Big Data & Supply Chains
JLL EMEA Industrial & Logistics
A logistics revolution in the making? 2018
Big Data and Supply Chains 3
Introduction
Good logistics and supply chain management are about In this short paper we consider some of the specific delivering superior customer service at a lower cost, but, in opportunities for big data to make supply chains more many respects, these twin objectives have become more efficient and reduce costs, assuming businesses can challenging as supply chains have become increasingly overcome all the obstacles to incorporating big data into complex and customers more demanding. Today, supply their operations. These initial observations are based on chains often extend across the globe and involve multiple supply chains staying much as they are now but becoming parties, while customers (whether businesses or end more closely integrated with enhanced transparency. consumers) want things quicker than ever before. In addition, we discuss the role big data could play over the Over time supply chains have become better integrated as longer-term in a far more radical overhaul of logistics and businesses have joined up their internal logistics operations supply chains, by potentially facilitating a more open supply and better aligned their logistics processes with their supply chain system (or systems) in which freight is moved in ‘smart’ chain partners. However, despite this, many supply chains containers through networks of ‘smart’ open access logistics still lack transparency. In particular, the visibility of end centres and hubs. This concept of a ‘Physical Internet’ is consumer demand and the movement and precise location designed to improve the efficiency and sustainability of the of materials, parts and goods along supply chains is often global logistics system.1 However, if it is to become a reality limited for many supply chain parties. in the future it requires numerous barriers to be overcome by a host of innovations. Big data is generally defined to refer to the vast amounts of structured and unstructured data available to businesses and other organisations and typically includes the advanced analytical processing capabilities that enable insights to be derived from this data. It has the potential to substantially improve the transparency of supply chains The visibility of end and, by doing so, make them more efficient and reduce consumer demand and the costs. However, this is much easier said than done. Indeed, movement and precise location without an appropriate data strategy and information of materials, parts and goods processing and analytical capabilities, businesses may along supply chains is often simply be swamped by massive amounts of data without limited for many supply securing any efficiency or cost benefits. chain parties.
1 ‘Physical Internet’ vision has been developed by Professor Benoit Montreuil of Georgia Tech. Big Data and Supply Chains
Six specific big data opportunities in supply chains
There are numerous opportunities for big data in supply chains, but many of these are to do with improving supply chain transparency.
In short, because traditionally it has been very of ‘substituting information for inventory’ which difficult for businesses to estimate or forecast has been highlighted in academic texts for 30 demand accurately they have had to hold years or more, but which has proved stubbornly additional inventory as a buffer against this difficult to implement.2 Big data has the potential uncertainty. Therefore, all along supply chains, to improve transparency in this way and, businesses – such as retailers or their suppliers therefore, could enable significant reductions – hold additional inventory because they do not in inventory. However, inventory cannot be know what their customers will demand. eliminated from supply chains because in nearly all cases the supply lead time is longer than However, if data on end consumer demand the demand lead time and goods are typically can be better captured and analysed and if this sourced and consumed in different geographies. information can be shared along the supply chain to give a more accurate and timely picture of demand, then each party could reduce the amount of inventory it holds. This is the concept
2 For example, substituting information for inventory is discussed by Martin Christopher in Logistics and Supply Chain Management, second edition, 1998 page 263. 5
The digital transformation of supply chains Big data can enhance supply chain performance and reduce costs
Generated Internet data
Structured Analytical Decision and and processing optimisation unstructured capabilities data Big data
1. Demand forecasting Static data e.g. Artificial intelligence 2. Visibility of supply Data saved from with the implementation the past of machine learning 3. Network planning 4. Transport operations 5. Risk mitigation 6. Smart warehouses
Dynamic data Data created continuously and in real time
Creation Stock Analysis Use of data of data of data of data
Internet
Big Data and Supply Chains 7
