Efficiency and Utilization
In my last post we talked about what typically happens at the end of the production month as the typical cost based performance metrics are abandoned so that required shipping quantities can be achieved. This phenomenon is referred to as the end-of-the- month syndrome or the hockey stick effect. Throughout the month, individual departments work to maximize their own individual performance measures (e.g. operator efficiency or equipment utilization), until they are forced by leadership to sacrifice their own metrics so that higher-level company objectives can be achieved. So what drives this curious behavior? The fact is, it is directly related to the metrics used to measure the performance of the organization, namely local efficiency and utilization. So where do you think these two measures came from?
Efficiency and utilization evolved as part of manufacturing management framework consistent with the standard cost system. The basic belief within the standard cost system assumes that the higher the level of efficiency and utilization, the better the profits will be. In effect, this approach assumes that both the plant’s and the company’s performance metrics are maximized when each of the individual departments within the organization are operating at maximum efficiency and utilization. The problem with this thinking is that by focusing on local performance optimization, it actually “damages” the performance of the total system. When choosing to maximize efficiency and/or utilization, the system becomes clogged with excess inventory, which in turn causes cycle times to lengthen and ultimately deliveries become late.
Every business that manufactures products is made up of various subsystems within the total system. The product has to be designed, engineered and the required materials and resources must be purchased. The product then has to be manufactured, marketed, sold, delivered and payment is received from the buyer. It’s extremely important for all of these departments to work together rather than working in isolation. If the company is to be successful, all must act like a winning sports team. So how can you measure bottom-line success?
The traditional bottom-line financial measurements of making money are net profit (NP) and return on investment (ROI). Net profit is an absolute measure of whether or not a company is truly making money, but NP by itself is not enough. For example, suppose a firm had a net profit of $1.0 million last year. Is this amount good for a company? We can’t answer that question unless we know how much the company had to invest in order to generate $1.0 million. If the investment was $5 million, then $1.0 million represents an ROI of 20 percent, which is a very good return. On the other hand, if the investment was $100 million, then the ROI is 1 percent which is not very good. These two bottom-line measurements are adequate to determine whether your company is making money, but they are totally inadequate for evaluating operational and investment decisions. As we saw in our case study example, the use of standard cost-based concepts could very well lead you to make the wrong decisions. So if these aren’t acceptable, then what can we use to improve our decision-making when we’re making a purchasing or investment decision?
A New Decision-Making Perspective
The foundation for this new approach is the development of a set of measures that can be used to correctly assess the impact of specific actions on the productivity and profitability of your entire company. These same measurements will also play a key role in the development of ways that will provide you with much better operational decisions in real time. This approach will use three operational and global measures specific to manufacturing companies:
- Throughput (T)
- Inventory ( I )
- Operating Expense (OE)
An important point to remember is in order to be universally applicable, the performance measures must be common to all manufacturing companies and they should also accurately describe the key activities that govern a plant’s performance. With this in mind, there are three basic activities that a manufacturer undertakes:
- Purchasing of raw materials and component parts
- The conversion of purchased materials into finished goods
- The sale of the finished goods to customers
The Theory of Constraints (TOC) offers companies a way to use the three operational metrics (T), ( I ), and (OE) to make better operational decisions. ( I ) represents the money invested in materials; (OE) is the money spent by the company for assets and expenses; and (T) is the money generated and received by the company from the sale of finished goods. Let’s look at each of these in a bit more detail.
If you look at a standard dictionary definition of Throughput, it simply states that it represents the total outlook. TOC’s definition considers only units sold, not units produced because the finished goods do not generate revenue until they are sold. Traditional accounting considers inventory as an asset, but TOC tells us that the finished goods are of no value until they are sold. The authors tell us that as support for this argument, just consider the amount of product that is written off, sold as distressed prices, or simply becoming obsolete. So, in order to link manufacturing performance to real profit, you need to measure sales rather than how many units you produced.
In my next post, we will continue our discussion on T, I, and OE and how these three measures can be used to make financial decisions in real time. As always, if you have any questions or comments about any of my posts, leave me a message and I will respond.
Until next time.
 L. Srikanth and Michael Umble,Synchronous Management – Profit-Based Manufacturing for the 21st Century, Volume One – 1997, The Spectrum Publishing Company, Wallingford, CT
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