When visiting Rome as a tourist how can anyone not admire the incredible structural achievements of Italian sculptors, artists and architects? As examples, in Rome you’ll find the Pantheon, the Coliseum, the Spanish Steps and all the wonderful piazzas.
When visiting Rome as a tourist how can anyone not admire the incredible structural achievements of Italian sculptors, artists and architects? As examples, in Rome you’ll find the Pantheon, the Coliseum, the Spanish Steps and all the wonderful piazzas.
And of course there are all of the churches, including St. Peter’s Basilica in the Vatican. The structure of these churches, including their tall columns and ornate decorations, is breathtaking. Inside each church I visited, as I gazed at the high ceilings, I asked myself this question: “How much of this beauty built by people centuries before us was the result of art, craft or science?”
And similarly, for today’s organizations implementing the various component methodologies of the enterprise performance management framework with imbedded analytics (e.g., dashboards, scorecards, strategy maps, costing models and customer intelligence reporting), how much is art, craft or science? Let’s further discuss this question.
Balancing a smart as well as healthy organization
Despite my Newtonian view, probably shared by a vast majority in business and IT, that an organization is a big machine that simply needs more gears, pulleys and dials to better operate it, many of my blogs and articles describe the importance of behavioral change management. I believe you have to be a psychologist and sociologist to successfully implement analytics-based performance management. I realize there is also a Darwinian view: That an organization is like an organism, and we must acknowledge its sense-and-respond behavior.
A Newtonian organization relies on fact-based information to make it smarter. A Darwinian organization relies on employee-centric programs and policies for improving morale to make it smarter. A balance of the Newtonian and Darwinian management styles is needed to be both smart and healthy. But please indulge me for the next few paragraphs, and let’s explore how science in the form of business analytics can be inserted into performance management methodologies to make it smarter.
The power of business analytics
- If you are a retail merchandiser with many stores and consumer items, you can use predictive analytics to continuously replenish a dynamically optimal level of inventory – without having too many items languishing on the shelves or any stock-out items.
- If you are a hardware manufacturer and your suppliers are delivering component parts late, your order-handling representatives can demand-shape customer orders by offering them slightly different product configurations, with a small discount, for the parts you have in stock.
- If you are a consumer packaged goods producer, you can run short-duration marketing campaigns, target and predict the desired sales mix volume, and instantly harmonize your production levels to optimize costs.
- If you are a business-to-consumer service, like a bank or telecommunications firm, you can analyze the purchase history and preferences of your customer micro-segments (and even of each customer). With that information combined with individual customer profitability information, you could tailor service offerings to up-sell and cross-sell to customers to optimize future sales and profits.
- If you are a human resources manager seeking to increase employee retention, you could analyze the characteristics and traits of employees who have left your organization. The variables might include their ages, the frequency and amount of their salary increases, their lengths of employment, and dozens of others. You could then apply these patterns to your existing work force to predict and order a list of the next most likely employees to resign (plus their reasons) and potentially intervene to prevent their departures – for those valuable employees you wish to retain.
- If you are a CEO desiring to better select and align your most influential balanced scorecard’s key performance indicators (KPIs), you could continuously test the degree of statistical correlation between the cause-and-effect leading to lagging KPIs. This way you can keep improving which KPIs to monitor and accordingly apply weights to influence and align your employees’ behavior with the strategic objectives.
The time for scientific management is now
Frederick Winslow Taylor, a luminary of industrial engineers, pioneered scientific management methods in the 1890s to systematically organize work. His techniques helped make Henry Ford wealthy when Ford’s automobile company applied these methods to divide labor into specialized skill sets in a sequential production line, and to set stopwatch-measured time standards as target goals to monitor employee production rates. Production at rates faster than the standard was good while slower was bad. During the same period, Alexander Hamilton Church, an English accountant, designed a method of measuring cost accounting variances to measure the favorable and unfavorable cost impact of faster or slower production speeds compared to the expected standard cost.
Since then, management science has gradually continued to support better decision making and improve an enterprise’s performance. However, the time has come to escalate the application of management science by leveraging business analytics.
An organization should be viewed as a broad series of ongoing laboratory experiments to refine its decision making. Managers at all levels should be formulating ideas and theories of what will best improve the organization’s performance. Based on these theories, the organization should constantly test the effects of its actions for validation.
Scientists, like biologists and astronomers, live by experimentation and test analysis. An organization should, too, whether it is a commercial business or public sector government agency or ministry.
