Can you trust your data?

To make sense of supply chain data requires consolidating the information into a central repository and then using tools or applications to pinpoint errors and inconsistencies. It also includes taking possible corrective actions that enhance overall quality and confidence in the raw information.

By Andrew Nelson, Matrikon Inc.

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Supply chain data can vary in quality from good to downright misleading. When these data are being used as the basis for business decisions, the quality of the resulting strategic directions can vary from “on-target” to “I’m not really sure.” With government regulations such as Sarbanes Oxley, “I’m not sure” has legal implications.

Data in the supply chain are often a bewildering collection of instrument measurements, laboratory measurements, material transactions, inventories and events. These data are measured and reported separately but together provide a coordinated picture of the business transactions and material positions in the supply chain. Data validation generally requires developing coordinated sets of data. To do this the data must be physically or logically consolidated into a central repository. After collection, there are several tools, or applications, that have proven successful in validating data, including: balancing, batch tracking, measurement conciliation and data reconciliation. These applications can pinpoint errors and inconsistencies and make possible corrective actions that enhance overall quality and confidence in the raw information.

How wrong can we be?

Inaccuracies in data can be caused by problems with the raw measurement or more subtle inconsistencies in timing or interpretation. Obviously, poor meter calibration can cause a value to be in error, but an accurate value incorrectly placed in time also can cause errors. The following list of data issues provides the scope for any data quality program:

  • Measurement inaccuracy — has to be controlled using auditable maintenance and calibration practices;
  • Missing measurements or transactions — a significant cause of information quality degradation;
  • Inappropriate time stamps on values — these errors are often reporting problems, but also can be caused by incorrect handling of time zones or daylight savings;
  • Blurry data standards — does the data value have the correct units of measure, is it a compensated value, an uncompensated value, etc.?
  • Poorly defined data — are the data values instantaneous or are they already an average; if so what is the period of the average?
  • Change management problems— has the value been changed, and by whom? Has data been compensated differently in the past such that it can no longer be used as a reliable comparison with future data?
  • Outdated value — has the source actually updated the value or is it just displaying what it had last time?
  • Improper source — is the measurement being measured in the location where it’s expected to be measured?
  • Measurement misinterpretation — the measurement may be correct but with the process operating in an alternative mode it may need to be interpreted differently.

A strategy to improve consistency

Data don’t get better on their own. There needs to be a strategy. This strategy requires that data be characterized and accessible. In other words you know what they are and you can get at them. The first step in improving data quality is organization. Several tools are available for making these improvements, including: characterization, creating a central repository, conversion and aggregation.

On its own, a single data point is hard to validate. But when analyzed as a component of a large data set it can be compared for consistency. However, for data values to be compared they must all be on the same basis. This means the same unit of measure, the same method of measurement, at the same reference conditions, applying to the same geographical scope and on the same timescale. Data from different sources in general will violate some if not all of these rules. The information needs to be logically characterized so that it can be appropriately processed for comparison.

To compare the monthly planned quantity of a specific type of feedstock in thousands of pounds that must be transferred to a unit, to the actual quantities of various feedstocks measured in gallons processed daily from various tanks, requires several levels of processing. Transactions at the tank level need to be aggregated to the unit, transactions at the individual feedstock level need to be aggregated to the feedstock types and daily transactions need to be summed for the month converting the units of measure.  This can only be done if every data value is characterized.

Data also must be accessible from a single point of access (Figure 1). This doesn’t imply that the data must reside in the same database but just that it must be accessible in a form that allows comparison queries to be executed. For the most effective use of the analysis tools, the data need to logically or if necessary physically reside in a single coordinated data structure. This coordination should be able to bring together real-time and transactional data into a single view.

Figure 1. A repository database can provide a single point of access to information in many business systems.

Figure 1. A repository database can provide a single point of access to information in many business systems.

Any attempt at data consistency will involve the use of conversion and aggregation. Trying to make disparate systems use the same units of measure, same naming, same time aggregation, same geographical aggregation and the same material aggregation is a losing battle (Figure 2).

Figure 2. Collecting data at a plant and sharing it within the corporate structure can be a complicated affair.

Figure 2. Collecting data at a plant and sharing it within the corporate structure can be a complicated affair.
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