tual performance to highest possible performance of a workstation or a production area. For an economic use, the utilization should be as high as possible. Thus, the WIP must be on an appropriate high level, so that the flow of material is not interrupted and the resources are used to capacity. But more aspects must be considered when defining the WIP in production. Firstly, with a high WIP capital costs and handling costs rise. Secondly, a high WIP causes long waiting times at workstations and thus longer throughput times for the production orders. Because of the increasing throughput times and the accompanying increasing scatter of throughput time, the lateness distribution and the schedule reliability are affected negatively.

As the example shows, there are conflicts between the logistic objectives. Producing companies must position themselves within the fields of tension arising. For this, the different objectives have to be evaluated and each company has to prioritize the objectives according to their corporate goals. Moreover, the entire PPC processes and parameters have to be oriented towards the chosen objectives. Thus, a profound understanding of the interactions between the PPC tasks and logistic objectives is required to perform a conscious positioning.

4. Interactions between PPC and the Logistic Objectives

For the success of a company it is fundamental to understand how the decisions in production planning and control affect the actuating variables and control variables and via these the logistic objectives of the supply chain. That is why the Hanoverian Supply Chain Model contains further levels to detail the processes and interactions in the PPC main tasks. Figure 3 shows the representation for the PPC main task “plan production”. There is an equivalent representation for all other main tasks of PPC (see Figure 1).

By performing the PPC main task “plan production” specific production orders are created based on the in house production program resulting from the prior PPC main task “plan production requirements”. At first, a lot size calculation is performed and the economically optimal production volume is determined. Next, the start and end dates of individual operations within the production orders are defined. The throughput of production orders is scheduled. Then, the availability of the required resources is examined and a detailed resource allocation plan is worked out. Together, the created production orders with due dates and amounts lead to the production plan. Now the feasibility of the production plan has to be checked. If the production plan cannot be implemented, the resulting information from prior PPC main tasks (e.g. the in house production program from PPC main task “plan production requirements”) have to be questioned. As soon as the production plan is regarded as feasible, it may

Figure 3. PPC Main Task Plan Production.

be approved. Main result of the PPC main task “plan production” is the released production plan, containing short-term and medium-term production orders. In the process sequence of PPC the production plan is passed on to the PPC main tasks “control production” and “mange inventory” for implementation. Considering the systems of logistic objectives presented in section 3 the production plan determines the planned input and the planed output of the preliminary production stage and the end production stage.

5. Locating Quantitative Logistic Models in the Framework

The Hanoverian Supply Chain Model is developed to serve as a framework for PPC and a company’s internal supply chain. It clarifies the interactions between the tasks of production planning and control, the actuating variables, the control variables and the logistic objectives in core processes. Existing conflicts between logistic objectives are discussed. Like that the decisions within the PPC tasks can be taken with clarity about the interactions and the consequences. Moreover, existing quantitative logistic models are included in the framework. These models can be used to calculate values for PPC parameters. Furthermore a positioning within the indicated fields of tension created by logistic objectives can be performed.

To exemplify the approach to use quantitative logistic models to support the fulfilment of the PPC tasks we will have a closer look at the calculation of lot sizes. This is a sub-task of the PPC main task “plan production” (see Figure 3). The aim of this task is to calculate the economically optimal lot size. Concentrating on a make-to-stock production a field of tension between setup costs, storage costs and logistic performance arises. The bigger the lot size, the less often workstations have to be changed and the smaller are the setup costs. On the other side, big lots lead to high inventory in following storages and the logistic performance of the production is influenced negatively.

Setup costs result from the changes of production facilities between producing two different lots. The setup costs incur for each adjustment. Therefore, the setup costs increase when reducing the lot size. Normally, the setup costs comprise of material and wage costs for cleaning the production facility, wage costs for adjusting the production facility and assembling special components, tool change costs, transport costs, machine costs during the setup time, start-up costs and administrative expenses for the creation of production orders [10] .

In contrast, the higher the lot size, the higher will be the storage costs, because more products will be stored. There are interest costs for fixed capital. Moreover costs for the care of the stored products arise. In addition, risk cost, for example for the loss of value, may occur. And there are depreciation costs, insurance costs and maintenance costs for the buildings and the technology of the storages [10] .

Besides the addressed setup costs and storage costs, the lot size has got a big impact on the logistic performance of a producing company. There is an effect on the throughput time, the distribution of the throughput time, the distribution of lateness of production orders, the safety stock level in the storage for finished goods to compensate poor logistic performance and the flexibility of production [10] .

