Christina Reuter, Felix Brambring and Thomas Hempel*
RWTH Aachen University, Germany
Received Date: April 14, 2016; Accepted Date: August 12, 2016; Published Date: August 19, 2016
Citation: Reuter C, Brambring F, Hempel T (2016) Approach of Information Provision for High Resolution Production Control. Arabian J Bus Manag Review S2:001.
Copyright: © 2016 Reuter C, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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Production information is essential for controlling a company’s production. The awareness and targeted use of data is decisive for constant and positive enhancements of a company. At the moment, many manufacturing companies are still at the very beginning of collecting and using data gathered during production processes. The cost-benefit ratio of the various steps on the way to a high resolution production control is not clear at all. To overcome the hesitation of many small and medium-sized companies in collecting and using more data from their production, migration paths with an explicit focus on the cost-benefit ratio of decisions have to be characterized. This approach will ease the way to gain a suitable information level of a production facility for different objectives. The aim of this paper is to present the targeted approach for the efficient provision of production information in order to enable a high resolution production control.
High resolution production control; Information provision; Production; Control; Simulation
Production information and derived data are essential for controlling a company’s production. The awareness and targeted use of data is decisive for constant and positive enhancements of a company. Many companies are still not aware of that fact and do not collect data in IT-Systems at all. The companies that do collect data from production are dealing with the problem that major parts of the collected data are outdated or biased and far away from real - time.
Poor data quality accrues through false aggregated data and false response from production. This could be the case if several machines are summarized even though this might not be appropriate. The biggest challenge of this development is to identify the relevant data and generate the correspondent information. Weaknesses in the production process can be found through selection of the most important data and identification of the best fitting granularity [1-3].
Today the granularity of different production data is often not sufficient. As numerous practical examples from projects with the industry have shown in the last years, the typical overall throughput time of a production process is composed as follows (Figure 1). The production process time is about 5% whereas the transition periods are about 95% of the overall throughput time. Transition periods are the time intervals between the last process step of a previous machine and the first process step of a following machine. Concerning the production processing time plenty of high resolution information is available in many businesses, however, transition periods are often mapped only rudimentary without much information. An exact resolution of the greater part of lead time offers a great potential to increase the logistic objectives of a company by means of high resolution production control (HRPC). Thus it is possible to specifically respond to customer queries, rush order situations or failure of work stations.
But not only information granularity is a problem: so far, the extensive acquisition of production data on the shopfloor has not reached small and medium-sized companies (SME), even though existing IT-systems cannot function properly if necessary and important information is not digitalized and accessible through the system. Recent studies about production in Germany show, what system is being used typically to record figures like inventory-, movement- and position data as well as disturbances (Figure 1).
Comparing major enterprises with SME, big differences can be noticed: 65% of the large companies are using production data acquisition (PDA) as their main instrument whereas with 37% of SME, the PDA is only their second most important instrument. PDA happens in most cases through PDA-terminals that are on the shopfloor and allow inputs by the workers regarding the current production situation. With 57%, SME preferably use documentation in written form for recording inventory-, movement- and position data and disturbances. Only three percent of SME allocate data with radiofrequency identification (RFID) technologies. Through electromagnetic waves RFID technologies allow locating and identifying materials and products. In spite of relatively low costs and high performance, RFID technologies prevail in the industry very slowly. This leads to high transaction costs and a high expenditure of time .
As described in the upper paragraphs, today many SME are still at the very beginning of collecting and using data from their production, even though new information technologies and developments like Industrie 4.0 contribute to the allocation of data in unforeseen quality and quantity .
One reason for moderate extent of HRPC at especially SME is, that the cost-benefit ratio of the various steps on the way to a high resolution production control is not clear at all and companies do not know which investments will amortize positively in a reasonable time-frame. To overcome the hesitation of many SME in collecting and using more data from their production, migration paths with an explicit focus on the cost-benefit ratio of decisions have to be identified and characterized.
Furthermore it is not clear, what to do with the collected data. At what time is a controlled action of production control necessary and when should production just continue without any external controlled input?
The aim of this paper is to present the targeted approach for the efficient provision of production information in order to enable a high resolution production control.
