Final Project: Big Data

Big Data x Logistic x Globalization 
‘Big data’ promises business benefits in terms of timely insights from data, real-time monitoring and forecasting of events, more fact-based decisions, and improved management of performance and risk. It has already had a considerable impact and changed the competitive landscape in many industries, for example, our targeted industry: logistic. We are going to focus on different functions of big data applied in different sub-industries under logistic: prediction on maritime shipping, real time monitoring on cold chain, decision making on logistic infrastructure, operational efficiency on supply chain. In our case, big data does help by implementing ‘intelligent operation’. The purpose of our group project is to establish a framework for identifying impact of globalization on logistic industry, understand how big data can contribute to make logistic industries safer and more sustainable under the age of globalization.

Big Data and maritime shipping.
In global trading and logistic aspect, big data provides free real-time information to the public, about ship movements and ports, mainly across the coast-lines of many countries around the world and thus lead to emerging innovation management practices. The initial data collection is based on the Automatic Identification System (AIS). As from December 2004, the International Maritime Organization (IMO) requires all vessels over 299GT to carry an AIS transponder on board, which transmits their position, speed and course, among some other static information, such as vessel’s name, dimensions and voyage details and share the data of their area in order to cover more areas and ports around the world. AIS is initially intended to help ships avoid collisions, as well as assisting port authorities to better control sea traffic. AIS transponders on board vessels include a GPS (Global Positioning System) receiver, which collects position and movement details. Messages include the following three basic types:

1. Dynamic Information, such as vessel’s position, speed, current status, course and rate of turn.

2. Static Information, such as vessel’ name, IMO number, MMSI number, dimensions.

3. Voyage-specific Information, such as destination, ETA and draught.

Big data and cold chain.

An increasing demand of daily products, vegetables, fruits as well as the needs of pharmaceutical drugs boosted the cold chain market where the logistics was attached with more importance on the control and stabilize of temperature and humidity in the vehicles and containers, and the cold chain monitoring platform in use of big data technology has come into the market. The M2M (machine to machine) communication requires the sensors for temperature and humidity which has been expected in the vehicles and containers that they carry, and this would help generate data during the transport which can be transmitted via mobile means and be monitored in real time. The globalization which has a positive impact on international trading would urge the immense use of this M2M communication on cold chain as it shall be taken as the supply chain integrity where daily products and pharmaceutical companies will need the proof to sell their products.

                        

                           Big data and Supply Chain Management
A “supply chain” is a broad term for the resources, activities, information, and people that are involved throughout the entire process of raw material to final product, or supplier to customer. Thus, supply chains are highly linked together and involve anything from transport to storage in warehouses to actual point of sale. Globalization increases the interconnectedness of businesses which leads to a growing complexity of supply chains. Hence, there is one magic word in the logistics industry: efficiency. Big data, with its many applications, has helped boost operational efficiency of supply chains in the logistics industry on a global scale.

Some widespread applications of big data include the following:

·         Radio Frequency Identification (RFID)

·         Enterprise Resource Planning (ERP)

·         Automated Storage and Retrieval Systems (ASRS)

        Automated Storage and Retrieval System in a Warehouse

Implementing these types of big data leads to higher efficiency and lower costs, enhancing the competitiveness of logistics companies today. However, it is often argued that globalization leads to a lack of fair trading opportunities. In fact, the World Trade Organization has received much criticism for being heavily influenced by the interests of wealthy nations and companies. Through globalization it seems that rich, well-established companies are becoming bigger, stronger and more dominant. Thus, they are able to afford the opportunities provided by big data while smaller companies are left struggling to survive in the market.

Big data in the logistics infrastructure

Big data represents more a wide range of analytical technologies than simply the ability to handle the large volume of data[1]. There are emerging innovative technologies applied making it happen for organizations to utilize the big data for a smarter decision-making. Industries such as logistics fund specializing in providing warehouses to the target market are the pioneers in employing big data analytics. The decision-making processes are typically driven by the results generated by analyzing the data on four key elements: the locations or site selection, economy of scales, market projections and customers. These four factors are interrelated rather than independent of each other.

