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Real-Time Analytics: The Importance of Strategic Alignment and Ease of Use

Real-Time Analytics - Key to Maximizing Profitability

Being able to analyze organizational data is essential in order to make changes which will improve corporate performance. The emergence of computerized systems over the years has allowed companies to take the guesswork out of decision-making. Collecting key data and having the ability to slice and dice it provides feedback for decisions regarding product and customer strategies, manufacturing facility improvements and investments, pricing and a host of other areas. All of these analytic efforts and the decisions made based on them are targeted at one major goal - improved profitability.

However, the ability to amass data and analyze it does not in itself guarantee dramatically improved profits and ROI. While data collection and analytic capability are fundamental components to effective enterprise operation, they are only the first steps in a journey toward achieving the best financial performance possible.

Roadblocks on the Road to Optimal Corporate Performance

Multiple Systems, Data Formats and Terminology

Large manufacturers have often initiated data analytics efforts in individual plants, of which there may be many within the organization. Different plants may make the same or similar products but use different terminology, such as product names or codes for them. They also often adopt different systems to manage their operations. To compound the problem, companies often grow by acquisition or merger, thereby inheriting different systems from those previously in place in their original operations.
Figure 1: Manufacturers often have a wide array of systems across the organization.

The net result is that frequently there is no centralized source for all company operating information, often due to the difficulty of integrating the many different systems, data types, database schemas and product information. This makes it impossible to be able to optimize operations across the organization, and without this ability, profits will never be maximized.

Concerns about Data Quality

Despite the availability of computerized systems for many years, manufacturers often still have concerns about the quality of the data they have amassed. Along with the technical challenge of integrating data from multiple locations stored in different systems and formats, this concern about data quality is a further impediment to efforts to centralize operating data.

Mismatched Metrics

The availability of data for analysis is all very well, but what typically happens within organizations is different departments using metrics they feel most relevant to their particular area. For example, sales and marketing have typically focused on using margin as the key gauge of profitability for products and customers. Production, on the other hand, is generally most interested in run rate or production speed - the number of tons, pounds, kilograms or units per minute, per hour, etc. This situation frequently results in heated conversations between departments about which are the "best" products or customers. None of this is any help to the CEO, whose goal is to maximize profit over the course of the quarter or fiscal year. There exists, therefore, a mismatch between metrics used within the organization, yet another roadblock to maximizing profits.
Figure 2: Data availability and analysis can lead to conflicts within the organization.

Using the Wrong Metric

To compound this problem, neither of the operating metrics used by sales and marketing or production will lead to decisions that maximize profitability. Consider the following simple scenario for three products.
Figure 3: Neither margin nor run rate alone is an effective metric to maximize profits.

Using margin-only to rank products A, B and C clearly shows A to be the more profitable product. Similarly, using production run rate to rank the three products shows B to be the "best" product. However, when margin is multiplied by production run rate to create a composite profit-per-minute metric rather than a profit-per-unit one, C emerges as the most profitable product, even though it had neither the best margin nor run rate. Since there are a finite number of production minutes in the course of the year, product C will actually generate over $1 million more profit per year than product B and over $1.5 million more than product A. Because it is a financial rather than efficiency metric, margin is generally used as the key metric for evaluating profitability. It is often believed that maximizing margins will lead to optimal profits. However, this simple example demonstrates that decisions to push sales to sell more of product A, to focus marketing programs on A to increase demand or to prioritize A for production time will not lead to maximal profitability. The superiority of C is quite easy to see in this three-product example, but many manufacturers make thousands of products, sell them to hundreds of customers and, as previously mentioned, operate with multiple production facilities. The importance of incorporating production run rate, or velocity, with margin has been apparent to many manufacturers, but the difficulty in creating a solution which solves this challenge has been overwhelming. Some companies have invested more than $1 million over a year of effort without being able to create a system that successfully combines margin and production velocity. Luckily, solutions are now commercially available.

Complexity of Systems

While highly sophisticated systems have become available to analyze any data that is collected, these systems often present drawbacks in two areas - 1) they are complex and require an expert to create the desired reports and 2) they are designed as generic applications and so cannot be easily adapted to create the required profit-per-time metric. These systems frequently require a trained BI expert or IT staff member, and business users typically have to wait to get the reports they need. In trying to get profit-per-minute-based reports, they may not be able to receive this at all. Since manufacturers must respond to changing market conditions, competition and changes in any cost, quick response is essential for analytics to be "real-time."