Production planning and control
The major tasks of production planning and control (PPC) are order generation, order release, capacity control as well as sequencing. The trigger of these different tasks is information collected from production as well as information from the supply chain and all indirect departments of a company, such as marketing or sales. The tasks of PPC, their correlation and the influenced logistic objectives are shown in Figure 2. Generating orders ensures the planned values for the input and output of the production as well as the planned sequence. The order release defines the period of time in which the orders are needed for production and sets the actual input for the production. Capacity control identifies and determines how much time a machine is running and how long each worker is engaged on each machine. Sequencing, however, determines the process order of each machine .
In the shown model each of these mentioned tasks can be assigned to an actuating variable which constitutes the instrument of each task. These actuating variables define the input and output as well as the sequence in which the orders are executed. Order release can be controlled by the actual input whereas capacity control leads to the actual output. The difference between the actual input and the actual output is the control variable work in process (WIP) .
WIP is a control variable and is defined by the actual input and the actual output. Therefore WIP influences three logistic objectives which are WIP itself, throughput time and utilization. It describes quantitatively how much capital is currently tied-up in the production and refers to all materials and partly finished products that are on an early stage in the production process. The task sequencing influences the variable actual sequence and leads to the control variable sequence deviation. Order generation defines the three actuating variables planned input, planned output, and planned sequence. The actuating variables actual output as well as the planned output define the control backlog whereas actual sequence and planned sequence define the sequence deviation. Backlog and sequence deviation influence the schedule reliability of the production. Self-evidently, the schedule reliability directly impacts the delivery reliability which the customer will notice instantly and which is a clear competitive advantage .
High resolution production management
The concept of high resolution production management offers the opportunity to receive accurate information and precise feedback. Rules for the design and format of these high resolution data need to be provided accurately in order to ensure an effective use. In particular, the design of the IT structure of the system needs to be established modularly. The user must to be able to adjust and use the IT system on his behalf and requirements. Beyond high resolution data, control capability is ensured by employee knowledge. This high resolution density of data can provide a better assistance in the production control process as well as in the decision making. As a result, this concept can ensure a data base by which a selective decision-making process can be initiated .
The overall system of human and technology have to be considered in order to improve the controllability of production systems. The major goal of high resolution production management is to support employees in the selective decision-making process by providing the right degree of information. In this way, qualified and funded decisions can be made by dealing with internal and external dynamic constraints. Considering that high resolution data from production is important, the following statements are essential for a high resolution production management:
• The frequency and accuracy of the feedback data from the shopfloor needs to be increased.
• Depending on the actual situation in production, the feedback data needs to be in a higher resolution.
Smart factory – production in times of ‘Industrie 4.0’
Vision of production in times of Industrie 4.0: Industrie 4.0 represents a world of production where intelligent machinery and sensors serve as a huge support for humans in production. To achieve a high efficiency in production, intelligent factories communicate with the employees as well as the machines. By collecting an enormous amount of production data, the current state of the production is always available for the IT-systems .
Furthermore, Industrie 4.0 implies a new level of organizing and structuring the supply chain. New products and services are characterized by their potential ability to be individualized on customer demand as well as the integration of the customers and business partners in the actual creation process . Industrie 4.0 is based on real time data which can be generated by the integration of all participants of the supply chain. As a consequence, the ability to collect data and derive the optimal creation processes is increasing .
Networked machines, storage systems and production facilities are able to exchange information and manage themselves without central supervision. Products manufactured in an intelligent factory distinguish themselves being locatable, identified and their current production status as well as all alternative ways to the final condition are known.
Industrie 4.0 offers production regarding the customers' wishes under a huge flexibility and also concerning disturbances. Better decisions are possible due to the transparency of production processes and the productivity and efficiency of the resources can be further improved .
Methods from control theory in a smart factory: To analyze and visualize the elements of a factory in times of Industrie 4.0 – also referred to as smart factory – and their connections, control theory offers great possibilities. The principle of the smart factory as a control loop system from control theory will be described in the following:
There are two different kinds of control loop systems: the open loop and the closed loop system. In an open loop system, the actuating input signal is independent of the output of the system. In contrast, the input signal of a closed loop system is influenced by the output due to a feedback signal . The focus of the modelling of a smart factory will be on closed loop systems since they allow to feedback information to the controller (e.g. time-dependent inventory levels, delivery dates, replenishment times etc.) and therefore react to external disturbances (e.g. fluctuations in demand) . The main objective of a closed loop control system is to control a dynamic system. A dynamic system is defined as a functional unit, whose input and output parameters vary with time. They therefore represent time-depending functions. Specific characteristics for a controllable dynamic system are linearity, causality and time-invariance. A closed loop control system consists of the dynamic system, its inputs and outputs, the controller as well as the sensor embedded in the feedback loop .
