The cost and count data of any business is critically important. Universally held judgment on business health is highly dependent on common measures of cost and count like revenue statements, earnings per share and asset value. But arriving at business success requires appropriate management of individual operational areas. Each function needs evaluation and fine tuning to ensure it contributes effectively and correctly to the whole. So for many businesses there are supplemental pieces of data, different from the core cost and count disciplines that must be monitored too as leading indicators of possible or probable outcomes.
With this awareness of the need to monitor for these early warning indicators at the department or functional level, comes the challenge of understanding exactly what the patterns of such indicators are. To answer that we must:
- Determine what data will be most relevant.
- Discern what patterns in the data are actually valid and meaningful.
- Have awareness of actions that will affect the data in support of the outcome desired.
Choosing Tools for Early Warning Indicators
There are two aspects of the quality management function that provide tools closely aligned with solving those three challenges.
,strong>Seven Basic Scientific Quality Tools
These tools focus on analyzing and then improving known processes many of them familiar from school mathematics curriculum.
- For quantitative analysis: check lists, control lists, Pareto charts, histograms and scatter diagrams.
- For non-quantitative insights: flow charts, cause and effect diagrams.
Regardless of the purpose, we are using these tools for the collection and analyzing of data around known or defined events or instances. Our best selection of the right tool is generally dependent on the effectiveness of our use of the classic quality management tools defined in the following.
Classic Quality Management Tools
These are the tools we use when our most significant challenge is to understand and define a complex situation. The most common versions of these tools come in the form of diagrams such as affinity, tree, matrix and activity. They are often supplemented with process decision program charts. Their purpose is to help us
- Explore and organize challenges in a formal manner that both defines scope and ensures completeness of investigation.
- Allow challenges to be better understood and accurately communicated.
The result of using these tools is the development of an actionable plan that can be monitored using the Scientific Quality Tools above.
Collecting & Monitoring Data
Once your business team has developed an understanding of the complex relationships through use of the diagramming tools the process of collecting, monitoring and analyzing your data can begin.
Many organizations rely on spreadsheets initially. Spreadsheets are useful, especially for early modeling, but they do have some key limitations for longer term use:
- They remain silos or islands of data that require much duplication of effort to be meaningful in a timely manner.
- They require effort to maintain data integrity, whether it is for ensuring data entered conforms to datatypes or for managing user security to prevent data corruption.
Timing a Move to Integrated Solutions
When activities grow more complex, your business should consider a move from spreadsheets to other integrated solutions for collecting and managing your decision data. The timing for such a move is generally indicated by:
- A need for single source accuracy and pinpoint timeliness to ensure that true decision intelligence is supported.
Understanding the contrast between simple spreadsheet management of decision data and the efficiency and accuracy of integrated solutions is another valuable step towards achieving decision effectiveness.
You can learn more about how our quality solution supports decision intelligence by reading our recent article in the Summer 2015 edition of GPOptimizer. We also invite you to register for one of the videos in our learning series such as: Decision Intelligence – Going Beyond Cost and Count.