Business Intelligence Status Report

Economic and regulatory pressures have made a broad set of technologies called business intelligence (BI), more important than ever for all enterprise application users. Users rarely feel satisfied with the amount of information they can extract, if they can extract any at all, from their enterprise applications. Enterprise resource planning (ERP) and BI (sometimes called analytics, which is specialized analytical software) are inseparable concepts, but they have taken different trajectories and evolved differently.

ERP systems have positively transformed many enterprises' business processes, yet many users feel oversold because ERP appears to inhibit access and lock up vital information. I In most traditional ERP systems operational activities are grouped together to form artificially created processes, which bear little resemblance to the actual business activities. For example, the focus of ERP has often been to get the correct figures into the general ledger (GL) and create a transactional glut (for more information on the genesis of enterprise applications, see Enterprise Applications—The Genesis and Future, Revisited.).

Conversely, BI, which has for few decades been called executive information systems (EIS), offers a new breed of similar, but more insightful and functional tools to help enterprises operate more efficiently and profitably. Many manufacturing and distribution enterprises of all sizes are amenable to leveraging software that would not only sense the daily pulse of the operations, but would also spot incongruities, analyze the performances of multiple areas, and initiate corrective adjustments. BI tools promise to help "rank and file" employees harness data too complicated for manual manipulation. For instance, few departments are as hard pressed for new tools as purchasing and sourcing, where rapid increases in materials costs, greater deviations in lead times, and supplier base growth and instability require ever increasing buyer dexterity. BI can provide this dexterity.
Enterprise software systems are designed as transaction processing tools and, nowadays the main job is to optimize an informed decision-making process for users at all levels of the organizational hierarchy. Recent trends seem indicate that access to key operational data is no longer under the purview of executives alone. Many manufacturing executives today are allowing (if not pushing and encouraging) access to operational performance data to the shop floor and in distribution centers to enable better and more timely decision-making by operators.

Most operational data in ERP systems—and in its younger siblings like supply chain management (SCM) or customer relationship management (CRM) is stored in what is referred to as an online transaction processing (OLTP) system, which is a type of computer processing where the computer responds immediately to user requests. Each request is considered to be a transaction, which is a computerized record of a discrete event, such as the receipt of inventory or a customer order. In other words, a transaction requires a set of two or more database updates that must be completed in an all-or-nothing fashion. The opposite of transaction processing is batch processing, in which a batch of requests is stored and then executed all at one time. In other words, transaction processing requires interaction with a user, whereas batch processing can take place without a user being present. Still, both approaches result with an immense number of records in the database.

To further refresh our memory, a database is a collection of structured data that is application-independent. This data processing file-management approach was designed to establish the independence of computer programs from data files, whereby redundancy is minimized, and data elements can be added to, changed, or deleted from the file structure without changing existing application programs.

Relational database are most commonly used in enterprise applications nowadays. A relational database is a software program that allows users to obtain information drawn from two or more databases that are made up of arrays of two-dimensional data (tables). Contrary to this, a hierarchical database is a method of constructing a database that requires that related record types be linked in tree-like structures. In this instance, no child record can have more than one physical parent record.

Relational databases are more powerful than the others because they require few assumptions about how data is related or how it will be extracted from the database. As a result, the same database can be viewed in many different ways. Another important feature is that a single database can be spread across several tables, which differs from, for example,, flat-file databases, where each database is self-contained in a single table. Accordingly, relational databases are prevalently deployed within enterprise applications.

Bundled with this are database management systems (DBMS) that access data stored in a database and present multiple data views to end users and application programmers. They are a collection of software programs designed for organizing data and providing the mechanism for storing; maintaining or modifying; and retrieving or extracting data on the database. A DBMS separates data from the application programs and people who use the data, and permits many different views of the data.

From a technical standpoint, DBMSs can differ widely, since terms such as relational, network, flat, and hierarchical all refer to the way a DBMS organizes information internally, which can affect how quickly and flexibly users can extract information. For example, a relational databasee management system (RDBMS) is a type of DBMS that stores data in the form of related tables, whose architecture is based on a formal method of constructing a database in rows and columns using rules that have formal mathematical proofs. In these systems, which originated in the work of EF Codd, relationships between files are created by comparing data, such as account numbers and names. In addition, an RDBMS has the flexibility to take any two or more files and generate a new file from the records that meet the matching criteria.

