The Value of Big Data In The Supply Chain

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1 BUSINESS WHITE PAPER The Value of Big Data In The Supply Chain From planning and collaboration to execution, information is power TABLE OF CONTENTS 1 Executive Summary 1 What Big Data Is And What It Isn t 2 What Big Data Can Do For Your Supply Chain 4 Finding The Nuggets: Using Process To Filter Data 5 Don t Get Lost In The Data. Get Process-Driven 6 Getting Started 7 In Summary 7 Software AG Can Help BIG DATA: BY THE NUMBERS For the sake of context, let s take a look at the data produced by some of the most popular social media companies. Each day 4 : YouTube users upload 48 hours of video Facebook users share 684,478 pieces of content Instagram users share 3,600 new photos Tumblr sees 27,778 new posts published Executive Summary It seems these days that you can t power up your phone or start your browser without seeing big data the ever-growing, increasingly detailed and incredibly splintered mountain of information being collected every minute of every day. From GPS tracking to buying habits, there s much to learn about customers, employees, partners and more. With the sheer volume of information available, it s very easy to get overwhelmed or lost in the data. So, how can you utilize massive amounts of data and find only the most important, relevant information? Part of the answer already exists within your organization: your processes. By leveraging your processes as a guide, you can determine the data that s most important to your business, and use it to increase the efficiency of your supply chain planning, collaboration and execution. However, while processes are a very useful guide, they re just the beginning. Examining massive quantities of data and discerning what s important and what isn t requires the aid of advanced analytical tools. What Big Data Is And What It Isn t Every day, millions of orders are placed, places are liked, reviews are written, photos are geotagged, sensors are digitizing previously unobtainable information, weather events are tracked and transportation asset positions are provided. Retail giant Wal-Mart, for example, handles over 1 million customer transactions every hour, feeding databases estimated at more than 2.5 petabytes that s the equivalent of 167 times the books in America s Library of Congress. 1 Facebook, the leading social-networking website, is home to 40 billion photos. 2 And decoding the human genome involves analyzing 3 billion base pairs which took 10 years the first time it was done in 2003 but now be achieved in one week. 3 The amount of data that is created and that can be consumed is almost limitless. And it s valuable information if you know what to do with it. That s big data for you. Simply put, big data is the ability to consume, manipulate and understand massive quantities and multiple types of data. How much data? Literally more than the human mind can conceptualize or analyze. And more than what on-hand database management tools can handle. And that s no exaggeration. Combing through these mountains of information requires the usage of advanced algorithms 1

2 or advanced analytical tools such as an intelligent business operations platform or Apama, a complex event processing platform specifically designed to tackle big data. Big data potentially gives you the power to identify simple and complex issues in real-time. Properly utilized, big data can give businesses real-time insight that they can use to make critical business decisions, see inconsistencies before they become problems, and increase efficiency at every step of their processes. This includes previously unfathomable methods of supply chain planning, procurement, supply chain fraud detection, partner collaboration and end-to-end supply chain execution. What big data isn t? Running multiple reports out of multiple data sources for spreadsheet analysis. It s simply too much. Too many data sources. Too many fields. Too much time. So much, in fact, that without dedicated resources or the types of advanced tools mentioned above, many enterprises miss out on the benefits of big data. And in the long run, that can be a huge misstep. Point is, the amount of data isn t shrinking any time soon. It s not a matter of if you get on board with big data, but when. And most importantly, if you re faster than your peers to leverage and capitalize on it. So what does that mean in the real world? By utilizing big data, companies can better analyze millions of orders around the globe to identify the at-risk orders, or those that are from first orders with large quantities and short lead times for a large, long-pursued prospect. In-memory big data allows for these issues to be addressed in sub-second response times by determining which transactions can be cost effectively managed and which cannot be resolved at any cost. For any business that lives and dies by its supply chain, that s not just big. It s huge. What Big Data Can Do For Your Supply Chain The kind of information big data brings to the table can have a profound impact on supply chain management, particularly in the areas of supply chain planning, procurement, collaboration and end-to-end execution. Big data allows for massive amount of data to be used, taking into account an unheard amount of PERFORMANCE VARIABLES Supply Chain Planning: Imagine taking years worth of production, supply, weather, order and shipping information and being able to understand not only the correlation of these issues but also the context of why and how decisions were made. Then imagine utilizing that data with the desired business plan to accurately plot multiple scenarios and backup plans for this year s holiday rush. Big data allows for massive amount of data to be used, taking into account an unheard amount of performance variables and the ability to leverage more complicated what if scenarios for advanced network optimization. Procurement: Imagine a world where data pure information could make procurement more efficient. With big data, organizations can base procurement decisions on more factors, enabling them to make better informed decisions that ultimately impact the bottom line. That world is here. Purchasing decisions are no longer solely made upon production requirements but a combination of multiple inputs including: Planned production Forecasted customer orders Available supplier capacity 2

