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Step 2: Find Your Data After you’ve established your KPIs and metrics, it’s time to go find the data you need. Start by looking at what you’re already doing. Different functional units of your business may already be doing analytics, but very often departments don’t communicate with one another, and no one person knows everything that’s going on. If you’re going to gain end-to-end visibility of your supply chain, you need to break down the siloes of information.

So, go out to the various departments and find out what analytics activities they’re doing and what metrics they’re currently mea- suring. Learn what data they’re using and where it resides. After you know what’s available, you can compare it to what you need and discover where the gaps are. Ask yourself these questions:

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» Is our existing data of good enough quality to use for analytics?

 

 

CHAPTER 5 A Six-Stage Approach to Getting Started 41

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» What systems, transactions, and data are we not measuring?

» In what format is the data?

» Where does the data reside, and where is the best place to capture it?

» Do we need to capture data outside of our internal systems?

» How can we assure the new data meets our quality requirements?

At this point, data quality is paramount. You need to profile and cleanse your data to ensure that it’s error-free. Because many data sources and data formats exist within the supply chain, this can be a major task.

By way of example, the details you have for your customer in one database are slightly different than what you have in another. Something as simple as this can lead to faulty results.

These challenges are compounded as you move toward more pre- dictive and prescriptive analytics. These techniques begin to han- dle unstructured data in things like emails or social media posts. Analyzing unstructured data means understanding the context in which words and sentences appear. Where the same word has more than one meaning, it’s easy to produce false results.

Data quality is one reason you need to be able to consolidate all the data you require on one analytics platform. Consolidation will enable you to integrate, standardize, and cleanse all your data so that it’s available in a single useable format for analysis and pre- sentation to your business users.

The second reason to consolidate your data is that you don’t want to be conducting ad-hoc analytics on each separate data source and then drawing each set of results together in order to reach an overall conclusion. Data blending ensures that all relevant data for any particular metric is integrated and presented in a format that allows for automatic analysis. This technique is at the heart of the move from descriptive to predictive analytics. Data blend- ing pulls in information from many different business systems including B2B networks, ERP, and transport/warehouse manage- ment systems. Data blending normalizes the information into a single flow before it is archived into the data lake, as shown in Figure 5-1.

 

 

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Two final points on data sources. First, many companies still use the phone, fax, and email as their main ways to interact with supply chain partners. A lot of supply chain information is still locked up in paper so find ways to make it available digitally and feed it into your analytics engine. Secondly, new data sources are appearing all the time, and your analytics engine will need to be able to accept data from those sources as soon as it becomes important to your business.

Step 3: Choose the Right Team An analytics initiative requires a multi-functional, multi- disciplinary team in order to succeed. In addition to the IT staff and data analysts, you want active and enthusiastic involvement from business managers and users in each functional area that will benefit from the initiative. This starts at the top, as explained in this section.

Executive sponsors The C-Level senior executives set the tone for any major change project  — and, of course, they control the purse strings! Your analytics has a much greater chance of success and widespread adoption with a board member driving the program. Within C-level ownership, analytics initiatives can easily stall if they fail to quickly show benefits in terms of business performance and return on investment (ROI). (See “Step 6: Measure Success” later in this chapter for more on ROI.)

FIGURE 5-1: Data blending brings data from different sources together.

 

 

CHAPTER 5 A Six-Stage Approach to Getting Started 43

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Data owners The data owners will primarily be the departmental heads of the functions where your analytics activities are focused. These are the people who help define what should and shouldn’t be mea- sured. They can explain how the analytics is going to be used within their departments and how they want to digest the infor- mation. Working with the data owners, you should ensure that

» They’re involved from the strategy and planning stages.

» They lead the input for metrics within their business areas.

» They provide regular feedback on the performance of their analytics and the relevance of their metrics.

» They receive visual analytics reports when they need them in a format they can easily digest.

» They have access to top line results but can drill down into the analysis as they require.

