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Defining Supply Chain Analytics

Supply chain analytics has the power to completely transform your business, especially if you’re in the manufacturing, automotive, retail, fast moving consumer goods (FMCG),

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and information technology sectors. So, this chapter sets out exactly what I’m talking about.

A simple definition Supply chain analytics lets you make sense of the data in your supply chain, so you can make better decisions. That’s really all there is to it! This is going to be a very short book. But wait — it’s not as easy as it sounds.

Anyone with any supply chain experience knows what complex beasts they can be. In most organizations, the supply chains have grown and evolved over many years. Each one is a potentially data-creating monster — and data can be in all sorts of places and in all sorts of formats.

Chapter 1

IN THIS CHAPTER

» Learning the different components of supply chain analytics

» Understanding the role of analytics in the supply chain

» Knowing what makes for good analytics

» Learning the difference between analytics types

 

 

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The global nature of today’s business has led modern supply chains to become more intricate and diverse than anyone could’ve dreamed of only a few decades ago. Many companies have a net- work of supply chains connecting various suppliers and part- ners. In addition, the growth of partner-to-partner relationships means that companies are frequently also a part of other organi- zations’ supply chains.

This complex supply chain environment presents a number of critical challenges for a business, including

» Lack of synchronization between business strategy and execution

» Lack of real-time visibility across supply chain operations

» Inability to properly schedule production, leading to costly asset underutilization

» Poor forecast accuracy, resulting in frequent stock-outs or excess inventory and safety stock levels

» Lack of flexibility in the manufacturing, distribution, and logistics footprints

» Inability to properly assess and prepare for supply chain risks

Applying intelligent analytics is the key to addressing all these challenges — and many more.

The Three Core Components of Supply Chain Analytics

Digitizing paper-based information has become a key strategic element for nearly every organization. In addition to the obvious data handling benefits, digitizing also creates new opportunities for data analysis, a.k.a. analytics. Analytics allows for automated number crunching on a huge scale and can quickly deliver decision-critical insights.

Of course, analytics isn’t a new discipline. Supply chain managers have been populating spreadsheets and trawling through report printouts for decades. However, that kind of analysis can be pain- fully slow, complex, and — let’s face it — tedious.

 

 

CHAPTER 1 Defining Supply Chain Analytics 5

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Analytics helps companies learn lessons from the past (not the dim and distant past, but the recent, relevant past) so they can make better decisions in the future. Ford, for instance, isn’t going to glean many useful insights from studying data from the Model T production line. The information has to be up-to-date and pre- sented in a way that makes it easy for users to extract the key information.

To deliver actionable insights (that is, insights that might result in taking action), supply chain analytics requires three core com- ponents, as shown in Figure 1-1:

» Data analytics: The process of examining datasets using specialized systems and software to draw conclusions about the information they contain. Within the supply chain, this requires collating and analyzing data from a series of complementary systems.

» Data visualization: The process of helping people under- stand the significance of data by placing it in a visual context. Patterns, trends and correlations that might go undetected in text-based data can be exposed and recognized more easily with data visualization.

» Technology platform: The underlying infrastructure — often including an analytics engine — that allows for the capture, storage, retrieval, aggregation, analysis, and reporting of all transactions taking place within the supply chain and with trading partners.

FIGURE 1-1: The core components of supply chain analytics that lead to better decision making.

 

 

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By combining these three components, organizations can create an effective supply chain analytics solution.

The term analytics engine was originally coined by Charles Bab- bage to describe his pioneering computer. Today it describes any comprehensive internal system for data analytics. The important thing to know is that an analytics engine is embedded into a larger system and makes sense of the data it’s handling.

How Supply Chain Analytics Works Supply chain analytics makes two main business processes  — order-to-cash (OTC) and procure-to-pay (P2P) — more efficient and effective. OTC is the downstream or sell-side process and includes all the steps required to receive and process a customer’s order, from the customer placing the order to the order being delivered to the final bill being settled. Figure 1-2 illustrates these steps.

P2P is the upstream or buy-side process of the business. It rep- resents the relationships and transactions that an organization has with its suppliers. It includes quotations, purchase orders, receipt of materials, and paying supplier invoices, as illustrated in Figure 1-3.

Both OTC and P2P have a lot of moving parts. Many stages, sub- stages, and interconnected systems are involved. At each stage, many people, including managers and staff, need to know exactly

FIGURE 1-2: The OTC cycle.

 

 

CHAPTER 1 Defining Supply Chain Analytics 7

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what’s going on. Having the right data at the fingertips of those who need it is key to optimizing these processes.

Supply chain analytics consists of the three stages shown in Figure 1-4:

» Stage 1: Obtain the Right Data. What data do you have? What format is it in? Where is it stored? Is it up-to-date and relevant for the type of analysis you want to undertake?

» Stage 2: Define the data for analysis. What do you want to measure? How many datasets do you want to include? How often are you going to refresh or update this data? How are you going to display the results? What type of reports do you need to create?

» Stage 3: Discover the insights. How are you going to visualize the data? How much drilling down will be required? How will executives need to be able to interrogate the data? Are you able to predict outcomes and trends?

Supply chain analytics can help an organization apply informa- tion to real business needs and pain points. For example, analytics can help improve forecast accuracy and reduce inventory (check out Chapter 2 for more info). For it to work effectively, however, the organization must know exactly what it wants to measure and why. For that, check out a more detailed explanation in Chapter 3.

FIGURE 1-3: The P2P cycle.

 

 

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What Makes for Good Analytics? To gain maximum insight, analytics systems must have access to all the data, from all stages in the process, and from all the sys- tems involved in making the process work. An organization can get some valuable information from looking at data in a single system, but the real power of analytics comes when you combine data from multiple systems, in a process called data blending.

Data blending provides a quick and straightforward method of extracting value from multiple data sources and allows analy- sis on the entire dataset. It can deliver a single view of a specific area  — such as perfect orders completed, for example  — that allows the organization to evaluate performance at a glance, both for itself and its trading partners.

Not all transactions in the supply chain will be in a digital for- mat. Many companies still use telephone, email, and fax to place orders. When data blending, an organization must remember the data that’s held on paper, and then work out how to include it within the analysis. For instance, faxed orders might be scanned and imported into an ERP system.

Data quality is an important concern. The information from an analytics solution must be

» Timely: The decision-making process is accelerating. Companies can’t wait days, weeks, or months for the

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