Big Data In Supply Chain Management: Differences, Selection And Technologies

Big Data: The Differences between Online and Offline Big Data

Discuss about the Big Data in Supply Chain Management.

According to Dumbill (2013), a supply chain is a complex and dynamic network of the supply and demand. On the other hand, researchers Marz and Warren (2015) define the term ‘supply chain’ as that particular system of operational activities, resources, data sets and organizations that facilitate the transportation of any service or product from the production facility to  the customer

At present, Amazon is considered as one of the largest cloud computing and electronic commerce US based multinational organizations. Headquartered in Seattle, in Washington, Amazon is not only considered as the largest internet retailers of the world but is also known widely for its well-managed supply chain and delivery systems. The following sections of the report would provide a detailed discussion of the manner in which   Amazon utilizes big data tools in its supply chain process.

The report would also shed light on the basic differences in between online and offline big data tools and the manner in which such tools might be chosen so as to gain optimized results.

Researchers McAfee et al. (2012) define big data as the ‘technical term’ that is used for depicting those large and complex volumes of information (both structure and unstructured), which are deemed unmanageable by traditional database systems. Thus, Big data can be considered as those initiatives and technologies that can be utilized for managing data sets far too diverse, massive and dynamic for conventional technologies.

Researchers Wu et al. (2014) are of the opinion that Big data technologies can be categorized into two sections: Online Big data and Offline Big data. In the following section of the report, detailed discussions on these subdivisions would be provided:

Online Big data: According to Chen, Chiang and Storey (2012) big data that is generated, managed, transformed and analyzed in real time, so as to support various ongoing operational activities and provide online services to users, is considered as online big data. Thus, online big data can be precisely described as the big data that is created and managed over the internet.

Online Big data tools are characterized by the ability to generate new data sets, along with the production of dynamic outputs.

Offline Big data: According to Chen, Mao, and Liu (2014) big data technologies that facilitate the analysis and management (needless to say transformation) of ‘big data’ in batch processes are considered as Offline Big data technologies. One of the most remarkable characteristics of offline Big data is the fact that not only these technological tools lack the capability of generating new data sets but are also restricted the generation of static outputs like reports and dashboard updates.

Selecting the Right Big Data Application for Achieving the Desired Outcomes

The differences between online and offline big data are:

Sl. No	Characteristics	Offline Big  Data	Online Big  Data
1.	Mode of operation	Offline, data managed in batch context (Lohr 2012).	Over the internet
2.	Capability  of generating new data sets	Not  capable	Capable
3	Nature of Output	Static	Dynamic
4.	Application Latency	Minimum latency, as the performance of the systems, must meet the end user expectations (Provost and Fawcett 2013).	Slow response is accepted, as the outcomes of such applications are not directly utilized in the operational activities of the organization.
5	Examples	Business intelligence tools, ETL applications,  Data warehouses, etc	NoSQL databases like MongoDB (Kim et al. 2014).

It is a well-known fact that the Big data technologies that are currently available in the market typically belong to two categories: operational workloads that allow the real time interaction with data and analytical workloads that allow the complex analysis of business data in an offline mode (Gandomi and Haider 2015). Since making  the correct choice of the big data  application (for achieving  the desired  outcome)  is one  of  the  most difficult  tasks associate with the incorporation  of big data technologies within the operational infrastructure of  an organization, a well- accepted  strategy  for doing  the same  has been outlined in the  section below:

Researchers Kaisler e al. (2013) are of the opinion that the very first step towards selecting the big data tool is to identify and define the problem that the business is currently experiencing, so as to make sure of the fact that  Big Data technologies would indeed be capable of  solving them. On the other hand, Demchenko et al. (2013) recommend that during his particular phase, the organizational heads should consider all the data silos that are available to them, irrespective of the significant extent some of them might appear to be typically resistant to any further analysis by any big data tool.

Authors Chen, Chiang, and Storey (2012) are of the opinion that once the problem has been defined, it is important to identify the possible outcomes of solving the same. Researchers Marz and Warren (2015) on the other hand are of the opinion that ranking the expected benefits would also help in selecting the appropriate big data tool (that is, the tool that provides the majority of the highly the functionalities outcomes becomes the winner!).

According to Gandomi and Haider (2015), it is important to define the policies that would be utilized to measure the progress of the task (that is, finding solutions to business problems with the help of big data technologies). The identification of this strategy would facilitate the process of monitoring the functions of the selected big data tool.

In the final phase of the selection process, it is important to identify the end users of the system and the functionalities that each of the big data technologies would provide to these users.

Researchers Provost and Fawcett (2013) are of the opinion that big data technologies that are selected as a result of considerations made in step 2, 3 and 4 are more often than not found to be the most suitable ones.

Big Data Technologies

Researchers Kim, Trimi and Chung (2014) have pointed out the following big data technologies that are currently available   in the market:

Predictive analytics: Those software and hardware that allow organizations to develop and deploy business models by analyzing the big data sources that are available to them are termed as ‘predictive analytical tools’ (Kaisler et al. 2015).

