FIGURE 1-4: The three stages of supply chain analytics.
CHAPTER 1 Defining Supply Chain Analytics 9
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numbers to be crunched. The information must be presented in as close to real time as possible.
» Accurate: People will make poor decisions if the original data is poor. The data that populates the analytics engine must be error free; otherwise, garbage in equals garbage out.
» Relevant: There is a temptation with analytics to measure everything just because you can, but this leads to data overload. A good analytics solution should be able to zoom in on the relevant facts needed to address specific business issues.
» Integrated: Looking at one data set in isolation is unlikely to derive the insight of combining multiple datasets. Analytics must present as complete a picture as possible (using the minimum number of datasets, of course!).
» Digestible: Raw data is hard to digest. Even text-based reports are better displayed visually. The easier the analytics results are to digest, the more widely they’ll be put to use.
No set of analytics results should take more than 10 to 15 minutes to digest. That not only includes the top-line results but also the data underneath that people will drill down into.
For any specific business process, try to include no more than ten different metrics. With more than this, the analytics can become very complex and the results very difficult to interpret and digest.
Types of Analytics For most types of data analytics — and supply chain analytics is no different — there are three potential approaches:
» Descriptive analytics looks at data and analyzes past events for insight as to how to approach the future. It is looking for the reasons behind past failure and success. What happened, where, and why?
» Predictive analytics uses historical and transactional data to determine the probable future outcome of an event or a likelihood of a situation occurring. It exploits patterns found in the data to identify future risks and opportunities. What will happen and what should be done next?
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» Prescriptive analytics automatically synthesizes big data, business rules, and machine learning to make predictions. It goes beyond predicting future outcomes by also suggesting actions to benefit from the predictions and showing the decision maker the implications of each decision option. What are my best outcomes and what do I need to do to make them happen?
Within the supply chain, descriptive analytics is still the most commonly implemented. Organizations are working hard to spot weaknesses and opportunities in their current operations. How- ever, predictive and prescriptive analytics are steadily becoming more popular as the analytics technology evolves and the matu- rity of supply chain managers using analytics increases.
Figure 1-5 shows a slightly more complex model, with more than three layers, but conveying the same basic idea. (You discover the additional layers in Chapter 3.) As you can see in Figure 1-5, each type of data analytics builds on the foundation of the last.
FIGURE 1-5: The landscape for supply chain analytics.
CHAPTER 2 The Importance of Supply Chain Analytics 11
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The Importance of Supply Chain Analytics
If you can’t measure it, you can’t manage it. That’s true in every part of business, and especially true in the supply chain. Supply chains are becoming more complex, more extended, and more
global every day. Properly managed supply chains can result in competitive advantage by increasing efficiency and reducing waste. The key is to be able to analyze supply chain performance at a granular level.
When companies gain insight and perspective from their data, it allows for more informed decisions to be made; this drives greater supply chain efficiencies, which ultimately contributes toward increased profit growth.
Big Data in the Supply Chain The volume of data in every supply chain is exploding from dif- ferent data sources, business processes, and IT systems. It’s not
Chapter 2
IN THIS CHAPTER
» Learning about Big Data in the supply chain
» Learning how analytics answers your supply chain questions
» Examining the benefits of supply chain analytics
» Discovering the role of B2B integration
» Learning how analytics acts as a strategic differentiator
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going to stop any time soon. As more and more companies have digitized their business processes, it’s created a virtual tsunami of data worldwide.
Recent research by the analyst firm International Data Corpora- tion (IDC) predicted that there would be 163ZB of digital data in existence by 2025. Yes that is zettabytes! (A zettabyte is one one trillion gigabytes.)
But volume is only one element of data to manage. For the pur- poses of this book, there are four:
» Volume: The amount of data that’s stored within a supply chain
» Velocity: The amount of data passing along a supply chain at any time
» Variety: The different types of data, in terms of both format and function
» Veracity: The need for that data to be reliable and accurate at all times
As the volume and complexity of data increases, so does the com- plexity and time taken to analyze that data and derive insights from it. I call that the time to insight.
The time to insight using traditional data gathering methods is typically measured in months. The process goes like this:
1. An organization sets a “drop-dead” date on which to freeze the data.
2. It spends 30 days or so collating and processing the information to put it into a usable format.
3. Take another 30 days for an army of analysts to make sense of the information, and perhaps a few more months putting it in some semi-digestible format.
