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What do you say when the CEO is asking whether the company will hit its revenue targets for the current reporting period? Can you tell the CEO instantly which customers may be facing late delivery, and which orders may not ship? Can you tell the CEO what is causing the late deliveries and how the company could get back on track?

 

Even if your company has an effective, integrated ERP system, chances are you won't have a good answer. Because, even the 'best of breed' ERP systems don't give the business stakeholders sufficient visibility into what's actually happening to ensure performance improvements. Are employees able to determine for themselves the future risk to the company? Are they able to test alternative decisions and evaluate the financial and operational impact quickly and effectively? They should, because that is where the business value lies.

 

Not too long ago companies suffered from having too little data with which to manage the company's operations. The ERP age has brought in a different problem of too much data, but too little information. This is not unusual because transaction systems, such as ERP, are designed to capture data and make a record of a transaction, principally for accounting purposes. They were not designed to provide insight gained from analyzing many similar transactions.

 

It is a well-known fact that financial services and telecommunication companies have pioneered the use of business intelligence (BI) solutions to enable them to analyze massive amounts of data they have accumulated over the years. As a result, considerable insight was gained from data mining and data analysis and thus, the need for BI capabilities grew in the '80s and '90s in other industries as well. In the past 5+ years, the interest, and indeed the need, for real-time access to operational data has increased dramatically. The promise of real-time operational BI that goes beyond the capturing of static data snapshots and enables users to identify and analyze business trends and patterns, is of major interest to supply chain management (SCM) managers because they know that better information leads to better decisions and outcomes.

 

But despite being a hot topic, and one that has been written about extensively in the last 10 years by industry analyst firms under its many guises of business intelligence (BI), operational BI, and business performance management — there has been only mediocre results. Why?

 

BI tools suffer from two major drawbacks that prevent them from providing greater value and therefore obtaining greater adoption: They cannot identify causality and, as a consequence, they cannot provide a prediction of future performance and risk.

 

In SCM, without the deep supply chain analytics required to identify causality, BI tools cannot identify future risk based upon the current state of the supply chain. For example, while the information that a receipt of a shipment indicates that 20% of the shipment is damaged is important; the real value comes from being able to identify the orders that are impacted and therefore, being able to evaluate the potential financial consequence. In other words, BI tools do not provide actionable information and therefore fail to address a critical aspect of the day-to-day lives of operational people: knowing what levers to pull to affect change.

 

What has been missing is the predictive analytics to connect changes to consequences, both operational and financial. There is little value in knowing about a problem after it has occurred (or just before it occurs when you do not have time to react.) And knowing that something has changed is also of little value. But knowing the operational and financial consequences or root causes of these changes, and therefore what to work on, has immediate value. And knowing what to do to overcome the risks that these changes present is of greatest value.

 

For example, there may be several orders that will be delayed because of a shortage of one component. This component could be a fairly small part of a company's overall purchase costs so the shortage may be overlooked using traditional BI tools. But its impact on future finished goods availability may be quite large. Predictive analytics identifies future risks associated with current changes. And once you know what future risks are faced by your company, the natural next step is to find ways of testing the effect or impact of choosing one course of action over another to mitigate these risks.

 

At the heart of evolving business intelligence into business value is having the right tools at your disposal to make intelligent decisions that affect the future performance of your company. This requires:

  • knowing sooner of risks to the business,
  • understanding the operational and financial impact of the risk,
  • assessing the action alternatives (through "what-if" analysis), and
  • taking correction action to avoid or minimize the impact.

 

The 'rear-view mirror' approach to BI has not produced the performance improvements desired and urgently required by today's businesses. Predictive analytics that can clearly demonstrate cause-and-effect have been the critical missing piece in the BI value proposition to-date. To maintain and grow its place as a strategic enterprise tool, BI must be redefined to encompass and address the issue of real-time performance management.



Originally posted by tmiles at http://blog.kinaxis.com/2012/02/connecting-changes-to-consequences-the-missing-piece-for-getting-business-value-from-business-intelligence/

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