The data-driven supply chain

Preparing Australian retail for the emergence of data-driven collaboration.

The retail supply chain is poised to undergo a transformation based on new technologies that will make data easier to collect (IoT), easier to interpret (AI) and easier to share (collaboration platforms). Online giants like Amazon and Alibaba have built their success, in part, due to their mastery of supply chain data.

In this report, we seek to analyse the readiness of Australian retailers, transport/logistics companies and manufacturers to participate and compete in the world of data-driven supply chains.

Want to learn more? Read the full report

Download now


The supply chain is vital to the retail industry as it represents both a large cost and a source of competitive advantage. Supply chain costs have been calculated by the Reserve Bank to represent 40% of retail selling prices so controlling these costs can have a significant effect on both retail prices and profitability. In other cases supply chains can be used to create a competitive advantage by delivering goods to the right place more quickly than rivals, thereby serving customers better.

The concept of supply chain management (SCM) is that more value can be created by managing a supply chain holistically than by managing its separate parts independently. At the core of SCM is the theory of collaborative advantagewhich says when two or more organisations pool resources and skills, incremental value can be created. Studies have shown supply chain collaboration (SCC) delivers benefits such as risk reduction, cost reduction, access to complementary resources, improved productivity, increased competitive advantage and increased profitability over time.

Discover more about data-driven businesses

Read the case studies

Explore the future of retail supply chains

Technology and adoption

Supply chain collaboration:
The dawn of new technology

Today, supply chain collaboration as we know it is about to enter a new era driven by technology. The ability to capture supply chain data will rise exponentially with 5.5 billion smartphones and 20 billion IoT (Internet of Things) devices in the market by 2020. This means it will be possible to collect data not just on sales and production schedules between retailer and manufacturer, but also on the location and condition of goods throughout the supply chain, consumer and team member movements (both in and outside stores), stock levels in consumers’ fridges and appliances, the availability of consumers to receive a delivery, and much more.

Data storage is already being transformed through the increasing capacity, flexibility and decreasing cost of cloud computing.

New technologies like blockchain and data exchanges – will mean information can be shared by all parties, not just point-to-point collaborators. Data analysis will also be transformed as autonomous or semi-autonomous platforms allow businesses to discover deeper insights, make predictions, or generate recommendations in virtual real-time. Most of all, high-speed data networks will enable collaboration to take place at a pace that allows timely business decisions.

As well as technological disruption, retailers face competitive disruption from online retailers like Amazon and Alibaba who are already masters of supply chain data. Both collect every customer click and use it to predict buying patterns, optimise the placement of inventory in the network and maximise both the speed and efficiency of delivery.

Watch our highlights video where leaders discuss the value of data-driven supply chains.

The 5G journey

"5G" is actually an umbrella term for the next major wave of cellular network technology.

5G will be a combination of transformational technologies that will address specific use cases. Whilst 1G, 2G, 3G and 4G were primarily about voice and then data, 5G will be about connecting everything with minimal delay, faster speeds and at larger scale with billions of devices (via the Internet of Things).

It's important to understand that the journey from 4G to 5G will not be a step change. Rather, many features that will eventually be collectively standardised as "5G" will be incorporated into forthcoming releases of 4G LTE standards and will progressively be deployed by carriers such as Telstra alongside 5G deployments.

There will be three major benefits to 5G:

Ultra-low latency

For mobile broadband services, 5G will approximately halve latency, which will greatly improve the responsiveness of internet applications. For mission-critical IoT applications, 5G will provide very low latencies at very high reliability, which will enable brand new industrial automation applications.

Increased speeds

5G will provide significantly faster data speeds and greater capacity, eventually providing ten times the speeds and capacity of 4G.

Massive machine-to-machine communications

4G technologies are today facilitating IoT sensor networks at scale, but 5G will increase scale to support billions of devices without human intervention.

5G will enable the new technologies that improve efficiency, speed and reliability through a number of features:

  • A Distributed Cloud Architecture means resources can be allocated from the cloud enabling a better user experience
  • Massive MIMO Antennas that can focus the radio signal towards multiple users simultaneously, increasing speeds and reducing interference
  • New protocols and frame structures, which will reduce latency for all services and improve reliability for mission-critical IoT services
  • Network Slicing, which enables a granular way to segment the network, allowing finely grained performance and cost optimisation to be applied to individual applications, industries and customers
  • Network Function Virtualisation enables the use of commodity network infrastructure, which can rapidly adapt to the changing needs of the network
Case study

Australia Post: Data driving functional capability

Australia Post has been around for 209 years, but the last seven have seen some of the biggest changes in its history, with the number of parcels they deliver overtaking letters for the first time. There is no doubt this has been driven by Australia’s growing appetite for online shopping – up a whopping 19.2% last year alone. For the company, this new e-commerce world is presenting both enormous opportunities – and challenges – as they move from their traditional business model towards an omnichannel, on-demand future.

