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.
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.
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.
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.