Such a closed control loop as an abstraction of a smart factory is shown in Figure 3. Data is collected from the shopfloor via various acquisition modes (manual, automatically, sensors, etc.). The high resolution data is sent to a central server, which can be represented by a cloud solution. In this cloud solution the high-volume and highvelocity data is processed to smart data. Smart data is enriched and organized data from production, which is linked with each other and has internal relationships and connections. Through smart data potential controlled actions into the production can be derived. The PPC as the regulator in this control circle of the smart factory is receiving that data from production through the cloud and is performing its tasks (Figure 2) based on that information to ensure a smooth production process.
Sensor solutions as enablers for a smart factory: Possible ways to increase the information density on the shopfloor and to achieve the vision of a smart factory are sensor solutions. Especially relevant for the todays PPC are manual acquisition (PDA – e.g. Barcode or QR-Code Solutions) and automated acquisition (automatic data acquisition [ADA] – e.g. machine transmits data automatically into the IT-System). Other ways of collecting relevant data for the PPC are e.g. laser sensors, RFID sensors, vibration sensors, camera solutions and many more. In the following, parameters that are important to take into account for selecting suitable sensor systems for digitizing the factory are listed below:
• size, weight, assembly effort
• long-term stability, durability
• interchangeability of the sensor element
• performance limits
• efficiency (price, including operating costs)
• failure rate, reliability, readability when
• without visual contact
• independent of orientation
• contactless identification
• bulk acquisition capability
• data density
• energy supply
• operational safety
In the following chapter, deficits in theory and hypotheses regarding a high resolution production control are described.
As described before, many SME are still at the very beginning of collecting and using data from their production, even though innovative information technologies contribute to collecting new data in high quality and quantity. One problem these companies are facing when they start researching about data acquisition from production is that the information needs of existing PPC-systems are not clearly described in theory. The level of minimum information from the shopfloor which is needed under given conditions of a production to ensure a smooth and controlled production process has to be defined and described. The level of minimum information is influenced e.g. by the structure of a production, by the maturity of the production process, by the command of the supply chain, by the degree of individuality of the manufactured product and many other internal and external factors.
The demands on the information supply in a complex job-shop production with many alternative machines are different to a straight assembly line production with an aligned production and material flow. Therefore, the demands on a system for data acquisition in a job-shop production are different than the ones on an assembly line production. The likelihood to miss a problem in a complex and confusing job-shop production is higher than in an aligned assembly line where problems attract attention a lot faster. The following two exemplary metaphors from daily life show the need for an adaptive information provision to have an adequate digital footprint of production in order to being able to control it.
Metaphor 1 - Fishing net: The size of the holes in a fishing net determines the type of fish that are planned to catch. Smaller fish will be able to swim through the holes in the net, bigger fish that are mature and big enough will be caught in the net. Transferred to production, it shows that for a given structure there should be an optimal sensor population which can receive enough data to generate an adequate digital footprint of the situation on the shopfloor.
Metaphor 2 - GPS maps: Structural changes of the street infrastructure (new highway, road or bridge etc.) often take long until they are implemented in maps of GPS systems. Until they are fully implemented, the controlling GPS system does not have sufficient information about the routing situation of the car. It assumes, the vehicle is off-road at the moment it drives on an unknown new road. Even though it is constantly rescheduling to find the best route, missing information prevents accurately dealing with infrastructural changes. Transferred to production, it shows, that for a given production structure it is paramount to have the right sensor population in order to get a correct digital representation. If the production structure changes, these changed circumstances must also be implemented in the accompanying IT-systems and sensor solutions.
The following description shows why it is of utmost importance to have the right sensor population in the company and to adapt it to structural changes:
Corporate IT-solutions are still too rigid. Therefore introducing and adapting software and sensors often takes longer than reorganizing processes. As a result, the real situation of the production differs from the one the ERP- systems display because alterations are not represented in a timely manner in the IT-systems.
