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Normalize Data and Terminology Often separate departments have their own data terminology and structures. These act as a barrier to enterprise-wide analytics. By making all data uniform, across all platforms and departments, information can be processed and understood much more easily, resulting in faster and more efficient decision making.

Ensure Reasonable and Achievable Goals The potential of supply chain analytics means organizations can over-reach when establishing their implementation. Business executives should clearly outline goals in areas such as revenue, sourcing, and customer service that each department can practi- cally achieve. Look for goals that can work in tandem to multiply their value.

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CHAPTER 7 Ten Tips for Using Analytics to Optimize Your Supply Chain 57

These materials are © 2017 John Wiley & Sons, Ltd. Any dissemination, distribution, or unauthorized use is strictly prohibited.

Start Small but Think Big Implementing analytics successfully isn’t a simple process. It takes time and effort to create the correct data structures and technical infrastructure to enable effective analysis. As with any IT project, start small and look for those quick wins. However, you should have a wider strategic goal for how you can extend analyt- ics into other areas of your supply chain. Start planning now.

Organize the Data Necessary for Business Growth

Not all data is created equal — some information is inherently more valuable to a company’s priorities. You must identify your top business priorities. Work out what data is needed to help meet those priorities, where it’s stored, and how it can be retrieved. Work out how you want the analysis displayed and how much drill-down is required into any specific data set.

Prioritize and Streamline Your Analytics Reports

Don’t try to do everything at once. Start off by prioritizing your goals and efforts in order to optimize the data. You should concen- trate on the top-line items before moving on to additional analy- sis in order to best understand the data and forecasts. Encourage the analytics team to include no more than ten metrics for any business process to avoid “paralysis by analysis.”

Turn Data into Decisions After you’ve analyzed the data, act on the results. The purpose of data analytics is to prescribe an actionable insight to the decision maker. Effective analysis highlights inefficiencies within your supply chain, and executives must be prepared to capitalize on the opportunities that the insights suggest.

 

 

58 Supply Chain Analytics For Dummies, OpenText Special Edition

These materials are © 2017 John Wiley & Sons, Ltd. Any dissemination, distribution, or unauthorized use is strictly prohibited.

Be Flexible, Be Agile The success of a supply chain relies on the ability to be more effi- cient and informed. Intelligent analytics is necessary to provide deeper visibility across the entire supply chain. However, business is dynamic and increasingly global. You must ensure your analyt- ics capability continues to measure the right things or be able to quickly introduce new metrics and KPIs as business changes.

 

 

These materials are © 2017 John Wiley & Sons, Ltd. Any dissemination, distribution, or unauthorized use is strictly prohibited.

Notes

These materials are © 2017 John Wiley & Sons, Ltd. Any dissemination, distribution, or unauthorized use is strictly prohibited.

 

 

These materials are © 2017 John Wiley & Sons, Ltd. Any dissemination, distribution, or unauthorized use is strictly prohibited.

Notes

These materials are © 2017 John Wiley & Sons, Ltd. Any dissemination, distribution, or unauthorized use is strictly prohibited.

 

 

These materials are © 2017 John Wiley & Sons, Ltd. Any dissemination, distribution, or unauthorized use is strictly prohibited.

 

 

WILEY END USER LICENSE AGREEMENT Go to www.wiley.com/go/eula to access Wiley’s ebook EULA.

 

  • Title Page
  • Copyright Page
  • Table of Contents
  • Introduction
    • About the Book
    • Foolish Assumptions
    • Icons Used In This Book
  • Chapter 1 Defining Supply Chain Analytics
    • A simple definition
    • The Three Core Components of Supply Chain Analytics
    • How Supply Chain Analytics Works
    • What Makes for Good Analytics?
    • Types of Analytics
  • Chapter 2 The Importance of Supply Chain Analytics
    • Big Data in the Supply Chain
    • Your Questions, Answered
    • Looking at the Benefits of Analytics
    • Why B2B Integration?
    • A Strategic Differentiator
  • Chapter 3 Understanding the Basics of Metrics and KPIs
    • Strategic Goals
      • Increase profitability
      • Forecast accuracy
      • Working capital improvement
      • Operating margin improvement
      • Risk management
    • Two Strategic Considerations
      • Top down versus bottom up
      • Positive versus negative variance
    • Maturity Models, Reference Models, and Benchmarking
      • Maturity models
      • Reference models
      • Benchmarking
    • Applying Goals to the Supply Chain
    • What to Measure
    • Looking at Reports
  • Chapter 4 Use Cases for Supply Chain Analytics
    • Demand Forecasting
    • Invoice Reporting
    • Inventory Visibility
    • Partner Performance Reporting
    • Procurement Reporting
  • Chapter 5 A Six-Stage Approach to Getting Started
    • Step 1: Identify the Business Problem
    • Step 2: Find Your Data
    • Step 3: Choose the Right Team
      • Executive sponsors
      • Data owners
      • Internal data users
      • External trading partners
    • Step 4: Select the Right Tools
    • Step 5: Start Small, Think Big
    • Step 6: Measure Success
    • Six Things to Avoid
  • Chapter 6 The Future of Supply Chain Analytics
    • Growing Pace and Variety of Data
      • Social data
      • Internet of Things
      • Mobile data
    • Becoming Integrated and Embedded
    • Prescriptive Analytics Is Maturing
    • Cognitive Analytics Is Coming
  • Chapter 7 Ten Tips for Using Analytics to Optimize Your Supply Chain
    • Establish a Cross-Department Analytics Program Team
    • Start with Your Business Objectives
    • Break Down the Communications Silos between Teams
    • Normalize Data and Terminology
    • Ensure Reasonable and Achievable Goals
    • Start Small but Think Big
    • Organize the Data Necessary for Business Growth
    • Prioritize and Streamline Your Analytics Reports
    • Turn Data into Decisions
    • Be Flexible, Be Agile
  • EULA

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