1 a. Give an overview of the trend

b. The history of the trend/concept (each stage)

c. The current status/evolution of the trend (each stage)

d. And where you see it in the future. Include any industry criticisms of the trend and finally your opinion.

BASED ON ARTICLE BELOW.

TopTrends in Management Accounting

Imagine if you reviewed the titles and content of The New York Times best-selling business books or of Harvard Business Review articles from the last 25 years. How many of them might cause you to react with a chuckle and say, "Oh, that one"? Do you remember any of the items in the following list? (Warning: Some advocates or book authors may be offended.) Quality circles (for total quality management, or TQM) One minute manager Business process reengineering (BPR) Management by objectives (MBOs) Six Sigma Matrix management Core competency Intrapreneuring Search for excellence Best practices Management by walking around (MBWA) I'm not saying those practices served no purpose. They did introduce useful ideas, but they didn't live up to their promises as they ascended. Many organizations jump from one improvement program to another, hoping that each new one will provide that big competitive edge, only to discover with hindsight that it was just a method du jour. Most managers would acknowledge that pulling one lever for improvement rarely results in a substantial changeparticularly a long-term sustained change. And the business media haven't helped. They hype what's fashionable at the time, mostly because that's their role. Will the management accounting trends that I describe here take root or be just another fad or fashion? Management Accounting Eras First let's look at some history.

1. Ancient EraRocks and stone piles.

2. Medieval EraPiles of precious metal and paper money. This situation ultimately led to the book published in 1494 by Luca Pacioli, an Italian mathematician and Franciscan friar, titled Summa de arithmetica, geometria, proportioni et proportionalit. It dealt with Hindu Arabic arithmetic and its offshoot, algebra, and contained Pacioli's treatise on Venetian accounting that described double-entry bookkeeping.

3. Industrial Age EraStandard cost accounting. In the 1860s, Albert Fink, a German-born civil engineer who worked in the United States, developed cost per ton/mile rates for the railroad industry using cost allocations. In the 1890s, to reflect Frederick Winslow Taylor's manufacturing scientific methods, Alexander Hamilton Church developed standard costing methods.

4. Regulatory Compliance EraThe Great Depression in the U.S. resulted in regulatory reforms to protect investors from shady financial reporting practices (1930s). In one sense, they were a setback to management accounting because the reforms established simplified rules that calculated inventory values and costs of goods sold (COGS), yet the overhead cost allocation methods were misleading because they were based on cost factors that violated costing's causality principle (the need for cause-and-effect insights).

5. Consumer EraThe emergence of activity-based costing (ABC). This next era arguably led to a transition from management accounting to managerial economics. ABC reflected "causal" cost tracing of increasingly diverse types of products, services, channels, and customers that resulted in an organization's relatively greater indirect to direct expense structure to manage the increase in complexity. In 1987, the book Relevance Lost: The Rise and Fall of Management Accounting, by H. Thomas Johnson and Robert S. Kaplan, documented the need for and benefits of upgrading costing practices from a highly aggregated "cost pool" with a single, noncausal cost allocation factor to using multiple disaggregated cost pools with causally related factors.

6. Predictive Analytics EraPredictive accounting. Today and moving forward, there's a shift in emphasis from a historical to a predictive view of strategy and operations. With cost projections, organizations can translate their plans and actions into monetary terms for decision evaluation and/or validation. Where are the emerging practices in management accounting that may likely evolve into lasting trends? They are in steps 5 and 6. Before getting to the trends, let's look at the role of management accounting. Contrary to beliefs that the only purpose of management accounting is to collect, transform, and report data, its primary purpose is first and foremost to influence behavior at all levels, from the desk of the CEO down to each employee. It should work by supporting decisions. A secondary purpose is to stimulate investigation and discovery by signaling relevant information (and, consequently, bringing focus) and by generating questions. Here is the IMA formal definition of management accounting: Management accounting is a profession that involves partnering in management decision making, devising planning and performance management systems, and providing expertise in financial reporting and control to assist management in the formulation and implementation of an organization's strategy. My intent isn't to debate or replace IMA's definition but to emphasize the importance of its need to support decision making.