To calculate the lot size, different methods may be used. Most of them focus the costs. A distinction is made between static and dynamic methods. Static approaches determine one lot size value for each item. This lot size is implemented as a fixed value into the PPC system for a certain period of time. As the underlying parameters may change the value must be verified regularly. In contrast, dynamic approaches define a new lot size for each production order, depending on the current conditions.

The opposing effect of the setup costs and storage costs implies that a cost-optimal solution exists for calculating the lot size. Using the traditional approach by HARRIS the optimal lot size is determined based on these two elements (setup costs and storage costs) [11] . MÜNZBERG extended this method and provides a logistic model to determine the optimal lot size based on more costs [12] [13] . The model considers the impact of the lot size on the logistic performance. The logistic objectives like work-in-process, throughput time and schedule reliability are transformed into costs. A logistic costs factor is implemented. Figure 4 shows the basic idea of the approach and the optimal lot sizes according to HARRIS and MÜNZBERG. Taking the logistic performance into consideration the calculation leads to significantly smaller lot sizes.

It becomes obvious, that the lot size is a parameter of PPC, evoking a company to make a decision. The determination of the parameter takes place in a

Figure 4. Logistics oriented model to calculate lot sizes and included cost types [12] .

stress field of logistic objectives. By defining a value for the parameter lot size a positioning takes places. The presented logistic model developed by MÜNZBERG can be used to perform this positioning. The framework Hanoverian Supply Chain Model discloses which models should be used in a certain case. By locating the quantitative logistic models within the framework, the partial models are combined into a unified context.

6. Future Research

More research could be conducted in future to extend the Hanoverian Supply Chain Model. There are several ideas.

The first idea addresses the topic of Industry 4.0. New technologies arise and are applied in production systems. Resulting from the increasing digitilaziation more and better data will be present in production systems. The technologies and the data can be used to enhance production planning and control. Research is needed to investigate, in which way production planning and control will change because of Industry 4.0.

Another idea addresses product return and circular material flow. There are new laws and standards concerning product return in Europe. Generally, material efficiency becomes more and more important for producing companies. To represent these trends a reverse supply chain could be added to the Hanoverian Supply Chain Model. There could be research about the consequences of a reverse supply chain for SCM and PPC.

7. Conclusion

A new framework for PPC and SCM―named the Hanoverian Supply Chain Model―is introduced. It was developed at the Institute of Production Systems and Logistics of Leibniz Universität Hannover in Germany. Firstly, the model provides a universally valid description of the tasks of production planning and control. In this way, profound knowledge about PPC is provided in a comprehensible way. The process descriptions can actually be used by companies to design or improve processes. Secondly, systems of logistic objectives are defined for five core processes (procurement, preliminary productions stage, interim storage, end production stage, dispatch) that represent a company’s internal supply chain. These systems include the relevant logistic variables for each core process. Moreover, the influence of the PPC tasks on the systems is pictured. The systems show the relevant relations at a glance. Thirdly, existing fields of tension between logistic objectives are fore grounded. These fields of tension have to be considered while fulfilling the PPC tasks. Quantitative logistic models can be used to compute values for PPC parameters and moreover to position a production within the indicated fields of tension. These partial models were located within the framework Hanoverian Supply Chain Model. Like this, companies can use the partial models to design specific elements of their production systems or to fulfill specific tasks of PPC according to strategic goals. The main achievement respectively main contribution to research of the Hanoverian Supply Chain Model is the integrated consideration of PPC and a supply chain. The interactions between the PPC tasks and the logistic objectives alongside a supply chain are pointed out. The model supports companies to design and conduct production planning and control in-line with strategic goals.

Acknowledgements

This paper presents results of the project "Integrative Logistics Model for Linking Planning and Control Tasks with Logistical Target and Control Variables of the Company's Internal Supply Chain" (SCHM-2624/4-1), funded by the German Research Foundation (DFG) and currently being conducted at the Institute of Production Systems and Logistics.

Appendix

The Hanoverian Supply Chain Model is published on an interactive website. The English version is provided at http://www.hasupmo.education/, whereas the German version is provided at http://www.halimo.education/. Beside the top level of the model the web site provides profiles with definitions and further information of the PPC tasks and the logistic objectives. Because the web site is freely accessible on the internet, it is an interesting tool for scientists, students and companies, who are active in the fields of PPC and SCM.

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Cite this paper
Schmidt, M. and Schäfers, P. (2017) A New Framework for Production Planning and Control to Support the Positioning in Fields of Tension Created by Opposing Logistic Objectives. Modern Economy, 8, 910-920. doi: 10.4236/me.2017.87064.
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