The ability to control production processes is disturbed because a parallel ‘planning reality’ occurs. The major cause of this particular deficit is given by the lack of information transparency and information return. This circumstance encourages employees not to trust the accuracy of the system and change the ability of proper decision making based on these system data.
For most companies the change of material numbering systems and bill of materials structures and its consequences on the framework in the IT-systems are a complex and complicated process. Many companies lack the ability to adjust master and transaction data after structural changes due to missing qualification. From the production control point of view an adjustment in the allocation is necessary but not realized because of the described reasons. A vicious circle begins which can only be evaded with a lot of capacity effort and planning .
To break out of this vicious circle, a planned approach and decision aid regarding the question what information are inevitable for having complete control over the production is needed. This leads to the first hypothesis of this paper, which is stated below:
For various structures of production, different minimal characteristics of the quality of information exist, from which a high resolution production control can be realized.
One reason for the small amount of sensor solutions on SME shopfloors is, that the cost-benefit ratio of the various steps on the way to a high resolution production control is not clear at all and companies do not know which type of sensor investments have a positive return on investment. To overcome the hesitation of many SME in collecting and using more data from their production, migration paths with an explicit focus on the cost -benefit ratio of decisions have to be identified and characterized. Therefore benefit curves have to be derived, to help with the identification of the sensor density, which brings the biggest benefit for enabling a high resolution production control. Migration paths to that vision give companies decision aid in what to do next to accomplish the long term goal of having a HRPC.
The following description clarifies, why a cost-benefit oriented roadmap for increasing the sensor population is reasonable: if a PPC system has a planning cycle of one day, which means it reschedules the production plan once a day, it might not be necessary to collect data only for that system in a frequency of milliseconds and therefore invest in capable sensor solutions that can achieve the demanded data acquisition every millisecond. In that case, an adequate investment into appropriate automated data acquisition would be perfectly enough to serve the current PPC.
Phrased differently, two questions come up: “How smart must a smart factory be, in order to best support the production process under given conditions and budget restrictions?” and “How can information from the shopfloor be easily and economically collected and synchronized with the information of the digital world? ” This questions lead to hypothesis 2, which is stated in the following box:
The capability to achieve the logistic objectives is depending on the availability of information from the production. The benefit of additional information has a declining course. In terms of a costbenefit analysis there is an ideal level of information for an existing production structure.
The first two hypotheses dealt with the topic of information acquisition from production and therefore having information available for production planning and control decisions. Another relevant question in the context of information provision for a high resolution production control is the following: assuming that there are high resolution data available - at what time is a controlled action of the production control necessary and when should the production just continue without an external input? Too many controlling actions on a running production can lead to many turbulences and irritated staff without any real improvement of the overall production situation.
In the following paragraph the funnel experiment by DEMING is described as an excellent example from control theory and quality management, which shows what happens, if there are too many interventions into a running process. The goal of the funnel experiment by DEMING is to drop a marble through a funnel and hit a target. A point on a level surface is designated as the target. A funnel is held a certain distance above the surface. A marble is dropped through the funnel. The spot the marble comes to rest on the surface is marked. This is repeated for at least 50 drops for a couple of different control strategies  (Figure 4).
The results one to three show the process quality with the adjustments described just below the pictures. Result four shows the vision what an adaptive HRPC could achieve in production with the help of intelligent sensors and the right amount of controlled interventions in production.
Many processes are endangered of being over-controlled, which is also known as ‘tampering with a process’. Tampering means adjusting a stable process for results that are undesirable or especially good and receiving an output that is worse than without intervening. This tampering with processes happens often both for working staff on the shopfloor and for controlling staff of the PPC. Without sufficient information concerning adjustment results, this approach can lead to disturbances in the process .
The experiment shows that too many interventions in a system lead to a decreasing predictability of the results. Action control limits and decision rules form the basis for profound decisions and have to be identified in order to avert tampering with the process. Current PPC systems or Advanced Planning Systems (APS) often do not follow that rule and intervene very often which can lead to many disturbances with only small or no benefits. The following example comprehensibly illustrates this issue (Figure 5).
An order is generated in October 2013 in the IT-systems. The original planned starting time is set for the beginning of July 2014. Until July 2014 the APS reschedules the starting time more than 8 times and for more than 230 days. The actual starting time of the order is very close to the first planning time in the beginning of July. This real example from industry shows, that the massive rescheduling of complex APS systems can cause a lot of disturbances without a big impact on the actual result. The descriptions above lead to the last hypothesis of the paper, which is stated in the box below.
