+1 (208) 254-6996 [email protected]
  

Step 6: Measure Success You must be able to demonstrate the benefits of your analytics ini- tiative. However, it can be tricky to identify benefits — especially financial ones — when your analytics investment is designed to deliver into operational areas such as performance efficiencies.

While showing financial benefits will always be the most impor- tant way of demonstrating the success of your analytics initiative, it’s essential you don’t overlook the benefits of improving supply chain metrics that lead to financial performance improvements in other areas.

Don't use plagiarized sources. Get Your Custom Essay on
Step 6: Measure Success You must be able to demonstrate the benefits of your analytics ini- tiative.
Just from $13/Page
Order Essay

 

 

CHAPTER 5 A Six-Stage Approach to Getting Started 47

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

For example, if you use analytics to bring preparation time for sale and operations planning meetings down from weeks to days, you free your team to be more productive and revenue-generating in their day-to-day roles. More accurate data is also going to make the decisions coming from those meetings far more accurate.

To help measure the impact of your analytics initiatives, take the following steps:

» Establish and share a performance baseline prior to analytics implementation.

» Identify and document risk and inter-dependencies — such as cultural resistance or technology barriers — that may impede the progress of your initiative.

» Be clear where your goals are supply chain, rather than financial, improvements.

» Select quantifiable financial benefits and design your initiative for quick wins that deliver them.

» Report on all improvements in supply chain agility and responsiveness — attach any resulting improvement to financial metrics where possible.

» Capture improvements in the actual process of generating and using analytics.

» Document and communicate the growth of demand for analytics in other areas of the business.

» Demonstrate how the analytics initiative has improved other business activities, such as placing a focus on driving best practice on process workflows.

» Include the feedback of customers and suppliers when assessing the impact of the initiative and reporting back to the business.

Aim for continuous improvement. So, like the analytics process itself, when analyzing your analytics initiative, you need to mea- sure and measure and measure again.

 

 

48 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.

Six Things to Avoid If you’ve been reading this chapter straight through, you know what you should do to get started with supply chain analytics. Here, I give you a few things you shouldn’t do:

» Use dirty data: If there are errors in your data, your analysis is going to be flawed. Your decisions will be based on poor data, and, once discovered, it will raise questions over any future analysis. This is the top reason analytics initiatives fail.

» Measure too much: If you try to measure everything, you’ll end up measuring nothing. Too many metrics brings back a huge amount of data, leading to confusion and a lack of focus. You’ll miss the insight in the data.

» Measure too little: At the other end of the scale, you can measure too little. Measuring a single metric in isolation from everything else may help improve that metric, but you’ll miss the relationships between metrics that drive improve- ment in your business processes.

» Create conflicting metrics: You need to define your goal clearly and build metrics around it. If you don’t do this, you could establish conflicting metrics. For example, setting a high fill-rate goal could inadvertently lead to inventory overstocks.

» Use outdated data and metrics: It seems obvious, but you need to make sure your data is up to date (or at least still relevant). On top of this, you must ensure that the metrics you already have still reflect your business goals.

» Not establish ownership: As with any technology program, executive buy-in matters. Failure to establish executive-level sponsorship is likely to lead to your analytics initiative stalling and slow adoption across your organization.

 

 

CHAPTER 6 The Future of Supply Chain Analytics 49

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

The Future of Supply Chain Analytics

Supply chain analytics has advanced a long way in a short time. Many companies today want to improve their business decisions by applying analytics to their corporate data. Yet,

as the pace of “business as usual” quickens and the variety of data sources continues to grow, it can become difficult for organiza- tions to keep up.

This chapter outlines some of the future trends in supply chain analytics, but here’s an executive summary: faster and more varied. In other words, to stay competitive, you need to make sure your analytics work as closely as possible to real time and handle as many kinds of data as possible. Analytics will become progres- sively more embedded with data systems, and decision makers will rely more on prescriptive and cognitive analysis techniques.

Growing Pace and Variety of Data Every day the modern supply chain creates a greater scale, scope, and depth of data. The Forbes article, “Ten Ways Big Data Is Revolu- tionizing Supply Chain Management,” suggests that there are now

Chapter 6

IN THIS CHAPTER

» Discovering the pace and variety of today’s supply chain data

» Increasing integration and embedding of analytics

» Learning how prescriptive analytics comes to maturity

» Seeing cognitive analytics on the horizon

 

 

50 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.

over 50 separate data types generated within supply chains, and more types are appearing all the time. The situation is further com- plicated by the fact that much of the new data is held in unstruc- tured formats such as business documents, emails, online chat facilities, and social platforms. Figure  6-1 summarizes the most common types of data, arranged by volume, velocity, and variety.

Analytics systems have traditionally been good at handling struc- tured data, but future systems will need to be equally good at capturing and analyzing unstructured data. Analytics engines will need to be able to combine multiple data types to provide a holis- tic view of supply chain operations.

Social data The phenomenal growth of social media has changed the way everyone communicates. Today, it’s one of the largest sources of unstructured data for informing supply chain actions. As social media data becomes more integrated into supply chain analyt- ics, systems will improve in a number of key areas, from demand sensing to customer acquisition and retention. For example, com- panies will be able to use the latest buzz on social media to inform product innovation or the timing of new product launches.

Internet of Things The Internet of Things (IoT) is set to reshape many parts of modern society. IoT is a growing number of Internet-enabled machines and devices that can communicate with each other over

Order your essay today and save 10% with the discount code ESSAYHELP