Patent Application
20080162273
IBM® is a registered trademark of International Business Machines Corporation, Armonk, N.Y., U.S.A. Other names used herein may be registered trademarks, trademarks or product names of International Business Machines Corporation or other companies.
1. Field of the Invention
This invention relates to managing a decision making process.
2. Description of the Related Art
Businesses suffer millions of dollars in losses each year as a result of flawed decision-making. Despite massive investments in technology to improve management practices, people continue to make bad decisions. Bad decisions can result from any level of the organization such as executive, middle management, and staff. There are a number of reasons why people frequently make poor decisions.
One common reason for poor decision making is that conclusions reached are often based upon poor data. For example, fragmented data, inaccurate data, and lack of data are potential causes of delay and bad decisions. Decision data and business metrics may reside in different databases. It may take hours to gather and correlate relevant decision data. Employees may not even know that some decision data exists.
Employees who follow their “gut instincts” can lose sight of business objectives. While these employees may think they are making correct decisions, the decisions may ultimately prove to have been poorly made as relevant factors and data were not adequately considered.
A decision maker needs to consider numerous factors such as corporate policies, business strategies, past decisions, risk management and market trends. In short, a root cause of many flawed decisions is that some critical factors or data may not have been considered during the decision-making process.
What are needed are software and hardware to improve the quality of decisions.
The shortcomings of the prior art are overcome and additional advantages are provided through the provision of a computer program product stored on machine readable media including machine readable instructions for improving the quality of decisions, the instructions include instructions for providing a tracking runtime component, a decision modeling component, and a decision analytics component wherein the tracking runtime component provides for tracking executed decisions; the decision modeling component provides for modeling executed decisions; and the decision analytics component provides for analyzing past decisions to improve the quality of the decisions.
Also disclosed is a computer system including a computer program product having instructions for improving the quality of decisions, the product includes instructions for providing a decision tracking runtime dashboard, decision tracking runtime subsystems, and a decision tracking repository; providing a decision tracking management tool, a decision recommendation tool, a decision execution tool, service handlers, simulation tools, and evaluation tools; providing a tracking controller, a data mediator manager, data mediators, a mediator handler manager, and mediator handlers; providing decision tracking meta data, decision knowledge base, and a data retrieval pool; providing a decision tracking modeling tool, a decision tracking transformation tool, decision tracking runtime elements, and runtime deployment tools; providing contextual decision variables, a contextual input link, performance feedback metrics, and a performance feedback link; providing a decision analytics dashboard, decision analytics service adapters, measurement ETLs, and decision multidimensional analytics data; and providing a dashboard GUI, a decision history management tool, multidimensional decision analytics, and measurement functions.
System and computer program products corresponding to the above-summarized methods are also described and claimed herein.
Additional features and advantages are realized through the techniques of the present invention. Other embodiments and aspects of the invention are described in detail herein and are considered a part of the claimed invention. For a better understanding of the invention with advantages and features, refer to the description and to the drawings.
As a result of the summarized invention, technically we have achieved a solution in which a computer program product stored on machine readable media includes machine readable instructions for improving the quality of decisions, the instructions include instructions for providing a tracking runtime component, a decision modeling component, and a decision analytics component wherein the tracking runtime component provides for tracking executed decisions; the decision modeling component provides for modeling executed decisions; and the decision analytics component provides for analyzing past decisions to improve the quality of the decisions.
The subject matter which is regarded as the invention is particularly pointed out and distinctly claimed in the claims at the conclusion of the specification. The foregoing and other objects, features, and advantages of the invention are apparent from the following detailed description taken in conjunction with the accompanying drawings in which:
The detailed description explains the preferred embodiments of the invention, together with advantages and features, by way of example with reference to the drawings.
The teachings herein provide techniques for improving the quality of decisions. A primary focus of a system employing the techniques is to bind the decision to up-to-date data and objectives. The techniques include a process having three parts. In a first part, a modeling process determines a context in which decisions are made and also measures an effectiveness of decisions. In a second part, a tracking process provides for tracking decisions as well as the context. In a third part, an analysis process analyzes past decisions. Each of the three parts is linked together, and operates to provide iterative refinement of decision making.
As a general example, a user will call upon the system to make a decision regarding procurement of a material from one supplier or another supplier. Inputs to the system include information regarding aspects such as cost, delivery charges, delivery schedule, quality, and warranty. Other inputs will include objectives such as profitability and customer satisfaction. The decision provided will simply be a selection of the best supplier with respect to the up-to-date data and objectives. However, the system will take advantage of other information such as internal clients, test data such as product failure modes for each type of material, and experiential data including customer satisfaction with products having each of the materials. Of course, procurement is but one example, and is therefore merely illustrative.
Accordingly, the system includes a variety of tools for making decisions. Linking the three parts together provides for improving the quality of decisions. Linking insures that the decision-maker has access to analyses of past decisions, up-to-date data and objectives, and appropriate decision-making tools and services. As one skilled in the art might surmise, each part of the technique includes a number of components. However, prior to discussing aspects of the various parts, certain definitions are provided.