The customer’s mobile number is one simple data set that is proving very powerful, connecting into all the organisation’s internal data sets and systems. By having access to that data and knowing where an item is in the supply chain, the company allows their customers to say, 'I want this item delivered here now, but in ten minutes time I want to change my preference,' and the supply chain has the flexibility and fluidity to keep up.

Australia Post’s biggest challenge now is how best to use data to drive change and improve collaboration. The recent launch of the Shipster platform is a prime example.

For the full Australia Post case study

Download here

Find out how another Australian company, Linfox, is taking a new approach to data

Our research: Overview

In our research, we sought to explore the phenomena of data-driven supply chains and investigate three key questions:

  1. What are the key readiness factors for the successful execution of a data-driven retail supply chain?
  2. What are the current readiness levels across retail, transport/logistics and manufacturing businesses?
  3. What gaps in readiness exist between supply chain actors (retail, transport/logistics and manufacturing) and between small and large organisations in these sectors?

We hypothesised that for data-driven supply chains to be adopted three conditions must exist i.e. management must believe in the benefits, the key technology pillars must be adopted and there must be collaborative relationships between supply chain partners.

To discover our key findings, keep reading on. Or, you can access the full downloadable report for an in-depth explanation of our research framework, methodology and results.

Download now

Key findings

Belief in business benefits

Technology deployment

Relationship with partners

Barriers to adoption

Our research: Conclusions

Collaborators need to align

Supply chain organisations have inherently different business drivers but the competencies of retailers, logistics companies and manufacturers are complementary. They are all in the business of combining to get goods and information from point of production to point of consumption and sometimes back again.

Collaboration relies on a coalition of the willing but if one party takes the lion's share of the spoils the coalition won't be willing for long. For data-driven supply chains to be adopted, it's important the benefits of collaboration are shared equitably and not just realised by one party (usually the retailer).

Discover the new technologies helping businesses collaborate in the retail supply chain

Data is the new oil but only when it can be used

Oil only became useful when we gained the ability to refine it. Once refined oil did indeed transform every industry especially by facilitating the development of the motor car and the aeroplane.

Likewise, data has been with us since retail began but has only become useful since we have been able to capture, store, analyse and share it with others.

Our respondents said: data is easy to collect but hard to manage, there are problems making it compatible across different apps, the tools to analyse it are in some cases immature, and the problems multiply when we attempt to share it across multiple platforms in the cloud.

To be useful data needs to be accessible and useable. To this end we are seeing retail enterprise systems being rewritten from the ground up with exposed APIs, the emergence of data exchanges that facilitate the sharing of data between multiple supply chain players, as well as the use of blockchain technology that helps verify hand-offs between supply chain actors. These developments hold the promise of overcoming current concerns with technology, making data useful and assisting in the adoption of data-driven supply chains.

Data is a resource that needs securing

Data is an intangible or operant resource, but when deployed in the supply chain it can be used to replace inventory, fixed assets, labour even physical locations through better sensing of and responding to demand. Data is therefore potentially as valuable as any of the tangible assets on a balance sheet and needs to be protected accordingly.

The Australian government conservatively estimated the cost of ransomware to the Australian economy to be approximately A$1 billion per year. It’s therefore not surprising that supply chain actors cite security as their number one barrier to adopting a data-driven supply chain. To remove this barrier, supply chain businesses should consult a trusted partner in cyber security to ensure shared data is protected and the benefits of data-driven collaboration are not lost.

This vehicle needs extra drivers

The transition to data-driven supply chains requires the traditional skills of supply chain professionals, but it also needs mathematicians who can interrogate the data and build the algorithms that turn supply chain data into useful business information. These skills are in short supply in the retail supply chain. Online retailers like Amazon have long recognised the benefit of PhD data scientists in their businesses. Traditional retail companies now also need to fill this skills gap.

Forming closer ties with universities who have pipelines of PhD students and have post-doctoral students as potential project resources may be a logical first step. The University of Sydney Business School collaborates with local and international industries for research in three interrelated areas:

  1. Data analytics: understanding what data to sense and through what channels, data cleansing and clustering, data analysis using innovative analytical predictive models, blockchains and data security;
  2. Innovative decision-support tools: dynamic optimisation models to improve supply chain efficiency, visibility, resilience and sustainability, enable integrated decision-making, allow effective cooperation and information sharing;
  3. The human factor: evaluating the consumer behaviour changes and incorporating the findings into the decision-support tools, understanding and formulating the behaviour of supply chain decision makers.