Contemporary Business Intelligence Tools

Contemporary business intelligence (BI) solutions should enable business users to easily author, publish, and distribute enterprise reports via a fully integrated report writer, with an easy-to-use report-creation wizard. Users will also have the power to customize and tailor reports to specific information needs. Report writing and graphing capabilities should enable even non-technical users to easily create and share clear representations of complex business conditions. In addition to being easy to use, report writers must also incorporate advanced features like exception filtering and highlighting, calculations with sub-queries, rankings, drill-through, and more.

Part Two of the Business Intelligence Report series.

In general, contemporary BI tools provide graphical analysis of business information in multidimensional views. Most companies collect a large amount of data from their business operations, and to keep track of that information, users would need to use a wide range of software programs, such as Microsoft Excel and Access (mostly on the lower-end of the market) and other, more sophisticated database applications for departments throughout their organization. Using multiple software programs makes it difficult to retrieve information in a timely manner and to perform analysis of the data.

The term business intelligence (BI) thus represents all the tools and systems that play a key role in the strategic planning process of the corporation, by allowing a company to gather, store, access, and analyze corporate data to aid in decision-making. Generally, these systems will illustrate BI in the areas of customer profiling, customer support, market research, market segmentation, product profitability, statistical analysis, and inventory and distribution analysis, to name only a few.

The BI applications have not experienced the "boom-and-bust" cycle of adjacent enterprise application areas, and their need has been neither over- nor under-hyped. It has recently become one of the key enterprise software sectors, given that skimpy IT budgets have espoused the importance of getting the most out of existing IT assets. BI should provide an environment in which business users receive information that is reliable, consistent, understandable, and easily manipulated. C-level executives and middle management have always had a need to understand their business' performance regardless of good or bad economic times, and while the output from BI might change, the need is always there.

Particularly relative are the recent massive demise of dot-com companies, moderately optimistic economic forecasts, the stringent Sarbanes-Oxley (SOX, see Attributes of Sarbanes-Oxley Tool Sets), and many other mandatory reporting regulatory requirements, such as those from the Food and Drug Administration (FDA) or Environmental Protection Agency (EPA), following up the high-profile corporate fraud scandals (e.g., Enron and WorldCom) or bioterrorism threats. These have increased executives' focus on understanding and managing corporate performance. One should also expect the future use of radio frequency identification (RFID) tags to further drive up the amount of data a business generates, and the importance of being able to report on and make sense out of that information (see RFID—A New Technology Set to Explode?).

BI tools have neither been terribly complex nor expensive to deploy (compared to their extended enterprise resource planning [ERP] counterparts), and have been helpful in facilitating decision-making process, yet, lately, they have become considered a necessity rather than only a luxury. Also, nowadays, decisions are increasingly made at ever lower levels in organizations, thus the need for reliable information is even more pertinent. For more of pertinent information, see Business Intelligence Success, Lessons Learned and What's Really Driving Business Intelligence?.
To that end, various BI solutions enable organizations to track, understand, and manage enterprise performance, and leverage information that is stored in an array of corporate databases and data warehouses (DW), legacy systems, enterprise resource planning (ERP), supply chain management (SCM), customer resource management (CRM) and other related enterprise applications. Naturally, the market has gone through a number of evolutionary steps on a journey that began with pesky "green bar" reports printed from mainframe computers in the 1960s and 1970s. These reports were infamously poor at pinpointing critical information. Moreover, they often arrived on managers' desks a week or so after month-end, and were a far cry after the fact that they were "rear view" reports.

Driven by technological progress in the decades since, the evolution of BI has embraced everything from queries and reports to executive information systems (EIS), which extract data to provide a view of quantitative performance measures on-line (a new generation will provide this information in near-real time). It has encompassed on-line analytical processing (OLAP) technology; data mining, digital cockpits, and dashboards to portals and other broadcasting tools. These all have has a common aim to provide more timely information, filtered for importance and in a context that supports better decision-making.

Nowadays, popular uses of BI include management dashboards and balanced scorecards, collaborative applications, workflows and alerts, analytics, enterprise reporting, financial reporting, and customer and partner extranets. These solutions, some of which will be described in more detail later in this series, will enable companies to gain visibility into their business, acquire and retain profitable customers, and reduce costs. They will also be able to detect patterns, optimize the supply chain, analyze project or product portfolios, increase productivity, and improve financial performance. Data visibility problems are possibly the easiest and most rewarding obstacles to solve when it comes to improving business performance.