As used herein, the term “relevant data” relates to data that the decision maker needs to aid in making the decision. The term “critical factors” relates to concepts such as strategies, goals, risk tolerance, and market trends. The term “ETL” relates to an extract, transform, load function performed on data. For example, data is extracted from a database, transformed, and then loaded in an application. The terms “runtime elements,” “runtime components,” and “configuration components” relate to elements of software for implementing the teachings herein. The term “metrics” relates to data that are typically used to measure effectiveness of decisions. The term “root cause” relates to the earliest event in a decision chain whose interruption would have changed the decision outcome. The term “stereotype links” relates to a predefined link between at least two sets of data. The term “critical decision variables” relates to those variables that contribute to making the decision. The term “multidimensional” relates to analyzing at least one type of information (such as the decision) with respect to at least one other type of information (for example, supplier warranty).
Referring now to
As disclosed herein, the system 99 includes machine readable instructions stored on machine readable media (for example, the hard disk 103) for providing improved quality of decisions. As disclosed herein, the instructions are referred to as decision method software 221. Typically, the software 221 includes instructions for generating runtime elements. The software 221 may be produced using software development tools as are known in the art. The software 221 may be provided as an “add-in” to an application (where “add-in” is taken to mean supplemental program code as is known in the art). In such embodiments, the software 221 replaces or supplements structures of the application for providing decisions.
Thus, as configured
It will be appreciated that the system 99 can be any suitable computer, Windows-based terminal, wireless device, information appliance, RISC Power PC, X-device, workstation, mini-computer, mainframe computer, cell phone, personal digital assistant (PDA) or other computing device.
Examples of other operating systems supported by the system 99 include versions of Windows, Macintosh, Java, LINUX, and UNIX, or other suitable operating systems.
Users of the system 99 can connect to the network 222 through any suitable connection, such as standard telephone lines, digital subscriber line, LAN or WAN links (e.g., T1, T3), broadband connections (Frame Relay, ATM), and wireless connections (e.g., 802.11(a), 802.11(b), 802.11(g)).
The decision modeling component 10 captures the context in which decisions are made. The decision modeling component 10 includes critical decision variables that lead to the decision, results of the decision, and utility functions that measure effectiveness of the decision. The decision modeling component 10 makes this information available for use with the tracking runtime component 20 and the decision analytics component 30. The decision modeling component 10 includes a decision tracking modeling tool 100 and a decision tracking transformation tool 110. To capture the context and effectiveness, the decision tracking modeling tool 100 constructs relationships between entities such as a strategy, a goal, a decision and an action. Based on a decision model constructed by the decision tracking modeling tool 100, the decision tracking transformation tool 110 transforms the decision model to the decision tracking runtime elements 120.
The decision tracking modeling tool 100 includes a set of stereotype links for defining a tracking relationship. Typically, for each decision action, the context to be captured includes critical decision variables. The critical decision variables typically include relevant data and critical factors. The stereotype links automatically couple the decision to the critical decision variables. Other stereotype links may couple the decision to the entities. In the decision tracking modeling tool 100, the stereotype links are used to make connections from at least one critical decision variable that leads to the decision, to execution of the decision via at least one action, and finally to the impact of the decision on other variables. In addition, the utility functions can be attached to each decision for measuring the effectiveness of the decision-making process.
The decision tracking modeling tool 100 selects which metrics are used to measure the effectiveness of the decision. By measuring the effectiveness of decisions, the decision tracking modeling tool 100 provides a way to identify a root cause of poorly made decisions.
In one illustrative example for the teachings herein, if the decision is made to purchase ten percent more raw materials because product sales are increasing at a ten percent rate, then the stereotype link is created to capture the purchase decision coupled to the product sales increase rate. Many more stereotype links may be created such as between the decision and market trends for the product. Another stereotype link may be created coupling the decision to product sales to measure the decision effectiveness. Percent increase in profits may be another metric used to create a stereotype link with the decision.
The decision tracking transformation tool 110 provides the decision model to the decision tracking runtime elements 120. The decision tracking runtime elements 120 extract information such as the critical decision variables and metrics from the decision model. For the example above, the decision tracking runtime elements 120 would extract the purchase decision, the critical decision variables such as the product sales increase rate and market trends, and the metrics such as the product sales and the percentage increase in profits.
The tracking runtime component 20 includes a decision tracking runtime dashboard 130 and decision tracking runtime subsystems 140. The decision tracking runtime dashboard 130 provides the decision-maker the opportunity to make decisions by at least one of a manual process and an automatic process. The decision tracking runtime dashboard 130 provides the decision-maker with many decision-making aids. For example, with the decision tracking runtime dashboard 130, the decision-maker can browse a history of past decisions. Typically, the decision-maker will be provided with the critical decision variables and metrics by the decision tracking runtime dashboard 130. The metrics provide the decision-maker the types of data that will be used to measure the effectiveness of the decision. Also, typically, the decision-maker can simulate decisions and evaluate alternative decisions. As another example, the decision-maker may request recommendations for specific situations. The decision tracking runtime dashboard 130 guides the decision-maker to bind decisions with the critical decision variables and metrics.