3 Currency fluctuations Contract rate increases The impending likelihood of disruptive events Available raw material storage capacity The current level of net working capital and the impact of increased purchases on the firms NWC levels The end result is a lower amount of net working capital outlay to support raw material purchases and more efficient buying methodologies. Supply Chain Collaboration: What if you could leverage an entire network of supply chain partners acting in real-time to supply chain events and changes? Big data makes it possible. With it, all partners understand how every other partner is responding to an event or issue and the responses are being completed in concert with each other without extended discussions to decide what the issue is and how best to resolve the issue. The ability to leverage the same vast amount of data at the same time is critical to success at every link of the chain. End-to-End Supply Chain Execution: With more data, you have a sharper picture of the factors that impact your global supply chain and a more precise idea of what that impact could be. You can feed your analysis with better input, giving you more meaningful output. Big data gives your organization the ability to seamlessly combine and correlate multiple data sources to ensure end-to-end supply chain execution is completed as efficiently and as effectively as possible. These data sources include: Data from enterprise owned Commercial Off-The-Shelf (COTS) apps, hosted or cloud-based Supplier provided data from EDI feeds to flat files to Web portal entries Telematic data feeds (e.g., GPS, temp control monitors, transportation geo-data) Weather, financial or other event streams By combining multiple data streams with other big data-related technology, such as geo-fencing, a holistic picture of a complete supply chain is obtained. Adding advanced analytical engines and sophisticated real-time visualization tools determines where are the most important exceptions that require action and what is the most effective and efficient manner to manage these exceptions. Machine-To-Machine Monitoring For Predictive Maintenance & Better Production Quality: Manufacturing is a critical component of a company s end-to-end supply chain. Thousands of production sensors provide vast quantities of data at sub-second speeds for analysis. Big data and advanced analytics ensure this vast amount of data is utilized to monitor production processes and identify when maintenance is required, or when an issue will affect production quality, before the production quality is actually affected. While quality testing is important, big data provides the ability to ensure QA tests are confirmations of high product quality and not used to catch errors or product quality issues. These are examples of the result. But let s step back for a moment. How do you get here? How does it all work? How, even with tools in place, can you begin to get to the data that is most critical for your business? Look no further than your own processes. Big data provides the ability to ensure QA tests are confirmations of HIGH PRODUCT QUALITY 3

4 Finding The Nuggets: Using Process To Filter Data There s big data. And then there s how your business works. The insight, power and results you want come from looking at both in tandem. With the massive amount of data collected, correlated and analyzed, it can be very easy to become overwhelmed lost in the data. Process serves as a guide to understand how different portions of data affect the outcome of a process in this case, the operation of your supply chain. Another real-world example: 10,000 transportation assets around the world are delivering products to you or from you at any one time. Data on speed, route compliance, estimated time of arrival and compliance to the customer requested delivery date are all being tracked in realtime. So, what assets need to have action taken because they are going to fail / not show up on time, and how do you know this? More importantly, how can you identify which transactions are at risk as early as possible in the process to avoid costly problems like expedited shipments, high inventory levels to compensate for performance failures and decreasing customer satisfaction? Understanding each event as a segment of an overall end-to-end process helps guide what data is valuable and what an appropriate response should be to any identified issues. In this case, the enterprise understands the exact length of time it takes to obtain product that is on the water, convert it to saleable inventory and move it to a specific customer while having a general idea of what the expected pitfalls will be along the way. So when a massively large order is placed with an emergency status lead time, the organization understands what inbound product can be utilized to build this order. It can also help determine if it is feasible to even deliver against the customer request or if it is not, the option of trying to reschedule the order can be taken rather than spending an inordinate amount of time and resources, only to fail. As a business, you should have known you were going to fail by understanding, from a process perspective, if there wasn t any possible way to comply with this customer request. In simplest terms, knowing how your business works (even with large inventories of cheese and sauce, you can t start or deliver the pizza until the dough is made) will determine what data is most important to succeeding (how long it takes to have flour delivered, how much flour is needed, etc.). The seemingly impossible mountain of data becomes much more manageable once you know what you re looking for. 4

