Comparison with the previous iteration

The findings relating to: “How are efforts to manage strategic

performance of supply chains affected by workflow automation”

summarized above covers aspects that are considerably different from the ones discussed in the first iteration of the AR cycle.

One of the possible reasons for this is the difference in the scope of the interventions in these two AR iterations. While the scope of the first AR iteration is the entire supply chain, the second AR iteration directly involved only a selected group of staff engaged in customer order promising and production planning, of a manufacturer and a selected population of suppliers. Data collection in the first AR iteration is based on evidence obtained form a relatively larger population in the supply chain, whereas the data collected in this AR iteration drew on evidence from only one organization perspective i.e. manufacturer.

Another possible reason for the contrast in the findings is the difference in the iteration focus. While the first iteration is mainly exploratory, this iteration is guided by Gowin’s Vee heuristics. This leads to the iteration being conducted with more rigor and focus, in that the findings are more specific and has greater depth. Also, in the first iteration, much effort was expanded on linking the performance variables to quantitative financial indicators.

Nevertheless, findings in this AR iteration are generally consistent with those in the first AR iteration. The decrease in enabling cost, for example, is consistent with the perception by workflow participants in the first AR iteration of a decrease in the time spent in making informed decisions. Moreover, the findings in this AR iteration provided a better insight into how supply chain communication efficiency gains can be obtained, pointing to a combination of decreases in conversational ambiguity, participation cost and disruptions as the main causes.

The suggestion that there is an increase in the number of workflows is also consistent with the findings in the first research iteration, but the main cause is different. While in the first AR iteration communication efficiency gains are seen as the main cause, here it is the decrease in the demand for leadership skills, which appears to play a major role. I believe these two findings to be complementary regarding primary causes, and confirmatory as regards the higher-level effect that workflow automation is likely to increase the number of workflows per unit of time in the context of strategic performance management of supply chains.

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An exception to this general consistency in the findings across iterations seems to be the identification, in this iteration, of an increase in the threats to management brought about by “employee empowerment” as a result of workflow automation.

In the first research iteration, however, I see a remarkable increase in the reliance on workflow automation. Are these findings contradictory? Apparently, they are. On the other hand, it appeared that the remarkable increase in the actual number of interactions observed in the first iteration may have been strongly influenced by the fact that SAM chief operating officer championed and provided unconditional support to the workflow drive. Although there is insufficient evidence to support this assumption, the influence of the chief operating officer may account for these seemingly contradictory findings.

5.9.

Chapter summary and concluding remarks

This chapter reports on the second iteration of the AR cycle whereby a pilot study is completed. The study is conducted at MAK, a wholly owned Japanese MNC (Multi National Corporation). The study scope covers the customer order processing and production planning events. This iteration is completed in 4 months. A feedback from the staff indicates that drastic improvements in the perceive efficiency and reliability of the order planning and promising has been achieved at MAK.

This AR iteration leads to the identification of 5 main dependent

variables affected by workflow automation. These variables are workflow

set up time (i.e. Team formation for collaborative workflows) and enabling cost for the network unit of analysis, degree of interactionand demand for Leadership skills for the entity unit of analysis and individual influence for the individual unit of analysis

The study suggests that the workflow automation caused a decrease in the demand for leadership skills, in enabling cost, and in degree of interaction. The study also indicated that workflow automation lowers barriers to Interdepartmental communication. This favors the occurrence of new virtual teams (i.e. workflows) involving different departments. On the other hand, the study indicates that these virtual teams tend to be perceived as bringing about more security threats to management, which can induce negative reactions from managers. Finally the study suggests that while strategic performance management of supply chain benefit from workflow automation, this is likely to be achieved as a result of a

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combination of workflow automation and other types of interaction including face-to-face and other verbal or written communications.

The findings presented in this research iteration may have been weakened by the fact that the study focus only on one unit of analysis i.e. entity. The results may have been distorted by idiosyncratic characteristics of both MAK (as entity) and the two events (i.e. customer order processing and production planning) being studied and by my own involvement. Therefore, the models presented should be understood as interpretive aids to be used as a basis for further research, as opposed to tentative generalizations of what is likely to happen in organizations.

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