As with organisations in many other industries, logistics services are under intense pressure to remain competitive to face severe market disruption. Disruptive innovation has reached this industry, which is currently experiencing strong inflexion points. The logistics sector is being forced to innovate as rapidly and quickly as possible. Therefore, organisations need to stay competitive and drive innovation processes. Given that multiple innovations exist, it is crucial to identify the relevant innovation areas that fit based on business needs
Focusing on customer excellence is one of the keys to understanding market competitiveness. In the journey towards customer centricity, it is essential to use technologies that can help accelerate the organisation’s innovation. In this realm, open-source technologies can play a vital role. However, it is not enough that enterprises and businesses only have the mindset and technology; transformational work is necessary to change every thriving organisation’s culture and working methods.
This section defines open-source technological solutions and how the solution fits into the logistics sector. Below highlights the four core technologies for digital transformation: cloud computing, IoT & robotics, cyber security, and finally – analytics, AI, and ML.
An IT infrastructure consists of application development software, application deployment software, system infrastructure software, system and service management software. Aside from this, IT infrastructure also includes both internal and external workflows and products.
One of the most famous infrastructure innovations today is cloud computing. Previously, clouds were mainly present in Virtual Machines (VM) and datacenters; now, the cloud has evolved with the mix of bare metal, VM, and containers. Cloud computing delivers computing services—including servers, storage, databases, networking, software, analytics, and intelligence—over the Internet (“the cloud”) to offer faster innovation, flexible resources, and economies of scale. In addition, cloud computing can facilitate efficient logistics because of an increased need for data and system availability, backup and restore, robust and scalable IT infrastructure. As a result, industries taking advantage of the cloud can reduce operating costs and run infrastructure more efficiently.
There are multiple open source solutions, communities, and companies that have been supporting and researching available infrastructure, cloud-native applications, and more. In the open-source community, one of the leading organisations that advocates, piloted, and created successful open-source infra projects is the Open Infra Foundation. The organisation has 100,000 members in 187 countries, on which the communities build the tools and infrastructure operators on data centre, containers, edge, network, Continuous Integration/Continuous Development (CI/CD) and beyond. One of the most known projects of Open Infra Foundation is OpenStack. In addition, the Linux Foundation also established the Cloud Native Computing Foundation (CNCF) project. This project drives forward a vendor-neutral space so cloud-native technologies can be used and further developed. The goal is to design and run scalable applications in modern, dynamic public, private, and hybrid clouds. Successful open-source projects include Kubernetes, Prometheus, Envoy, and many others. There are more than 150,000 contributors to the CNCF project and 770 members worldwide.
Another organisation that consolidates some ethos of open-source infra is opensourceinfra.org. The organisation continuously updates different open-source infra solutions from the topics: operating system, computing, wikis, development tools and more. Other organisations such as IDC also performs multiple market research and publishes a market study of the Open Source Ecosystem that provides a market glance of open source software projects that contribute to infrastructure software, application development and multiple software technologies
The Logistics industry aspires to be competitive in the market and deliver the best solution, and there is no doubt that open source IT systems can contribute to these successes. A mission-critical IT infrastructure must be created, maintained, and sustained in the logistics sector. It should also support multiple technological innovations such as cloud computing. A sound IT system and infrastructure gives a personalised workplace environment that maintains security and data quality communicated and networked properly to run multiple scenarios and infrastructure.
The IoT and robotics are technological innovations that are highly interconnected. Even though these technologies have similarities, the application of this innovation varies from one use case to another. These innovations consist of hardware, processes, intelligence, software, and embedded systems.
In the open-source IoT and edge computing space, the LF Edge organisation leads to creating an interoperable framework for edge independent of hardware, silicon, cloud, or operating system. The project outcome is the creation of hardware and software standards for the future generation of IoT and edge devices. One product of the LF Edge umbrella is the EdgeXFoundry, an open-source, vendor-neutral, edge IoT middleware platform that enables autonomous operations and intelligence in the edge.
The implementation of IoT in the logistics sector is inevitable. One of the advantages of collecting and understanding data from the network is creating new business opportunities and revenue streams. In addition, through IoT solutions and data insights, there can be a better customer dialogue for improvement to fine-tune services in the operational processes in the industry.
In the realm of the semiconductor industry, an organisation called RISC-V is bringing open collaboration by gathering multiple stakeholders to tackle extensible software and hardware freedom on architecture. RISC-V forwards free and open International Society of Automation (ISA) standards so computing design and innovation can pave the way for the next 50 years. The organisation is a global organisation with 2 thousand plus members in 70 countries.
In the open-source robotics space, a non-profit organisation called Open Robotics supports the development, distribution, and adoption of open-source software in robotics research, education, and product development. One of the organisation’s projects is the Robot Operating System (ROS). It is a set of software libraries, algorithms and tools that help build robot applications in open source.
There are multiple advantages to implementing robotics and automation in asset-heavy sectors like logistics. An example domain where robotics is of help is in the warehouse. Robots are deployed in the warehouse to assist workers in picking, packing and sorting cargos with efficiency and precision. Self-driving vehicles and automated guided vehicles in warehouses, ports, and trucking business is also the future of transport. IoT and robotics can lead to higher production rates and productivity, more efficient use of assets, improved quality, improved safety for workers, and reduced lead times.