4. At last the data is presented to the board and key executives, who put together next year’s plan based on data that’s, by this point, six to nine months old.
The entire life cycle of some consumer electronics products is shorter than that!
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Such analysis methods are simply not adequate for dealing with the rapid-fire pace of change and disruption in today’s mar- kets. Some companies have previously invested heavily in data analytics for their supply chains, but these attempts have often been for specific business functions, rather than company-wide initiatives. Mid-level managers are good at analyzing their own specific areas, but they don’t always see things from a company- wide perspective. One department might come to conclusions based on their analysis that another department’s research con- tradicts, leading to “My facts are better than your facts!” argu- ments. Sound familiar?
The much-needed insights are hidden in a rapidly growing data lake, and it takes so long to extract the insights from the data that they ultimately aren’t of much practical, actionable value. Worse yet, when the final analysis does come back, it’s often siloed within the specific department that commissioned its extraction. There’s no overarching, enterprise-wide view of the data, so the data could potentially return false insights that don’t correlate with overall corporate strategy.
The power of modern supply chain analytics lies in two areas:
» It enables a company to fine-tune its supply chain by analyzing data and visually presenting results at a pace and in ways that haven’t been possible in the past.
» It enables a company to take an enterprise-wide holistic approach that relates all relevant information — wherever it is and whatever format it’s in — to specific business pro- cesses or corporate objectives.
Your Questions, Answered IDC is in no doubt that analytics is the backbone of the future of the supply chain. A recent OpenText sponsored report, “Unlock the Value of Your Supply Chain Through Embedded Analytics,” looked at how embedded analytics could unlock more value from a supply chain. IDC said, “The best supply chains will be those that have the ability to quickly analyze large amounts of data and disseminate business insights to decision makers in real time or close to real time.”
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Taking a 360-degree view of the supply chain is the only way to collaborate effectively with top performing partners, improve business operations, and drive revenue. That’s the high level. At the day-to-day level, it’s simply about getting your questions answered — questions like the following:
» Who are my top- and bottom-performing trading partners based on specific KPIs (such as complete orders, accurate shipments, on-time deliveries, and processing of payments)?
» For which trading partners/customers has the volume (order/payment) increased or decreased by more than 20 percent over the last six months?
» Who are my top trading partners and how many transac- tions have I completed with them?
» Which of my customers sent me the most orders during the holiday season? Which ones sent a lot of changes?
These questions all have a common theme: Who are your suppli- ers and trading partners, and what are the facts about your inter- action with them? Is this partnership strengthening or hurting my supply chain or my customers, therefore my pocket?
By receiving timely and insightful answers to your specific supply chain questions, you’ll bring significant benefits to your business. I delve into some of those benefits in the next section.
Looking at the Benefits of Analytics The use of intelligent, embedded analytics can benefit every part of a supply chain operation. You can realize the following benefits:
» Planning and scheduling: Planning and scheduling is one of the most important parts of any supply chain. Get this wrong, and you’re going to lose a lot of money. Supply chain analytics provides better insights into inventory levels across different locations. You also gain high-quality decision support through near real-time information, which can be vital if something goes wrong.
» Improved responsiveness: Supply chain analytics helps you determine what stock you need in your supply chain. You
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can predict what’s likely to happen, so you can determine what items to buy, what items to discard, and what items you’ll need more of. Armed with this information, you can be much more responsive to trends and events.
» Improved demand planning: With supply chain analytics, you can examine past trends to predict what stock — and in what amount — your customers will require in the future. For each customer, you can see what items are selling well and what items didn’t sell well. This increases customer satisfaction and can inform product development.
» Order optimization: Supply chain analytics lets you optimize both the items you’re ordering and the overall ordering process. You can increase the number of on-time orders, decrease the cost of acquiring items, and make sure you get what you order. Order optimization brings upstream benefits as well. For example, production is more efficient when assembly lines aren’t waiting for parts.
» Real-time supply chain execution: You can see your orders and supply chain in real time and be alerted as soon as an issue arises. You don’t have to worry about where orders are, what’s happening with them, or whether you need to make changes. Taking this one step further, prescriptive based analytics wouldn’t just see what’s happened; it shows you the options you have to do something about it right then — for example, what other suppliers are ready to source from immediately.
