The digital supply chain inclination encompasses many technologies, from more accepted categories of software that “digitize” a method, to newer technologies like AI that can more brilliantly transform supply chains. The broadest form to look at digital transformation as it pertains to the supply chain is to estimate end-to-end processes, studying the digital technologies that can help organizations do a better job all the way from the supplier’s provisioners to the end customer.
Amongst the satiated menu of digital technologies, some are moving hot, such as autonomous mobile robots (AMRs), and AI. Analysts say some of them hold the possibility to build off of each other, so that information from IoT- connected things, blockchain constructs, AI-driven apps, or analytics platforms add up synergistically. Rising is an era when supply chains can be more self-correcting, holding analytics Platform as a Service (PaaS) foundations.
Since supply chains are under extreme pressure to do more with less, and labor reserves including warehouse partners and truck drivers are limited, it’s no surprise that AI and robotics are stimulating interest. AI can be used in numerous forms: AMRs and other kinds of robotics such as collaborative piece choosing arms use AI for tasks like how to operate to a pick location or how to collect a carton or pill bottle. AI and predictive analytics have been employed to high-level disciplines like supply and demand plan, but through traditional labor recording systems are mature, warehouse operators now have an advantage in using machine learning to enhance their intra-day and medium- to long-scope labor planning.
PaaS is Cloud-oriented and may feature machine learning institutions that can be adapted to various applications, as well as Cloud storage and calculate infrastructure needed to save and analyze data accumulated into what’s commonly known as “data lake.” More significant PaaS providers, such as Microsoft with its Azure platform, also allow business intelligence tools to visualize what machine learning has revealed. Using application programming interface (API) requests, needed data from various sources can be tapped instantly and analyzed.
Blockchain adoption in the supply chain is still new but will improve, and once widely adopted, could project nicely with AI to support an age of self-adjusting digital supply chains. Capgemini’s 2018 analysis on blockchain found that 87 percent of surveyed businesses were at least in the early stages of leadership, though only 3 percent were actively applying it.
Others understand that blockchain will indeed see an advantage in supply chains, but it might end up being an exceedingly incremental improvement. The complete benefits of a digital supply chain will appear from the reciprocation of technologies if organizations can integrate machine learning, artificial intelligence, and then blockchain applications, which is a great combination that is going to be the triad of the digital supply chain space. The amalgamation of these three trends can become a tremendous force enabling humans to live in a digital world. With comprehensive digital projects, it’s typically best to separate them down into proof of concept demonstrations, pilot a resolution, and eventually scale up the application in the working supply chain. AMRs fit well with this methodology, owing to the fact that they can be tested at comparatively low cost, perhaps utilizing a “robot as a service” model, and then moved out for broader use if they meet expectations.
Other digital technologies, such as applying new forms of supply chain preparation that leverage AI or machine learning, are necessarily costlier to test and calibrate up than AMRs but may do more to modify a supply chain.