The Contemporary Logistics Industry
The Importance of the Industry, the Need for Change, and the Nexus to Big Data & Data Science
1. Welcome to the Logistics Industry
The Warehousing and Logistics Industry in Australia generates an enormous amount of data every day (DHL 2013), which creates great opportunities for businesses. However, this data is not currently being used to maximum efficiency (Zhong et al. 2015). Furthermore, if used correctly, data and statistical analysis provides immense opportunity for businesses and generates significant competitive advantage (Wang et al. 2016). By leveraging the data which already exists within the business, organisations can extract substantial value over multiple facets of the business, including guiding high-level strategic directions, assisting day-to-day tactical decisions, and reducing the cost-overhead caused when Service-Level Agreements (SLA’s) are not met. Jia, Wang & Wei (2015) discuss that the key to achieving this business success is through real-time data analytics and user-friendly visualisation. Moreover, Davenport & Harris (2007) emphasise the direct link between a business’s degree of competitive advantage and its degree of data intelligence. The use of data, and the realisation of in-depth statistical analysis is also an area where the warehousing and logistics industry is yet to achieve great outcomes (Bamberger et al. 2017).
The vital importance of the logistics industry cannot be undervalued. The Australian Logistics Council (2014) indicate that the Logistics industry contributes 8.6% to the Australian GDP, handles 5 million tonnes of goods per day (1.8 billion tonnes per year), and an increase of only 1% in the productivity of the logistics industry will benefit the economy by approximately $2 Billion. Therefore, the profitability of the Logistics industry is vital not only for companies within the industry, but the entire Australian economy. One way that the industry can maintain its effectiveness and competitiveness in the modern-day business environment, is with the effective use of data, analysis, and innovation.
The key factor in supply chain technology over the past 10 years has been Big Data, which underpins and enables everything (Abdulrahman et al. 2017; Cecere 2013; Kersgens 2019). Big Data is generally considered as information that is presented in large data sets, with large data volumes, and complex data structures (Khoury & Ioannidis 2014; Waller & Fawcett 2013). As opposed to newer technology (such as Machine Learning and Artificial Intelligence) which improves how the data is used, Big Data represents what the data is (structured information in data bases, high-volume video streams, and Website Comments).
To draw a comparison, outside of the warehousing and logistics industry, Big Data is seen in examples such as social media data, mobile phone call records, commercial website data, geographical information, search engine data and also smart card data (Subramanian et al. 2017). Whereas from within the industry, this data can be seen in examples such as high-volume transactional data, RF-ID and Barcode data, GPS data, and driver mobile-data-terminals (MTD’s) (Ayed, Halima & Alimi 2015; Mikavica, Kostić & Radonjić 2015). There is no shortage of high-quality data available in the industry, all of which achieve the five qualities of Big Data: volume, velocity, variety, value, and veracity (Wang et al. 2016). It is now the responsibility of the organisations within the industry to use this data holistically and effectively.
2. The Money Behind the Industry
The question needs to be asked: Why is data so important? Why should the warehousing and supply chain industry be focussing on data? According to the Australian Bureau of Statistics (ABS) (2018a) and as visualised in Figure 2, between 2013 and 2018, the industry has increased over three key areas:
1. Number of employees has increased by 16,000 to 586,000 employees (up by 2.8%);
2. Total income has increase by $7.3 Billion to $165.2 Billion (up by 4.6%);
3. Wages and Salaries have increased by $1.8 Billion to $34.9 Billion (up by 5.4%);
While it is beneficial that the industry is growing in these key areas, the same ABS report (2018a) indicates that the industry is also struggling financially. It notes two important areas of financial concern:
4. Total expenses have increased more than income, growing by $8.5 Billion to $150.5 Billion (up by 5.9%);
5. Earnings before Interest and Tax (EBIT) has decreased by $1.3 Billion to $14.7 Billion (decrease of 8.8%).
These results from these five metrics indicates that the industry is growing and expanding, however is struggling with profitability. There appears to be a slight correlation between the number of employees and the EBIT, as the employees reduced in 2014–15 corresponding with an increase in EBIT in the same year; while it was opposite in 2016–17 where employees increased and EBIT decreased. However, there are a number of hidden factors which would influence a company’s EBIT more than the number of employees, such as commodity prices (Vague 2019). Furthermore, the total income remained steady over the years 2014–15 and 2015–16 before increasing in 2016–17 by $5 Billion, which is 4.6% higher than the baseline. Notwithstanding, the total expenses in the 2016–17 year also increased, but by $7 Billion, which was 6% higher than the 2013–14 baseline. What this means is that the current accepted business models used in the industry are leading to increased costs, with little increase in income to compensate. If this trend remains, the industry will be in serious financial trouble. In order to rectify this trend, an increase in efficiency and improvement in operating process is needed in order to reduce costs and increase profitability. This is also where Big Data, Data Analytics, and Data Science can add the most value to the industry.