1. Better demand forecasting 4. Improving transport operations The most obvious specific opportunity for big data is where it Big data has a clear role to play in improving the efficiency can enhance forecasts of end consumer demand, by enhanced and the reducing the cost of transport operations, especially predictive demand analytics. For example, if retailers are in the final or last-mile delivery involving multiple drops. better able to utilise data on spending, together with other These deliveries are much more costly than primary data that may affect short-term future spending, such as the ‘inbound’ deliveries to warehouses and over the past 10 years weather, they ought to be able to make their supply chains or so have risen up the agenda of many businesses because more agile and responsive, by delivering an improved service of the growth of e-commerce. Big data could improve the to customers at a lower cost, see Otto case study. scheduling and routing of deliveries by factoring in dynamic data, such as real-time traffic incidents or congestion, see Ocado case study. 2. Enhanced visibility of supply Related to enhanced demand analytics is the potential of big data to improve the transparency of supply by better tracking 5. Risk mitigation and tracing products in the supply chain. The ability to track Big data could help mitigate supply chain risks. Risk and trace the movement of supplies and goods through supply mitigation has shot up corporate agendas over recent years chains has improved significantly over recent years because of following the Japanese tsunami and the flooding in Thailand the wider use of technologies but big data could reinforce this in 2011, which proved hugely disruptive for certain supply trend, with in the Internet of Things (IoT) often considered as chains. Since, then many companies have sought to get a providing the next generation of track and trace. better handle on their global supply chains including using big data to monitor events – such as the weather or potential industrial action – that could affect their suppliers. 3. Better network planning and design If big data can provide an enhanced picture of current and future demand and how supply chains operate in seeking to 6. Smart warehouses match supply with demand, then it could improve network Finally, big data has the potential to improve the efficiency planning and design. Typically, businesses undertake and reduce the cost of warehouse operations including periodic reviews of their distribution networks to ensure they by making warehouses ‘smart’. Clearly many warehouses remain fit for purpose and may seek to adapt these in line already operate with high levels of information and with expectations of growth or change. However, often these communications technology (ICT), such as Warehouse network optimisation exercises are undertaken with very Management Systems, but ‘smart’ facilities will be more incomplete data and very limited insight into possible future widely connected (including via the Internet of Things and changes. The availability of big data could plug these gaps sensors) and transmit data which could enhance both particularly via insights into potential future demand trends. warehouse operations as well as the overall performance of This would make network planning more robust and ought to the building, such as its energy consumption. help make outcomes more ‘future proof’. Big Data and Supply Chains
Otto Germany German online retailer Otto together with Blue Yonder, a leading provider of artificial intelligence (AI) solutions in retail, has developed an algorithm that calculates how its customers’ purchasing habits change on a day-to-day basis. The algorithm analyses as much information as possible including historic sales data, prices, discounts, short and longer term weather forecasts and other factors that influence sales for every item in Otto’s range. This helps Otto decide which products need to be ordered from its suppliers and in what quantities and enables the retailer to have items in stock before the customer orders them. With more accurate sales forecasts, Otto can avoid having quantities of items left over at the end of the season. Otto’s artificial intelligence system works independently and automatically. It takes on tasks previously Ocado carried out by people because it is faster UK and more efficient. Ocado, a UK online grocer and technology provider, has developed a routing software which uses big data to create a real-time optimisation algorithm. The software makes Prologis and several million route calculations per second to identify the best routes for its delivery vehicles fulfilling online orders. EEGLE It automatically assigns deliveries to a delivery vehicle France and establishes the order of individual deliveries to ensure on-time delivery. When an order is placed, Ocado’s webshop In partnership with Resolving, a digital management instantly communicates with the routing software to software company, Prologis has launched a new digital tool establish available delivery slots and the software called EEGLE which enables its customers (warehouse incorporates the new slot into the route occupiers) to better manage their logistics facilities remotely. optimisation for the desired delivery day. Building sensors send data to a 3D model of the logistics facility and customers can view the data and the 3D model on the EEGLE interface via a PC, tablet or smartphone. This new digital tool is first being implemented in a 56,000 sq m built to suit facility in Ile-de-France for French retailer Cultura. The tool enables customers to monitor energy consumption, manage maintenance, detect operation malfunctions and improve Nestlé overall operations. UK Nestlé, the world’s largest food and drink company, and XPO Logistics, a leading global provider of transport and logistics solutions, are co-creating a new distribution centre at Castle Donnington in the UK. The facility, a digital and smart warehouse, will be occupied predominantly by Nestlé for its consumer-packaged goods and will function as a testbed environment for XPO’s technology prototypes. The facility, scheduled to complete in 2020, will use big data, by integrating predictive data and intelligent machines to deliver one of the most advanced distribution management centres in the world. Moreover, as a smart building, the distribution centre will also use big data inside the building to improve its sustainability (energy-saving LED lighting, environmentally ammonia refrigeration, air source heat pumps for administration areas and rainwater harvesting). 9
Longer term potential for radical change
The existing explosion of big data is largely based on the world wide web and the digitisation of supply chains that this enables.
