Logistic Curves

Synonyms

Production operating curves

Definition

Logistic Operating Curves (LOC) qualitatively and quantitatively depict the interaction between logistic objectives in the form of curves.

Extended Definition

The company-internal supply chain comprises the core processes: source, make, and deliver (Fig. 1, upper). Each of these core processes focuses on different logistic objectives. These objectives create a field of tension between the logistic performance and logistic costs (Fig. 1, middle). Moreover, the objectives to some extent both contradict and complement one another. Finding an optimum within this field of conflict is impossible for enterprises. Instead, the company has to position themselves between the logistic objectives. Among other uses, Logistic Operating Curves provide an excellent tool for accomplishing this (Fig. 1, lower).

Logistic Curves, Fig. 1

Core processes, objectives, and exemplary Logistic Operating Curves

Usually, the actual procurement process is decoupled from the production via a warehouse of raw and/or semifinished goods. In order to guarantee a strong logistic performance, this store is supposed to ensure a high service level with a minimum delivery delay while at the same time maintaining as little stock as possible in order to keep the logistic costs down. As the Storage Operating Curves (Lutz 2002; Glässner 1995; Nyhuis 1996) show, these targets are to some extent contradictory. In this case, the logistic objective “stock level” is also the controlled variable which can, for example, be set via the reorder point in the ERP system. When the stock level is high, a high service level and minimum delivery delay are to be expected since all of the stored parts are generally available. As the average stock level decreases, fewer of the demands on the store can be met. As a result the service level sinks and the mean delivery delay increases.

In the field of production, the logistic objectives “throughput time” and “schedule reliability” (logistic performance) as well as “utilization” and “WIP” (logistic costs) are of key importance. The Production Operating Curves show that when there is a high WIP level, the output rate and with that the utilization of a workstation is for the most part independent of the WIP. Should the WIP, however, fall below a certain value, output problems arise due to a temporary lack of work. In comparison, the throughput time grows for the most part proportional to the increasing WIP. Short throughput times as a result of low WIP are also generally related to minimal variance. From the perspective of the subsequent production areas, the greater planning certainty arising from this causes greater schedule reliability (Nyhuis and Wiendahl 2009).

If we assume a make-to-stock production when discussing the distribution core process, the Storage Operating Curves, already outlined above in regards to procurement, can be applied. In comparison, with a make-to-order production, the logistic objectives from the perspective of performance are high schedule compliance and short delivery times, whereas from the cost perspective the objective is a small store of finished orders, i.e., completed orders should only wait briefly before being shipped to the customer. In this case the controlled variable is the delivery time buffer. If a larger delivery time buffer is selected, the majority of promised delivery dates can be met. The delivery time buffer also directly impacts the delivery time extending it by the same amount. Moreover, a very large number of orders will be completed before the actual planned delivery date, subsequently giving rise to a bigger store of finished products. As can be seen in the Schedule Compliance Operating Curves, with shorter delivery time buffers, the delivery times and the stores of finished products also decrease. When the static distribution of the lateness is constant in the preceding production area, the schedule compliance decreases (Nyhuis and Schmidt 2011).

Theory and Application

The Logistic Operating Curves are impact models derived either from deductive or deductive/experimental modeling. They reproduce interactions between logistic objectives. Their mathematically calculated progression is dependent on various parameters. If the parameters change, the shape of the operating curve adjusts. This allows logistic measures to be evaluated with the aid of the Logistic Operating Curves.

The shape of the Storage Operating Curves is dependent on both the fluctuating demands on the store output side as well as the replenishment time and the quality of the supplier’s delivery (i.e., with regards to quantity and due date). The greater the supplier’s due date reliability the steeper, for example, the slope of the Service Level Operating Curve is. This means that in order to ensure a desired service level, a lower stock level is required. A number of parameters, e.g., technical disruptions, load variance, capacity flexibility, or lot sizes, just to name a few are taken into consideration by the Production Operating Curves. Logistic measures that impact these parameters can thus be evaluated based on the changes in the operating curves. The Schedule Compliance Operating Curves are determined by the distribution of the output lateness of the preceding production stage. Logistic measures such as those for improving the due date reliability or for narrowing the distribution of the due date reliability directly impact the shape of the Schedule Compliance Operating Curves. Thus with less variance in the lateness, a shorter delivery time buffer is occasionally necessary in order to realize a defined target due date compliance.

A variety of possible applications for the Logistic Operating Curves arise from the connections demonstrated here. These are summarized in Fig. 2. Since the Logistic Operating Curves describe the correlations between the logistic objectives and the possibility of influencing them, they represent an ideal foundation for increasing and monitoring the certainty and capability of logistic processes in an enterprise. The Logistic Operating Curves can thus be drawn upon for evaluating processes within the frame of monitoring logistic process in enterprises particularly in the production as well as for deriving potential. They show, for example, which throughput times and WIP level can be achieved with the existing structural conditions without having to expect noteworthy breaks in the material flow or a loss of output. When applying them within the frame of production planning and control, the system parameters such as the delivery time buffer, safety stock, or throughput times can be derived and set in agreement with the goals. Depicting the logistic objectives in a diagram also makes it possible to determine which of them should be weighted the most depending on the current operating and/or market situation as well as depending on the system specific conditions. At the same time it can be shown how the changes in the parameters impact the logistical quality indicators.

Logistic Curves, Fig. 2

Application areas for Logistic Operating Curves

Should it turn out that the set target values are not attainable without supporting measures; the operating curves can be drawn upon according to the possibilities introduced here for reinforcing and evaluating planning activities and thus work as an aid in stabilizing the process certainty. Thus alternative, implementable planning and control strategies can be evaluated and selected according to logistic criteria. Logistic Operating Curves can also be directly integrated into planning and control methods (e.g., lot sizing, scheduling, order release). Moreover, applying them provides continual, method-based support for orienting the planning and control on the logistic objectives. When designing production processes, Logistic Operating Curves can be implemented as an aid to resolving diverse problems. They can, for example, assist in evaluating alternative manufacturing principles (in view of logistics) or new logistic concepts, determining the customer decoupling point or planning the layout. The basis for all of the mentioned applications is a Logistic Positioning which provides the target values and thus also represents a link between all of the individual functions.

Cross-References

Changeable Manufacturing

Factory

Logistics

Machine Tool

Manufacturing

Manufacturing System

Production

System