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As discussed by Provost and Fawcett (2013), Data science refers to the interdisciplinary study of all the critical processes and the business systems for the extraction of the mission-specific data in diverse forms, including the structured data and the unstructured ones. This field is related to the domain of Data analysis and may be considered similar to the data statistics, mining and specific forms of Data Prediction Analytics. As argued by Rodriguez et al. (2015), with the advent of the Internet and the technological advancements of the Information and Communication Technology, E-businesses have adopted a variety of techniques, not only for the production and the supply chain management but also for considering the consumers as a direct stakeholder (Xie et al. (2016). As a result, the point of concentration has shifted from just the production to the consumer satisfaction as well as customer loyalty.
The report aims to deliver a number of advanced and efficient techniques for the Data Collection, Data Storage and Recommendation system for allowing the customer to choose from a wide range of bakery products of the E-business named Cookies Limited. The report includes all the effectives necessary for the recommendation data analysis and collection. Apart from this, the study involves the insight into the Consumer-centric Product design to target the points of consumer satisfaction and loyalty that may turn up as the key contributors towards the increased sales in this ultra-competitive e-business industry. The report suggests the implementation of the Intelligent Matching System for the storage of the relevant data and the retrieval of the appropriate data from the database. Besides, another important section of the report is the plans and the strategy that are formulated for the continuity of the e-business in case of any disaster such as a power outage, which has been briefly discussed.
According to Weng et al. (2013), one technique for the analysis of the user information and the prediction of the choices is the study of the community characteristics. The community characteristics are studied by evaluating the relational probabilities of the users and the flavors. The study involves a dendrogram that consists of the nodes to represent the user ant the variety of cookies/flavors.
Figure 1: The community data in the form of a hierarchical random graph
(Source: Newman 2013)
The random graph proves useful in this study, as the probability of the relatively higher node is less than the probability of the lower node. The nodes are joined by the edge that has the probability corresponding to the least common predecessor of the dendrogram. The differences in the probabilities result in the random graph for the community analysis of the prediction of the flavors as per the customers (Newman 2013).
The system generates matching results using limited information. It creates and indexes the collected data to the database.
Intelligent Matching System:
According to Wang (2015), the Intelligent Matching system is a technique for the management of the data, which includes the searching, the indexing and the retrieval of data from the database. The operations follow a sequential data sort and search algorithms and the queries involve the human-resembling inference methodologies.
Figure 2: Structure of the Intelligent Matching System
(Source: Kasabov 2013)
Matching model; Star shaped relationship graph method:
The graph model uses two types of relationship graphs namely the Product graph and the User relationship model. The blue lines display the mapping functions for the community analysis.
Figure 3: Matching function graph of the Product graph and the user relationship graph
(Source: Jeong et al. 2016)
Thus, by matching the characteristics of the users and the attributes or the flavors of the cookies or the other products, the relation mapping is done for the recommendations.
In case of the data storage issues for the recommendation systems, the access to the database becomes slow, if the data is stored in the hard disk. So, the bottleneck problem is avoided by storing the data in the memory directly. According to Pham and Jung (2014), the data can be stored at a reduced price of 2 GB. By choosing a particular Memory Reader, the access can be made more efficient. Suppose the user prefers a particular cookie flavor, the system uses two hash tables.
Figure 4: A sample hash table for the data storage
(Source: Sánchez et al. 2016)
The two tables may be headed as UId and the CId. None of the tables consumes more than 24 bits for the storage. If the platform chosen is Java, then an “int” takes 32 bits. Consequently, the the UId and the Cid bits are shifted to the upper 24 bits and are stored in the lower bits. The chosen technique is efficient because it performs the bitwise operations, reduces the estimated memory space and facilitates the fast access to the databases.
Figure 5: Data collection and storage
(Source: Bobadilla et al. 2013)
According to Moskowitz et al. (2012), the consumer-centric product design not only determines the extent of the consumer services but also involves the consumer experience from the awareness or the online website visiting stage to the purchasing stage followed by the very important post-selling stage to determine the consumer feedback. The design is most likely to discover the consumer behavior towards the sequential services and the organizational dependencies on them for the improved business.
