Table 7: Results of the Offered New Product Problem
f g 1.5
c i 1
Figure 4(a): Deduction Graph of Department A
c j 1
Figure 4(b): Deduction Graph for Department B
5.0 CASE REFLECTION
This section discusses the feedback obtained from the CEO and three managers (A, B and C) of Company SPEC about the application process of the analytic infrastructure. The applicability of the proposed approach was evaluated based on the criteria of feasibility, usability, and utility (Platts, 1994).
The infrastructure was evaluated as feasible on overall. The CEO stated that the required information for this analytic method was appropriate. The information is real and relevant. The timing of the application process was appropriate; it only takes half a day to finish. Manager C pointed out that one of the many benefits attributed to the analytic process was the insight gained into the problem that was being modelled. He further said that “in the process of model building, we are forced to ask questions that may never have been asked and to examine the information generated from big data analysis”. However, Manager B felt that a longer time scale would be needed if they would like to look into the model in more depth and examine the developed competence network in detail.
The infrastructure application was rated as quite clear. The outcome of the testing shows that the combination of data mining technique with deduction graph was useful. The participants felt that the proposed structure and network rules provided a useful guidance for building a useful competence network model. In term of ease of use, the analytic infrastructure was rated as easy to understand. The CEO commented that ‘the mathematical property takes some time to understand but okay afterward’. Manager A felt that the process was very useful for them to develop a ‘competence network’ from a firm perspective. He further pointed out that ‘each of us sees the factory operations through a unique set of lenses that is determined by our personal experiences, and our capabilities. Thus, none of us, as part of a functional group, have a good understanding of the competence sets entirely’. All the participants agreed that the process was appropriate and they had high confidence in the decision reached.
The utility of the proposed infrastructure was rated highly by the managers. The CEO commented the proposed technique and process helps Company SPEC to make use of the information generated from big data to offer new insights into product development and operations improvement. This method can be widely used in manufacturing operations, and the CEO has great interest to continuing applying the proposed analytic infrastructure in Company SPEC.
In general, all participants felt that the process provided a structured approach for decision making and the competence network helped to illustrate the competence set expanding process vividly. The application of the analytic infrastructure in Company SPEC indicates that the method has high feasibility, utility and usability. As the case was conducted in an eyeglasses company, it indicates that it was feasible for applications in the manufacturing settings. The CEO described it as ‘a road map that provides many alternative ways to arrive at the destination’. The feedbacks also highlighted a number of research issues that remain to be addressed.
This section discusses the results of this work, and the wider implications for managers and academe. The ﬁndings are grouped and evaluated under two main areas: the value of an analytic infrastructure; and the value of competence network.
6.1 The Value of an Analytic Infrastructure
Big data analysis is far too frequently carried out relatively informally and generated vast amount of ‘isolated’ information. Managers might spend signiﬁcant time to make sense of the analysed information. This often occurs in an ad hoc way based on the manager’s past experience. This is understandable; faced with complexity, and the need to act, managers will tend to seek the comfort of the known (Tan and Platts, 2003). An analytic infrastructure provides a mechanism for combating this tendency. Our research shows that managers liked the structured approach that enables them to develop a visual decision path that captures the logic behind the variety of decisions made over the course of the competence set analysis process. Although the speciﬁc problem might be unique, they felt reassured that an approach to addressing it was well known. The CEO commented that with the analytic infrastructure, Company SPEC can utilise the full potential value offer in the big data analysis.
6.2 The Value of Competence Network
The main finding of this research has been the development and testing of an analytic infrastructure. This method combines deduction graph and data mining techniques. The combination can overcome the shortcoming of both methods. The existing data mining technique is useful to discover unknown information, but it cannot totally address the supply chain problems. Although such techniques might help managers to produce a lot of information, they are unfocused, and hence inefficient. A lot of effort and time is needed to sort out the information generated and to identify those that are relevant and viable. Therefore, instead of just generating vast amount of information using existing data mining software, managers need a better approach to structure, and link various stream of data to create a coherent picture of particular problem – so that a better insights into the issue being analysed could be gained. The proposed analytic infrastructure shows the interrelationship of different competence sets visually, so the decision-makers have the clear view about the expansion of the competences sets. The analytic infrastructure is efficient to support decision making by offering managers more alternative choices and suggesting the optimal expanding process of incorporating a company’s own competence sets with others.
Thus, although the term ‘big data’ is not new, the application of big data in supporting supply chain operations is a relatively new area (Cecere, 2013; Zhou et al., 2014). The case study results indicated that the proposed approach enables Company SPEC to: a) gain new product development ideas; and b) understand how different sub-firms or departments could work together to optimise the manufacturing processes and to produce new products in the most cost effective way.