For the illustrative example above, the decision tracking runtime dashboard 130 provides the decision-maker with the percent increase in product sales, product sales, market trends, and percent increase in profits. In one scenario, the decision-maker can simulate the purchase of ten percent more raw materials to determine the percent increase in profits. In another scenario, the decision-maker can evaluate the product sales resulting from both the purchase of ten percent more raw materials and a purchase of thirty percent more raw materials. In yet another scenario, the decision-maker can request a recommendation for the purchase of raw materials from the decision tracking runtime dashboard 130.
The decision tracking runtime subsystems 140 extracts the decisions and associated information such as the critical decision variables from the decision tracking runtime dashboard 130. From the decisions and associated information, the decision tracking runtime subsystems 140 creates runtime components.
The decision analytics component 30 includes a decision analytics dashboard 160 and decision analytics service adaptors 170. The decision analytics dashboard 160 enables one to view and analyze decision data in different dimensions. The decision analytics dashboard 160 uses the decision tracking runtime elements 120. The decision analytics dashboard 160 enables one to build decision multidimensional analytics data 180. Typically, the decision multidimensional analytics data 180 is where analysis functions are applied. For the illustrative example above, one can analyze the decision to purchase ten percent more raw materials by reviewing the resulting product sales and any increase in profits. At a higher level, one can determine how the purchase decision relates to the strategy and goals. The results of these analyses can be stored in the decision multidimensional analytics data 180. For another analysis, one can retrieve information from the decision multidimensional analytics data 180 to review all past decisions to purchase raw materials and the resulting effects.
The decision analytics service adapters 170 provide an interface between the analytics dashboard 160 and the decision multidimensional analytics data 180.
The decision tracking runtime elements 120 are input to runtime deployment tools 200. For the illustrative example above, the runtime elements 120 include the decision to purchase ten percent more raw materials and the critical decision variables such as the percent increase in the product sales rate and market trends. The decision tracking runtime elements 120 may also include metrics such as the products sales and the percent increase in profits. The runtime deployment tools 200 configure both the tracking runtime component 20 and the decision analytics component 30 using the decision tracking runtime elements 120. The runtime deployment tools 200 also store the runtime elements 120 with the decision tracking repository 150. Additionally, the decision tracking repository 150 stores the runtime components from the tracking runtime component 20. The measurement ETLs 190 extract data from the tracking repository 150, transform the data, and load the transformed data to the multidimensional analytics data 180.
Typically, the first step 91 includes sending and receiving data (the executed decision 113 and the critical decision variables 123 for example) with both the decision modeling component 10 and the decision analytics component 30. The first step 91 also includes receiving data such as relevant data and critical factors from outside sources.
A second step 92 calls for modeling the executed decision 113. Typically, the executed decision 113 context and effectiveness are captured with the decision modeling component 10. The software 221 typically provides for reviewing the set of stereotype links and coupling the executed decision 113 to the critical decision variables 123 and to the performance metrics 125. The second step 92 may include creating decision tracking runtime elements 120.
Typically, the second step 92 includes sending and receiving data (the decision tracking runtime elements 120 as one example) with both the tracking runtime component 20 and the decision analytics component 30.
A third step 93 calls for analyzing past decisions 113. Typically, analyzing the past decisions 113 is performed with the decision analytics component 30. The third step 93 may include building the decision multidimensional data 180. The third step 93 may also include performing analyses on the decision multidimensional analytics data 180.
Typically, the third step 93 includes sending and receiving data with both the tracking runtime component 20 and the decision modeling component 10.
The capabilities of the present invention can be implemented in software, firmware, hardware or some combination thereof.
As one example, one or more aspects of the present invention can be included in an article of manufacture (e.g., one or more computer program products) having, for instance, computer usable media. The media has embodied therein, for instance, computer readable program code means for providing and facilitating the capabilities of the present invention. The article of manufacture can be included as a part of a computer system or sold separately.
Additionally, at least one program storage device readable by a machine, tangibly embodying at least one program of instructions executable by the machine to perform the capabilities of the present invention can be provided.
The flow diagrams depicted herein are just examples. There may be many variations to these diagrams or the steps (or operations) described therein without departing from the spirit of the invention. For instance, the steps may be performed in a differing order, or steps may be added, deleted or modified. All of these variations are considered a part of the claimed invention.
While the preferred embodiment to the invention has been described, it will be understood that those skilled in the art, both now and in the future, may make various improvements and enhancements which fall within the scope of the claims which follow. These claims should be construed to maintain the proper protection for the invention first described.