With the applications of geospatial technology, big data in logistics infrastructure industry are to be collected in real time model, thus lead to more efficient and accurate strategic decision-making power.

Conclusion
In summary there is a lot of headroom for Big Data approaches to fill under globalization. The digital revolution in the logistics will continue with no doubt. The transformation into an information-driven business will make logistics smarter, faster and more efficient. 

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[1] ‘Using Big Data for Smarter Decision Making’ IBM BI Research, 2011

Big Data in Logistics

Preface

Large or small, no enterprise could remain aloof today as big data has been flooded in every sector, every economy, and everywhere in our lives. Big data is hyped as the next big thing by tech geeks; along with cloud, social media, and mobility, the four transformative megatrends that will shape the global economy over the next decades, as MGI report (2011).

Big Data, though with only two short years since its inception, has been profoundly transforming our lives in all dimensions. More than that, big data, as is claimed, is disruptive in terms of velocity, volume and variety. As such, big data has been termed as data deluge. It is reported that 90% of the world’s data has been created in the last two years, it is being created more overwhelmingly with the easy access to internet through mobile devices, sensors, wearables and social networking tools. It is of no avail to predict what the data landscape would look like even tomorrow. What must be done is to harness the power of big data and uncover the benefits it can bring to our business, or what changes the big data may revolutionize in terms of efficiency enhancement, cost reductions, risk management or attracting customers.

Admittedly, logistics sector is typical of a data-driven business. Logistics and big data are a perfect match. DHL Big data solutions demonstrate how the global logistics company plays ahead of the game by capitalizing on the untamed data asset in an innovative way.

Introduction 

It is the era of big data that we are entering in spite of ourselves at an accelerating speed. According to the EMC/IDC Digital Universe Report, the digital universe is growing 40% a year into next decades. It is doubling in size every two years, and by 2020 the data that we create and copy annually in the digital universe is projected to reach 44 trillion. If the foregoing future had been forecast in a sense of ambiguity, let’s watch how the date is being generated the moment we are working on the paper. Some quick facts are instrumental to our understanding how we are awash in a flood of big data every minute. The following snapshot shows the amount of data created online in one minute (NOT an hour or a day, but a minute):

• 2,000,000 Google Searches
• 685,000 Facebook Updates
• 200 Million Sent Emails
• 48 Hours’ worth of video uploaded to YouTube
• 347 New Blogs Posted

What is the implication of the above data? Unstructured, massive, and real-time? Or all of them. It is really big, isn’t it? But we are impressed not only with its being big but its ubiquitous. Big data has been making inroads into all aspects of our lives one way or another. For enterprises, it is time to make a shift of mindset to dig the value and gain competitive advantage from the untamed data by data-driven business insights. It is challenging but there is plenty of headroom for valuable innovation.

Logistics and supply chain, of all the industries, is supposed to be the most ideally situated for using big data analytics given that massive amount of data are being generated every day in supply chain business activities. For instance, the tracing of goods from origin to destination produces an extensive flow of information including geographical, customers, packages, transportation carriers, energy consumptions, and among others. When the enormous amount of data generated is aggregated on a global scale, it turns out to be a huge mine of data. Enterprises embracing the advent of big data configure the capabilities to extract values from such mines as to boost operational efficiency, increase customer experience and loyalty, fortify risk management and exploit innovative business models. Big data and logistics are made for each other, according to CSI report (2013). 

DHL is a prime example of how it has significantly benefited by tapping into the power of big data, and gained its competitive advantage globally. For instance, by using GPS and sensor data, they optimized the route, thereby fuel usage reduced, and orders delivered more efficiently. That is only a part of the benefits that big data analytics unleashed. However, there are many enterprises in the logistics sector that didn’t apply the big data. A survey by Accenture (2014) showed that only 30% of the target companies in the logistics and supply chain sectors have implemented the tool of big data analysis as an organizational imperative to run their businesses though more than 90% have a big data strategy in place to be executed in the next 6-12 months. 

