Trends & Issues

Turning Data Analytics into Value in the Supply Chain

Producing data analytics is not the same as converting analytics into value-producing decisions that maximize the supply chain ROI. Turning data analytics into action is an imperative for the digital global supply chain, but it requires a new way of thinking.
— By Gerald Donald

Embracing digital supply chain solutions has never been more important, a fact emphasized by the COVID-19 pandemic, which rapidly altered sourcing and delivery of goods and services.

Many companies were caught in the middle of a gap of their own making—the gap between data analytics and action.

Data analytics needs to be integrated with strategic planning and creating value through a flexible and adaptable supply chain. The global digital supply chain has enormous ability to create real value far beyond any cost savings when technology and processes come together. Technology and processes working synchronously can initiate quick action based on real-time, current integrated data analytics that is accessed and used by all business leaders involved in supply chain processes.

However, this in turn means that data analytics users must be prepared to understand the context of analytics and the insights they are delivering. Turning data analytics into value requires giving people the knowledge and tools that facilitate decision-making.

Meeting Supply Chain Challenges
Supply chain management has never been more challenged than it is today, due to factors such as unexpected global events disrupting supply chains, trade wars, changing consumer buying practices, demand for greater social equity and environmental sustainability results, and advancing technologies like artificial intelligence (AI).

Being able to have a view of end-to-end inventory movement and location, assurance of adherence to ever-changing government regulations, and the ability to respond quickly to natural disasters are yet more demands placed on supply chain managers. Data analytics can provide current information, forecasting, and the predictive guidance that supply chain management needs for visibility and resilience.

The supply chain transformation has been in process for a while now, yet there is an obvious need for more progress. Otherwise, value is left on the table. Many organizations have already automated some of the manual processes, like RFP and purchase order tracking, invoice matching, and logistics tracking.

However, for many, their efforts ended there because of issues like legacy systems and functional siloes. For example, procurement professionals and purchasing managers in decentralized systems do not utilize data analytics that bring consumer demand changes and materials sourcing together.

Enterprise-Wide and Future Focus
To understand how little digital progress many companies can claim, consider the KPMG supply chain model that fully embraces technology to reduce supply chain management complexity and uncertainty.

Preparing the supply chain for future sustainability requires an organizational mindset change. Global procurement is not a lone function in minimizing country costs. It becomes an integrated process that implements new technologies to bring digital enablers and humans from different supply chains together. One of the main reasons a gap exists between data analytics and value creation is that technology is introduced into the supply chain to support legacy processes and systems, rather than rethinking the organizational approach to digitization.

To overcome this gap, KPMG suggests organizations should invest in cognitive decision centers that provide a cross-functional view of the supply chain. Functions like sales, marketing, finance and procurement learn to utilize AI-generated insights and data analytics to identify the best path to optimize enterprise-wide performance. In the traditional system, each unit operates autonomously, and each has key performance indicators it strives to optimize. One function’s efforts can negatively impact the performance of others.

After data is brought into the system from a variety of internal and external sources, the cognitive decision center framework for the digital global supply chain has three layers.

Internal data sources include suppliers at various tiers, manufacturing, distribution, transportation, wholesalers and customers. External data is drawn from weather data, social media, news feeds, government health agencies and other sources applicable to the business. The data for demand, supply, manufacturing quality, risk, inventory and transportation is given cross–function visibility.

At the next level is the application of AI, and an optimization engine to do analysis for the purpose of providing cross -functional decision support. This step involves the decision-making framework, machine learning and decision collaboration.

At the top level is the intelligent executive and the decision-making process. Humans augment the decisions, and there is global supply chain orchestration.

Real-time decision-making is supported by tech-based programs that track raw materials and finished goods from point-of-origin to the final point-of-sale. Issues that arise, such as scheduled freight going off track, are addressed quickly. Track and trace can be used in many ways, such as ensuring goods like food or drugs are always within the required climate control range, or identifying the sources of labor from a human rights perspective.

Other critical technologies are blockchain for documenting the flow of goods across borders, and predictive analytics to predict potential supply chain disruptions, such as the COVID pandemic or changes in country governance.

Real-time decision-making is supported by tech-based programs that track raw materials and finished goods from point-of-origin to the final point-of-sale.
Creating a Single Version
An important element in maintaining a digital global supply chain is ensuring the managers, workforce, contractors and suppliers have the necessary digital skills.

SFI developed a platform for digital supply chain integration for manufacturing companies ready to transform maintenance, repair and operations supply chain performance. Data from across the enterprise is digitized and collected, so that supply chain integration presents a single version of company performance in areas like sales and cost allocations.

The dashboards used to present the information enable supply chain decision-makers to identify production issues, preventive maintenance needs, parts redundancies, and areas where sources of supply can be consolidated. Data is presented in actionable reports for faster decisions based on accurate information, and elimination of duplication of effort among different departments inputting data for different purposes. It also gives more coordinated maintenance, and better ability to predict part failure through analytical forecasting and machine learning. This leads to less downtime, lower inventory costs, and better use of employee time as historical data automatically feeds automation and triggers reorders of replacement parts and supplies.

KPMG and SDI identified the challenges of digital supply chain integration and found similar issues contributing to the slow progress in digital global supply chains in producing full value. One is the continued reliance of organizations on mismatched legacy systems. Others include silo-based procurement and inventory processes creating inter-departmental conflict, due to performance measures dependent on isolated factors. SDI also mentions there is sometimes an unwillingness of supply chain functions to turn over any control to technology.

These are challenges that can and should be overcome, in order to move into the future.