From Available-to-Promise (ATP) to Keep-the-Promise (KTP): An Industrial Case of the Business Intelligent System

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1 From Available-to-Promise (ATP) to Keep-the-Promise (KTP): An Industrial Case of the Business Intelligent System Eduard Babulak 1 and Ming Wang 2 1 Fairleigh Dickinson University- Vancouver, Canada 2 Fairleigh Dickinson University- Vancouver, Canada Abstract-. Available-to-promise (ATP) is a business function that provides a response to customer order enquiries, based on resource availability. Due to dynamics of manufacturing there always exists a gap between what has been promised and what can be physically fulfilled. Yet there is little or no margin for error in the present business environment characterized by the zero inventory, Just-in-time delivery and fluctuating demands. The way forward is to improve quality of the decision-making at the order fulfillment stage. The KTP (Keep-the-Promise) system is proposed as the decision support tool to help operational managers decide how to keep the promise made by an ATP system. This paper firstly analyses the need for improving quality of the decision making at the order fulfillment stage. It then examines the decision making process and proposes framework of the KTP system. The core of the KTP system is the discrete-event simulation engine coupled with business rules. Index Terms Available to Promise, Scheduling, Discrete Event Simulation, Enterprise Resource. 1. INTRODUCTION Available-to-promise (ATP) is a business function that provides a response to customer order enquiries, based on resource availability. It generates available quantities of the requested products and delivery due date [1]. ATP functions have been available in all commercial ERP and SCM software such as SAP s APO, Oracle s ATP server and i2 s Rhythm etc. Today an ATP system will dynamically allocate and reallocate resources to promise.. It simultaneously considers supply-chain-wide resources, including raw materials, work-in-process (WIP), finished goods, and even production and distribution capacities. In the business environment of zero inventory and Just-in-time production, what an ATP function actually does is to generate availability outlook with respect to customer order enquiries. Subsequently actual customer orders need to be scheduled against the availability outlook. Thus, some literatures claim that there are two levels of ATP: one for generating the availability outlook (push-side of the availability management) and another for scheduling customer orders (pull-side of the availability management). A key role for effective availability management process is to coordinate and balance the push-side and pull-side of the ATP [2]. Problems at the order fulfillment stage: Theoretically an ATP system books availability of materials and capacity against customer orders. The promised orders and allocated resources will then be disseminated to the relevant business units for the physical fulfillment (i.e. production and distribution). There is a gap between what has been promised and what can be actually fulfilled due to the following reasons: 1) Quality of the information: The availability information kept in the IT system (system availability) is not always synchronized with the actual availability (physical availability). The availability information is typically updated periodically as it is very expensive to collect relevant data and update the database in real time. Due to the potentially inaccurate view of the availability, unrealistic promise can be made to customers [3]. 2) Simplicity of the resource allocation model: Many ATP tools are mostly fast research engines for available database, and they schedule customers orders without any sophisticated quantitative methods. Research on the quantitative sides of ATP is still at an early stage, and there are only a limited number of analytical models developed in supporting ATP [2]. 3) Dynamics of manufacturing and there are always some expected events, for instance, Machine breakdown or absentee of operators, resulting in variations in capacity availability; Inconsistent quality, for example, one or two batches have much lower than average yield, resulting in variations in quantity availability. Late arrival of some materials. A surge demand for some products, resulting in higher priority for certain orders. It means that the resource availability is not deterministic but stochastic in nature. In old times, many companies relied on extra capacity and stock to cushion the variations. But it is no longer the case in the present business environment of zero inventory and Just-in-time production. In practice, it is up to the operational manager to find a way to bridge the gap. It is their responsibility to make decisions and take actions to smooth things out and ensure that the promise can be kept. Very often it is an uphill struggle in the environment characterized by the zero inventory, Just-in-time delivery and fluctuating demands. There is little or no margin for error. A promise made to a customer by an ATP system could just fall apart because of one erroneous tactical decision at the order fulfillment stage. 1

2 Need to improve the decision making process: Under tremendous time pressure, the operational managers often make decisions based on their experience and intuitions. It is imperative to improve quality of the decision making process. To this end, the paper proposes a decision support system. The system enables the managers to evaluate available options against the promise in order to select the best one to implement. Ultimately the system aims to help the managers decide how to keep the promise made by an ATP system, and therefore can be named as KTP system. From the planning viewpoint, the KTP system acts as a fine-tune mechanism between the ATP at the frontend and the production scheduling at the back-end, as illustrated in Figure 1. This could be the most effective way to reconcile discrepancies between the system availability and the physical availability. Figure 1: KTP (Keep-the-Promise) System in the Hierarchy production performance. He will evaluate the options and select the best one based on his own judgment. Finally he will prepare a set of instructions for operational people to implement. Reports like - Production Schedule - WIP Report - Shipment