Databases: NoSQL databases used for storing graphs, documents, and key values.

Search and discovery tools: Big data solutions also include tools that are capable of extracting information from large repositories of structure and unstructured data.

Steam Analytics: Researchers Demchenko et al. (2013) are of the opinion that software tools capable of filtering, aggregating and analyzing big data coming from several online sources are termed as stream analytic tools and are to be considered as one of the most essential big data technologies.

Data virtualization: According to Kim, Trimi and Chung (2014) data virtualization is defined as that particular technology that facilitates the delivery of information from several distributed sources, including big data sources supported by Hadoop, in real time frame.

According to researchers Waller and Fawcett (2013), Big data technologies can be utilized to manage the core functionalities of any supply chain process, namely business planning and operation. The following section of the report provides a detailed discussion on how the utilization of big data technologies can benefit the above-mentioned functionalities.

Business planning:

Forecasting: According to Feki, Boughzala and Wamba (2016), each activity and element associated with the Supply Chain process generate huge volumes of data that can be utilized effectively by Big data tools so as to make accurate supply and demand forecasts.

Marketing and sales intelligence: It is a well-known fact that order management applications and tools are quite useful in collecting large volumes of customer information. Appropriate big data technologies can be utilized to analyze these data sets, as a part of a customized market research, so as to as to identify the geographical regions or business domains in which business activities can be expanded (Sanders 2014).

Operations:

Crowdsourced package delivery systems have been one of the greatest contributions of big data technologies in supply chain management systems: this has primarily been because big data technologies allow the match between the available drivers and the delivery times requested by the customers (Erevelles, Fukawa and Swayne 2016).

Operational efficiency: Big data technologies have made significant contributions in increasing the operational efficiency of supply chains. Operational activities, like that of monitoring the supply routes, capacity planning and responding to bottle neck situations in real time are some of the activities that can essentially be benefitted by the incorporation of Big data technologies (Waller and Fawcett 2013).

Researchers Erevelles, Fukawa and Swayne (2016) are of the opinion that most retail organizations, the task of optimizing the retail and logistics chain are considered as one of the most difficult activities. However, the authors consider this very activity to be one of the greatest opportunities that can be taken for improving their services, all the while reducing their costs. 

In an attempt to grab the above mentioned opportunity, Amazon has introduced the concept of ‘anticipatory shipping’- a method that allows the organization to start shipping even before the customer has placed the order (Whitmore 2014). In fact, in 2012, the enterprise had filed a patent application for the system, describing its functionalities as the “method and system for anticipatory package shipping,” and was awarded with the patent in the following year.

Although the details of the said system (algorithm to be very precise) have not yet been revealed by the organization, the patent summary description of the same reveals it to be a system that allows the retailer to send packages to particular geographical regions, without any specific address. These particular attributes of the system have encouraged Leveling, Edelbrock and Otto (2014) to consider it as a significant improvement over traditional predictive analytical tools.

The information available from the patent document indicates that Amazon utilizes the data available from the previous activities of a user to run the anticipatory shipping system (Cuthbertson, Furseth and Ezell 2015). These activities might include the time at which he/ she had logged in, the time that has been spent on reviewing the products available on the website, the links that have been clicked and the queries placed by the customer with the sales team. As for example, in case the local residents of  Sydney order a lot of  scarves in August, the local order fulfillment centers  of the city would be stocked with  scarves at this particular time of the year. However, the shipments would only be made when Amazon’s shipping system receives the actual orders (and the delivery addresses) (Whitmore 2014).

According to Leveling, Edelbrock and Otto (2014), the ‘anticipatory shipping’ method demonstrates the finest manner in which Big Data technologies can be utilized in optimizing supply chain processes: in fact, the introduction of this particular system now allows Amazon to provide guaranteed 2-day deliveries, thus ensuring customer satisfaction.

Conclusion

Experts  in the  domain of  information technology have  often  pointed out the fact that Big  data and supply chain are  a ‘made  for each   other’ pair: partly due  to the  enormous  amount  of  data generated  by supply chain  processes on a daily basis and partly  due to the immense strength  of  Big   data technologies  in analyzing voluminous  data and generating patterns from the same.

The preceding sections of the report provide a detailed discussion on the big data technologies that are currently being utilized for the management of supply chain and logistics. The discussions provided in section 2 of the report sum up to the fact that, based on the mode in which the analysis of Big data collected from various sources is made, Big data technologies can be subdivided into two categories: online big data operating in real time domain and offline big data emphasizing on the batch processing of enterprise data. On the other hand, the discussions provided in section 3 of the report reveal that the selection of big data technologies to be utilized in an organization require to be made based on the benefits available to it.

Section 4 and 5 of the report provide an in-depth discussion on the benefit of the utilization of Big data technologies in supply chain process.  In the light of the discussions made in these sections, it can thus be concluded that the incorporation of Big Data technologies increases the efficiency of supply chain processes to a significant extent.

References

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