Over time, as more and more devices become connected to If realised – even if on a modest geographical scale – the the internet, through the Internet of Things (IoT), the volume ‘Physical Internet’ concept would likely lead to the better of data will skyrocket as billions of objects become ‘smart’ utilisation of logistics assets (facilities and transport assets) and networked. through seamless open sharing, and change the ways these operate. For example, the facilities could become more In this scenario of ‘hyperconnectivity’, in which objects and automated due to standardised containers and material assets in supply chains are all connected and capable of handling equipment with more robots, and intermodal communicating with one another – and with the deployment transport operations could become more significant of blockchain technology to enable secure transactions – it is because flow consolidation would make rail freight more possible to imagine a future where in many respects supply viable. The end objective is a more efficient and sustainable chains effectively become self-organising with freight moving logistics system. However, it requires a host of innovations in ‘smart’ containers through networks of ‘smart’ warehouses. across multiple fronts – technological, business, legislative, This is the vision of the ‘Physical Internet’, a hyperconnected social, cultural and process – to become a reality. global logistics system based on the open sharing of logistics facilities and standardised modular containers.
The vision of ‘Physical Internet’is a hyperconnected global logistics system based on the open sharing of logistics facilities and standardised modular containers. Big Data and Supply Chains
Conclusions – implications for property
• From supply chains to demand chains. Big data has the • More demand for on-demand, dynamic, warehouse potential to enhance the transparency of supply chains to space. As big data enables supply chains to become improve efficiency and reduce costs. By providing better more responsive and agile so businesses are more likely insight into end demand, big data should make supply to require more flexibility from their logistics property. chains more agile and responsive, with less inventory This could result in growing demand for on-demand, or overall. Big data could shift the focus from supply chain dynamic, space which businesses can source through management to demand management. online market place providers. • Less inventory more throughput. With less inventory • Growing demand for data centres. The growth in big overall but greater insights into end demand, the function data will lead to growing demand for data centres, which of logistics facilities will continue to evolve away from whilst specialist in terms of their fit out are typically storage to a greater emphasis on throughput. Clearly, incorporated within industrial/distribution structures. as noted above, inventory will not be eliminated and so • Longer-term – a new logistics system? In the longer demand for large storage facilities will remain, but we term if big data, in combination with other developments, expect to see growing market demand for facilities such ushers in a new type of smart, hyperconnected logistics as transhipment and cross dock centres linked to quickly system, then the demand for logistics space could change meeting customer demand, which big data should enable more radically. This is the ultimate vision of the ‘Physical businesses to better track and predict. Internet’ and it would drive significant changes in the • More decentralised distribution networks. Distribution demand for logistics space. Logistics facilities would networks could become more decentralised thanks increasingly become open access, with more automation to the increased ability to pinpoint more accurately and robots with major hubs located at intermodal or customer demand. multi-modal locations. 11 Contacts
Guy Gueirard Head of EMEA Industrial & Logistics +33 (0)6 59 03 74 54 [email protected]
Jon Sleeman Head of EMEA Industrial & Logistics Research +44 (0)207 087 5515 [email protected]
Alexandra Tornow EMEA Industrial & Logistics Research +49 (0)69 2003 1352 [email protected]
Raphaele Naud EMEA Industrial & Logistics Research +44 (0)203 147 1135 [email protected]
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