Plan Figure 2: Steps in the decision-making process Information on resource availability, product-resource matrixes, product switch strategy etc Internal or External Disturbance Establish Production Status Explore Alternative Options Scenario Analysis Time Function Procurement Production Distribution Sale Instruct Corrective Actions Long-term Strategic Network Mid-term Short-term Material Requirement Master Production Production Scheduling KTP Distribution Transport Demand Demand Fulfillment & ATP KTP, a tactical decision-making tool, to reconcile discrepancies between the system availability and physical availability 2. THE KTP SYSTEM 2.1 Understanding the Decision-Making Process Figure 2 shows a set of steps the operational managers may take to decide what to do in response to an internal or external disturbance. The internal disturbances include machine breakdown, higher than average rejects and others. The external disturbances include shortage or late arrival of materials, changes to shipment plan and so on. Any of the disturbances will prompt operational managers to act and therefore trigger the decision making process. The manager needs to establish the production status first. This is done by getting information on the production progress and the WIP (work-in-progress) status. Then the manager will consider available options to minimize impacts of the disturbance on the The decision making process is characterized by the following features: 1) Reactive the process is largely triggered by either internal or external disturbance. It could happen randomly and unexpectedly. The occurring frequency of the disturbances is proportional to the work load and could be very high at the production peak period. 2) Tactical: In many situations the decisions are concerned with reallocating resources to utilize some extra capacity or shifting some products from one line to another to expedite the production. The effects are very local and short-term. Most of them may not be recorded in the database and appear in the system availability. 3) Detailed: The adjustments are often made to individual machines, production lot or even individual components/products. 4) Short time span for the decision making: The resource capacity is time-sensitive. An extra capacity is valuable only before time elapses. Thus, as a decision-support tool, the KTP system must be capable of retrieving relevant data from the IT system and process the data into a format from which the managers will be able to establish the production status and identify deviations from the target instantly. Secondly the KTP system will predicate outcome of a particular option under the manager s consideration and likely impacts on the promise, for instance, whether the output from the production can match the shipment plan after an adjustment made to capacity allocation. Most of 2

3 all, the processing speed is essential because of the time pressure. 2.2 The Framework of the KTP System Figure 3 illustrates the framework of the KTP system. The heart of the system is the model that represents the production process and that can simulate the production under different conditions over a defined period. In other words, the model can predicate likely output from the production process and enable managers to compare it with the shipment plan. There two types of input data files to the system: Type I defines resources, products and their relationships (i.e. routines). It also includes operational rules such as batch priority, dispatching rules and so on. Type II is the transaction information on the production progress such as work in progress (WIP) status, daily production progress report and so on. By comparison, Type I is the information on the process and resources, which are relative stable and only change when one alters resources and launch new products. Type II is dynamic and changes as long as production runs. Type I defines the model of the production process, whilst Type II makes up the initial status of the simulation. The model is used to predicate likely outcome from the manager s options. Therefore, the output from the simulation should be organized into a format from which the managers can evaluate the production performance with ease. It is proposed to organize the output data into two parts: 1). Primary measurements; and 2). Detail Report. The first part highlight major concerns to the managers, for example, the simulated production output versus the shipment plan. The second part includes key production performance measurements such as throughput, resource utilization, WIP status, and average cycle time as well as the drill-down facilities. The model configurator is used to create and maintain the simulation model. The option definition module could include pre-defined options known to the managers. Each of the options is associated with a set of changes to the production model. Once the manager selects a particular option, the model will be modified interactively through the model configurator. It enables the manager to run the production with the selected option and evaluate the output against the shipment plan. The manager can use the model configurator to change the model directly to evaluate the production performance. Input Data File I Resource capacity & capability definition Product category Routines Loading rules Others Data File II Production Schedule WIP Status Machine Status QC Status Others Figure 3: Framework of the KTP System Option Definition Resources Products Product-Resource relationship Loading rules others Model Configurator Model of Production Process Output Primary Measurement Simulated output vs. shipment plan Detailed Report File Output Resource utilization WIP Cycle time The next section describes the effort in developing a pilot KTP system for an industrial application based on the framework discussed above. 3. CASE STUDY 3.1 Project Background A KTP system was developed for a semiconductor plant in Singapore. The plant belongs to a global corporation who is a leading provider of microprocessor. In a typical global supply chain environment, the microprocessor production is carried out in three different countries: silicon wafers are fabricated in Germany plant and then sent to Malaysian plant for assembly and Singapore plant for testing. The Singapore plant receives microprocessors from Malaysia plant and immediately releases them into the production where chips are tested according to specification. After tested, microprocessors are shipped to worldwide customers or distributors. The production manager works on the planning window of one week. On Thursday he has the information on what to receive from the Malaysia plant in the following week. The same information also acts as the scheduled release to the Singapore plant. The manager also has the shipment plan which decides quantities of microprocessors ready for shipment by end of the week. Although the ERP system generates the production schedule accordingly, the production manager has to make constant adjustments to the production as result of inconsistent quality with silicon wafers, variation in available capacity, changing order priority and, most of all, product flexibility. By product flexibility, it means that microprocessors can be used for different types of applications, for 3