Using Predictive Analytics within Business Intelligence: A Primer

Predictive analytics has helped drive business intelligence (BI) towards business performance management (BPM). Traditionally, predictive analytics and models have been used to identify patterns in consumer oriented businesses, such as identifying potential credit risk when issuing credit cards, or analyzing the buying habits of retail consumers. The BI industry has shifted from identifying and comparing data patterns over time (based on batch processing of monthly or weekly data) to providing performance management solutions with right-time data loads in order to allow accurate decision making in real time. Thus, the emergence of predictive analytics within BI has become an extension of general performance management functionality. For organizations to compete in the market place, taking a forward-looking approach is essential. BI can provide the framework for organizations focused on driving their business based on predictive models and other aspects of performance management.

We'll define predictive analytics and identify its different applications inside and outside BI. We'll also look at the components of predictive analytics and its evolution from data mining, and at how they interrelate. Finally, we'll examine the use of predictive analytics and how they can be leveraged to drive performance management.

Overview of Analytics and Their General Business Application

Analytical tools enable greater transparency within an organization, and can identify and analyze past and present trends, as well as discover the hidden nature of data. However, past and present trend analysis and identification alone are not enough to gain competitive advantage. Organizations need to identify future patterns, trends, and customer behavior to better understand and anticipate their markets.

Traditional analytical tools claim to have a 360-degree view of the organization, but they actually only analyze historical data, which may be stale, incomplete, or corrupted. Traditional analytics can help gain insight based on past decision making, which can be beneficial; however, predictive analytics allows organizations to take a forward-looking approach to the same types of analytical capabilities.

Credit card providers offer a first-rate example of the application of analytics (specifically, predictive analytics) in their identification of credit card risk, customer retention, and loyalty programs. Credit card companies attempt to retain their existing customers through loyalty programs, and need to take into account the factors that cause customers to choose other credit card providers. The challenge is predicting customer loss. In this case, a model which uses three predictors can be used to help predict customer loyalty: frequency of use, personal financial situations, and lower annual percentage rate (APR) offered by competitors. The combination of these predictors can be used to create a predictive model. The predictive model can then be applied and customers can be put into categories based on the resulting data. Any changes in user classification will flag the customer. That customer will then be targeted for the loyalty program. Financial institutions, on the other hand, use predictive analytics to identify the lifetime value of their customers. Whether this translates into increased benefits, lower interest rates, or other benefits for the customer, classifying and applying patterns to different customer segmentations allows the financial institutions to best benefit from (and provide benefit to) their customers.
Data mining can be defined as an analytical tool set that searches for data patterns automatically and identifies specific patterns within large datasets across disparate organizational systems. Data mining, text mining, and Web mining are types of pattern identification. Organizations can use these forms of pattern recognition to identify customers' buying patterns or the relationship between a person's financial records and their credit risk. Predictive analytics moves one step further and applies these patterns to make forward-looking predictions. Instead of just identifying a potential credit risk, an organization can identify the lifetime value of a customer by developing predictive decision models and applying these models to the identified patterns. These types of pattern identification and forward-looking model structures can equally be applied to BI and performance management solutions within an organization.

Predictive analytics is used to determine the probable future outcome of an event, or the likelihood of a situation occurring. It is the branch of data mining concerned with the prediction of future probabilities and trends. Predictive analytics is used to analyze automatically large amounts of data with different variables, including clustering, decision trees, market basket analysis, regression modeling, neural nets, genetic algorithms, text mining, hypothesis testing, decision analytics, and so on.

The core element of predictive analytics is the predictor, a variable that can be measured for an individual or entity to predict future behavior. These predictors are based on models that are created to use the analytical capabilities within the generated predictive models. Descriptive models classify relationships by identifying customers or prospective customers, and placing them in groups based on identified criteria. Decision models consider business and economic drivers and constraints that surpass the general functionality of a predictive model. In a sense, statistical analysis helps to drive this process as well. The predictors are the factors that help identify the outcomes of the actual model. For example, a financial institution may want to identify the factors that make a valuable lifetime customer.

Multiple predictors can be combined into a predictive model, which, when subjected to analysis, can be used to forecast future probabilities with an acceptable level of reliability. In predictive modeling, data is collected, a statistical model is formulated, predictions are made, and the model is validated (or revised) as additional data becomes available. One of the main differences between data mining and predictive analytics is that data mining can be a fully automated process, whereas predictive analytics requires an analyst to identify the predictors and apply them to the defined models.

