A case study was conducted to evaluate the applicability of the proposed approach in Company SPEC, a leading eyeglasses manufacturer based in China. Recently, in light of the hype of big data potential value, Company SPEC had established a unique information management department to collect and analyse different source of data: a) existing customers’ preferences and characteristics; b) videos and photos of available eyeglasses products; and c) social media (i.e. tweets, google, Facebook, etc.) clues on potential new product ideas.
In particular, the SPEC Company determines the preferences of their customers by analysing their registered information and recent shopping history from data warehouse. The SPEC Company has more than 6 million registered customers and their shopping history is changing all the time. Moreover, the company gathered feedback from their customers about their preferences. In order to identify each eyeglasses product and generate new product ideas, the company collected different source of data such as videos, photos, number of comments and number of followers from the most popular websites (i.e. eBay, amazon) by using Web Crawler, Web Page Cleaning and HTML parsing technologies. It is worth to mention that all these collected information has vast amounts of data where people produce and share every second. For example, On Facebook alone we send 10 billion messages including photos and videos per day, click the "share" button 4.5 billion times and upload 350 million new pictures each and every day (Thibeault and Wadsworth, 2014). Moreover, most of the information is unstructured data (i.e. photos, videos or social media) which means it cannot easily be put into tables. Furthermore, take Twitter posts as an example, the data quality and accuracy are less controllable. Thus, in order to harvest great values from big data, the trustworthiness of the data is a significant issue that Company SPEC needs to address.
Currently, Company SPEC was capable to analyse available data using the existing data mining techniques. The aim is to harvest the available unstructured data to serve as ideas for new production innovation and operations improvement. The approach, however, could lead to different part of information on the eyeglasses products. For example, in order to produce a new product, managers might get different answers from customer feedback, website information and user comments. The management was unable to combine (i.e. make sense) of the isolated group of processed information to create a coherent understanding of potential new product development ideas or trends. As a result, the management team was not confident that current approaches to extract understanding from big data are appropriate to assist them in future decision making.
The proposed big data analytic infrastructure is not just a combination of conventional big data techniques and deduction graph model, it was employed to assist managers in Company SPEC in making effective use of big data to support decision making (i.e. development of future products) as well as improve supply chain operations. It is based on real company data and overcomes the information connectivity problem. It uses different conventional big data techniques to harvest useful information from big data. For example, Apache Mahout for machine learning algorithms in business, Tableau for big data visualisation, Storm for analyse real-time computation system and InfoSphere for big data mining and integration. Then, deduction graph can be used to combine the useful information gathered to support managers in making a comprehensive decision towards a particular issue. Especially, Company SPEC is keen to explore how to make use of the value from big data to enhance their manufacturing department competence sets (i.e. that further strengthen product innovation capabilities etc.). The following sections describe the detailed application of the proposed analytic approach in Company SPEC.
4.1 Company SPEC Manufacturing Processes
Company SPEC employs more than 200 employees, and annual turnover is about 33million reminbi. The firm has two main manufacturing departments: A and B. The case was championed by the Chief Operating Officer (CEO). Both factory managers of departments A and B, and the manager of the information management department (manager C) were also took part in the testing process.
Analysis of existing big data (by the information management department) indicated 5 different types of glasses will satisfy most customers’ preferences and have vast potential for future development. The identified glasses are: Anaglyph glasses, Polarized 3D glasses, Active shutter 3D glasses, Sunglasses, Prescription colour-changing glasses. The company also identified the relevant competence sets needed to manufacture the five different eyeglasses i.e. a, b, c, d, e, f, g, h, i, j, k, l, m, and n (please see Table 1). For example, set a is precision tools machining skill, set b is surface hardening technology; set c is low temperature ion plating technology, and so on.
Table 1: Relevant skills for product development
Specifically, different types of glasses require different competence sets to produce. Table 2 shows the needed competences to make a specific product. For example, to produce active shutter 3D glasses will require active 3D technology (d), infrared receiver system (h), triple flash skill (m) and liquid crystal panel technology (n).
Polarized 3D glasses
Active shutter 3D glasses
Prescription colour-changing glasses
(“ √” means required)
Table 2: Different competence sets required by products
Having identified the required competence sets for different products, both factory managers were asked to point out the existing competence sets available in departments A and B. The existing competence sets of department A () were identified as:
c: low temperature ion plating skill
d: active 3D technology
e: fast liquid crystal technology
Whereas, the existing competence sets of department B () were:
a: precision tools machining skill
b: surface-hardening technology
f : resin lens manufacturing
A quick analysis shows that both departments A and B doesn’t have all the required competence sets to produce the five newly identified products. Thus, to make products that require new competence sets, the departments should purchase the competence set from other departments or expand its existing sets. The selling price for competence set in each department is estimated in Table 3. For example, the selling price for competence c in department A is 1 unit, and 1.5 unit for competence f in department B.
Table 3: The selling price for each department
Based on the selling price, the expanding cost for department A is shown in Table 4 (a), and for department B in Table 4 (b). The expanding cost for learning new skills takes into account of the training time, labour, energy, funds and so on. There are also compound nodes, such as d^e and a^b. In order to produce the new products, the needed skills will be obtained by learning from existing skills or by purchasing from other departments directly.
Table 4 (b): Competence expanding cost for B (B owns competence a, b, and f)
Based on the above analysis，the two manufacturing departments should focus on different product families. From the competence sets learning costs, we can figure that department A is more suitable to manufacture Anaglyph glasses, Polarized 3D glasses and Active shutter 3D glasses. Whereas, the department B should responsible for sunglasses and prescription colour-changing glasses manufacturing. Table 5 shows the products to be produced in departments A and B. In the Table 5, anaglyph glasses, polarized 3D glasses and active shutter 3D glasses is denoted as X1, X2, X3 respectively, whereas sunglasses and prescription colour-changing glasses is denoted as Y1 and Y2.