Artificial Intelligence in Supply Chain Management

The management of supply networks has significantly increased in difficulty in recent years. While product portfolios are becoming more complex, physical flows are growing longer and more interconnected.

The COVID-19 epidemic has made the market more volatile, which has increased the demand for flexibility and adaptability. Moreover, regionalization and the optimisation of flows are being sparked by heightened awareness of the ecological impact of supply chains. As a result, stakeholders and businesses are paying more attention to supply-chain resilience.

Supply-chain management systems powered by artificial intelligence (AI) are projected to be efficient tools to help businesses overcome these challenges. All company functions, from sales to procurement, may address the opportunities and limits with an integrated end-to-end approach.

AI has the potential to change the game because of its capacity to analyse massive amounts of data, comprehend relationships, offer visibility into processes, and promote smarter decision-making. Yet, maximising the benefits of these solutions requires more than just technological advancements; in order to fully utilise AI, businesses must take organisational action.

How supply-chain management is evolving:

A network of interconnected processes, including manufacturing, procurement, marketing, and sales, is known as the supply chain. Companies can optimise profits before interest, taxes, degradation, and amortisation for the business as a whole by balancing trade-offs across functions via integrated planning.

The development of supply-chain management is highlighted by the experience of one major building materials firm. The company recently expanded the four dimensions of the mission of it's own supply-chain function: improve operational sustainability; deliver premium levels of service and implement requirement sensing for brief changes; integrate it's own production and logistical supply chains; and strengthen the organisation.

In support of this, the company increased the size of its central supply-chain department and created the position of chief supply-chain officer, who answers directly to the CEO.

These methods have strained supply-chain operations, which now have to work as a "central cross-functional brain" within big businesses. In many businesses, the focus of supply-chain management has evolved from merely enhancing local function performance to dynamically optimising the company's worldwide value. Sales-and-operations planning has transformed into integrated business management in a number of industrial industries (including chemicals, agriculture, metals, and mining). The COVID-19 pandemic's recent supply shortages and the spike in demand they caused have made it even more critical for businesses to strengthen their central-planning capabilities.

To improve performance, supply chain or business strategy teams must be bigger and more relevant. The following challenges must also be addressed by businesses:

• forecasting demand for various product categories and regions
• dynamically discovering trade-offs with thousands or even hundreds of interconnected variables and countless technical restrictions
• The wider value chain can be managed by integrating AI solutions (such as computation optimisation, proactive maintenance, or expert data quality), which will ensure that plans are carried out and can quickly adjust to variance effects (such as demand shocks, manufacturing stoppages, and transportation disruption).

A crowded market for solutions:

The good news is that organisations can now access and use AI-based solutions to help them achieve supply-chain management efficiency that is next-level. Demand prediction models, end-to-end transparency, inter - enterprise planning, dynamic planning optimisation, and physical flow automation are examples of solution features.

These features all build on prediction models as well as correlation analysis to help supply chains understand the causes and effects of their actions.

By effectively integrating AI-enabled supply-chain management, early adopters have reduced logistical costs by 15%, increased inventory levels by 35%, and increased service levels by 65% as compared to slower-moving competitors.

Many alternatives have surfaced as a result of the substantial wealth at stake. Market disruptors as well as established IT providers are getting into the game. Demand planning, which has been transformed by incorporating machine learning and utilising new data sources, real-time inventory management made possible by the IoT and connectivity, and dynamic margin optimisation of end-to-end chain stores with digital twins are just a few of the new services being offered.

For instance, e-commerce and retail have been at the forefront in demand forecasting.

Making the appropriate choice is crucial. Modern supply chains are extremely complicated. Therefore it takes clever design and business case adaptation to handle them. They must also work nicely with the overall plan of the firm. Companies are able to address crucial choice points with an acceptable level of understanding and avoid needless complexity thanks to this alignment.

The priority is to do it right because implementation can take a lot of time and money in both humans and technology.