A decision tree is a variable within predictive analytics that allows the user to visualize the mapping of observations about an item and compare it to conclusions about the item's target value. Basically, decision trees are built by creating a hierarchy of predictor attributes. The highest level represents the outcome, and each sub-level identifies another factor in that conclusion. This can be compared to if-else statements, which identify a result based on whether certain factors meet specified criteria. For example, in order to assess potential bad debt based on credit history, salary, demographics, and so on, a financial institution may wish to identify multiple scenarios, each of which is likely to meet bad debt customer criteria, and use combinations of those scenarios to identify which customers are most likely to become bad debt accounts.

Regression analysis is another component of predictive analytics that allows users to model relationships between three or more variables in order to predict the value of one variable in comparison to the values of the others. It can be used to identify buying patterns based on multiple demographic qualifiers such as age and gender which can be beneficial to identify where to sell specific products. Within BI, this is beneficial when used with scorecards that focus on geography and sales.

Practical applications of all of these analytical models allow organizations to forecast results to predict financial outcomes, hopefully increasing revenues in the process. Within BI, aside from financial outcomes, predictive analytics can be used to develop corporate strategies throughout the organization. What-if analyses can be performed to leverage the capabilities of predictive analytics to build various scenarios, allowing organizations to map out a series of outcomes of strategic and tactical plans. This way, organizations can implement the best strategy based on the scenario creation.

Sybase and MicroStrategy Team on Vertical Market Portal Applications

"EMERYVILLE, Calif., Nov. 1 /PRNewswire/ -- Sybase�, Inc. (Nasdaq: SYBS) today announced a comprehensive, multi-year licensing, technology and service agreement with MicroStrategy Incorporated (Nasdaq: MSTR). The alliance offers customers MicroStrategy's Intelligent E-Business� software coupled with customer relationship management (CRM) and business performance management (BPM) applications. Under the terms of the partnership, Sybase will embed and re-market MicroStrategy Intelligent E-Business� Platform for the Industry Warehouse Studios� (IWS) offerings; thereby leveraging MicroStrategy's core analysis, personalization, and broadcast technology within Sybase's complement of analytical CRM and BPM applications". As stated by Eric Miles, senior vice president and general manager of Sybase's Business Intelligence Division, "As Sybase expands its growth in the business intelligence, CRM and BPM markets, it is critical to form strategic partnerships with companies that share our vision".

Each of the Sybase Industry Warehouse Studios consists of five analytical customer relationship management applications and one industry-specific module. The suite of six applications, designed for sophisticated, business performance management and customer relationship management include marketing campaign analysis, customer profile analysis, sales analysis, loyalty analysis, customer care analysis, and business performance management (the vertical component which delivers operational scorecards and analytical reports).

MicroStrategy powered versions of the Sybase Industry Warehouse Studios for Property & Casualty and Life Insurance, Telecommunications, Healthcare, Retail Banking, Credit Card Companies, and Capital Markets will be available during the first quarter of 2000 on UNIX and NT platforms. Industry Warehouse Studio applications start at $100,000 (US).
According to Sybase "The Company is leveraging core enterprise product strengths to capitalize on the emerging enterprise portal market to provide powerful new solutions that deliver on the promise of e-Business." Due to their decreasing hold on the overall database market, Sybase is attempting to focus vertically in an attempt to improve their profitability. (Sybase saw share value decrease 44% in 1998, revenues have decreased for the last two years, and they have suffered four years of negative earnings per share). MicroStrategy has been very successful in the portal arena, and their stock has appreciated over 300% in the last three months alone (at the time of this writing, the stock was selling at $94 per share). In addition to this agreement, MicroStrategy has also announced alliances with Unisys and NCR. If Sybase is successful in leveraging this marketing relationship, it should help restore some market confidence in the firm.

Factors Inhibiting the Widespread Adoption of Business Performance Management

Although business performance management (BPM) offers outstanding benefits, such as helping organizations align their performances to their business processes and their overall organizational strategies, widespread adoption has been slow at best. BPM vendors need to ask themselves why this has been the case, and what they can do to increase their market penetration. A step in the right direction would be to identify BPM's competitors within the overall business intelligence (BI) market, analyze the market penetration that BI solutions have sustained, and determine how BPM can reposition itself to increase its competitive edge.