There has been an increased effort to improve cyber security on enterprise open source products. This improvement covers multiple implementations of tools to defend computers, servers, mobile devices, electronic systems, networks, and data from malicious attacks. It is also essential to always follow and seek the input of open-source projects’ security guidelines; for example, Kubernetes has published guides on how to secure clusters in containers. Another example is Ubuntu, wherein the operating system’s security is enterprise-grade and has been leading in security practices. To add to the list is the Linux Foundation project called Open Source Security Foundation (OpenSSF). This organisation envisions having an open-source ecosystem that uses and shares high-quality software wherein security is handled proactively. It is a community wherein members can provide information about different open source projects defects, mitigation, quality, and supportability. Developers can also do secure development practices and use the tools for preventing, remediating and mitigating security issues.
Cyber security has been a significant focus of different organisations, especially in the logistics sector which was affected by cyber attacks and cyber threats in the past. Examples of these threats are an intrusion into the company’s firewalls, systems, networks, and data. It is crucial to the logistics sector to have a trusted software supply chain service provider where companies can benefit from economies of scale in DevSecOps (Development, Security, Operations). The service provider can prevent the attackers from accessing and modifying the IT systems and complex software development supply chain. This proactive approach can prevent costly security fixes, disruptive technology ecosystem changes, and avoid liability when sensitive customer information is breached.
There is a growing amount of data from multiple systems and applications in different organisations in the logistics sector. Examining these data requires discovering hidden patterns, correlations, preferences, and trends. Analytics is the complex process of analysing big data to uncover these patterns, so organisations make informed business decisions. On the other hand, artificial intelligence is a technology that can create intelligent systems that can simulate human intelligence using data. At the same time, machine learning is a subfield of artificial intelligence, which enables machines to learn from data or experiences without being explicitly programmed. These three are interrelated to each other and can work together to provide a common goal, to provide the best value in data through intelligence, automation and experiences.
Data analytics, AI, and ML technologies can help the logistics sector get the right business intelligence and solutions by analysing the enormous amount of data collected in this sector. Through analytics, these diverse data sets of different types — structured, semi, and unstructured data can be used for use cases for analysing reactive, preventive, and predictive scenarios. As a result, the logistics sector can take advantage of this innovation for faster and better decision making, cost reduction and operational efficiency and stay competitive.
Guided by open-source values, organisations like AI Infrastructure Alliance (AIIA) have built a canonical stack for AI and machine learning built-in open-source software. The organisation has developed AI/ML dev lifecycle footprints that can guide the processes from data ingestion, data cleaning, data validation, and transformation, training stage to deployment stage. In each of the development lifecycles in the blueprint, open-source software has been a driving force. Some examples of these are PyTorch and TensorFlow. Python is also vastly used in the data science space and has been used to build key libraries, such as scikit-learn, pandas, and NumPy. The Twiml solution guide also published a machine learning landscape presented in Figure 1 below.
The landscape describes multiple verticals, such as tools, ML platforms, data management storage, compute, and consumption space. Most of these services are in the open-source space. Below are examples of projects that contribute to the ML infrastructure.
| Vertical | Open Source Technology |
| AI/MLOps Platform | Kubeflow The Kubeflow project is dedicated to making deployments of ML workflows on Kubernetes simple, portable and scalable. This MLOps platform provides a straightforward way to deploy best-of-breed open-source systems for ML to diverse infrastructures. |
| Data Platform and Products | MongoDB MongoDB is a document-oriented database program. This database uses JSON-like documents with optional schemas. This database can be used for operational reporting and is highly reliable for IoT related use cases. In addition, it can handle large volumes of data, is scalable, and supports IoT architectures. OpenSearch OpenSearch enables easy data ingestion, searching and data aggregation. It also has functionalities to view and analyse data. It is a popular database for search, fast data lookup for historical data and log analytics. Kafka Apache Kafka is a distributed event streaming platform used for high-performance data pipelines, streaming analytics, data integration, and mission-critical applications. Use cases that work are messaging, website activity tracking, operational monitoring data, log aggregation, stream processing, event sourcing and commit log. Redis Redis is an open-source (BSD licensed) in-memory data structure store, used as a database, cache, and message broker. Redis provides data structures such as strings, hashes, lists, sets, sorted sets with range queries, bitmaps, hyper logs, geospatial indexes, and streams. CephCeph Storage is an open, massively scalable, simplified storage solution for modern data pipelines in a cloud-native setup. |
| Consumption | Ubuntu Ubuntu is a widely used operating system used by data scientists worldwide. It is also the most popular Linux distribution used on public clouds with machine learning offerings. Data science professionals can also develop locally and quickly move to production using this operating system. Kubernetes Kubernetes, also known as K8s, is an open-source system for automating deployment, scaling, and managing containerized applications. Kubernetes can be set up in a multi-cloud environment and container orchestration is streamlined from cloud to edge. |
Open source is the future of sustained innovation. Many companies have recognised the value that adopting open source technology can bring. Organisations formerly wary of open source software are increasingly embracing the benefits and competitive advantages of using it, including logistics.
By adopting an IT strategy that embraces open-source software, logistics industries benefit from open standards, lower cost of ownership, economically practical support and reduction or avoidance of vendor lock-in. In addition to these benefits, the logistics sector can maximise the use of this technology for customer-centric product development. Multiple solutions in the open-source arena can help solve these use cases using technological innovations such as IoT, robotics, cyber security, cloud computing and data analytics, AI and ML.
Find out more about logistics sectors challenges, use cases and how open source technologies can help transform the industry.
Read our whitepaper entitled: Seeking satisfaction: Customer-centric digital transformation in logistics. It is a guide on making your customers happy using open source technologies and digital transformation for logistics leaders.
In this whitepaper, we tell logistics leaders and practitioners a compelling story of customer centricity as the core component of logistics business strategy. In addition, we give further insights to:
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