4 instance, server, desktop, mobile equipment and so on. Each application is associated with its own testing specification. A microprocessor that fails the test for one application can be downgraded to another application. Thus, it is very likely that one production lot of microprocessors will be split to several sub-lots after a testing operation, each for one type of application. Subsequently several sub-lots with same type of application can be combined into one lot to go through next testing operation. The lot splitting and combination could occur very regularly if quality of wafer fabrication is inconsistent. Externally, a surge demand for one application, for instance, mobile equipment, will result in a higher order priority. It means that quantities of microprocessors for the mobile application must be met first, even it may delay the delivery to some other applications: Under certain circumstances, the manager has to switch product, i.e. diverting some microprocessors to mobile application from other product lines. The diverted microprocessors require to be tested again and it completely upsets the original capacity allocation. The dynamics of the operation force the manager to make constant adjustments to capacity allocation, production lot grouping and priority etc. Each time when he makes an adjustment, he wants to know whether the production can meet the shipment plan at the end of week. There is no margin for error: he can either make it or miss the shipment. To this end, the KTP system seems the ideal tool to help the managers to make better decisions. 3.2 On the KTP System In this case, Witness simulation software was used as the platform to model the testing operation within the plant [Lanner]. This is because the software is well known for its user-friendliness and rich functionality in model building. Secondly the project team had only a very limited time of three months to come out the solution. Figure 4 is the functional framework of the KTP system using Witness simulation software as the platform. Major issues or difficulties occurred during the development are discussed below: 1). Data acquisition: The entire operation within the plant is supported by three IT systems: SAP ERP system, WorkStream Manufacturing Execution System (MES) and QC system. The data both Type I and Type II are available from the IT systems but not directly usable to either define or run the model. The interface is created to retrieve all relevant data out the IT systems and organize the data into different files in the categories recognizable to the managers and engineers, which are: IT Systems SAP ERP WorkStream (MES) QC System Figure 4. Overview of the KTP System using Witness simulation software as the platform Input (Excel files) No of Testers Operator & Shift Resource Grouping Device & Product Route & UPH Scheduled Lot Release WIP File Yield assumption Witness Simulation Software Model Configuration File Simulation Model of Production line Output (Excel files) Daily Production Output Tester Utilization WIP Status a) Resource file: type of tester, number of testers, number of operators, resource grouping, shift pattern. b) Product file: device type and product grouping. c) Routine file: relationships between products and resources. In this case, it is relationship between devices and testers. The key attribute of the relationship is the processing time, or units per hour (UPH). d) Material Arrival file: information on receiving microprocessors from the Malaysia plant. The information is equivalent to the scheduled production lot release to the Singapore plant. e) WIP file: quantity by device in front of each tester at a particular time. The data sets the initial state of the simulation. f) Yield file: average yield rate and percentage of splitting by product at testing process. It is based on the average values but subject to changes to reflect the actual situation or scenario analysis on impacts of inconsistent quality on the production output. In this case, all the data are kept in MS Excel after they are retrieved from the IT systems. There are several advantages: Firstly it is easy for engineers to verify the data. Secondly the manager can manipulate some data directly to reflect his option, for instance, altering resource grouping, and increasing capacity by adding overtime or subcontract tester. Finally it appears that both engineers and managers like to copy some data for other analyses. 2). Knowledge acquisition: This was the most difficult part of the development. A lot of effort was spent in discussing with the managers and engineers to understand how they react to expected events and make 4