Identifying Vendor Differences

Identifying the way vendors are positioning themselves in the market may help users find the vendors that most closely meet their requirements. Although there is a great deal of feature and functionality crossover, vendors market their differences aggressively. This may create confusion for user organizations. Differentiators among vendors are generally seen in business benefits, market positioning, and organizational uses, which often translates into how solutions are adopted and used.

BPM Vendors

Leading BPM vendors include Applix, Cartesis, CorVu, Clarity Systems, Actuate, and Hyperion. Analysts forecast that the BPM market will reach roughly $1 billion (USD) by 2011. While BPM vendors provide similar features as their BI counterparts, they primarily focus on planning, budgeting, forecasting, consolidation activities, etc. that center on an organization's financial performance. This can include sales and marketing efforts, human resources management, and vertical market solutions.

Traditional BI Vendors

Traditional BI vendors include Cognos, Business Objects, Information Builders, and MicroStrategy. In 2005, analyst consensus placed the overall BI market between $4 billion and $6 billion (USD) with high growth rates for subsequent years. BI vendors provide users with the ability to create and leverage data from within a data warehouse, and extract, transform, and load (ETL) functionality that pools data from across various applications to create a centralized data repository. Additionally, reporting, online analytical processing (OLAP), analysis, scorecard, and dashboard functionality provide the user with interface and front-end analysis tools. Lastly, many BI vendors develop solutions based on various vertical markets, or business functions, to meet the general needs of organizations out of the box, and increase their usage across the organization by providing specialized solutions.
In addition to vendors with a strong presence in either BPM or BI markets, several vendors have expanded their product offerings and marketing strategies to compete in both spaces. Included in this list are Actuate and Hyperion, which have crossed over from BPM to include BI. Within the BI space, Cognos and Business Objects are examples of vendors positioning themselves in both markets. These crossovers give users more flexibility. For BI vendors, their expansion into the BPM market gives their customers the advantage of a BI platform, vendor viability, and features and functionality. Additionally, many customers that implement BPM solutions do so as expansions within their BI frameworks.

Operational BI Vendors

Operational business intelligence (OBI) has emerged to provide organizations the forward-looking analysis and real-time decision-making ability lacking in traditional BI. Operational BPM and BI use similar tools to measure and define an organization's performance, and to compare those defined measurements to identified metrics. However, the focus of each market differs slightly. BPM focuses on the departmental management of metrics, or key performance indicators (KPIs), to manage the application of strategic planning. OBI leverages the use of BI to embed those tools within organizational processes. OBI includes the development of analytics and dashboards to monitor various metrics and provide collaboration tools to interface with various departments. OBI tends to appeal more to operations users and lines of business (LOB) managers, while performance management tools appeal to financial applications users.

Factors Inhibiting the Widespread Adoption of BPM

Aside from market size and current market penetration, the perception of BPM is that it has less presence than its BI counterpart. In reality, BPM and BI each play to a different audience in terms of usage within the organization. BPM's main focus is financials, including budgeting, consolidations, planning, and so on. However, BPM vendors may offer some similar features and functionality as BI vendors. BI vendors focus on the breadth of their product offerings, which include data warehousing, OLAP, reporting, usage of dashboards, etc. This means that BPM vendors might have to fight to get their "foot in the door," because BI plays to a wider market.

Installed Base

BPM vendors compete in a skewed market where they are immediately disadvantaged. Why? BI has a large installed base. BI vendors use aggressive marketing campaigns to target their current customer bases and to increase standardization within organizations. This presence often creates a roadblock for BPM vendors. A BI vendor's installation base and crossover strategy makes the vendor a natural contender for growth within user organizations. BI vendors have greater success because it is easier to sell to a current, satisfied customer than to find new customers. Additionally, many BI vendors develop crossover strategies or market their BI functionality to meet an organization's BPM needs.

Platform Standardization

Standardization on a single platform by the information technology (IT) department represents a significant obstacle to BPM vendors. One of IT's goals is the creation of a stable and manageable environment. BI standardization involves the use of a common BI platform to meet the needs of an entire organization. It also allows BI vendors the advantage of expanding their installed bases to generate more revenue and to align themselves more closely with the IT department.