Implementing an AI-driven transformation:

A supply chain transformation is an ambitious project, and businesses should be well aware of the difficulties. The potential rewards, however, are substantial: businesses that successfully coordinate the management of four specific areas will be in a position to gain much better visibility and decision-making, all supported by AI.

Value development strategy, road map, and value identification:

These pockets of creating value across all activities, from buying and producing to transportation and, ultimately, commercial, need to be identified and prioritised by firms as a first step. Although fewer than one-third of businesses do an independent assessment at first, doing so can guarantee that businesses have a complete list of all value-creation prospects.

Clearly establishing a supply and distribution strategy enables better alignment with the company's digital programme and supports the business plan. Additionally, a remedy assessment enables business owners to pinpoint the organisational adjustments, process redesigns, and skills necessary to improve performance and also to create an organizational road map.

Design of the intended solution and choice of provider:

Finding a single vendor that can satisfy all of these requirements is becoming increasingly unlikely due to the complexity underlying supply chains, which includes demand forecasting, planning optimisation, and measuring digital execution. Executives should understand that the proposed solution by the providers, whose objective is frequently to push for just a single end-to-end solution, won't always be the best one for their organisation.

The digital supply-chain approach can be supported by solution design & vendor selection. The most effective strategy frequently consists of combining several solutions from various sources that are put into place by various systems integrators. While choosing a suite of solutions, businesses must give integration first importance.

Systems integration and implementation:

Many businesses lack the necessary expertise to adopt technology across their entire organisation.

If a company chooses a solution, there is a risk of implementation running behind schedule and over budget and losing sight of the main goal, which is to appropriately address value-creation drivers from the outset. Only 25% of supply-chain executives said they believe their goals and the motivations of the system integrators are compatible.

An all-encompassing strategy for supply-chain optimisation:

Businesses should approach implementation and system integration holistically. Companies can implement methods that provide value in the short to medium term and are more enduring in the future by improving the end-to-end value (see sidebar "An end-to-end strategy to supply-chain optimisation").

Full value capture, capability development, and change management

Companies must take care of essential auxiliary components like organisation, change management, and building capacity even as they concentrate on technological solutions. According to our research, this task is frequently difficult. For instance, only 13% of executives say their companies are adequately equipped to handle their talent gaps.

Companies must spend in organizational change and capability building to secure adoption of new solutions. Workers will need to adopt new working practises, and it will take a coordinated effort to inform the workforce of the reasons why changes are required. Incentives will also be needed to reinforce the desirable behaviors.

The challenge of supply-chain management is now even greater, but assistance is on the way. Teams will have the ability to get deeper insights from AI more frequently and precisely than ever before. But, this visibility by itself won't be sufficient to maximise the benefits of supply-chain AI solutions. Any significant technology investment should be accompanied by organisational modifications, business process upgrades, and skill-updating initiatives. Only then will businesses realise the anticipated Return.

AI-driven supply chain advantages:

Why have supply networks not been completely revolutionised by artificial intelligence? Several of us predicted that artificial intelligence-powered automation would cause "the death for supply chain management" a few years ago.

We recognised the opportunity to transform supply chains into self-managing systems that efficiently manage end-to-end activities with minimal human involvement. Companies haven't yet succeeded in realising the dream of AI-managed supply chains, despite significant investments.

Aera Technologies and BCG recently conducted a study to identify the root causes of businesses' inability to fully utilise the benefits of AI within supply chains. We discovered that the application of technology by businesses—not the technology itself—is the basis of the problem. Most continue to concentrate on employing AI for analysis and prediction, such as demand forecasting and production planning. The more advantageous application of employing AI to make repeatable judgements by identifying patterns in vast data that humans could perceive has not been pursued by businesses.

Companies must implement an integrated, AI-powered learning system across all functions in order to realise the full potential. Based on internal and external data, this system makes judgements and continuously learns first from results to enhance performance. Instead of only giving practitioners information, who are still required to make decisions, analytical engines simplify decision making.

A new operational model must be implemented, among other enablers, in order to achieve success. The correct investments will help businesses become more resilient to market volatility & talent shortages and reach greater sustained performance.