Transforming the Supply Chain for Today’s Digital Economy

Today’s digital economy, where customers want tailored products delivered immediately via their preferred channel, is putting enormous pressure on companies to modernize their traditional supply chains. In addition to meeting the needs of the now generation, these new, digital supply chains must support macro trends such as globalization of manufacturing, new models such as the sharing economy, and the rise of Big Data and the Internet of Things (IoT).

Supply Chain Revolution

The manufacturing and supply chain model has remained largely unchanged for more than a century. Since Henry Ford pioneered assembly line production, the industry has seen only incremental shifts.

In recent years, however, signs of a supply chain revolution have emerged. The introduction of mechanization was quickly followed by computerized inventory, routing and record keeping. Further, more sophisticated enterprise resource planning (ERP) software enabled better forecasting and management.

Today, digital advances such as Big Data and optimization tools remain the most visible drivers of change in the supply chain. However, fundamental shifts in the industry are also having a profound impact on the pace of change:

• Globalization: Products are developed for and sold into global markets and components are sourced from global suppliers. Further, manufacturing often shifts from one location to another to take advantage of favorable labor costs and government incentives.
• Product complexity: Information technology is not the only area of product complexity. From vacuum cleaners to vending machines that are able to cook food, products are more complicated to build than they were 10 years ago. One reason is that advanced smart technology, such as wireless connectivity, is being integrated into many traditional products, including clothing, medical devices and home appliances.
• Reduced product development cycles: In the hunt for a competitive advantage, companies in many markets, particularly technology, have shortened product renewal cycles. Similarly, huge advantages can be realized by a company that is the first to bring a new innovation to market.
• Mass customization: Customers are demanding customization and personalization, whether in appearance or function, of products that have been traditionally mass marketed.
• Pace of business change: In a globalized, digitized world, the pace of business is faster than ever. Manufacturing and supply chain operations must be able to respond and adapt quickly to changes in products, customer demands and business models in a particular region or market.

Each of these driving factors has been influenced by technological advancement. Likewise, technology can supply the tools necessary to transform operations and meet the current and future requirements of the digital economy. Important technologies include smart manufacturing plants and supply chain components, where sensors and controls are connected to one another; increased volumes and quality of data generated by a connected environment; advancements in analytics capabilities and cognitive computing to make sense of the data; and the ability to translate data into business intelligence.

Driving the Smart Supply Chain

Perhaps the most talked-about technology is the IoT, where sensors, devices and controls are embedded in industrial systems and communicate with each other. Access to large amounts of data—often in real time— and the connectivity and interaction of sensors and components promise to improve decision making immeasurably, with major implications for efficiency and profitability. Estimates vary, but the IoT is expected to have an economic impact of up to $3.7 trillion a year by 2025, according to a report by the McKinsey Global Institute.^[1]

In the area of logistics, the benefits of a connected ecosystem are staggering. Data in isolation may not be particularly informative, but with connected information flowing from suppliers, manufacturers, retailers and other partners, companies can piece disparate facts and metrics together to tell a story or pinpoint a trend.

The picture test is a nice analogy. If you show someone a church, then a man in a tuxedo and then confetti, individually they don’t mean much. Taken as a whole, these images could lead to the conclusion that a wedding has taken place. Supply chain managers who analyze volumes of data can discern stories and patterns. These, in turn, might show hidden inefficiencies in the supply chain, or could indicate new opportunities to free up capital, decrease costs and improve service levels.

These examples refer to structured data, which is produced by supply chain organizations and their customers and sits in standardized databases. But, unstructured data with the power to enhance operations lies everywhere. Imagine being able to absorb all user feedback about a particular product that’s been shared online in forums, review websites, even social media platforms. Such data would support predictive analysis about demand patterns and component production by enabling the organization to identify features and elements that might be added or removed. A supplier’s inventory management system would advance from the current just-in-time model to one that actually anticipates the needs and requirements of manufacturers, thus reducing risk and overhead costs.