Figure 5: Consumer centricity aspects for Cookies Limited
(Source: Harris 2013)
The economic downturn shifted the power of the brands from just their products to the consumer experiences and their preferences. As argued by Huang and Benyoucef (2013), the brands to respect the consumers provide them great service along with product qualities won the race. The prime reason for the design of such a system is that the consumers have now the authority to compare the products of other bakeries efficiently across various commercial platforms, following the advent of the social media. As discussed by Mattila et al. (2016), the most probable of the challenges faced by Cookies Limited for the consumer-centric design may be the lack of information transformation across all the operational departments.
The consumer value may be regarded as the prime marketing concept for Cookies Limited, especially because it lies in the food industry. Thus, the consumer analysis is vital for this case. According to Maglio and Spohrer ( 2013), the resources of Cookies Limited can be classified as the tangible and the intangible resources along with the conceptual operant and the operand resources, which are critical for better consumer services. The operand resources are those exchanged thorough the product delivery. The operant resources are those, which are exchanged based on the service-oriented methodology (Maglio and Spohrer 2013).
Figure 6: Resources and the Consumer value dimension
(Source: Mackeviciute and Skudiene 2013)
The resources have respective dimensions. The core product for Cookies Limited is the cookies. So, the dimensions of the products are the cookies’ price and the flavor quality. On the other hand, the operant resources consist of the service of Cookies Limited towards the consumers. According to Fiore et al. (2013), the service has dimensions namely Fundamental service, Competence, and Complementary cookies and gifts.
Fundamental service: These are the basic services of the operations until the consumer is provided with the intended cookie product. These include the delivery services, the transaction actions and bill involving, the hours of cookie service etc.
Competence: It comprises the knowledge of the operations within Cookies Limited, the skills of the employees and the consumer-centric product service capabilities. The skills and the knowledge base of the Cookies Limited employees must be capable of handling the IT operations regarding the product service business.
Complementary cookies and gifts: This section concentrates upon the post sales or the sales associated actions, that need to be taken by Cookies Limited. This includes the complementary sales of the products, partner sales, complementary cookies for above-limit purchase, complementary gifts for kids and relevant products for the consumers etc.
Cookies Limited should consider the consumer value for the consumer-centric product design. As discussed by Anker et al. (2015), the Customer Relationship Management is an useful tool to generate the consumer relationship value. The CRM helps to select the consumer form the database, and for creating separate groups for the loyal and esteemed customers. The benefits of CRM are-
The analysis of the consumer reviews and the no of visits to Cookie Limited can determine the extent of the consumer loyalty and the consumer satisfaction. More the satisfaction of the consumers more is their loyalty i.e. increased visits and repeated purchases from Cookies Limited. The operand resources namely the cookie price, the cookie flavors, and the quality are profoundly connected to the combination of Consumer loyalty and the satisfaction. The consumer-centric product design is primarily based on the consumer service values and the dependency of the core product values on the service factors.
According to Wu et al. (2015), the recommendation system is a child class of the Information Filtering system and is implemented to estimate the liking or the preference of the consumer assigned to a particular item or a number of parallel items.
Cookies Limited is intending to provide the customers a choice of multiple products. To facilitate this, the Product Recommendation System is in vogue to offer a wide range of choices for the products. The system deploys an Information Filtering system, which predicts the user’s characteristics and his/her choices for the product.
Information Filtering System: The system is classified into two filtering categories based on Context and Collaboration.
Category based filtering
· Uses the data contents of the user or the cookies/flavors
· Uses language processing and Information search
· Uses Metadata (Product description)
· Shallow range of recommendation
· Requires small sized data
· Uses user estimation
· Uses cookie/flavor similarities
· Uses other user’s estimation data
· Wide range of recommendation
· Requires large sized data
· Assigns methods based on user and item/product
Table 1: The recommendation systems
The Business Continuity plan for Cookies Limited must address the following aspects to counter the power outage issues.