We have demonstrated how the proposed infrastructure gives integrated support throughout the process, providing a more comprehensive functionality than is provided by the existing data mining or deduction graph model approaches discussed earlier in this paper. The deduction graph model captures and interrelates different competence sets, providing a comprehensive view of the firm capabilities for strategic analysis. It provides a proven way of eliciting and quantifying the relationships necessary to use the information harvest from big data. Using this analytic infrastructure, managers can model different supply chain operations and product development decisions and use the results to aid in supply operations strategy decisions as well as enhancing innovation capabilities.
To our knowledge, this is the first attempt that incorporated the big data analytics and applied it in a synergistic fashion with the deduction graph technique. The evidence provided in this paper reveals the promise of this combinatorial approach, which we believe is worth further developmental efforts from big data and supply chain operations management scholars.
While the proposed approach is potentially useful there are a number of research issues that remain to be addressed. Ongoing refinement and improvement is a fundamental component of valid research. First and foremost is to test the approach on a wide variety of product designs and supply chains in order to determine the general applicability of the approach. The second issue involves the assumption that each decision maker can freely exchange information and is willing to purchase and sell competencies at prescribed prices. The last issue is that the mathematical approach to acquire the optimal results is quite complex and tedious. Thus, future research should be carried out (for example, a software) to simplify the deduction graph computation.
The author would like to thank Miss Fan Chen for her help in the case study data collection and analyses. The authors also want to thank Nottingham University Business School Spark Fund for the support of the research.
Property 1: All the are 0 or 1 integer and the objective equation should subject to:
Property 2: , So applying it in the eyeglasses manufacturing company should be:
Based on [Bf(k)] ≤ , applying to the company should be :
Property 3: the objective equation should be subject to all the restrictions. And we assume that the budget for both departments is 6. It will show the restrictions applying for department A and B as follows.
Because the skill c, d and e are the existing skills of department A, subject to:
Because the skill a, b and f are the existing skills of department B, subject to:
Due to the cost should lower than the budget, subject to:
Notable,are binary variable which is whether be 0 or be 1. Also, should be less or equal than 6 and are integers.
Lam, C., 2010. Hadoop in Action. Manning Publications Co. Greenwich, CT, USA.
LaValle, S., Hopkins, M.S., Lesser, E., Shockley, R., Kruschwitz, N., 2010. Analytics : the new path to value. MIT Sloan Management Review. 52 (1), 1-22.
Li, H. L., 1997. Incorporating competence sets of decision makers by deduction graphs. Oper. Res. 47 (2), 209-220.
Li, J. M., Chiang, C. I., Yu, P. L., 2000. Optimal multiple stage expansion of competence set. Eur. J. Oper. Res. 120 (3), 511-524.
Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., Byers, A. H., 2011. Big Data: The Next Frontier for Innovation, Competition, and Productivity. McKinsey Global Institute, San Francisco.
Manyika, J., Chui, M., Groves, P., Farrell, D., Kuiken, S.V., Doshi, E.A., 2013. Open data: Unlocking innovation and performance with liquid information. McKinsey Global Institute, San Francisco.
McAfee, A., Brynjolfsson, E., 2012. Big Data: The Management Revolution. Harvard Business Review. 90 (10), 60-68.
Mervis, J., 2012. Agencies Rally to Tackle Big Data. Science. 336 (4), 22.
Minelli, M., Chambers, M., Dhiraj, A., 2012. Big Data, Big Analytics: Emerging Business Intelligence and Analytic Trends for Today's Businesses. John Wiley & Sons Inc, Hoboken.
Mishra, A.A., Shah, R., 2009. Inunion lies strength: Collaborative competence in new product development and its performance effects. J. Oper. Manage. 27 (4), 324-338.
Mishra, S., Modi, S.B., Animesh, A., 2013. The relationship between information technology capability, inventory efficiency, and shareholder wealth: A firm-level empirical analysis. J. Oper. Manage. 31 (1), 298-312.
Mohanty, S., Jagadeesh, M., Srivatsa, H., 2013. Big Data Imperatives: Enterprise Big Data Warehouse, BI Implementations and Analytics. Apress, New York.
Oh, L., Teo, H., Sambamurthy, V., 2012. The effects of retail channel integration through the use of information technologies on firm performance. J. Oper. Manage. 30 (1), 368-381.
Ohlhorst, F. J., 2012. Big Data Analytics: Turning Big Data Into Big Money. John Wiley & Sons, Hoboken.
Ozer, M., 2011. Understanding the impacts of product knowledge and product type on the accuracy of intentions-based new product predictions. Eur. J. Oper. Res. 211 (2), 359-369.
Platts, K.W., 1994. Characteristics of Methodologies for Manufacturing Strategy. Int. J. Oper. Prod. Manage. 13 (8), 4-17.
Sakr, S., Liu, A., Batista, D.M., Alomari, M., 2011. A Survey of Large Scale Data Management Approaches in Could Environments. IEEE Comunications Surveys and utorials. 13(3), 311-336.
Schmenner, R.W., Vastag, G., 2006. Revisiting the theory of production competence: Extensions and cross-validations. J. Oper. Manage. 24 (6), 893-909.