3. The Challenge
The challenge comes when implementing change and disruptive technology within the industry (Christensen, Raynor & McDonald 2015). According to two different ABS reports (2018b, 2019), the workers in the warehousing and logistics industry are 70% blue-collar, 60% unionised, 70% men, and 80% have not attained a university qualification. Resultingly, the trend is that managers of the industry are people who have always worked within the industry (for example, truck drivers, forklift drivers, freight handlers, pickers/packers). Bodenheimer (2005) discusses that these business managers are seriously hampering efforts to transform the industry due to three key reasons: lack of awareness of the benefits and capability of data, fear of change that Big Data and Data Analytics can affect in the industry, and excessive focus on business core competency and not on developing data analytical capability (Herrick, Gorman & Goodman 2010).
4. The Recommendation: IoT Network, RFID Tags, and Facility Layout Optimisation
One way for the industry to improve, is a three-pronged approach utilising IoT, RFID, and Predictive Modelling. The Logistics Industry can learn a lot from the path paved by the Retail Industry in this area.
4.1. Inspiration from the Retail Industry
Bottom: MSPT (2019), RFID Sticker
Almost every retail store in Australia has Electronic Article Surveillance (EAS) systems installed to prevent the theft of goods (Geason & Wilson 2017). These systems primarily use low-frequency (10–1000Hz) ‘gates’ at the entry and exit of the store (Giselher 2003) to detect any adhesive tags, containing amorphous or ferromagnetic metal, attached to any unsold goods (Figure 3 Top). However, the preferred option is for a small coil of metal (usually hidden behind an adhesive barcode) that acts as a capacitator between a pair of high-frequency (1.75–9.5MHz) ‘gates’, creating what is conventionally known as an RFID system (Figure 3 Bottom). This second option is preferred due to two reasons: 1. A unique identifier can be assigned to goods, allowing retailers to see exactly what goods were taken out of the store and when; and 2. The system is incredibly cheap, with capacitator’s (the ‘labels’) costing only a few cents, and inductors (the ‘gates’) costing only a few thousand dollars. Therefore, if a retailer is able to set up this system to track goods in and out of stores, at the loss of a few cents every time they make a sale, it begs the question why can’t warehouses use it to track goods throughout the facility?
4.2. How it Would Work in Logistics
The system would utilise an Internet-of-Things (IoT) network, whereby RFID tags would be attached to incoming goods upon receipt in to the warehouse. Then, as shown in Figure 4, the forklifts & pallet-jacks would have an equivalent sensor to recognise and record the goods being moved. Once a virtual handshake occurs between the tag and the sensor, the goods can be tracked throughout the warehouse without the need for barcodes and long-range scanners. The challenge would come when considering how to handle situations like multiple RFID tags on the one pallet, or breaking down pallets in to smaller parcels, or how the goods would be tracked once they are handed over to the transporter. However, these challenges can be overcome, and the efficiencies that it would bring to the business include increased worker productivity, increased tracking visibility, and increased warehouse layout efficiency (by analysing pick paths and highly-frequented locations). This solution is not yet established in the Warehousing Industry, and is most definitely an innovative idea that can prove beneficial to any business.
4.3. Facility Optimisation
Utilising this information, combined with details about the movement of inventory within the warehouse (for example: fast, medium, and slow moving stock), companies can develop much more efficient warehouses. This opportunity is focussed around the utilisation of predictive models to improve stock location, shelf configuration, goods routing, and asset utilisation. Some deep-level data analysis can develop Heat Maps and Kernel Density Estimate Maps, as shown in Figure 5, in order to optimise the warehouse. An integrated system can be designed to allow the data to be utilised by both internal and external stakeholders, which can be accessed through a cohesive web-portal. Moreover, seasonal time-series models can be developed to improve stock rotation and inventory management. The benefits of collecting and analysing Big Data within the industry are substantial and valuable.
