Manufacturing Leaders are asked to meet strategic goals set by corporate leaders. These goals are often stated in financial terms concerning cost control and margin increase. To accomplish this, Manufacturing Leaders initiate numerous programs to keep the cost of goods under control and improve production efficiency. Often times their methods involve gathering production data from the plant floor and manually inputting the data into an Enterprise Resource Planning (ERP) system or Excel spreadsheets. Manufacturing Leaders keep track of factors such as total downtime, downtime reasons, setup time, run speeds, and Overall Equipment Effectiveness (OEE), among others. This data is then used to determine where to invest continuous improvement dollars.

And you know what. It works…well, kind of.

Manufacturing Leaders are finding that they cannot get the anticipated return on their continuous improvement investment since some programs work, while others miss the mark altogether. It is a drunken walk. You may get where you want to go, but it takes a very long time and you fall down more than once. Often times you never make it. Unfortunately, this means that the task is then left to a new Manufacturing Leader. What is needed is a way to get to where you need to be as quickly and efficiently as possible.

To analyze where the problem is, we need to step back to the driving strategy. To improve their competitive position, corporate leaders often define strategic goals such as improving return on capital deployed or improving margins. A positive change in these types of financial measures often indicates that a company is managing its business well. If margins continually rise then profits go up, stock prices rise, and shareholder wealth is created.

Once the corporate strategy is in place, it becomes the Manufacturing Leader’s responsibility to manage their performance in a manner that results in meeting the strategic goal. For example, if the goal is to increase margins, the Manufacturing Leader will look at ways to keep costs per unit under control and decrease them over time. This can be done by reducing the costs of raw materials, labour and energy. This brings us back to our previous discussion: Manufacturing Leaders need to look for the right places to invest their continuous improvement dollars, in order to achieve these strategic goals.

Leaders need to take effective action. Unfortunately, it is not completely clear what that effective action is. Their recourse is to fall back to rules of thumb or groping around in the dark. It becomes ‘hit’ or ‘miss’. The hope is to ‘hit’ more often than not; there has to be a better way.

In talking with manufacturers and looking at leading industry research, the inability to make the best investment decisions comes from a lack of visibility to the plant floor. Manufacturing Leaders understand the strategic goal but cannot see into the plant floor to take effective action. The plant is a ‘black box’, raw material, energy and labour go in, finished goods and scrap come out.

Collecting data manually and inputting it into ERP systems, Business Intelligence (BI) tools or spreadsheets is one attempt at seeing into the ‘black box’. Unfortunately the data results are is Aggregated, Inaccurate and Lagging; we call it “AILing,” as in “to cause pain”, “uneasiness”, or “feeling unwell.”

Aggregated data is a problem because the Manufacturing Leader cannot view details of what is actually happening. For example, the Manufacturing Leader may know exactly how much product his plants are producing and exactly how much money, in terms of raw material, labour, and energy, is being used, but that is all he knows. He cannot break it down any further, or determine the contribution to unit costs by shift, asset, product or operator. Manufacturing Leaders have the data but they lack the visibility to determine what the corrective course of action is to impact their key metrics.

Inaccurate data is a direct result of the data gathering process. Manually collected data cannot possibly be correct. Besides the normal input errors, manually collected data suffers from an immediacy problem, since when an operator is asked to log reasons for downtime and the time taken to resolve them; the data is often entered in at the end of the shift. This is due to the immediate problem of having a stopped machine super ceding the need to collect data accurately. The operator, rightly, needs to get the machine up and running as quickly as possible – accurate data be damned. The end result is that continuous improvement programs are using inaccurate data to determine where to invest in order to meet strategic objectives. Unfortunately, inaccurate data means investment decisions in the wrong programs, causing the return on the manufacturer’s investment to suffer. We call this chasing Continuous Improvement Ghosts.

Lagging data is money poured down the drain. Even if the data is accurate, by the time the Manufacturing Leader sees the data, it is too late to do anything about the problem. The money has been lost, swept away at the end of the day. What is needed is a way to get the right data into the hands of Manufacturing Leaders and their staff in real-time, to correct a problem as it is happening. If you are waiting for the end of the shift, the end of the day, the end of the week or heaven forbid, the end of the month; it is too late. The money is lost.

In order to meet the financial strategic goals established by the corporation, Manufacturing Leaders need the right visibility to determine what the effective action needs to be. The key to this, is having visibility directly into the plant through accurate, real-time, high resolution information. Manufacturing Leaders need to see into the ‘black box,’ that is their plants, and shine a light directly on the problem areas to eliminate them.

		George Dalle Ave George Dalle Ave is the Director of Solutions Development at Shoplogix Inc.