Jon Payne, sales engineering manager, InterSystems. Image: James Pike
InterSystems’ Jon Payne outlines how deployment of AI and ML technologies in a company’s digital transformation can keep the human at the centre.
Amid rapid technological advances and new developments in technology, the role individuals play within businesses can often be overlooked. Although technology has a huge part to play in the smooth operation of a business, people remain critical to ongoing success too. As organisations look to increase resilience, agility and innovation, the use of advanced technologies to provide humans with insights to make smarter, more informed decisions, and shift their focus to more value-adding initiatives, is groundbreaking.
In the supply chain sector, for example, people continue to have a significant role to fulfil in logistics. Although automation is fast advancing, organisations still need people in warehouses for pick and pack, to deal with advanced shipping notices, for dispatch, to interact with customers and to build brand loyalty, among many other things. People remain essential in the first mile and the last, and it is here that organisations are achieving the biggest improvements in performance by applying technologies such as artificial intelligence (AI) and machine learning (ML).
These technologies complement domain experts and their years, if not decades, of expertise. They empower individuals to become more proactive by using prescriptive insights that radically improve outcomes and increase productivity. As well as enriching the quality and breadth of every decision, this use of technology in conjunction with humans replaces the loss of ‘tribal knowledge’.
This refers to the knowledge acquired by longstanding employees who know the idiosyncrasies of specific territories, markets or customers. When they retire, this knowledge can go with them, leading to lost opportunities or unforeseen difficulties.
For example, in a warehouse setting, it might be that a long-standing manager is aware that a particular delivery always arrives slightly early, despite the timings indicated on the schedule. Should they leave, and that knowledge with them, this could create an issue when the delivery arrives early the following week and the loading bay is not ready to receive it, causing delays.
The use of AI, ML and prescriptive insights, can help organisations to avoid the consequences of such departures. The new manager could be prompted to be ready early for this particular delivery each week, based on historic data. The overall effect of using technology to supercharge the abilities of domain experts is to greatly enhance operational efficiency and ensure resilience in the face of personnel change.
Optimising resource-allocation
A prime example of where these technologies can have a positive impact is demand-sensing, which has historically been a highly complex operation. Without the ability to sense demand as it evolves, retailers with multiple outlets can easily be caught out by the success of product promotions in specific locations. For example, take Spar, a major food retailer in Austria, which found it lacked data to enable adequate store inventory control at shelf-level to improve on-shelf-availability. Stores could run short of goods because they had to rely on inaccurate, stale and reactive data calculated by a central office.
Consequently, Spar sought an end-to-end resource planning and point-of-sale system to help managers gain the necessary control. For that purpose, it deployed a unified data platform to give store managers a comprehensive and accurate end-to-end view of their sales, inventory orders and deliveries. It was able to do this at scale, but the advances did not stop there. Using embedded AI and ML the company optimised replenishment through real-time sensing of 800 promotions in each of its 1,500 stores. This significantly improved demand forecasting.
In another example, Paltac, a major cosmetics wholesaler in Japan, has improved labour efficiency when moving 50,000 items from 1,000 manufacturers to 400 retailers operating 50,000 stores. Undeniably a massive undertaking, this formed part of the company’s digital transformation programme which aims to increase revenues on the retail sales floor.
The company used a data platform with AI and ML to develop its in-store support application – in effect the first step of the digital journey. The application is available as a desktop and smartphone app so project leaders can assign work and communicate with team members in the field. Team members can also share promotional plans and document their observations.
As a result, Paltac has seen improved productivity along its supply chain, providing a unified workflow with real-time, accurate information that continues to improve revenues and operating costs and achieve an unprecedented on-time, in-full (OTIF) metric of 99.999pc. As the system develops, the company will integrate AI to automate personnel assignments and a portal will provide manufacturers and retailers, the two ends of the supply chain, with monitoring and analytics capabilities.
Using technology to augment human agency is already having a major impact in sectors such as retail and supply chain, but its introduction needs careful handling and close attention must be paid to change management. Employees need to understand how the technology will make them more productive and help them work smarter.
See, understand, optimise, act
No matter the industry, empowering humans is essentially about using data to achieve real-time visibility and make more informed decisions. Organisations need to wrangle the data from across the entire enterprise to gain a ‘sense and respond’ solution, with the responses orchestrated by humans using prescriptive insights. The use of a smart data fabric architecture can provide organisations with those predictive and prescriptive insights that address demand surges, disruptions and constraints. This type of data architecture, powered by a unified data platform, helps businesses to overcome the conventional lack of end-to-end visibility by integrating and normalising different types of data and applying embedded AI and ML.
Optimisation of human decision-making within organisations essentially relies on four elements: see, understand, optimise and act. The first is end-to-end visibility. The second is the data-driven insight. The third is the end-to-end prediction and orchestration. And the ‘act’ element comes from the achievement of end-to-end aligned decision-making, transforming the productivity of the whole enterprise.
By adopting this approach through a single, unified data management platform, business leaders will benefit from a much more productive and responsive human workforce and an infinitely more efficient business.
By Jon Payne
Jon Payne is manager of sales engineering at InterSystems and has 37 years’ experience building and delivering software in over 30 countries.
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