FIGURE 6-1: The growing variety of data in the supply chain.
CHAPTER 6 The Future of Supply Chain Analytics 51
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the cloud. It’s driven by the smart data at the heart of automated machine-to-machine communication. There are many potential applications for IoT in the supply chain. For example, consider the visibility of stock in transit using Radio Frequency Identification (RFID) — an electromagnetic, field-based identity and tracking solution — tags, and satellite-based Global Positioning System (GPS) devices or optimal logistics route planning and fleet uti- lization with sensors constantly measuring everything from fuel consumption and tire pressure to the temperature of perishable food in transit. With so much of the process automated, advanced analytics ensures everything is working properly and provides real-time insight on which to base agile business decisions.
Mobile data The convergence of cloud computing, mobility, and location- based services will drive a new stream of supply chain analyt- ics. Companies will be able track materials in motion anywhere and in real time. This convergence also offers the potential for completely new models of agile supply chain deployment based around advanced analytics and GPS-enabled systems. For exam- ple, when a customer enters a store, the retailer can transmit price-matching guarantees and personalized offers based on a combination of consumer preferences, relevant product promo- tions, and available stock levels.
Becoming Integrated and Embedded More and more companies are finding that embedding analyt- ics is the way to go for efficiency and profitability. While still at early stages, analytics are increasingly being embedded in systems including demand forecasting, integrated business planning, sup- ply chain optimization, and logistics management. Accenture’s global operations Megatrends study, “The Big Data Analytics in Supply Chain: Hype or Here to Stay?”, has found that embedding analytics into supply chain operations accelerates supply chain processes by almost one and a half times over ad-hoc analytics systems. Companies experienced a 4x improvement in order-to- cash cycles and over 2.5x improvement in supply chain efficiencies.
A new generation of supply chain and B2B integration solutions are appearing with an advanced analytics engine already installed. These cloud-based services give access and visibility to the data
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that passes over the network. The power of this approach is that it gives an end-to-end visibility of business processes while pro- viding the right information to the right people at the right time. If you’re using a B2B integration platform to transact with trad- ing partners, for instance, you have near real-time access to all purchase order and invoice data so you can spot inefficiencies and waste in both your order-to-cash and procure-to-pay processes.
As data grows and analytics solutions mature, the types of analytics you can undertake are also evolving. Today, most companies are still conducting descriptive — what happened? — and predictive — what will happen? — analytics, but the use of prescriptive analytics is growing. Cognitive analytics is also on the verge of offering the potential to move beyond data insight.
Prescriptive Analytics Is Maturing Prescriptive analytics builds on the foundation of descriptive and predictive analytics. Using predictive and prescriptive analytics can provide a competitive advantage because it enables compa- nies to understand and interpret past and future events and base better business decisions on that information. Figure 6-2 high- lights the different steps required to achieve a cognitive approach to supply chain analytics.
At this point, prescriptive analytics adoption is still low. A recent survey entitled “Forecast Snapshot: Prescriptive Analytics, World- wide 2016” from Gartner found only 10 percent of companies have currently implemented prescriptive analytics. This figure is set to increase 35 percent by 2020, according to the research firm. The slow uptake of prescriptive analytics reflects the relative maturity of supply chain organizations. Two trends suggest that implemen- tation: First, technology solutions with prescriptive capabilities are increasingly available. Secondly, like every other technology adop- tion curve, the maturity curve for analytics is shortening rapidly. What would’ve taken years to achieve can now happen in months.
FIGURE 6-2: The maturity of supply chain analytics.
CHAPTER 6 The Future of Supply Chain Analytics 53
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Cognitive Analytics Is Coming Cognitive analytics promises to unlock the full value of digital unstructured data — emails, social media, and IoT sensor data — and help companies make smarter decisions. It combines older analytics approaches with natural language processing, neural programming, machine learning, and artificial intelligence to rapidly find answers in massive amounts of data. You can ask a question in the same way you’d ask another person, and receive an answer within minutes.
Cognitive analytics creates unique queries on the fly, based on natural-language input. You don’t have to program structured queries into the system and apply them to datasets. Think of a service call to a customer center that has cognitive analytics. The organization no longer has to send a repair engineer out to diag- nose and fix the problem. Instead, the automated system iden- tifies similar problems and patterns querying maintenance logs and service call audio files, and implements a successful, auto- mated resolution.
Cognitive analytics flips conventional thinking on analytics. Rather than asking “How do I create a query that will interro- gate the data sets to get the answer I want?”, it asks “How do I structure and access the datasets I need to make the answer to my question complete and reliable?”
Implemented properly, cognitive analytics offers a whole new way to improve your entire supply chain, personalize your cus- tomer experiences, and enhance knowledge-sharing within your organizations and with your trading partner network.
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CHAPTER 7 Ten Tips for Using Analytics to Optimize Your Supply Chain 55
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Ten Tips for Using Analytics to Optimize Your Supply Chain
This chapter presents ten useful tips for getting started with supply chain analytics and making the process as smooth and successful as possible.
Establish a Cross-Department Analytics Program Team
Running a global supply chain operation requires intricate plan- ning, sourcing, delivery, and measurement. This takes different people with different skills. Use a cross-departmental program team to ensure proper business value is being delivered in the most technically sound and efficient manner.
Chapter 7
IN THIS CHAPTER
» Understanding the need for planning and strategy
» Looking at common data structures and terminology
» Setting achievable and actionable goals
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Start with Your Business Objectives Supply chain analytics works best when it addresses a real pain point within the business. A top-down approach helps align your metrics to definable business outcomes, but there are times when a bottom-up approach is required to deal with specific failings within a business process. The optimum analytics solution is a balance between approaches.
Break Down the Communications Silos between Teams
Businesses that segregate operations can’t fully harness the potential of cross-functional platforms. By aggregating all data into a single, multi-department system, a company’s analytics capabilities grow dramatically as information flows seamlessly and cultural resistance is reduced.
