Business Intelligence Blog from arcplan

Big Data for Manufacturers: Customer Feedback Should Influence R&D


In 2001, The McKinsey Global Institute published a comprehensive report on big data, Big Data: The Next Frontier for Innovation, Competition, and Productivity, which explores the value that companies across various industries may yield as result of the big data explosion. So far we’ve explored the impact of big data on retail and healthcare companies, but today I’ll explore how big data analytics impact the manufacturing industry.

The manufacturing sector stores more data than any other sector, according to the McKinsey report. Manufacturers will likely get the most benefit from big data analytics since they have so much “raw material” to work with (from machinery metrics to sales systems). Manufacturing is a relatively efficient industry, with many advances made over the last few decades to streamline processes and improve quality through management practices like lean & six sigma (and lean six sigma!). But big data can be the impetus for the next wave of improvements in manufacturing, especially in R&D.

Research and Development
Streamlining the R&D process results in greater efficiency and reduced costs for US manufacturers and is important for products to be competitive in the global economy. But in 2012 and beyond, manufacturers should be going further, leveraging big data to influence design decisions. This means incorporating customer feedback into the process, designing products and adding features that customers actually want. McKinsey calls this “design to value” or “value-driven design.”

Surveys: I’ve taken consumer surveys that ask questions like “How much more would you be willing to pay for x feature?” and I now understand why companies are asking this. They are culling data from consumers about what features are desired and if they are included in the product/service, what is the value, i.e. how much are people willing to pay for it. Gathering concrete insights is one step toward big data analytics influencing R&D. Manufacturers should be listening to what consumers want and refining their designs accordingly. It’s just smart business.

Here’s a concrete example: Domino’s Pizza. You might not think of Domino’s as a manufacturer, but it is – the company is a serious dough manufacturer, producing and distributing dough to more than 5,000 US stores.

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Types of Return on Investment


Our series on Business Intelligence ROI has explored the importance of ROI for BI projects, provided examples of the types of BI projects that never pay off, and evaluated the methodology for calculating BI ROI. We saw that if a project has measurable returns it is more likely to get off the ground and get you acceptance for future BI projects.

Many of you who are tasked to calculate the ROI of your BI projects were never taught such a thing in school, so let’s break down another element that will help you do your calculations: types of return. Here are 5 types you should evaluate:

1. Revenue enhancement
Simply put, your organization will generate more money as a result of doing your project. Shareholders appreciate these types of projects – you’re reaching the right group of customers who see value in your project – and are willing to pay.

An example of this type of ROI would be one of arcplan’s grocery chain customers – their arcplan BI solution ties together three separate IT systems (one for sales, one for ordering, and one for inventory) and allows them to get a handle on inventory shrink (the loss of products between the point of manufacture and the point of sale…think brown lettuce or rotten tomatoes). arcplan allows the right people to see how many tomatoes are stocked in stores, how many are coming in from the warehouse, and how many are selling. The system allows the grocery stores to sell more tomatoes since they have better-looking inventory and less rotten tomatoes since they’re only ordering the amount they need in each store.

2. Revenue enhancement/margin protection
This means that your organization will increase profits through better efficiency. This does not necessarily mean more revenue but just higher profitability as a result of streamlining your current process.

The grocery store example from above also fits this type of ROI. The same shrink avoidance system allows stores to not only sell more tomatoes, but also to throw out less, thus protecting their profits (less shrink = more profit).

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Look Ahead with Leading Indicators


Just as you can’t drive to work with your eyes only on the rearview mirror, you can’t drive your business forward by focusing on the past. Yet that’s what you’re doing if you’re relying solely on lagging indicators such as revenue, profit, or Cost of Goods Sold (CoGS) to manage your organization’s performance. These factors are important, but once they’re calculated, it’s too late to impact them. What you need are good leading indicators that allow you to spot trends and see issues before they balloon into real problems.

Leading vs. Lagging Indicators
Leading indicators are early predictors of sales and profit, and in combination with lagging indicators, they give you a holistic view of your company’s performance. Lagging indicators such as revenue, sales, expenses, and inventory turnover help you understand whether or not certain objectives have been met. They can depict trends when periods are compared, but by then, you’re too late to profit from the early discovery of the trend. Lagging indicators are calculated at the end of a period (month, quarter, etc.), so you won’t know whether or not a goal has been met until nearly the end of the period. Even if you run some ad-hoc reports throughout the period, you likely can’t get to the root of a problem in time to impact the outcome. Chances are, things were going wrong long before the lagging indicator on your dashboard turned yellow.

On the other hand, leading indicators pinpoint the source of future problems and help you predict whether or not the target values for your lagging indicators will be met. Leading indicators enable your company to avoid problems and operate more cost-effectively. For example, rather than tracking product returns (a lagging indicator), reporting a 90-day customer complaint trend allows you to fix problems earlier and less expensively. Drilling down into the complaints themselves, you might discover that a particular product has a defect that your quality assurance team didn’t catch. Removing the product from your shelves may save you a lot of trouble in the long-run, reducing complaints (and the negative feelings your customers may be starting to harbor toward you) as well as the cost of returns (returns aren’t free – they cost retailers nearly $14 billion a year).

Tracking receivables turnover (a leading indicator) enables the company to better manage its cash (a lagging indicator).

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