4.4. Details About the Data
The data that would be collected would of course be massive, with thousands of transactions occurring every hour. In order to achieve the level of granularity expected by the customer, the data would need to be stored in a Data Warehouse and optimised to be available to the end user (internal or external customers) via the means of a web portal or a data feed. With consideration of data privacy, external customers would only need to access the data associated with their products; while internal customers (operators, managers, analysts) would want to understand all customers in order to sufficiently optimise their operation. Furthermore, the need to transform the input data would be vital in order for it to be used by the final user, as simply seeing the timestamp of an electronic handshake is not enough; different users would want different requirements of the data. Noting the size of the data collected, the ability to analyse such data would not be possible using simple Excel spreadsheets and pivot tables (which is the prevailing analytical application of choice within the industry). Hence, the need to invest in Big Data is vitally important, not just in infrastructure and hardware, but also in software and technology in order to fully capitalise on the performance and productivity increases. Therefore, while this opportunity is great for a business, a mature Big Date pipeline is needed to properly handle the amount of data generated, and a thorough business policy is needed to govern the use of the data.
5. Some of the Good Things Happening in the Transport and Logistics Industry
With this in mind, there are also a number of other good things which are happening in the transport and logistics industry, particularly regarding Big Data and Data Science. Four such examples can be seen in the work by Transport for London, the Shipping Industry, UPS, and Amazon.
5.1. The Optimisation of Transport for London
Marr (2015) reports how Transport for London (TfL) is using the Big Data of their Oyster Card system (the London equivalent of Sydney’s Opal Card). TfL use this data to optimise the efficiency of the city, particularly in regards to journey mapping, infrastructure load profiles, unexpected events, personalised travel news, and route optimisation. The use of Big Data in the TfL business has optimised and improved the business substantially.
5.2. Granular Tracking in the Shipping Industry
Lacey et al. (2015) discusses an example seen in shipping with small IoT devices affixed to shipping containers. These devices transmit key information — like GPS location, temperature, humidity and G-force — at predetermined time intervals. These devices may cost from $50 up to $500, and allow for granular-level tracking of assets, and transparent supply chain processes. When customers have assets worth hundreds of thousands of dollars, the benefits of having the visibility greatly outweighs the purchasing expense.
5.3. Why UPS Don’t Turn Left
Mayyasi (2014) writes that in 2004, UPS announced a new policy for its drivers: only turn left when absolutely necessary. In 2001, UPS used the Big Data behind GPS data to build a route optimisation system, solving the Vehicle Routing Problem (NEO 2013) for their drivers, and allows them to avoid crossing traffic when turning left (the equivalent of turning right on Australian roads). As a result, Kendall (2017) claims that since this change, UPS is using 10 million less gallons of fuel (37.9 million litres), emits 20 thousand less tonnes of carbon dioxide gas, and delivers 350 thousand more packages per year. The efficiency and use of Big Data in UPS has yielded some great results for UPS.
5.4. Amazon as ‘Warehouse King’
Amazon Web Services (AWS) provides cloud-based storage and processing, which reduces cost for businesses deploying products and services on the cloud. This has allowed businesses like Netfix, DropBox and Yelp (who are all AWS clients) to penetrate their respective market, and has led Amazon to become known as the ‘Cloud King’ (Lawler 2011). By extension of their own external services, Amazon has used Big Data to improve their warehouses, allowing them to ship more than 1 million items per day from their warehouses — some of which are over 117,000 square meters large (Sisson 2018) — and to deliver orders to the receiver within 30 mins of order placement via the use drone technology (Lebied 2017). Moreover, by analysing their own data, Amazon found that employees moving between stationary shelves/racking was inefficient, thereby opting to set up robots to reverse the process (as seen in Figure 6) and instead bring the shelves to the workers, increasing efficiency and picking capacity of every worker (Brown 2018; Wingfield 2017). Clearly, there are many efficiencies to be attained by implementing Big Data technology within the warehousing business.
6. Conclusion
The warehousing and logistics industry is, and will always be, a vital aspect to the Australian economy. The industry employs nearly 600,000 people and earns nearly $200 Billion every year, and any increase or decrease in the productivity by a mere 1% can affect the economy by approximately $2 Billion. However, the industry is struggling financially, and is in dire need of adopting new technology and new ways of operating with data if it is to remain profitable in the coming years. This need is recognised at an industry level, with the trends affecting supply chains indicating that Big Data, IoT, Blockchain and AI will be the biggest disruptors in 2019. Conversely, at an organisational level, businesses are aware that data analytics is available, but the adoption of such technology is cautionary and apprehensive; despite such positive examples seen in businesses like Transport for London, UPS and Amazon. Furthermore, by utilising technologies such as the Internet of Things (IoT), RFID Tags, and Predictive Analytics, businesses can optimise warehouses and increase the efficiency and visibility for internal and external stakeholders. Effectively, this will increase the productivity and profitability for the industry, making it more sustainable for the future.
8. References
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