Unlocking the Value of Supply Chain Data

Reaping the benefits of structured and unstructured data need not wait for some distant future. In an early and relatively unsophisticated form, the IoT is already a reality. Most manufacturing sites have hundreds, if not thousands, of sensors, devices and machines that produce huge amounts of data.

However, there are significant challenges to unlocking the value of this data. Even capturing the information accurately can present issues. A seemingly straightforward data point, such as a supplier’s name, can be affected by different possible spellings and formats. That is why capturing data in a regular, standardized way is a critical first step to analysis.

Because Jabil works with about 250 major customers across 100 plants and operates a global supply chain of 17,000 component suppliers, equating to some 700,000 parts, data analysis is more of a challenge for us than for most other organizations. Through a system of sophisticated filters based on hundreds of years’ worth of collective supply chain experience, and leveraging a deep understanding of factors that can make data suspect, the Jabil team vets quality to ensure our own data and that of supply chain partners is consistent and able to be cross-referenced.

Once consistent and high volumes of quality data are available, Jabil uses analytics to extract business value. A proprietary, intelligent digital supply chain platform called InControl™ oversees all of Jabil’s capture data, quality evaluation and analysis. This platform promotes deep understanding of the supply chain ecosystem, identifying opportunities for improvement and anticipating issues well in advance.

InControl works across four main types of analytics:

1. Descriptive: Metric-type analytics that report the status of the business
2. Predictive: Analytics that use past and present data to predict a future state
3. Prescriptive: Actionable analytics that show what steps need to be taken to achieve the desired result
4. Cognitive: Analytics that analyze and find patterns in huge data sets

By applying complex event processing and cognitive analytics, Jabil can orchestrate the entire supply chain ecosystem. By applying both predictive and prescriptive analytics to large sets of operational and supply chain data, we gain valuable insights to accelerate decision making and increase the level of accuracy compared to just a few years ago.

Cutting Costs, Reducing Supply Chain Risk

The beauty of analytics is that it is totally agnostic and not dependent on individual knowledge or informed guesswork. For example, Jabil procurement tended to focus on big ticket items, but analysis showed that there was not necessarily a correlation between these items and the greatest areas of risk or opportunity.

Through the use of analytics, Jabil has achieved up to 15 percent in savings on inventory spend. We have removed hundreds of millions of dollars in inventory based on more-accurate demand and lead times, while decreasing supply chain risk for customers from potential material shortages, non-conformance and poor quality by 10 percent.

Achieving Big Improvements  

The convergence of Big Data, the IoT, analytics and digitization gives companies the opportunity to make significant changes in supply chain performance, efficiency and capabilities. Instead of incremental improvements, companies should be concentrating their efforts on achieving substantial change with improvement impacts of 20 percent and greater. This calls for radical alterations to business processes.

Once a company starts on its journey, developing a digital supply chain platform and analytical capability is clearly paramount. However, changing the culture is just as important as adding new technology. Change management is critical when converting the company’s processes from manual Excel spreadsheets to a unified digital process connected through a single platform and architecture. These changes also affect the way work is done and the skills needed to succeed in the new workflow. For example, instead of depending on individual subject matter experts, organizations must capture their collective knowledge in the digital data platform and leverage it via the power of analytics.

This change in work processes must be driven from the top down and the bottom up by encouraging and rewarding the adoption of digitization and analytics.

Leading with a Digital Supply Chain

In the supply chain industry, which has been characterized by slow evolution, there is a tendency to shy away from rapid change. However, only by embedding data and analytics at every level of the supply chain and manufacturing process can companies gain the full picture.

Creating an intelligent supply chain depends on capturing data from multiple sources; establishing data control and consistency; applying rigorous analysis; and acting upon insights gained from this methodology. Those that take the lead might become the Henry Ford in the new era of smart manufacturing driven by cognitive computing and the IoT.