Big data is the untapped logistics asset, says a report from DHL. In the age of emerging technologies such as mobile devices and sensors, big data would bring unprecedented transformations in the logistics sectors. In particular, with the surge of e-commerce move and delivery on an end-to-end basis, innovations in logistics supply chain will be overwhelming, which will be bound to change the landscape of global economy. 

Industry Profile Report 

Differences of Big Data from existing technologies (Michael, Michele, 2013): 

Unlike past eras in technology that were focused on driving down operational costs mostly through automation, the ‘Analytics Age’ has the potential to drive elusive top-line revenue for enterprises. For those enterprises that become adept with Big Data analytics, they will simultaneously minimize operational costs while driving top-line revenues to net substantial profit margins for their enterprise.

Big Data analytics uses a wide variety of advanced analytics, as displayed below: 

Deeper insights.

Rather than looking at segments, classifications, regions, groups or other summary levels you will have insights into all the individuals, all the products, all the parts, all the events and all the transactions etc.

Broader insights.

The world is complex. Operating a business in a global, connected economy is very complex given constantly evolving and changing conditions. As humans, we simplify conditions so we can process events and understand what is happening. But our best-laid plans often go astray because of the estimating or approximating. Big data analytics takes into account all the data, including new data sources, to understand the complex, evolving, and interrelated conditions to produce more accurate insights.

Frictionless actions.

Increased reliability and accuracy that will allow for deeper and broader insights to be automated into systematic actions.

The spectrum of opportunities and pitfalls in the adoption of Big Data: 

Opportunities 

The key to success for organizations seeking to take advantage of this opportunity is (Minelli, Michael, (2013)):

1. Leverage all current data and enrich it with new data sources.

2. Enforce data quality policies and leverage today’s best technology and people to support the policies.

3. Relentlessly seek opportunities to imbue your enterprise with fact-based decision making.

4. Embed your analytic insights throughout your organization.

With so much data being generated, the real challenge is finding the right data and deriving actionable intelligence.

Pitfalls

For those ready to dive into a Big Data implementation, be sure to weigh the pros and cons. Three of the most common problems in big data deployments are incomplete data collection, false starts, and disruptive drains on IT and data-professional staff productivity.

Here are some insights into the pitfalls to avoid:

1. Failure to capture critical data. 

With haste and inexperience, you might miss relevant data that could illuminate revenue opportunities or ways to reduce customer churn. If competitors start taking advantage of what you miss, the entire business could be vulnerable.

2. False starts. 

Taking multiple shots at big data will delay implementation. The impact of any delay will only be magnified if competitors beat you to a breakthrough.

3. Resource drains.

IT and data-management teams are under pressure to maintain daily operations, deliver new reports and analyses, and incorporate new capabilities. Overburdening employees with too many roles or short-staffing the day-to-day work is not the way to go. In fact, many successful practitioners report that their big data teams are quite separate from preexisting BI, data warehousing, and data management teams.

Factors driving technology leaders to manage Big Data (Philip Russom 2013):

1. Big data just gets bigger. 

Embracing big data to keep pace with its growth, get a business return and fold it into enterprise data architecture. Thus, it is important to beef up data management infrastructure and skills as early as possible. Otherwise, an organization can get so far behind from a technology viewpoint that it is difficult to catch up. From a business viewpoint, delaying the leverage of big data delays the business value. Similarly, capacity planning is more important than ever, and should be adjusted to accommodate the logarithmic increases typical of big data.

2. Resistance is futile: big data will be assimilated into enterprise data.

Technology leaders have to start somewhere, even if it is a data management silo devoted to one form of big data. Typical silos manage Web logs, sensor and machine data logs, and persisted data streams. Yet, it is also important to determine how each form of big data will eventually fit into an overall architecture for enterprise data.

