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Analytic Applications: Relevant Information for Decision-Making Purposes

Published
  • April 19 2002, 1:00am EDT

Relevant information is essential to any business decision, which in the hands of an informed individual leads to better business decisions. However, most organizations struggle with the ability to provide decision-makers with the important information that they need in a timely manner. These organizations are confronted with this information struggle because they either do not understand what relevant information is needed and/or they do not know how to obtain it efficiency. By defining analytics such as key performance indicators (KPIs) or metrics and implementing business intelligence technology, an organization can overcome this information struggle and provide relevant information to its decision-makers.

Analytics – Key Performance Indicators and Metrics

One of the values that reporting environments such as an operational data store, a data warehouse or data mart provides is timely, relevant and accurate information. Information within these systems is stored at both detailed and summarized levels which facilitates the ability to perform research and analysis.

KPIs are significant predefined measures that provide individuals with the information they need to assess previous actions. They target performance and provide the ability to assess past performance. KPIs allow individuals to focus on those areas that require attention, thereby, managing their time more efficiently. Defining and creating KPIs can be very challenging because the individuals tasked with creating them must understand the goals of the organization, the business questions that must be addressed and where to obtain the data.

Analytical applications are business intelligence solutions that facilitate the access and maintenance of KPIs and other important metrics of an organization. The following diagram graphically depicts the flow of data from an enterprise resource planning (ERP) application to an analytic application.


Figure 1: Flow of Data from an ERP Application to an Analytic Application

A few of the analytics that we have developed for our clients includes the following goals and KPIs or metrics with the business question that it addresses:

 

Goal

KPI/Metric

Business Question

Improved Productivity

Order/Revenue Volume by Stage

Analysis of Revenue Cycle Inefficiencies

Revenue Cycle Aging by Stage

Automated Alerts of Inefficiencies

On-Time Payments

What volume of customer inquiries will be converted into revenue and cash?

What percentage of transaction volume is on hold and why?

What stage causes the greatest delays?

At what point in the revenue cycle are transactions put on hold?

What is the value of cash receipts for the period and what is the percentage of on- time payments made during the period?

Improved Customer Satisfaction

Cancellation Analysis

Conversion Rate by Stage

On-Hold Percentage by Stage

On-Time Shipments

 

Return Analysis

At what point do most cancellations occur?

Are sales personnel closing deals at the expected rate and are conversion rates increasing or decreasing over the year?

What is the value and potential profitability of transactions on hold and does seasonal factors influence the number of transactions on hold?

Is the on-time delivery rate improving or getting worse from last year and overall, and does actual performance compare to target for on-time deliveries?

What product has the highest return measured by dollars and why are customers returning a particular product?

Improved Profitability

Revenue Funnel

 

Alert Summary

 

Gross Margin by Milestone

Accounts Receivable Aging

Automated Alerts of Low Margin Deals

Cost of Capital Analysis of Revenue Cycle

Days Sales Outstanding

Cash Receipts Forecasting

Based upon current funnel activity, what is my forecasted revenue and cash flow?

What transactions do I need to address immediately?

Is gross margin increasing or decreasing over prior periods and do seasonal factors affect it?

What is the value of the outstanding invoices and how long have they been outstanding?

Which deals are below the target gross margin?

What is the potential savings from improving the collection process?

How long does it take to get paid?

What are the forecasted cash receipts?

Improved Cycle Time

Total Cycle Time

Cycle Time by Stage

 

Intra-Period Cycle Time Comparison

How long was the total sales process?

How long was each stage in the sales process?

Are component cycle times meeting target and is there one component that consistently exceeds target and causes the greatest delay?

 

The KPIs and other metrics listed above are extremely beneficial to any decision- maker whose goal is to improve productivity, customer satisfaction, profitability or the sales cycle.

Access to Relevant Data

While information is abundant within an ERP application, it is often not easy to access, in a format that is not suitable for analysis or the data is segregated by business function such as order entry, accounts receivable, inventory or general ledger. From an online transaction processing (OLTP) design perspective, segregated business functions by software module limits the expansiveness of the data model, enables each module to "stand alone" and narrows the focus of functionality so that it can be rich in features. However, this approach hampers reporting from an enterprise or multi-business function perspective.

Data that is captured or produced by transactional systems, such as an ERP application, is stored in entities within a database that are grouped by business function or ERP module. For example, data from a sales transaction would be captured by the inventory, order entry, accounts receivable and general ledger modules of an ERP application.


Figure 2: Sales Transaction Data

While the grouping of data according to ERP module makes sense from an OLTP design perspective, it creates challenges when data needs to be analyzed across business functions or ERP modules. These challenges are technical complexities of relationships between entities from one ERP module to another. Although entity relationships do exist among ERP modules, the relationships are often numerous and complex.

Reporting by business function or ERP module can provide meaningful information, but it is also limiting. For example, one can analyze inventory levels to determine quantity on hand as well as the composition of inventory items by accessing data contained within the inventory ERP module. However, if one wanted to forecast inventory levels based upon current inventory against customer orders not yet fulfilled, data would need to be accessed from the inventory and order entry ERP modules.

Conclusion

The combination of functional requirements and technology when collectively defined, implemented correctly and effectively utilized will create a competitive advantage for the organization. Analytical applications provide individuals with an infrastructure to facilitate the availability of relevant information in the form of KPIs and other metrics. Having access to up- to- date analytics such as KPIs and other metrics creates a greater awareness as to the performance of the organization against its established benchmarks. By understanding, which areas are performing below expectations, resources can be focused on those problem areas, thereby, keeping individuals focused on the goals of the organization and increasing operational efficiencies.

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