How to roll out a big data strategy for long-term results in the manufacturing sector
Numbers don’t lie. Facts are facts. For many people, particularly those that are “left brained,” data and numbers are the basis for which all decisions are made. In some cases, the mere presence of numbers in a measurement or statement will give it all the credence necessary to be the absolute truth. But, when those numbers turn out to be no more credible than guesses, they lead to bad decisions.
Distribution centers can span millions of square feet, have thousands of employees and receive and load tens of thousands of truckloads a year, producing massive amounts of data. So, management teams have become ever more reliant on numbers to make important business decisions. This means that numbers not only must be accurate; they also must have a high degree of correlation.
The purpose of this article is not to discredit scientific data, but rather, it is to expose a common problem in business decision making—abuse of numbers to bring confidence to statements that lack both scientific proof and empirical data. The article also highlights the dangers of using aggregate numbers to draw conclusions about discrete events.
Abusing numbers to lend credence to statements
Science, mathematics, and numbers are meant to provide exact values, free of bias and emotion. So, people develop confidence in using numbers as a basis for educated decisions.
When numbers are used as the basis of a subjective assessment, they lose credibility as a scientific measurement. For instance, many surveys will ask you to rate your degree of satisfaction or agreement with a certain statement on a scale of one to ten. Although the results of such surveys may serve a useful purpose, they also do not provide an absolute and accurate measurement. In any such surveys, an alphabetical (e.g., A to J) or other scale could easily replace the numeric rating system.
Management will often set hard boundaries (e.g., a number lower than 9, a score between 7 and 8) to draw conclusions and possibly initiate major changes that may be detrimental to the organization. The use of numerical value may give the appearance of a scientific approach, but the fundamental assessment still resides in a subjective interpretation of the questions. Ratings may vary between supervisors and across job roles. All ratings, numerical or otherwise, have a subjective component to them.
Using aggregate numbers to assess discrete situations
The quest to simplify metrics and provide management with an overall aggregate number to assess performance and/or benchmark the organization or different business units within it can lead to erroneous conclusions and bad decisions. An aggregate metric may reflect overall performance, but it makes a very poor tool for diagnosing problems and initiating corrective measures. The following examples illustrate the pitfalls of aggregate measurements.
In distribution centers, “cases per hour” (“CPH”) is a standard KPI used to benchmark operations and measure throughput. Again, this is an aggregate value driven by many variables, such as pick density (average number of cases picked from a given location), layout, slotting, weight of cases, length of the travel path, equipment (condition, age, speed), etc. Not only do these variables differ from distribution center to distribution center, they also fluctuate on a daily basis within one given facility. Using such a metric for internal benchmarking may lead to a conclusion that one facility excels, when in reality it could be just average or worse. Using CPH for benchmarking purposes becomes even more problematic when applied across different industries; for example, a satisfactory CPH in the foodservice distribution industry may be a mediocre measure in the food retail distribution sector.
Instead, distribution centers should leverage data from their discrete labor management system to show data by employee on a real-time basis. That way, the supervisor can monitor activity and check in with employees when they are not achieving a given target and identify the root cause. Seeing the data alongside live observation will validate the assessment of the employee’s performance.
Discrete measurement for specific decisions
Understanding that aggregate measurements are not adequate for addressing discrete components of your business should lead you to seek other measurements that address specific needs. For instance, the distribution industry uses discrete engineered labor standards to measure the productivity of its workforce and to plan labor requirements. This form of measurement is specifically designed to account for the difficulties an employee encounters during each work assignment—thus, it can drive better and more confident decisions about workforce management, labor planning, process evaluation, and many others issues. Furthermore, advancements in business intelligence and dashboards make it easier to rely on multiple discrete measurements rather than a single aggregate KPI.
Managing operations efficiently, but also effectively
As distributors are under constant pressure to reduce labor costs, it’s natural to look for one or two metrics to assess performance of each operation so that you can focus your attention on “low performers” that might offer the biggest improvement potential for your bottom line. But, simply looking at one metric like cases per hour and making decisions based on that metric could be leaving money on the table.
Danielle Woodward is a senior manager in West Monroe’s workforce optimization practice. She can be reached at firstname.lastname@example.org.
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