There does exist an optimal level and frequency of interventions by production control in order to ensure a smooth production process. In Figure 6, the three major hypotheses are stated together, which have to be corroborated in the progression of the presented approach. In the next chapter, the approach for information provision for high resolution production control will be outlined.
The aim of this paper is to outline the approach for an efficient provision of production information to enable a high resolution production control. The approach for high resolution information provision is based on the hypotheses described in the previous chapter. The major steps and methods which are linked to the hypotheses stated earlier are shown in Figure 7.
The first step of the approach was an intensive research of literature to identify other approaches of high resolution production control. The identified deficits in the theory build the basis for the new approach. In order to corroborate the first hypothesis, the next step is an intensive requirements analysis. The goal of the analysis is the identification of the influence of different production structures on the requirements for the information needs of a high resolution production control. Discrete event simulation tools like Plant Simulation or FabSim will assist with the identification of the impact of different structures of a production on the requirements for the information needs of HRPC. With the help of Design of Experiments (DoE), experiments will be created which will be the basis for the simulation and help to come to a solution. The results will be several morphological boxes where characteristics of a production are linked to their influence on information needs from production in order to establish a HRPC.
The next steps to corroborate hypothesis two are described in the following paragraph. Since the second hypothesis states, that the capability to reach the logistic objectives is depending on the information availability from production, the quality of the information gained from the shopfloor needs to be high enough to make use of them. Therefore the maximum allowable measurement noise for various given production structureshas to be identified, in order to achieve the targeted logistic objectives. Afterwards, the identification of the steps will take place, in which the information density that is acquired from production will be increased by consideration of the cost-benefit ratio. This will help to increase the measurement frequency to a certain level, from where the benefit of a better achievement of the logistic objectives is smaller than the return on investment of collecting thast extra information from production via additional sensors. The result of this step is a cost-benefit oriented migration plan for increasing the information density from the production.
The last step of the approach is to identify the optimal level of interventions into the production by production control in order to ensure a smooth production flow. In this step, it is important to identify the action potential from which interventions in the production should be performed. Just as synopses of nerve cells, detailed feedback data from production firstly needs to be collected in production and subsequently aggregated in order to be available for PPC processes. Thus, profound planning and regulation decisions can be made by the IT and qualified employees. This gathered and aggregated information serves as a basis for the identification of the right amount and intensity of interventions into the production .
To identify that action level, research in control theory literature concerning a reasonable frequency and intensity of interventions into a system will be performed. After that, the transfer of potential methods to production control will have to be checked. The approach will be concluded by simulation experiments to determine a reasonable degree of production control interventions (Figure 8). Therefore DoE will be used again, to come up with experiment designs. The result of the last step is the identification of the right control frequency of a HRPC.
The described approach will be validated in the Demonstration Factory in Aachen as well as in several selected SME. The Demonstration Factory in Aachen (DFA) is a special demonstration plant which produces real goods in small series such as framework parts for the electric vehicle ‘StreetScooter’ or e.Go. The production environment of the demonstration plant is available for research purposes and provides access to already installed intelligent information systems and technology and real-time transaction data (e.g. information about produced goods, material flow). To validate the approach, e.g. comparisons of several ways of information acquisition will be compared as well as the analysis of the influence of controlled interventions in the production by production control will be performed (Figure 8).
In this paper, the general framework and the approach for information provision for a high resolution production control has been introduced. The presented approach consists of three different steps: it begins with a requirements analysis to identify the influence of different production structures on the requirements for the information needs of a HRPC. The outcome of the first step are morphological boxes for different production structures. Afterwards, the maximum allowable measurement deviation of the digital footprint of a production with its production structures will be derived in order to reach the targeted logistic objectives. Outcome of the second steps will be cost-benefit oriented migration plans to increase information density. The last step of the approach is the identification of action levels for the HRPC, from when interventions in the production should be performed in order to have a smooth production.
Within the application in the DFA as well as in exemplary SME the significant potential of HRPC will become evident. Future research will need to subsequently complete the presented approach in order to realize its full potential. It is planned to further detail the approach in submitted research projects which are dealing with Industrie 4.0 and its implications on production.
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