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.
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. But despite being a topic explored and written about extensively, there has been only a moderate uptake and mediocre results. Why? Pure business intelligence (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.
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 risks and events, is of major interest to supply chain management (SCM) managers. Driven to improve operations performance, supply chain managers know that better information about their operations and processes lead to better decisions and better supply chain performance.
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 and why? Can you tell the CEO what is causing the late deliveries and how the company could get back on track? You should, because it is in these answers where the business value lies. We just posted a paper that highlights what's at the heart of evolving business intelligence into business value.
I believe 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 means:
- knowing sooner of risks to the business,
- understanding the operational and financial impact of the risk,
- assessing the action alternatives, 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. Ventana Research conducted a study in 2007 that showed “Organizations at the higher maturity levels see operational BI as a key means of enabling front-line workers and operational managers to spend less time in struggling to locate and access information and more on activities that benefit the business, such as improving efficiency and customer service.” The potential value is recognized, but has remained stubbornly out of reach.
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 in advance what exactly has changed and the operational and financial consequences or root causes of the change (and therefore what to work on) has immediate value. And knowing what to do to overcome the risks that the change presents 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.
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. For example, you may be faced with a demand spike by a customer that represents a large portion of your revenue, but a lower margin because of their purchasing power. Does it make both long term and short term sense to satisfy this demand? What other customers will be impacted? Will your suppliers be able to provide you the components on time and in sufficient quantity? Where else could you get the components and what would be the impact on your margin? The ability to create multiple scenarios to predict detailed results is not possible without deep supply chain analytics.
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.
I expand more on these thoughts in my paper – anyone can download it using the link in the original post.
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