Using the "Right" Metric Enables Strategic Alignment

The use of a profit-per-time metric such as profit per minute has two very important benefits: 1) the aforementioned ability to have an operating metric on which to base decisions that will lead to optimal profits and 2) the alignment of all departments with a common metric. All the daily, weekly and monthly decisions about which products to make, who to sell them to, where to make them and how much to charge for them can then be made collaboratively without heated debates about which are the "best" products, etc. Profit per minute unites sales, marketing, production and finance under a common metric.
Figure 4: Profit per minute aligns all departments and the CEO under a common metric.

The second key element of strategic alignment that results from using a profit-per-time metric is the actual reason that this metric helps maximize profits. Company shareholders are primarily interested in return on assets (ROA), which, although generally reported as a percentage, is in reality the profit achieved during the fiscal year. It is, therefore, a profit-per-time metric itself. Using profit per minute to measure the profitability of products, customers, plants, production lines, sales regions and markets provides a metric that is the operational equivalent of ROA.

Using the "Right" Data Elements Solves the Data Quality Issue

Previously, it was mentioned that one impediment to the beneficial use of data analytics is concern over data quality. The way around this issue lies in using data that can be relied upon. Not all data is created equal. Certain data elements are more likely to be accurate than others. In order to create a profit or cash contribution-per-minute metric, one needs only a minimum of eight data elements. Certainly, other elements can be included for more extensive analysis, but the eight essential elements are all that are required to calculate profit per minute. These are shown in the table below:
Figure 5: Only eight data elements are required to calculate a profit-per-minute metric.

The beauty of requiring only these elements lies in their nature. The first six come directly from the customer invoice. Any errors in them are likely to be reported by the customer to the manufacturer, so they are likely to be of high quality. The seventh element, direct product cost, is likely to be of high quality for the same reason - any errors would be quickly referred to the supplier by the manufacturer. The eighth data element, production run rate or velocity, can be obtained from a variety of sources as shown in Figure 6.
Figure 6: Production velocity data can usually be found from one of a number of sources. 

Even if companies do not have actual historical run times, they are likely to have sample data, standards or engineering specifications for production speeds by product. These are sufficient to be able to create the aligning profit-per-time metric.

Providing the "Real-Time" in Real-Time Analytics

Responsiveness to changing costs, market conditions and competitive pricing is essential if a manufacturer wants to maximize profitability. Having data centralized, using high-quality data and even being able to generate a profit-per-time metric are not sufficient to provide agile response. A true real-time analytic solution must be able to be easily used by the business user - whether an executive, a product manager, pricing or financial analyst - so that the data can be viewed (based on profit per time) at a high level, such as at the business unit or product group level. The application must then allow the business user to easily drill down into suspected problem areas and be able to view the data at the individual product, customer etc. level. The business user should also be able to create a new custom report, such as profit per minute for a given product sold to a particular customer or made at a specific plant.

Remembering that manufacturers are dealing with thousands of products and probably hundreds of customers, the data must be presented in a way where problem areas are easy to spot. Graphical views often make this easier than tables - special views such as a topographical map that plots cash per unit (analogous to margin) against units per minute generates contour lines of equal profit/cash per minute. Because, as explained previously, profit or cash per minute is directly analogous to ROA, the position of products, customers, plants, etc. represented by bubbles on the "topo" map can be compared to the contour line that represents target ROA for the company. Decisions to change the targets for marketing programs, reduce or increase prices, initiate productivity improvements or rationalize products can all be made easily and in a timely fashion when the analytic application has high flexibility of use by the business user and graphics designed for easy ranking by profit per minute. Functionality such as what-if planning, which models the impact of changes in quantities, prices, costs and production speeds, allows even greater ability to make the decisions which impact profitability.
Figure 7: Graphics that easily display data based on profit per minute allow operating decisions to be made much more quickly. 

Real-Time Analytics Require a Variety of Elements to Maximize Profitability

Maximizing profits and ROA is the number one goal of any manufacturer. Real-time analytics support this goal. In order to make profit optimization a reality, certain components and steps are essential - 1) centralize all data where it can be properly analyzed, 2) use only data that is of high quality, 3) use an operational metric (i.e., profit per minute) that aligns with the corporate performance metric of ROA and also aligns all departments, enabling quick decision-making and 4) use an application that allows business users to analyze data and model changes so that decisions that increase profits can be quickly made.
Figure 8: Data centralization, using high-quality data and employing a profit-per-time metric in an application designed for business users will ensure that real-time analytics will maximize profitability.


Richard Batty is director of Product Marketing at Maxager. He is a 22-year veteran of the IT industry, with experience in marketing, product management and operations. He has a background at Hewlett-Packard and an IPO startup. He may be reached at rbatty@maxager.com.

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