5 Don t Get Lost In The Data. Get Process-Driven. Why gain visibility into supply chain processes? According to Forrester Research, there are five accelerators that will define the business landscape in Businesses and government will get obsessive about customers. New business models must delight the customer. New processes will shift from transactions to relationships. New tools that empower customers will upend today s processes. Operational excellence matters now more than ever. Poor operations can undo a great customer experience. Operational excellence delivers values to key stakeholders: regulators, shareholders, and customers. IT vanishes into the business. Business-ready technology abstracts configuation from technical complexity. SaaS and cloud deployment models reduce dependence on IT. Workers and customers become self-sufficient with technology. Finding and leveraging talent will be universal and strategic. The globalization of human talent will be a strategic advantage. Mobile is the new office. Ubiquitous and contextual collaboration will affect processes. Regulatory and competitive pressures will force constant change. New business models will drive established firms and startups alike. Increasing compliance requirements will affect processes. Emerging sustainability metrics will lead to new processes. The proliferation of data-based business opportunities will expand today s view of process. Figure 1: Accelerators for Becoming Process-Driven These five accelerators will require companies to become process-driven. But it can be argued that businesses must be process-driven prior to 2020 as items one through three in Figure 1 are not only already occurring but are also becoming exponentially more important. The bottom line is that the five accelerators noted by Forrester Research will require businesses to adopt new and better process methodologies faster than IT can provide them. That means business users will have to become process, technology and unique subject matter experts or obtain technology that enables process excellence like big data. Supply chain visibility is only the first capability that a supply chain requires to adapt to the impending business environment changes. Now think about the accelerators in terms of the example in the previous section. It s easy to see that breaking down an entire end-to-end process into process segments, signal collection and event understanding allows an enterprise to leverage big data in a meaningful manner. 5 Forrester Research, Moore, Connie, The Process-Driven Business of 2020, April 16,

6 Getting Started Selecting the initial supply chain process for a visibility project can be a daunting task, and data requirements can be hazy when starting. But collecting massive amounts of available data can result in misunderstanding correlation as causality. What you need to remember is that you re collecting data, for a specific purpose and identifying that purpose maintains focus for the analysis of that data. When it comes to big data, many enterprises aren t used to analyzing the vastly greater amounts of data. Too much data can be indigestible and can cloud an issue or inspire analysis paralysis. Focus on single problems that require fast solutions with the context of how to leverage this single problem into a much larger overall strategic vision. So stay focused and keep it simple. Select a process with impact on customers, partners, revenue and profitability. A company s order-to-cash process, for example, touches all of these participants and directly affects gross revenue, net profit and customer satisfaction since it is the primary interaction point with customers. The order-to-cash process also can be neatly distributed into process groups that affect single departments and have clearly and easily defined beginning and ending points, enabling a definitive path to ensure a clear focus for the analysis of the data is provided. Figure 2: The order-to-cash process can be an ideal first process for a supply chain visibility project. 6

7 In Summary At every link of the supply chain, information is power. And big data offers today s businesses more information than ever. However, with the vast amount of data that was previously unobtainable, organizations can lose focus on their objectives or easily suffer from analysis paralysis. Understanding your processes will make it easier to filter down to the data you need. The challenge is selecting where to begin and assessing how that will positively impact customers, partners, revenue and profitability then recognizing your data gap. This will help in selecting a big data solution that helps your business, not bury you in information. With an understanding of process and the data you need to make smarter decisions, you can transform big data from a behemoth to a big advantage. Software AG Can Help For more than 40 years, Software AG has been inventing new and better ways to help organizations achieve business results faster. Our ability to deliver end-to-end Business Process Excellence transcends the capabilities of any other organization of any size, anywhere. We can transform your company so business and IT are aligned around common, achievable business goals like smarter transactions with your business partners. Specifically, Software AG offers the industry-leading Apama Intelligent Business Operations and Terracotta In-Memory Data Management Platforms to help you get the most from big data. To learn more about Software AG Digital Enterprise solutions, visit: corporate/products. 7

8 Find out how to power up your Digital Enterprise at ABOUT SOFTWARE AG Software AG helps organizations achieve their business objectives faster. The company s big data, integration and business process technologies enable customers to drive operational efficiency, modernize their systems and optimize processes for smarter decisions and better service. Building on over 40 years of customer-centric innovation, the company is ranked as a leader in 15 market categories, fueled by core product families Adabas-Natural, Alfabet, Apama, ARIS, Terracotta and webmethods. Learn more at Software AG. All rights reserved. Software AG and all Software AG products are either trademarks or registered trademarks of Software AG. Other product and company names mentioned herein may be the trademarks of their respective owners. SAG_Value-of-BigData-In-Supply-Chain_WP_Nov13