In a previous article, we discussed what Machine TruthTM is and its importance. Gathering this data automatically is undoubtedly important and a certain amount of work can be done with this data. But it is just machine data. The next step with Machine TruthTM data is to put this data in context by associating the data with additional non-machine data. The non-machine data to use is really dependent upon the business and strategic goals of the organization. A common set of non-machine data to use includes the customer for whom the job is running, the operator running the machine, and the suppliers of materials. Another important item is the production standards used by the organization.

Why is non-machine data important? Consider a purchasing agent that secures a better price for the paper used in the process. By every measure used by this organization, the purchaser has done a good job in saving the company money. However, at the shop floor, it is found that this paper curls a little bit more under the humidity conditions of the plant and leads to the machine jamming which in turn results in more downtime which leads to lower production and increased costs. By associating Machine TruthTM data with supplier information, the loop between purchasing and production can be closed. This data can give the purchaser the ammunition they will need to go back to the supplier to ask for improvements or to change suppliers all together.

Associating the Machine TruthTM data with the labour used to produce the product is important on several accounts. First it provides us with the production per labour hour metric. It may not always be advantageous to increase production by increasing the labour as the increase in costs will eat into the margin. Secondly, associating production with the labour allows the organization to determine which operators are struggling and in which areas in order to allow the organization to target the appropriate training programs to bring all their operators to the required level.

Finally, let’s consider the association of Machine TruthTM­ data with organizational standards. Among other things, these standards describe how long it should take to set up a machine, the expected run speed, the expected labour hours, and the expected production.

Consider the following diagram. With conventional metrics of uptime and production, Operator B would be considered as having the better day. However, the success of Operator B’s day should really be measured against how well he performed against the standards developed by the organization. If the standards indicate that this operator should have had 70% uptime and produced 700 cases, then he did not have a good day.

Conversely, Operator A had less uptime and produced fewer cases but had to endure more changeovers. If this Operator performed better against the standards provided then he would have had the better day.

The important take away is that Machine TruthTM data by itself may be useful it can only take you so far. It is necessary to put this data into context in order to get the full impact of what this data is telling you.