The rise of the seamless supply chain
From reaching a wider customer base, to delivering products faster and increasing flexibility, the digital revolution has opened up a whole host of opportunities for brands and retailers. However, it has also made the global supply chain more complex and sprawling than ever before.
Materials and products pass through multiple geographies, as well as numerous suppliers, manufacturers, distributors and service providers that rarely communicate with each other. The consequences of this disorder and fragmentation are numerous.
In order to capitalise on the dormant value represented by today’s swelling but ill-optimised supply chain, businesses should look to harness technologies that facilitate a more seamless and efficient model.
Data fragmentation
As an unhappy by-product of digitisation, we now have a hugely siloed global supply chain where massive volumes of data are collected across a diverse range of sources, processes and systems.
This fragmentation has created large-scale macroeconomic inefficiencies and rendered decision-making too complex to be efficiently managed manually. The lack of insight and visibility caused by data fragmentation is resulting in businesses around the world losing vast sums every year.
Siloing and fragmentation inhibit effective planning and execution, leading to product distribution gaps and excess or insufficient inventory. The result is trillions of dollars of missed growth for the global economy.
The importance of capturing this lost value is clear to all supply chain stakeholders, and many are turning to emerging technologies in an effort to address the manifold issues with today’s systems. According to Christian Titze, VP Analyst at Gartner, “innovative technologies, such as AI and machine learning (ML), can potentially and significantly disrupt existing supply chain operating models.”
As such, the adoption of AI and ML in the supply chain sector is set to increase dramatically in the near future, according to a recent survey from Gartner. Though only 31% of supply chain leaders are currently using AI for decision automation and 34% for decision augmentation, when asked if they plan to use AI for either use case within two years, the number answering in the affirmative rose to 76% and 78% respectively.
So, how exactly can AI and ML remedy the complexity and fragmentation of the global supply chain?
The analytics- and AI-powered supply chain
The rapid emergence of artificial intelligence and advanced big data analytics has prompted many leaders to investigate their applications in the context of supply chain optimisation.
At the core of AI’s value proposition is the ability to organise, analyse and enhance business information. In so doing, it’s capable of delivering greater efficiency and transparency, and thereby a competitive advantage to organisations that choose to utilise it.
The business benefits of an AI-powered supply chain include reduced operating expenses and enhanced planning capabilities to respond to current and expected customer demand more effectively. Perhaps most important, understanding of customer buying behaviour can lead to recommendations and insights on what products should be on the retail shelf, and what inventory may no longer be needed, or needed at the same levels. This leads to higher gross turnover at an improved margin, as inventory holding and distribution costs as percentage of sales are reduced. AI solutions can also help to automate various supply chain processes — such as demand forecasting, production planning and predictive maintenance — on an end-to-end basis, which drives core business profitability and market share together with flexibility and resiliency.
Big data analytics, meanwhile, allows organisations to improve decision making by unearthing hidden patterns and unknown correlations from unwieldy datasets. This form of advanced analytics expands the dataset for analysis beyond the traditional data held on enterprise resource planning (ERP) and supply chain management (SCM) systems, and applies powerful statistical methods to draw value from data. Large brands can obtain insights from the data sources and platforms of their trading partners, to complement their own ERP with more current and predictive data.
The new insight generated by big data analytics augments decision making across the entire supply chain, from front-end operations to strategic decisions made at the back-end.
With all this in mind, it’s clear that AI, ML and big data analytics are set to become increasingly influential factors in the operation and optimisation of the supply chain, tearing down silos and moving towards a seamless model. Retailers, brands and manufacturers therefore need to embrace the power of AI and big data analytics for supply chain management, or risk losing out to their competition.