For more information on the University of Sydney ITLS

Find out more about how PhD-accredited data scientists are transforming Target USA

Smaller businesses need allies

SMEs see more benefit in a data-driven approach than their enterprise counterparts, but are less advanced in deployment. DDSCs represent a threat to SMEs as they are less likely to have the necessary resources to deploy. This puts SMEs at risk of becoming relegated to low-value, old world, transactional relationships with their customers and suppliers. To remain relevant SMEs must look for allies in the technology sector who can provide them access to data-driven supply chain data, analytic capability and collaboration platforms as a service.

Case study

Otto: Using predictive analytics to place inventory in the network

The future can be glimpsed at the Otto Group headquarters in Hamburg, where the German e-commerce merchant is using Artificial Intelligence, big data and Machine Learning to make quick and accurate decisions that reverberate along the length of their supply chain.
Otto uses AI platform Blue Yonder (a start-up in which they own a stake) which is capable of analysing around 3 billion historical transactions and 200 variables. As a result, stock can be optimally adjusted to demand and sales, customer satisfaction and earnings can be increased.

For the full Otto case study

Download here
  • Surplus stock reduced by 20%
  • Product returns down by 2 million items a year
  • Faster delivery
  • 90% accuracy on what will be sold within the next month

Target USA: A systems led approach to data-driven retailing

There are not many retailers that have forty PhD data scientists in the infrastructure team but Tom Kadlec, Target’s Senior Vice President of Infrastructure & Operations, says the data scientists’ contribution is increasingly vital for a company determined to delight its ‘guests’ by harnessing the power of innovation and technology to make the shopping experience as seamless as possible.
In a process Kadlec describes as ‘decomposition’, all the company’s systems are being broken down, simplified, completely rewritten from the ground up and then exposed to an API. This means data can be shared quickly and simply within the organisation, and is readily available to developers in a useable form. Kadlec feels if Target is to become a truly data-driven retailer, then this is an essential first step.
Kadlec sees a key focus of his role as providing the organisation with data on the four pillars of the organisation: Guests, Team Members, Assets and Products.

For the full Target USA case study

Download here

Smartphones and network design

Network design has always been part of supply chain management. Placing retail stores, distribution centres and other resources in optimal proximity to customers can be a key source of efficiency and agility in the supply chain.

The problem with network design data is that it traditionally consists of a series of historical snapshots – sometimes up to five years old – and normally requires the services of a demographer as an interpreter. Now, the smartphone can provide more dynamic and contemporary inputs to network design.

By understanding the movement patterns of smartphones in a market and linking them to ‘Helix Personas’ Telstra’s Location Insights service can provide not only demographic, but also behavioural and psychographic profiles of customers as they move in a geography, in virtual real-time. It can also give a true assessment of the efficacy of infrastructure and the true reach of trade areas. Given the high term costs of five-year leases for both retail and industrial premises, this is valuable data.

Case study

Linfox migrates its fleet to IoT

For Linfox, one of Australia’s largest logistics companies, the role of data today is critical, and only likely to get more so. Said Linfox CEO, Mark Mazurek: "Linfox has a significant fleet and warehouse network. Data is central to keeping our business compliant, safe, efficient, profitable, and most of all, connected."
Linfox has recently partnered with Telstra and MTData to upgrade its FoxTrax system by implementing an advanced telematics and management solution. The Internet of Things (IoT) technology will be rolled out to the whole Linfox truck fleet and will deliver advanced transport and logistics data and quality benchmarking information to enhance public and driver safety on Australian roads.

For the full Linfox case study

Download here
"Partnering with Telstra and MTData allows us to better:
  • monitor and measure safety compliance throughout our fleet,
  • coordinate our vehicles efficiently,
  • reduce congestion on the roads and
  • ensure a higher level of safety for the community."

"Our ambition in Telstra is to help our customers become digital enterprises by translating data into insights that empower outcomes."

Michelle Bendschneider

Executive Director of Global Products, Telstra.

New technologies connecting retail supply chains

The organisations that will thrive in the era of the data-driven supply chain will be those that can most effectively manage their data supply chain and can effectively collaborate with their supply chain partners around that data.

Technology already plays a critical role in providing those capabilities. Looking forward, three key questions then become:
  1. What new forms of data will drive efficiencies and new customer experiences?
  2. How do we use the massive amounts of data required to drive optimisations and create those new customer experiences?
  3. What are some of the technologies that can take supply chain collaboration to new levels?

Instrumenting the data-driven supply chain: The Internet of Things and IoT networks

A raft of new in-store IoT devices and capabilities are shaping both store operations and the customer experiences they can deliver.

