How the Intelligent Enterprise Is Reshaping Direct Spend and Supply Chains

By some estimates, the world generates 2.5 quintillion bytes of data every day. That’s the sum of all the grains of sand on Earth, every three days! Yet only a sliver of that volume, much of it residing on enterprise servers, is fully leveraged to drive a deep understanding of the enterprise and how to improve it. What value lies untapped within all that data? What insights get overlooked? What efficiencies go unrealized? As business leaders grapple with these questions, they rely increasingly on emerging cognitive technologies like artificial intelligence, machine learning and blockchain. When these technologies are coupled with cloud-based multi-enterprise networks, thought-leading companies are able to unearth, analyze and act upon critical insights across business lines and foster the emergence of intelligent enterprises.

And not a moment too soon. With increasing customer demands, competitive pressure, market volatility and cross-border trade tensions buffeting nearly every industry, businesses need to be nimbler than ever before to navigate the rapidly shifting currents. Only those enterprises that embrace cognitive technologies can manage business risk and opportunities to create enduring competitive advantage.

The promise presented by cognitive technologies is perhaps greatest in the area of direct or supply chain spend, where the need for agility, reliability and predictability in commerce is especially acute. Businesses that anticipate supply-chain bottlenecks and remedy them before they arise preserve the smooth flow of operations, reduce the potential for disruption caused by tariffs, input shortages, natural disasters, labor unrest or even military conflict.

While many companies want to apply cognitive technologies, many lack the ability to put these technologies into practice. Artificial intelligence, machine learning and blockchain are still new technologies, and many enterprises do not have the in-house skills to design, deploy and manage a complex intelligent enterprise project.

The lack of internal skills, however, is not the greatest challenge to adopting the intelligent enterprise approach and, in fact, is quickly diminishing. Data science is one of the hottest university topics, and companies can recruit new graduates, find staff from other firms with established data science skills or hire one of the numerous consulting firms specializing in the area.

The biggest challenge facing companies who intend to use cognitive technologies is that these technologies are hungry beasts. Their diet consists of lots of clean, timely and consistent data. Too often enterprises run their organizations with multiple systems, in-house databases or even spreadsheets that are fragmented across business units, countries and business processes. The reasons for this fragmentation are many – mergers, acquisitions or simply an attempt to fine-tune processes for segments of the business. Regardless of the cause, the reality is that cognitive technologies will not run on top of spreadsheets or homegrown applications and databases with inconsistent data models and taxonomies.

In 1984, Eli Goldratt changed the way companies view supply chains in his book The Goal. One of Goldratt’s key tenets is that the sum of local optima is not equal to the global optimum. In short, this means that optimizing individual parts of a system or business will not yield the best result for the overall system or business. This concept is as true today as it was in 1984 and particularly relevant for cognitive technologies in the supply chain. While locally tuned systems and processes for a particular plant, business unit, country or process can make some improvement, these improvements will be dwarfed by the benefits delivered by an intelligent enterprise approach. The path lies in driving consistency in processes and systems across as much of the business as possible. This will create lots of tasty data for your cognitive technology projects to eat.

In the direct spend and supply chain spaces, this means normalizing data models and processes, ideally into a single system. However, this concept can be dramatically extended beyond a single enterprise with cloud-based supply networks that enable real-time coordination among many buyers and suppliers. Whether a trading partner is located across the street or across an ocean, digital networks lend businesses the ability to design products together, streamline innovation, and create value for their mutual customers. In today’s interdependent economy, transparent collaboration is necessary for success at every link along the value chain from design, to procurement to manufacturing to distribution. In addition to making supply chains faster and more agile, supply networks create a complete supply chain picture with consistent, timely and relevant data to feed your intelligent enterprise.

Digital supply networks shed light on the interconnected operations of buyers and suppliers, enabling business leaders to make up-to-the-minute decisions (or change them as needed) based on inventories, utilization, cycle time and external factors. Through digital networks, organizations manage their direct spend by connecting with the people, partners, processes and information needed to manage all design-to-deliver activities in a simple, smart, open way. The immediacy of these networks enables businesses to respond swiftly to fluctuating market conditions, while their predictive capabilities help them identify and address these changes before they occur.

This concept is simple enough: Standardize processes, technologies and data across the supply chain to drive the intelligent enterprise’s cognitive technologies. Supply networks amplify the benefits of these technologies with data across many supply chains. From a practical standpoint, what are the areas to consider that will deliver the biggest benefit for the supply chain?

1. On-demand customer-centricity. We’ve all experienced this in our personal lives – simple buying experiences with extremely reliable deliveries. While Amazon is an often-cited example there are others that may be more instructive. In the area of auto parts, Rock Auto, Tire Rack and Summit Racing have easy online ordering, clear inventory visibility and fast, reliable shipping directly to a consumer or to a local repair shop.

Imagine the challenge of managing complex supply chains with thousands of suppliers, many thousands of items and customers with ever increasing expectations. A real-time supply network would help these kinds of supply chains meet customer expectations and deliver intelligent enterprise insights based upon supply chain and other data sources. For example, cold winters spike demand for snow tires in northern climates and weather forecasts would help predict demand and plan inventory. In sunny California, winter fuel formulations contain more ethanol/water and clog fuel systems, causing an increase in repairs.

2. Anticipating shortages and excess. One of the most essential supply chain goals is matching supply and demand. A global supply network can see not only demand and supply trends but also identify commodities or regions with increased lead times or less ability to fulfill upside requests, indicating a global tightening of supply or even shortages.

More dramatically, marrying supply and demand data with business disruption information like natural disasters or labor or political upheaval gives companies insight into the operational risk their supply chain faces. Companies who can glean these kinds of insights from their supply chain gain an advantage in locking in available supply or finding alternate supply faster than their competitors.

3. Increased supply chain transparency. The reality of today’s interconnected economy brings tremendous opportunity but also increased ethical and reputational risk. A quick internet search will yield dozens of examples of major global brands being tarnished because of actions deep in the supply chain. Whether it’s cobalt mining, cotton harvesting or rare earth minerals in industrial applications, brand owners are being held responsible for the behavior and ethics of their supply chains.

Supply chain transparency and validation is a perfect fit for supply networks and technologies like blockchain. Not only can buyers gain visibility across multiple tiers of their supply chain, but they can also have confidence in the accuracy and validity of their supplier and material provenance to protect their brands.

In procurement, digital networks are taking on many of the manual activities previously involved with sourcing, purchasing, contracting and payments. As a result, professionals can increasingly focus on strategic priorities such as strengthening relationships with suppliers, pursuing innovation and efficiency in coordination with them, and managing risk affecting mutual operations. At a time when business has never run faster, the intelligent enterprise provides a much-needed solution for a simpler, smarter way to thrive. The most foresighted businesses rely on connected partners, supply chains and cognitive technologies to move their businesses forward. Thanks to digital networks and the cloud-based applications connecting them, business leaders are creating immense opportunity for their organizations — and empowering them to manage their spend more intelligently.

The author is Keith Baranowski, global vice president and general manager, direct spend solutions, at SAP Ariba, the world’s largest business network, linking together buyers and suppliers from 3.6 million companies in 190 countries.


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