shewhart process control chart has been widely used, but it is not sensitive in detecting small shift. A number of alternatives have been proposed to improve the capability of control chart. The double sampling (DS) control chart is aimed at improving the capability to detect any small shift out-of control condition by observing the second sample without interruption. The capabilities of DS control chart were measured as the expected sample size (as a measure of inspection cost) or the control chart power (as a measure for customer risk). Optimization of these criteria is used to determine the control limits. In this paper, we optimize both producer and customer risks under a certain expected number of sample sizes as the constraint. Comparing the result to the previous procedure that only optimize customer risk, the proposed optimization procedure gives the same first control limit but smaller second control limits with higher value of control chart power.