According to Cook (2015), the first step is to determine the business operations and mainly the electronic components that may be affected by the power outage. The components include the computer hardware, the elevators, the security and alarming systems of Cookies Limited and the processes include the bakery operations besides the heating or cooling facilities. The strategy suggests to prepare an inventory planning for the equipments that require to be turned off while the outage occurs and get restarted on power restore. Another important measure is to determine the extent to which the bakery services are to be disrupted. The technical planning includes the implementation of Surge Protectors for power supply to the electronic items. The testing operations involve the creation of backup for the battery system, the fire protection operations, system securities etc.
Alternative and emergency power sources:
There has to be standby backup power sources for emergency electronic operations. One recommendation is the use of battery-driven and solar powered lighting and oven operations.
Establishing secure locations for the assets:
There must be enough shelter space for Cookies Limited’s employees, the suppliers of the raw materials or the consumers (customers), who may be present at the time of the power outage. The shelter must contain the minimum of the physiological requirements for the people present.
The emergency or backup power sources must be deployed. The best to unplug the electrical components to prevent any type electric surge following the power restore.
The first step includes the turning on of the most vital equipments first. However, a span of 15 minutes must be allowed to prevent the overloading of the electric supply system and to permit the stabilization of the electric supply (Cook 2015).
According to Snedaker (2013), The significance of every phase of the business continuity planning is that each phase has importance regarding the key aspects of the business continuity plan namely Recovery from a disaster, Recovery of the business, Resuming the BAU, and contingency strategy.
The phases of the business continuity planning are-
Figure 7: Phases of the business continuity strategy
(Source: Heng 2015)
The different and the efficient plans for the continuity strategy of Cookies Limited are-
Figure 8: Plans for the business continuity
According to Cheni et al. (2013), the service operations and the continuity planning not only focuses on the production data center but also on the key features of the consumer end. The most important recovery tactics include the OEM insurance and the Quick Ship tactics.
The Original Equipment Manufacturer’s insurance: The monthly bill may cost Cokies Limited 6-8% of the maintenance cost. The manufactures or the vendor offers an insurance of replacing the damaged equipments.
Quick Ship: The vendors or the third-parties also provide quick shipment of the damaged and the replaced hardware.
According to Agneeswaran and Venkataraja (2012), the commercial recovery sites facilitate the continuity of the operations in the case of hardware and equipment disasters.
Hot site and cold sites: The hot sites are always ready to provide operational platforms. The hot sites are armed with office space, telephone jacks and other computer components for the tackling of the disaster issues. The cold sites offer the same office spaces but not any hardware and other equipments; they only provide the place for the hardware to be shifted to(Agneeswaran and Venkataraja 2012).
Mobile sites: The mobile sites provide spaces for computationally smaller hardware. They can be formed at convenient small places like the parking lot of the bakery office (Agneeswaran and Venkataraja 2012).
Data storage: The data storage facilities consist of the Off-site data storage options and the technique called E-vaulting. The budget and the resource analysis may help in setting up off-shore data centers for the secondary storage during disasters, at another branch or any other geographically convenient location. The E-vaulting involves the transposition of data from the subscriber’s place to the hot sites. A popular implementation of Electronic Vaulting is through the use of the PC/LAN (Agneeswaran and Venkataraja 2012).
The research successfully addresses the key mission-specific aspects of the E-business methodologies for the Cookies Limited namely the Data collection and storage for the Recommendation and the Intelligent Matching systems. Besides, the Star-relationship graph and the Hierarchical random graph are simply one of the best techniques to conduct the community study for the users and the relative attribute study for the products. Apart from this, the Consumer-centric product design is detailed in this report. This portion of the study will highlight the prime necessities of considering the consumers as the direct stakeholders of the production and the sustenance of the market value for Cookies Limited. The plans and strategy suggested in the Business Continuity section are the effective set of techniques, which are implemented for the continuity of the business and the recovery from any disaster including a heavy power outage.
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