Retail success is now directly related to the speed with which businesses innovate. As a result, the 'typical' retail employee is changing, with retail technology teams larger – and younger – than ever before. But while dedicated technologists are increasingly expert in the latest innovations, from Artificial Intelligence to Blockchain and machine learning, how can these solutions be successfully deployed to drive business value?
These disruptive technologies are phenomenally exciting but what business problems are they specifically set to solve? Where is the balance of technology expertise and retail experience required to map the innovation cycle against the level of risk and reward? Retailers may have made extraordinary changes to create technology-first business models, but when it comes to radical innovation, it is essential to understand the art of the possible, not the probable. Andy Hawkins, Product Director, Adjuno, discusses the changing face of technology deployment and technology expertise within retail.
Disruptive for Innovation
The speed with which disruptive technologies move from hype to mainstream is faster than ever; and in a retail market that has been completely transformed by technology over the past two decades, it has never been more important to be at the cutting edge of innovation. A handful of retailers are already exploring Artificial Intelligence (AI), for example, to achieve a completely automated product design to manufacture to customer model; a process that leverages social media derived insight on trends in fashion to create new designs that are produced and shipped with zero human interaction.
Clearly such innovations are in the experimental stage right now and used for just a tiny proportion of the overall product range, but they reveal what is possible. With growing numbers of retail companies now actively recruiting an entirely new type of employee – even hiving off the technologists into separate Millennial and Gen Z friendly areas – technology as an enabler is now an underpinning concept of retail operations.
And a key area of innovation is the supply chain. In the bid to deliver the perfect customer experience, there is a growing awareness across the business of both the upsides and risks associated with supply chain functioning (and misfunctioning). The technologies that support effective supply chain operations are changing both in response to escalating consumer expectation and innovation adoption cycle.
But while the technologies are keen to embrace innovation are the latest 'disruptors' such as AI, Blockchain or machine learning really viable options for supply chains given the vulnerability of the retail business model?
Recognising that successful and innovative deployment of technology is increasingly differentiating success from failure, retailers are creating ever larger and more youthful retail tech teams. These individuals are savvy, knowledgeable and ferociously well informed; they are also fearless and keen to push the boundaries of innovation.
This new generation creates a different challenge for the tech vendors traditionally used to coaxing retailers into investment and innovation: when retail customers have larger tech teams chock full of Gen Zs confidently exploring cutting edge solutions than many software development companies, just what can a vendor bring to the party?
In many ways the model has been turned on its head: rather than tech vendors bringing innovation to retail experts, the vendors need to leverage their market expertise, their supply chain knowledge and experience, for example, to demonstrate to gung-ho tech experts just how these disruptive technologies can be adapted to deliver value – and when.
Right now, for example, while Blockchain has legal issues to overcome regarding data storage and ownership, and AI requires both greater technical knowledge and a significant financial investment, machine learning is most definitely a mainstream solution and one that retailers can confidently embrace.
Machine Learning for Automation
Automation is the primary goal at every stage of the supply chain, to improve efficiency and consistency and drive out cost. Certainly, automation within Distribution Centres (DC) has been on the rise for some time, from the integration of voice activated picking to improve accuracy and turnaround to the use of robotics to create a completely lights out operation.
Organisations are also leveraging end to end supply chain solutions that exploit business rules to automate processes. But there are still many areas in which the system decrees escalation is required because the business rules have not been defined – or met - and managers are required to handle the exceptions. From incomplete orders and shipment authorisations, to prioritising the unloading of containers, the decision to move goods out of the factory or into the DC too often requires human intervention.
This is where machine learning is set to make a huge difference through addressing large numbers of these current exceptions by supporting further, intelligent automating of additional business rules. Essentially, the vast majority of exceptions that have not yet been converted into business rules still rely on an element of gut instinct or expertise to be applied. Machine learning will be able to track the decisions being made by expert users and begin to translate those decisions into an automated solution. Essentially the new business rules will be more complex, but they will be based on the patterns of behaviour identified by machine learning solutions and enable businesses to incrementally and confidently automate essential activity throughout the supply chain.
While retailers are increasingly embracing technology led innovation, cultural change remains an issue. And one of the most compelling aspects of machine learning is the lack of cultural boundaries to overcome: it is essentially building on the straight through processing that has been enabled by data driven decision making over the past decade. Managers have already eradicated a raft of manual tasks as a result of fast access to trusted real-time data in conjunction with clearly defined business rules. Now they will be further released from mundane, day to day activities and empowered to focus only on those critical events that could potentially jeopardise the supply chain. In a volatile global marketplace increasingly affected by unprecedented carrier delays and potential trade wars, this ability to minimise the effort spent on business as usual will be invaluable.
Machine learning will also open the door for the next stage of fundamental change by revealing technology enabled opportunities to individuals across the business. While initially, machine learning will enable administrative savings and small efficiency gains, in the longer term machine learning and AI will eventually impact everything from product design through to analysis of demand and forecasting sales through to managing inventory flows through to engagements with the consumer.
Forward thinking retailers are no longer hamstrung by technology fear; in contrast their tech teams are keen to embrace the latest disruptive opportunities. But this remains an industry in flux – and it will be those companies that effectively manage investment at the right stage of the adoption and innovation cycle, that are able to match tech expertise and market experience, that will be successfully manage the shift to tech-first operations.