3. Advanced analytics is the primary path to business value from big data. 

The current uptick in advanced analytics among organizations is driven by the availability of new big data, plus the new business facts and insights that can be learned from its study.

4. Joining big data with traditional data is another path to value. 

For example, 360-degree views of customers and other business entities are more complete and bigger when based on both traditional enterprise data and big data. In fact, some sources of big data come from new customer “touchpoints” (mobile apps, social media) and so belong in customer view.

5. Big data can extend older applications.

This includes any application that relies on a 360-degree view, as mentioned above. Big data can also beef up the data samples parsed by many analytic applications, especially those for fraud, risk, and customer segmentation.

Dealing with New & Disruptive Technologies

Global economies, through innovation, have evolved considerably to form the nature of how business is conducted today. Rapid technological developments cause some firms to collapse while others have used new technologies to exploit business opportunities. Indeed, big data, being a disruptive technology, means that it is both a threat and an opportunity in the business environment. It can be a threat if companies fail to exploit the potential of big data while competitors are quick at adopting this new technology. Hence, companies should seize the opportunity of big data to conduct business more efficiently in the twenty-first century. As DHL (2013) points out, logistics companies such as DHL manage an enormous amount of goods and data sets every day. This means that the logistics industry basically relies on big data usage as a core part of their business models. For this reason, the success of logistics companies is linked to managers’ ability to fully exploit this new disruptive technology. Therefore, important questions are:

• How can the disruptive nature of new technologies be used to one’s advantage?
• How can innovation build upon these new technologies?

These questions are particularly interesting in today’s world where technology evolves faster than ever, loads of information is readily available via the internet, and communication occurs instantly amongst each other. Indeed, in addition to traditional resources such as labor, capital, and land, information has become a critical element in shaping competitive advantage in today’s world (DHL 2013). According to Wu, Ming, Wang, and Wang (2014), “knowledge has become a main source of wealth, and knowledge workers are the most vital asset, and how to manage knowledge is the most important task for organizations and individuals” (p. 6314). In a sense, this means that it is no longer a competitive advantage to simply have knowledge; instead companies nowadays must be able to use knowledge intelligently. Big data allows logistics companies to make intelligent use of their knowledge through package tracking, customer relationship management software, and Enterprise Resource Planning (ERP) software such as SAP and Oracle for example.

According to George, Haas, and Pentland (2014), big data involves huge sets of data, meaning these data sets must be managed in smart ways. People need to utilize powerful techniques to discover trends and patterns within a huge set of data. Therefore, nowadays the logistics industry needs employees with these technical skills. Indeed, from a human resources perspective it can be said that employees working with big data need the proper qualifications and training in order to efficiently work with big data applications. Therefore, logistics companies need people who have mathematical knowledge (e.g. statisticians) as well as computer skills who can work together with marketers in order to identify broader market trends. Equipped with this knowledge of market trends and needs, logistics companies can then design products and processes in ways that more effectively fulfill market needs.

Characteristics of Building on Emerging Technologies

Building upon emerging technologies such as big data requires the logistics industry to merge big data into the life cycles of existing products and services. DHL has done a particularly good job at accomplishing this. This can be seen from the fact that it has improved its operational efficiency, customer experience, and b2b supply chain management in recent years (DHL 2013). As a result, DHL has been able to please customers and reduce its operational costs. It is important to note that although using big data is helpful, this technology also has its limits. This is because of the existence of many indirect sales channels such as online platforms, Smartphone applications and portals. These indirect sales channels cause logistics companies to not have an entirely clear picture of customer activity.

