Companies and governments have been discussing the challenges that lie ahead as we enter a period of rapid innovation and change unlike any the world has ever seen.
In a previous post we have made a number of observations about the increasing importance of data and connectivity in realizing the Fourth Industrial Revolution and singled out one of the key demands facing supply chains in this next wave of industrialization: increased public pressure for transparency. From cradle to grave, consumers want to know the history of the products they buy and whose policies they thereby support. The post says:
If business, government systems and public authorities prove capable of embracing a world of disruptive change, subjecting their structures to the levels of transparency and efficiency that will enable them to maintain their competitive edge, they will endure. If they cannot evolve, they will face increasing trouble.
Much has been said of the potential of data to revolutionize business and relationships. But data will only get us so far on its own. We also need it to be relevant, timely, and usable between supply chain partners.
Above all, we need data to be open.
Open data will be a crucial tool for supply chains to meet transparency and efficiency challenges. This is why, to the greatest extent possible, supply chain data should be open: freely accessible, presented in a format that is comparable and reusable and, ideally, utilized in a timely manner.
Not only will supply chains that embrace open data improve their public accountability and efficiency, they will also reap the social and economic benefits of opening up data for end users. Instead of languishing in a corporate computer system to be viewed by a handful of managers, the data can be accessed and used by employees to drive entrepreneurship, innovation and problem solving – increasing its economic and social value exponentially. This is why open supply chain data will determine which companies are at the forefront of the Fourth Industrial Revolution.
In forward-thinking cities and countries, the open data revolution is already underway. In Uruguay, ATuServicio.uy provides direct access to all performance indicators of the country’s health services. From waiting times to user ratings, data on health services can be compared and citizens can make informed decisions on healthcare providers. Investigative journalists have already found anomalies in some of the data provided, and this has encouraged private healthcare providers to standardize and improve their data collection.
In Canada, supply chain consulting group MBSL is developing what they call the Cargo Carousel System. Loaded with a combination of sensor technologies for RFID, GPS, light exposure, temperature, humidity, weight and movement with real-time satellite reporting and alerting, this system offers open data on a host of variables that can be analyzed and then automatically utilized throughout the value chain to increase efficiencies and throughput volume while reducing losses, damages, greenhouse gas emissions and insurance expense. Examples like this could be the rule rather than the exception if we embrace an open data future across multiple sectors.
Modern supply chains have grown exceedingly complex, and organizational silos have become a fundamental part of modern supply chains. Silos usually result in isolation of data for that particular part of the organization.
For example, a warehouse management system that is not currently connected to the other warehouses in the network cannot accurately generate a demand forecast for a given time period. As a result, this warehouse may be more likely to suffer setbacks from not having enough product on hand, which could lead a consumer to work with a competitor. As explained in McKinsey Quarterly, advanced analytics allows a company to “fine-tune the mix of raw materials and finished products, as well as the routing of manufacturing flows, in real-time,” allowing an organization to make changes on a recurring, frequent basis.
Although these organizational silos worked for decades, the rise of cloud-based technologies has changed the model of thinking. The use of the cloud enables an organization to monitor supply chain operations across a variety of settings. In the aforementioned example, cloud-based technology may be applied to help the warehouse management system, transportation management system, and ERP communicate between one another, which further assists the manufacturer in producing a more accurate forecast of consumer demand. Consequently, the use of cloud-based technologies could be applied to this example to generate a means of reorganization to current manufacturing processes to better meet consumer demands.
Notably though, it is unclear who may actually own the data: the company that invested in collecting and analyzing it, or the entity from whom the data was collected? Here, Europe and North America may move in different directions, promising more heartburn on both sides.
What to do? As a first step, business leaders and policymakers need to take a more permissive approach. They need to find ways to fit Internet of Things technologies into existing regulatory frameworks rather
than oppose it. The battles over Uber and AirBnB around the world show this won’t be easy.
At the same time, both sides need to acknowledge a need for a new democratic and economic ethos of what might be called ‘algorithmic transparency’: the need for public scrutiny of how big data works in practice. The crises facing European car companies over the integrity of emissions tests show the importance of this new principle of democratic, capitalist accountability – the modern equivalent of
audited company financial statements.
Offering a trillion-dollar opportunity, intelligent assets are already unlocking new sources of value creation for both companies and individuals. They are in the process of significantly changing business operations, from product design to the supply chain to how value is created after sale. From a public
sector perspective, intelligent assets have already started transforming important parts of societies’ ecosystems, including resource networks, transport systems and built environments.