If not handled properly, metrics can end up driving efficiency and not effectiveness. You don’t really need to get better at something that’s doing your business no good. If you involve departmental managers at every stage of the initiative, you have a feedback loop to ensure you always measure the right things and have a clear idea of the new things you need to look at.

Internal data users The day-to-day users of analytics within your supply chain are essential to building success. Supply chain analytics puts a lot more power at their fingertips. They are no longer simply just reading a dashboard. They are now able to drill down into issues and make decisions based on the analytics data. Working with the data users, you should ensure that

» They fully understand the functionality of the analytics solutions they’re using.

» They have an understanding of the analytics techniques being used.

» They understand how the analytics data relates to the company’s KPIs and their own performance metrics.

 

 

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» They’re empowered to make business decisions based on the analytics reports.

» They receive visual analytics reports when they need them and in a format they can easily digest.

External trading partners The power in supply chain analytics comes from building closer and more effective working relationships and business processes with trading partners. Much of the information you need for daily operations comes from external sources. Working with trading partners, you should know

» Which trading partners you need to onboard

» How you’re going to onboard them

» What data you need to capture and share

» Whether you have the means to capture that data if it’s external to your systems

» What you expect from them as a result of your analytics initiative

» Whether you want them to provide input into your initiative

Supply chain analytics can radically change how you work with trading partners. This moves beyond simply transactional pro- cessing to the creation of knowledge-sharing networks where analytics data is shared between partners and forms the basis for collaboration across a range of KPIs.

Step 4: Select the Right Tools Data analytics has traditionally been the preserve of the IT department. However, a new generation of tools are making ana- lytics much more accessible to business people. There are two approaches to modern supply chain analytics: Select the tools you need to create a standalone analytics solution, or select supply chain systems with an analytics engine already embedded into business process flows. In this section, you look at each of those two options.

 

 

CHAPTER 5 A Six-Stage Approach to Getting Started 45

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Standalone analytics solutions Standalone analytics solutions provide deep and rich analyt- ics functionality; however, they still require an element of IT resources to integrate to backend enterprise systems or business processes. A typical standalone analytics solution features the following capabilities:

» Data capture

» Data modeling

» Data reporting

» Data visualization

» Intuitive dashboards

Although you’ll benefit from best-of-breed functionality from choosing standalone software packages for each of the items in the list, there are clear drawbacks in terms of integration, main- tenance, and support. The learning curve and training expense of teaching internal staff to use each package also has to be taken into account.

Embedded analytics solutions Increasingly analytics is becoming embedded in business sys- tems. Embedding allows the functionality within the system to quickly implement analytics into a supply chain. Being built into the business system can boost performance and reduce costs while delivering analytics within a package that internal staff is already comfortable using.

An embedded analytics approach makes sense when the system you choose

» Can capture data from across a complete business process

» Can bring together internal and external data

» Can capture data from other systems and databases

» Can bring together data into a single actionable format

» Has comprehensive visualization and reporting functionality

» Has a wide range of pre-existing business and operational metrics

» Has the ability to create the custom metrics you require

 

 

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One potential drawback of an embedded approach is that the ana- lytics within these systems can be limited to aspects of the sys- tem’s capabilities. For example, many ERP systems enable you to analyze simple metrics, such as the performance of purchase orders or orders created in the system, but are incapable of look- ing across entire business processes or including data from other data sources.

Step 5: Start Small, 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 the say- ing goes, don’t try to boil the ocean. You’re not going to have an analytics-driven organization from day one. Start small, and look for quick wins.

Highlight one key business objective where analytics has the greatest potential to deliver real gains to your business, and focus on that for starters. You can derive a manageable amount of KPIs and associate a small number of metrics with each. As your ana- lytics initiative begins to demonstrate benefits to your business users and executives, you’ll see demand for analytics grow organ- ically within your organization. However, you should always have a wider strategic goal for how you can extend analytics into other areas of your supply chain. With the initial success, you’ll be able to take a structured approach to increasing the business functions you cover and the metrics you measure.

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