In an online article from our week 9 class readings, Van Hove (2014) highlights the importance of original innovation. According to the author, if a company wants to innovate successfully then it is not enough to simply replicate the innovative actions of strong competitors. In short, Van Hove (2014) claims that in order to innovate effectively, employees need a clear idea of why they do what they do as a business. People are more motivated to add value to their organization if they can identify with the organization’s strategies and goals. Furthermore, to maximize innovative prowess, managers should encourage employees to share creative ideas and to be persistent in achieving agreed upon goals.

We generally agree with these arguments made by Van Hove (2014). To us, the author is basically saying that innovative companies are ones that are able to excite their employees and to make them ambitious. This is done by first inspiring the employees through ambitious goals and secondly by increasing the employees’ identification to the company. In this way, intrinsic motivation of employees will be harnessed, creating an environment conducive to original innovation.

While this approach to innovation is true when managing people, it does not always apply when managing products or processes. In contrast to the author, we believe it is often important for companies to replicate their competitors’ actions, especially when it comes to utilizing emerging technologies to enhance business opportunities. Van Hove (2014) states that “businesses exist because of opportunity and opportunity is why we innovate”. The author seems to be saying that firms should exploit environmental opportunities by creating innovative products or processes. This is a logical statement, however, in today’s world opportunities are often linked to innovations themselves. In other words, innovative technologies themselves represent opportunities for companies to innovate further.

Strategic Adoption of DHL

Deutsche Post DHL Group contains two powerful business brands in the marketplace: The Deutsche Post who is Europe’s largest mail services operator and market leader in the German mail and parcel market; and DHL, the leading global brand in the logistics industry. Originally founded in 1969 by Larry Hillblom, Adrian Dalsey and Robert Lynn, DHL primarily carried out their delivery between San Francisco and Honolulu, but expanded aggressively to countries that could not be served by other courier providers through its blue ocean strategy since the 1970s. As of 2014, DHL has around 285,000 employees in 120,000 destinations in all continents. 

In 2009, DHL presented its strategy 2015 which emphasized three key objectives for its development: become the provider of choice for customers, an attractive investment for shareholders and the employer of choice for staff. The strategic approach of its four business divisions (mail, express, global forwarding and freight, supply chain) in its annual report of 2012 and 2013 fully reflects its objectives. By making the important progress towards these three key objectives, in April 2014, “Strategy 2020: Focus. Connect. Grow” has been announced, the new strategy aims to build on these successes and further accelerate DHL’s growth. Slightly different from its previous four business divisions, the new one consists of post/ecommerce/Parcel, express, global forwarding and freight, supply chain. As the newly named division, post/ecommerce/parcel would continue to design a market-based cost structure, provide the highest quality to its customers, motivating its workforce and keep them informed as well as tap into new online and offline markets. By acquiring Allyouneed.com, DHL has established an online supermarket where they offer same-day food delivery with its retail customers; and DHL offers one of the largest online marketplaces in Germany on MeinPaket.de, all these efforts are in line with its goal of offering effective digital communications as the internet has become a useful tool for its customers to access its services. When looking at DHL’s strategic approach in the recent five years, we shall be able to find that DHL’s strategic approach is trend-setting and refuses to be left behind in general. 

Stepping into the big data era, where this new technology has been forecasted to play a vital role in logistics industry, as the trend leader, DHL would definitely strive to explore the benefits that big data could bring in. 

The contribution to operational efficiency

1. Real-time route optimization

In 2010, DHL and Blue Dart, part of DHL Group, launched an “intelligent” pick-up and delivery vehicle called Smart Truck, which combines a number of innovative technologies including a route planner, and this is able to adjust the routes based on incoming shipment and traffic data. DHL Smart Truck reduced number of miles travelled by 15% and length of average route by 8% during its pilot stage, and also reduced fuel consumption and CO2 emission.

2. Crowd-based pick-up and delivery.

DHL MyWays is an app enabling flexible parcel delivery through crowd-sourcing on which consignees can state where they want delivery and other app users in the area can build points by making ad hoc deliveries in their area.

