7.1 Conclusion
The aim of this research was to design a new maturity model where the purchasing function or department was combined with integrated Big Data applications. As a result, there was a new Big Data Purchasing Maturity model designed, with an attached scorecard for determining the score on the curve. Since Big Data is an upcoming trend among companies and there are less known applications of Big Data in relation to purchasing, the literature review and designed model had a more explorative background. Therefore, there are less
best case scenario’s implemented in the research. The stated main research question and sub research questions are based on the problem as detected and mentioned by Bright Cape. The (sub) research questions will be answered as follows:
“What is the current situation of Big Data in purchasing?”
Based on the explorative research, there is a little to no integration of Big Data in purchasing. The literature review shows the initial steps for possible integration options, but there are not any best practice cases written. In addition to that, based on the internal interviews, there are some examples given about the first steps which are taken to look at data by process mining and with purchasing dashboarding. Hence, both Big Data integration into a company and a strategic purchasing function are recently in strong evolvement. Therefore, it is assumed when combining both parts of Big Data and purchasing, and implementing the integration in the corporate strategy, that they can reinforce each other strongly and strengthen the company.
“How does a purchasing maturity level relate to a Big Data maturity level?”
When analysing the given maturity models, related to Big Data and purchasing, there are many similarities. Despite the differences among the maturity models related to Big Data or to purchasing itself, the structure is based on the same guidelines. The first stage is mostly related to an initial or basic phase, followed by the first steps of implementing and structure the processes. The third step is mostly defined to standardised descriptions and goes on the curve to automotive processes and ends on the curve with a predictive and fully integrated process. It is model specific to define a certain number of steps, where four or five are the most common chosen.
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“How are the different steps designed and specified in the new Big Data Purchasing
Maturity model?”
The different steps are designed in the Big Data Purchasing maturity model, as stated in appendix X. There are four different stages defined, where the first stage is related to an initial and/or pre-mature phase for applications of Big Data and purchasing as a supplementary function. The second stage is based upon a situation where the person or position is skilled to perform a more strategic purchasing task, which is related to Big Data. The third stage is defined to a more standardised process, as organised in a roadmap. There are applications of communication among machines. Finally, the fourth and ideal stage is designed as a fully integrated and autonomous decision-making system, driven on Big Data and is aligned with the corporate strategy. The eight different dimensions are the foundation of the topics which are in the model, for covering all important subjects. These dimensions build on the eight dimensions of the Industry 4.0 Purchasing Maturity model.
7.2 Limitations and further research
This research looks into a wide area of different theories and practices, however, there obviously is a chance that the information is already outdated in the area of data science. In the last couple of years, the development is at such a high rate, that it is hard to keep it updated with the most recent information, what could be mentioned as a limitation for the designing part of the maturity model. Next, the overviews of maturity models show that the models of purchasing are in another time span than the Big Data models. Thus, the purchasing function has to be developed in the last decade, but unfortunately that is not seen in the models. Further, another limitation of this research has to be mentioned that the background of the interviews is too abstract. For a deeper and more complete view into the purchasing department, the variety of respondents should be more extended in the next research. At this moment, a limited number of respondents can give a biased view. Additionally, the respondents should be separated in, for example, private or public companies, or to globally and local companies et cetera. Then the analysing part is more related to the companies operating according the same rules and options. Now the governmental rules are an important issue in the discussion and analysis. Additionally, the respondents of the interview are held with employees working on different levels in the organization. That can be a limitation to the experience and own view on the purchasing department.
77 Another limitation about the research is the umbrella terminology of Big Data. For the research it is crucial to define the threshold when there is Big Data in the process or it is just normal data. In almost all the literature about Big Data, and there is many literature, none of the researchers give even global guidelines about the volume of data. To make sure the literature is not outdated, the same threshold is tried to detect among professors and researchers at the University of Twente. More than 20 skilled persons react with the fact that there is no threshold and it is not able to have one. Therefore, it was really hard to work further on this point and results in a limitation of the model with Big Data integrations.
For further research should be suggested to update or improved the newly designed Big Data Purchasing Maturity model. Now, the model is primarily based on people with knowledge and experiences in Big Data or in purchasing, but mostly without combining knowledge or experiences. For further processing, it is suggested that the research include respondents with experiences and knowledge of both parts and hopefully with best case scenarios. When more companies involve their best practices, and with more companies in different industrial areas, the model can be more specified and with increasing details.
During this research, the final model is not tested among the respondents or other companies, because of the short time frame. For further research, the model have to be tested among the external respondents, and see if they have the same score as they assumed during the interviews. Additionally, the researcher can also fill in the Big Data Purchasing Maturity model, based on the given answers during the interviews. As a double check, both answers can be compared and see what the similarities and differences are. Some influencing factors of the result could be the different knowledge level about both Purchasing and Big Data, the experience with maturity models and the development of the company. It could be helpful to use the internal and external respondents, since their answers are part of this new Big Data Purchasing Maturity Model. The internal respondents are also able to fill in the model, since they can give insights about the different levels in relation to Big Data integrations.
Finally, in further research it could be possible that there are the coming years barely people with best cases about Big Data and Purchasing together. In that case, some methods as a world café, brown paper session or a brainstorm session with people from both functions together, will create the dialogue underneath. That can also result in some more useful details and developments for the model.
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