McKinsey states that “the ability to monitor and manage objects in the physical world electronically makes it possible to bring data-driven decision-making to new realms of human activity – to optimize the performance of systems and processes, save time for people and businesses and improve quality of life”. As assets become more intelligent they learn to communicate and collaborate amongst themselves, eliminating the need for human intervention. These advances in autonomous ‘machine learning’ technologies enable the automatic optimization of a digitized process, which goes beyond providing decision support for asset owners or managers. Where physical interaction is still required, machine to machine (M2M) collaborative networks are able to deploy autonomous devices to attend to a problem (e.g. drones, Cargo Carousel System, robotics). These advances in IoT technology are likely to represent the next major step change in asset productivity.
A lot of the technologies required to achieve an IoT marketplace already exist today, but the question of how to most effectively scale them up and capture the value potential they offer requires concerted, cross-disciplinary, organisational and even sectorial efforts. A successful transition involves three major factors:
Firstly, the need for large-scale interoperability demonstrators is key. It is essential to deploy test beds facilitating open-source interoperability creation, and they should also assist real-life collaboration using best practice tools aimed at creating systematic approaches to IoT.
Secondly, there is great synergistic value in open innovation that needs harnessing. Businesses, government and innovators will eventually realise that it is more profitable to collaborate than innovate as individual entities. Moving away from the existing, siloed approach to one of open innovation will enable IoT value creation above and beyond what we are seeing today. An effective market environment is one where challenges are opened up to the best innovators and researchers, emerging as well as incumbent, using collaborative approaches with clearly defined problems and challenges.
And finally, increased data sharing will allow organisations to more effectively overcome challenges and profit on IoT opportunities. Because IoT value resides mainly in the variety and volume of data, organisations sharing data on assets and resources, whether IoT or not, will again find synergistic value creation opportunities.
The interplay between circular economy and intelligent asset value drivers provides a fertile ground for innovation and value creation. Circular economy value drivers include extending the useful life and maximizing the utilization of assets, looping assets, and regenerating natural capital. Intelligent asset value drivers include collating knowledge about the assets’ location, condition, and availability. A broad range of opportunities emerges when these value drivers are paired. While it is impossible to predict all possibilities going forward, there are already numerous conceivable ways in which this interplay could drastically change the nature of both products and business models.
Broadly speaking, intelligent assets can supply these three main forms of knowledge about assets and resources that enable value creation in a business environment:
Knowledge of the location of the asset. Asset tracking – determining the location of an asset, either in real time or based on connected checkpoints – is a significant enabler of sharing models. It is also an important opportunity for users to bring down the costs of logistics and other operations, and use their resources more effectively. This is especially important for businesses that have mobile, high value assets deployed across multiple locations, since operational performance depends on balancing resource utilization, rapidly redeploying resources, and keeping assets in service. Tracking assets can also greatly facilitate auditing, helping companies comply with accounting standards at a much lower cost.
Knowledge of the condition of the asset. The collection of sensor data that monitors an asset’s condition – the technical or biological performance or state of an asset, including specific responses to environmental conditions – enables users and/or suppliers to use defined thresholds or rules to initiate actions or notifications that allow for condition-based (predictive or preventative) maintenance, repair, decommissioning, or change of use pattern. Knowledge of the condition of an asset includes recording the use pattern of the asset, as well an asset’s material composition and its potential change during transit or the use cycle – as for example recorded in embedded product inventories.
Knowledge of the availability of the asset. Data on an asset’s availability – including whether
an asset is idle but also the supply/demand dynamics for an ensemble of assets – allows for the increased sharing of assets among different users as well as the development of new business models that promote the shift towards a more service-oriented economy. Availability also includes knowledge about asset ownership, and in energy systems availability includes knowledge about usage and demand of energy at a given location and a given point in time.
While circular economy business models can yield increasing asset and resource productivity, IoT plays an important role in making data available and turning that data into useful knowledge. It is truly a question of being able to maximise the use of data. Since both the IoT and circular economy perspectives imply systems thinking and cross-sector collaboration, market leaders in this space will be those who most successfully collaborate, share and use an open approach to innovation.
It is clear today that calls for more efficient and transparent supply chains will not subside. A supply chain that wishes to meet the challenges that lie ahead, and to capitalize on technology and innovation as drivers of the Fourth Industrial Revolution, can only achieve these goals through meaningful commitment to open data. As consumers’ expectations evolve and rapid technological advancements continue, “openness” will cease to be optional for supply chains.
Open data will enable supply chains to not only survive but excel as innovators in the next phase of industrialization. It is a revolution whose time has truly come, and there is no better time than now for supply chains to be a part of it.
The greatest threat to our planet is the belief that someone else will save it ~ Robert Shaw