3. Operational capacity planning

One research project at DHL called “DHL Parcel Volume Prediction” is investigating the correlation between external factors like Google search results, weather conditions or even flu outbreaks and online parcels orderings. This analytic tool results in the operational capacity planning improvement.

Development of New Business Models

1. Market intelligence for small and medium-sized enterprises

DHL Geovista is an online geo marketing tool for SMEs to analyze business potential which is currently under piloting. It is used for predicting realistic sales and analyzing local competitors based on the scientific models.

2. Address verification

A correct address is the prerequisite for a punctual delivery. DHL Address Management is able to return incomplete or incorrect incoming data with validated data from its database.

Nevertheless, all new technology involves trade-offs between risk and return (HBR, 2013). With regard to big data, there are a series of challenges that not only DHL, but all logistics players shall overcome for the sake of the successful implementation, such as data transparency & governance, and data privacy. However, with the rapid development of internet where data is driven to be a crucial point for the success of logistics sector, DHL, the entrepreneurial logistics provider, will be expected to perform more efficiently.

References:

Big data: The next frontier for innovation, competition and productivity, McKinsey Global Institute, 2011. 

http://www.forbes.com/sites/danielnewman/2014/06/03/big-data-means-big-disruption/

https://www.t-systems.com/news.../WhitePaper_Big-Data-Logistics.pdf

http://www.emc.com/leadership/digital-universe/2014iview/index.htm

www.infosys.com/infosys-labs/.../bigdata-challenges-opportunities.pdf

Big Data in Logistics: a DHL perspective on how to move beyond the hype, December,2013

Big Data Analytics in Supply Chain: Hype or Here to Stay. 2014, Accenture Global Operation Megatrends Study. 

Minelli, Michael , (2013). 'What Is Big Data and Why Is It Important?'. In: (ed), Big data, big analytics: emerging business intelligence and analytic trends for today's businesses . 1st ed. : Hoboken, N.J. : John Wiley & Sons, Inc . pp.(13-14).

Philip Russom, (2013). Managing Big Data. TDWI Best Practices Report. TDWI Research.

Mohanbir Sawhney, Robert C. Wolcott and Inigo Arroniz. (2006). The 12 Different Ways for Companies to Innovate. MITSloan Management Review.

DHL. (2013). Big Data in Logistics: A DHL Perspective on how to move beyond the hype. Troisdorf, Germany: Jeske, M., Grüner, M., Weiß, F.

George, G., Haas, M.R., & Pentland, A. (2014). Big Data and Management. Academy of Management Journal, 57(2), 321-326.

https://annualreport2013.dpdhl.com/dpdhl_gb2013/static/export/docs/DPDHL_2013_Annual_Report.pdf

file:///C:/Users/1/Desktop/DPDHL_2014_Annual_Report.pdf

http://www.dpdhl.com/content/dam/dpdhl/Investors/Publications/DPDHL_Annual_Report_2010.pdf

http://annualreport2012.dp-dhl.com/outlook/opportunities-and-risks/strategic-focus

http://www.dpdhl.com/content/dam/dpdhl/Investors/Publications/Annual_Reports/DPDHL_Annual_Report_2011.pdf

http://unstats.un.org/unsd/trade/events/2014/Beijing/documents/postal/USPS%20-%20Big%20Postal%20Data.pdf

http://www.transglobalexpress.co.uk/news/351/

http://www.delivering-tomorrow.com/drilling-deep-to-find-big-data-riches/

https://hbr.org/2013/04/innovation-risk-how-to-make-smarter-decisions

Van Hove, M. (2014, July 11). Transformation Through Strategy And Innovation. Retrieved from http://www.strategos.com/transformation-strategy-innovation/

Wu, Z.Y., Ming, X.G., Wang, Y.L., & Wang, L. (2014). Technology solutions for product lifecycle knowledge management: framework and a case study. International Journal of Production Research, 52 (21), 6314-6334.