Business Performance Management Basics: An Overview of Business Performance Management and Its Benefits to the Organization

The market uses the terms business performance management (BPM), corporate performance management (CPM), and enterprise performance management (EPM) interchangeably. Vendors and industry analysts use these terms to describe performance management, but essentially they all mean the same thing. BPM represents the next generation of business intelligence (BI), and is defined as the use of software to help organizations manage their processes and measure their key performance indicators (KPIs) in order to optimize performance and help drive corporate strategy.

This article will focus on the key aspects to take into account when considering implementation of performance management software:

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the way KPIs are defined by an organization's focus
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the meaning and importance of data mining
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the importance of scorecards and dashboards in driving business decisions
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the benefits and challenges of implementing a BPM solution

BPM versus BI: A Brief Overview

BPM applications allow organizations to implement an approach to data analysis. Data mining tools identify trends and enable organizations to plan intelligently for the future. Additionally, performance management software provides organizations with visualization features (such as dashboards), which give them the opportunity to view summarized data and to drill down to operational data stores for relevant details. This differs from traditional BI software, which identifies data patterns by using historical rolling data to drill down on dimensional data over time.

Traditionally, organizations developed month-end processes to generate financial reports or queried data at specific intervals in order to provide data to decision makers throughout the organization. Additionally, throughout the organization, reporting processes were implemented to provide users and decision makers with regular static reports over time. With increases in competition and potential client bases (due to globalization and technological advances), organizational needs have evolved, and require more powerful reporting tools to capture significantly higher amounts of data more often. Businesses are shifting to accommodate increased data demands, and are attempting to become proactive in their corporate planning. The realization that BI and data warehousing concepts can be leveraged to drive business decisions has helped drive the evolution of BI toward encompassing business performance functionality.

Key Performance Indicators and Data Mining

The terms KPI and data mining are often used to discuss the benefits of BPM and the ways in which BPM drives business decisions. Knowing what those terms mean, however, does not alone guarantee business success. Instead, organizations should identify appropriate ways to apply KPI and data mining in order to determine the metrics required for making the right strategic decisions.

KPIs are defined as the critical metrics set by an organization to reflect its financial or nonfinancial success. They help organizations identify and monitor factors that are quantifiable, measurable, and important to the organization's overall success. Although KPIs can help drive business decisions, they are only beneficial if they are set properly and reach the right people at the right time. For example, with traditional BI online analytical processing (OLAP) cubes, sales data can be reflected multidimensionally with rolling sales data over a three-year period. This, however, pales in comparison to dashboard functionality, which allows a sales manager to see up-to-date sales figures in real time, and to compare them against predefined metrics. The sales manager can then drill down on the data to access and analyze operational data in order to determine a plan of action.

Hooking ERP Up with MES: Good, But Not Sufficient Yet

Without such a tight and near real-time integration, there is much anxiety and frustration within any enterprise that is in search of a more competitive, profitable, safe, and agile factory. How can any manufacturing company reduce non value-adding administration and empower their workforce to take immediate remedial actions?

Namely, the typical current state of affairs from the perspective of a senior vice president (SVP) of operations could be summarized as follows:

1. On one hand, the ever more pressured manufacturing environment demands acceleration of the stock-keeping units (SKU) mix and shorter lead times, all due to ever more demanding and fickle customers; but
2. On the other hand, the real world situation is of little overall enterprise and/or SKU-level profit visibility, and the company has to rely on (suboptimal) average key performance indicators (KPIs), with emergency scheduling (constant firefighting) on paper or Excel documents.

In such “clueless” environments, there are “blind spots” everywhere in terms of determining yields and losses, hidden capacity opportunity, and masked process routing and constraints by reactive work practices. Also, there are increased risks of quality non-compliance leading to manual quality assurance (QA) processes, whereas continuous improvement efforts are floundering and remain unmeasured. In a nutshell, the hands-on plant people do not seem involved and are ironically not accountable for what they should be.

The future state should logically be the inverse of the above, and the usual “first remedial step conclusion” is to gather the glut of data from data historians and MES databases, and then decide what to do. But, without smart and intuitive plant applications that have visualization and contextual business intelligence (BI) capabilities (and that are thus accepted by the plant staff), this will all be yet another exercise in futility.

The reality check reveals an “inconvenient truth” that many MES investments fail to deliver hoped for performance management outcomes due to people issues. Namely, after 18 months or so, the embattled company in case might have an overall equipment effectiveness (OEE) dashboard that the plant engineers occasionally look at (and which might have cool colors on it), but without a pervasive effect (actionable info) and acceptance across the plant (and entire enterprise).