Business Analytics Embedded in EPM Methods

Business analytics and Big Data are hot topics. They are here to stay because complexity, uncertainty, and volatility are on the rise. When some managers hear these terms, they react with trepidation and think, "I took a statistics course in school and just wanted a passing grade and be done with it!" Today, the need for analytics may be the only sustainable long-term competitive advantage. Why? Because the traditional generic strategies, such as being the lowest-cost supplier or providing product or customer differentiation, are vulnerable to agile competitors who can quickly match a supplier's price or invade your customer base. Analytics is about investigation and discovery. Queries, like drill-downs, simply answer questions. Business analytics creates questions. Further analysis stimulates more questions, more complex questions, and more interesting questions. But most important, business analytics also has the power to answer the questions. Here are a few examples of emerging applications that will help you get more and deeper insights from EPM methods:

Strategy maps typically have 15 to 25 strategic objectives displayed in boxes. They also contain arrows that causally connect the strategic objectives in the traditional four perspectives of a strategy map: (1) learning, growth, and innovation; (2) processes; (3) customer satisfaction and loyalty; and (4) financial. The arrows represent the selected key performance indicators (KPIs) and usually are displayed in a simple PowerPoint diagram that communicates the strategy in a single page. With analytics you can gain rich insights into how actions or projects more or less support the implementation of the strategy. You also can apply correlation analysis where the thickness of the arrows that connect the strategic objectives reflects the explanatory value, which is the magnitude that a change in one KPI impacts another KPI, that one strategic objective's KPI has on the dependent KPIs it is presumed to influence in other strategic objectives. The thickness validates the quality of the selected KPIs. With higher correlation (i.e., greater thickness), there is insight to where spending provides a higher return on investment (ROI).

The activity drivers in an activity-based costing (ABC) system assign the activity costs to their final cost objects (such as products, services, channels, customers, and business sustaining). Ideally, they should be exactly proportional. That is, if the quantity of an activity driver increases 20%, its activity cost should also increase 20%. This isn't the case in poorly designed ABC systems. Again, with correlation analysis, the quality of the activity driver can be validated. If there is low correlation, then a new activity driver can replace it and thus increase the cost accuracy of the final cost object. This also provides better insight as to what's driving the costs.

As I described in trend No. 1, there's an expansion from calculating product profitability to calculating channel and customer profitability using ABC principles. This results in ranking customers from most profitable to least profitable. Some of the reasons that differentiate highly profitable from unprofitable customers can jump off a report's pagesfor example, excessively frequent orders rather than bundled. The "what do things cost?" is amplified with the "why do things cost?" But the "why" question that differentiates highly profitable customers from unprofitable ones isn't always answered easily. With analytics' recursive partitioning and decision trees method, a computer can tell you why. Customer profit level is a dependent variable and is a result of many factors. In the customer master file are dozens of independent variables (such as number of sales orders, types of orders, the location of the customer, and special services the customer may demand) that can be compared and interpreted as the key differentiators of profit levels. From that information, companies can take profit lifting actions.

In trend No. 3, I described the shift from the annual budget to rolling financial forecasts using driver-based resources expense modeling methods that calculate a single-point profit forecast. In some cases, three scenarios may be projected using best-case, baseline, and worst-case assumptions for a few variables, such as sales volume. But why stop with three and just a few variables? Why not estimate on a range of seven estimates for a dozen variables assumptions (such as material prices or labor wages)? With 7 12, then 84 projections and rank-order can be displayed in a profit distribution graph. With such a distribution curve, analysts can better understand what factors most lead to higher profits (other than the obvious sales volume and product mix) and apply sensitivity analysis to better understand which variables (drivers) might be increased or decreased to improve overall profits.

There are dozens of other examples where analytics can support the management accounting function well beyond simple and primitive ratio analysis, such as sales expense as a percentage of sales, inventory turn ratios, and return on equity (ROE). Analytics is here to stay. The buzz about "data scientists" isn't hype. Trend No. 4 recognizes that progressive accounting functions now realize that competency and capabilities with analytics provides a competitive edge