Computer vision

Low-cost, high-quality application-specific cameras backed by advanced AI-based image and video processing make computer vision a realistic tool to apply to many retail problems. Computer vision built into intelligent shelving systems can:
  • monitor stock levels
  • gather customer intelligence including dwell time
  • demographic profiling
  • sentiment via facial analysis
Computer vision, augmented by other sensors, can be used to enumerate items that consumers pick from shelves almost as accurately as a standard checkout process (see exhibit 1) and can be used to understand consumer responses to advertising, store layout and product presentation.
Exhibit 1: BingoBox
  • Chinese-based start-up, BingoBox, is tackling the crowded convenience store market by using IoT enabled, AI-driven unmanned stores.
  • Customers use their smartphone to gain entry then as they leave, their goods are scanned via RFID and charged to their WeChat Pay or Alipay account.
  • By removing checkouts and automating inventory management, BingoBox believe a team of four staff members can manage up to forty BingoBox stores.

Narrowband cellular technologies

Two new mobile network technologies: 4G Cat-M1 and Narrowband IoT (NB-IoT), now being deployed by Telstra, are purpose-built to support low data rate IoT applications.
Unlike short-range technologies, narrowband cellular technologies allow compatible tagged stock to be connected to a pervasive wide area cellular network – offering the potential for cheaper end-to-end tracking.
Mobile network based compatible devices are particularly interesting for sensitive freight such as fresh produce and pharmaceuticals.
Exhibit 2: Peloris
  • Australian Peloris Global Sourcing has used IoT to help create a whole new market – exporting fresh milk to China.
  • With help from Telstra, Peloris implemented continuous end-to-end IoT monitoring of location, temperature and other condition data that has helped Peloris achieve endorsement from China’s Inspection and Quarantine Bureau for rapid border clearance of food imports. Shipments that would have taken weeks to clear can now be cleared in thirty-six hours.
  • The same approach can be used to optimise handling of other sensitive products such as fresh produce and pharmaceuticals.

Intelligent data-driven supply chains: Artificial Intelligence, Machine Learning and Deep Learning

Machine Learning algorithms take in large amounts of data from which humans manually select relevant features that the algorithms then use as a basis to learn how to create predictive models.

Deep Learning removes the need for humans to manual identify relevant features. Instead, deep learning algorithms construct an ‘artificial neural network’ (based on one model of how the human brain may work) to autonomously learn which features are needed to make more accurate predictions or more useful insights.

Conversational commerce Devices such as Amazon’s Echo, Google’s Home and Apple’s HomePod and platforms such as Facebook chatbots allow human interaction with services by natural language.

Retail supply chain collaboration: Blockchain and data exchanges

A blockchain is simply a database that continuously processes data and transactions, with copies distributed across many parties and reconciled using automated consensus algorithms.
Blockchains enable trust without a direct or indirect relationship between transacting parties. If one party makes a commitment or records an event such as a delivery, there is an immutable record visible to all relevant parties.
Some areas where blockchains are likely to have impact in the mid to long term include:
Traceability and provenance
Blockchains have been proposed as a good way to record the origin and path to market.
Fraud protection/minimisation
Blockchains can record features that provide strong evidence of product authenticity, which can then be matched against the actual product received.
Visibility and transparency
In complex supply chains, blockchain may provide a mechanism to give consumers and investors proof of ethical and sustainable origin and production practices.
Digital advertising
Blockchain-based advertising marketplaces are being developed to ensure legitimate presentation of ads and whitelisting of inventory sources.
Data exchanges are digital platforms where data from supply chain participants and others can be brought into an integrated, privacy-controlled, rights-managed, enabling participants to build data-driven solutions that unlock new value and capabilities for themselves and others. Data exchanges are the hub for ‘Data Communities’ – ecosystems that share, add value to and derive value from common data.
We know participants need timely access to data from across the supply chain to optimise operations. Data exchanges have the potential to solve many of these problems by:
  • Providing a single common interchange interface
  • Radically reducing the cost of on-boarding new analytical solutions and data sets
  • Reducing operating costs – such as normalisation and data quality management
Work is needed to develop acceptable and secure data ownership and commercial models for Blockchain and data exchanges. As data exchanges are ecosystem-centric, their utility is largely based on network effect and there are few industries in which a common data exchange has reached the critical scale required to be deemed industry infrastructure.


The emergence of new technologies to capture, analyse and collaborate with data promises a new era of supply chain management, but our research shows there are some barriers to be overcome.

To unlock the promise of a data-driven future, supply chain actors need to align objectives with partners, find a way to secure the data they share, find new skills to make data useful and win the familiar organisational arguments around investment priority. The world will not wait. Already we can see online retailers like Amazon, Alibaba and OTTO using supply chain data to gain significant advantages over their rivals, and traditional retailers like Walmart and Target USA are catching up.

In the end, it may be traditional retailers with their established networks of stores and distribution centres, ability to collect online and offline data on consumers, analyse markets using movement data from smartphones and a customer base willing to do some of the last mile work of the supply chain that have the most to gain. What can be said with more certainty is that the big gains in retail have and always will come from taking share from competitors or operating more efficiently. The data-driven supply chain promises both.

Want to learn more? Read the full report

Download now