A company’s Supply Chain is a complex ecosystem involving many different entities that have to work together to achieve the common goal of successful, timely delivery of an order or service. It can empower businesses and make them thrive. However, because of new technologies and capabilities, and changing customer demands and expectations, the landscape is shifting dramatically, and businesses are finding it necessary to reinvent their supply chains to keep up.

Transforming from a traditional into a digital supply chain is important as it empowers businesses to exploit the opportunities that new technology presents. The next-gen supply chain must be an agile entity that detects, responds and adapts to fast-changing scenarios and volatile markets swiftly – not just to avoid delays and losses, but also to enhance customer experience and gain that competitive edge.

To transform the supply chain into a digital entity, a fundamental necessity is real-time data that is reliable, visible and accessible to all parties. The agility and flexibility of a supply chain will be determined by how efficiently it uses data, and the most successful supply chains of the future will be the ones that are data-driven.

Everyone has data – a supply chain produces large amounts of it from multiple sources, courtesy computing advances and digital technologies – but that’s no guarantee of success. Disparate applications and tools, best-in-class though they may be, cannot provide a holistic, integrated view of this data that is critical to getting actionable insights.

To build a digital, data-driven supply chain, enterprises must adopt a unified supply chain management platform that gives “a single version of the truth” – a software that provides end-to-end visibility, standardizes processes, consolidates operations, seamlessly interacts with other systems and delivers real-time data. A system that connects all supply chain partners and stakeholders to the same platform and provides everyone access to the same data sets.

A data lake can address the challenges of storage, access and uniformity. A data lake is a central repository on the cloud that stores huge volumes of current and historical raw, uncleansed data. A data lake can help enterprises harness the large volumes of data generated by the digital supply chain – from ERP systems, Internet of Things (IoT) devices, third-party systems – as well as ensure that all involved parties are using the same data.

The supply chain platform should be on the cloud, if enterprises want to unlock the full potential of digital technologies. It is the most effective way to share real-time data and make it accessible to all partners – be it the internal finance team, suppliers or logistics providers – from anywhere, anytime and from any device. A cloud-native platform is also a more secure, cost-effective, scalable and low-maintenance solution. Enterprises are increasingly recognizing the value of such an investment: the cloud supply chain management market size is estimated to grow from $3.26 billion in 2016 to $8.07 billion by 2021, at a Compound Annual Growth Rate (CAGR) of 19.8% during the forecast period.

It’s easier to leverage the AI capabilities of unified supply chain platforms when all the data is on the cloud. The technologies that power today’s supply chain software can exploit large, ever-increasing volumes of data, extracting its value and refining it. An AI-powered, cloud-native platform can automate various processes, analyze data – real-time and historical – rapidly and offer critical insights that facilitate the decision-making process and drive innovation.

Its components — machine learning (ML), robotic process automation (RPA) and natural language processing (NLP) — can enhance data quality and harness the power of intelligent automation, thereby reducing repetitive, manual labor, speeding up processes, cutting down errors and improving efficiency. Gartner predicts that by 2023, at least 50% of large global companies will be using AI, advanced analytics and IoT in supply chain operations. The global market for AI in supply chain is expected to exceed $10 billion by 2024 at a CAGR of 40% in the period from 2018-24.

As the supply chain gets more complex and more global, predictive analytics is becoming an important tool for supply chain managers. Predictive analytics, used on large data sets, helps establish credible patterns and make forecasts with greater precision, a valuable tool for supply chain planning, especially in volatile market conditions. The number of supply chain professionals using predictive analytics is on the rise – a survey in the annual MHI Industry Report stated that 30% said they use predictive analytics in 2019, compared to 17% in 2017. Of the 260 supply chain leaders Gartner surveyed in late 2017, 96% said they use predictive analytics.

Real-time data, in conjunction with AI and predictive analytics, can be utilized to create a digital supply chain twin, or a part of it, to simulate situations based on which the impact of plans, strategies and decisions can be tested.

Be it predictive analytics, RPA, or digital twin, new technologies need large volumes of data to produce credible results. Data is the fuel for the next-gen supply chain, as well as its yield, and enterprises must adopt digital technologies to generate, store and analyze data. The ultimate aim is to use data to optimize the supply chain, which will translate into business success and growth.

About GEP

GEP helps global enterprises operate more efficiently and effectively, gain competitive advantage, boost profitability, and maximize business and shareholder value.

Fresh thinking, innovative products, unrivaled domain and subject expertise, and smart, passionate people — this is how GEP creates and delivers unified procurement and supply chain solutions of unprecedented scale, power and effectiveness.

To learn more about our comprehensive range of software and services, please visit www.gep.com

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