5 tactical decisions. In particular, the project team tried to find out the answers to the following questions: a) What information is most relevant to the operational manager? Concern No.1 is whether the production can meet the shipment plan. The manager wants to know what would be the likely production output as the result of my decision. To this end, the KTP system extracts the simulation results from Witness simulation software and displays them in MS Excel for the managers to view. The first portion of the data is the simulated daily production output by devices, as shown in Table 1. b) What options the managers normally take and what are relationships between identified options and the resources? The idea was to pre-define options and create the linkage between an option and the resources defined in the model. Once the manager selects a particular option, the model will be updated and run automatically to show the manager the likely outcome from the production. In this project, the KTP system enabled the manager to enter options by: i. altering the data in MS Excel files, such as resources, products, routines, material arrival and yield assumption. The model configuration file has defined relationships between the data in MS Excel and the elements in the simulation model. If a particular data in MS Excel files is changed, the model will be updated automatically. ii. changing the rules as defined in the model configuration file. The rules include dispatching, splitting lot, combining lots and others. To run the system, the manager firstly activates the interface facility which retrieves the transaction data from the IT systems and updates the files in MS Excel automatically. Then he can simulate the production from now until end of the working week. Once the simulation ends, he can look at the results displayed in MS Excel, particularly the daily simulated production output and weekly accumulated total by devices, as shown by an example in Table 1. The result can be used to compare with the shipment plan. If there is any discrepancy, the manager can manipulate the input data in MS Excel or the rules in the model configuration file with respect to his option and rerun the simulation to see if the newly simulated production out can meet the shipment plan or not. Through simulation, the manager can evaluate various options before make changes to production 3.2 Reflections from the project Although the project schedule was very tight, the KTP system was in place for the operational managers to use. The managers response to the KTP system was very positive as it had delivered what they wanted: likely production output against shipment plan. The manager used the KTP system at least once per shift (normally at the end of shift) to see if there is discrepancy between the simulated production output and the shipment plan by end of the working week, taking account the production status at the end of the current shift. If there is any discrepancy, he will use the KTP system to evaluate the options to see how to make adjustment to the forthcoming shift in order to eliminate the possible discrepancy. Because of the very tight project schedule, the project had used Witness simulation software as the platform. It seems that the discrete event simulation coupled with rule-based definition function could be the heart of the KTP system. However, much more work needs to be done to create a generic KTP system, particularly in the following areas: 1) Interface with IT systems: one type of plug and place interface to retrieve all relevant data from IT systems in a real- time basis. 2) User-interface for the operational managers, which should consist of three portions: i. View the input data; also facility to manipulate certain data. ii. Intelligent presentation of the simulation results iii. Options, pre-defined and recommended Simulation speed: For operational managers, processing speed is essential because of the time pressure. For this project, the processing speed was the main problem and the time taken to run simulation was the bottleneck. Ideally, for each scenario, the simulation processing duration should be limited to within 5 minutes. As the result, the managers will be able to run 3-5 scenarios within 30 minutes. 5

6 Table 1: Simulated daily production output by product 4. CONCLUSION From perspective of planning and operation, there is a gap between what has been promised and what can be physically fulfilled due to dynamics of manufacturing. Current developments in E-Manufacturing sector promote reliable business intelligence while supporting a real-time information and decision support systems [7]. The results of merging business intelligence with flow of manufacturing processes leads ultimately to business and industrial automation via ubiquitous internet and cyberspace [8, 9]. The fact is that there is little or no margin for error in the present business environment characterized by the zero inventory, Just-in-time delivery and fluctuating demands. The way forward is to improve quality of the decision-making at the order fulfillment stage. The KTP (Keep-the-Promise) system is proposed as the decision support tool to help operational managers decide how to keep the promise made by an ATP system. The core of the KTP system is the discrete-event simulation engine coupled with business rules. It pulls relevant information from enterprise resource planning (ERP) system and manufacturing executive system (MES). It simulates the production under various conditions over a pre-defined period. The simulated production output is then used to compare with the shipment plan. Any deviation will prompt the managers to take corrective actions over the forthcoming production. Thus, the KTP system acts as a platform for the managers to reconcile discrepancies between the system availability and the physical availability. From IT perspective, much needs to be done to the KTP as a generic decision support tool. Throughout 1990s ERP systems had taken the centre stage in the electronic enterprise. Corporations have spent a great amount of resources and effort in implementing ERP systems. Many corporations today have a solid IT infrastructure in place with a high degree of information integration. The challenge is how to move to the next stage of utilizing the data for the decision-making. Thus, the KTP system can be considered as an attempt in this direction. However, as illustrated by the case presented in the paper, most of the data are available from IT systems, but they are not directly usable for the decision making. To a certain extent, we believe that Business Intelligence (BI) software can solve the issues. However, to make better decision, managers have to consider how to reallocate resources and evaluate likely outcomes under options. It requires a predictive engine to support such an analysis and the discrete event simulation could be an ideal platform. A complete Business Intelligence (BI) system should include both the data analysis capability and a predictive technology. The former concentrates on gathering and analyzing large quantities of unstructured data, whilst the latter enables managers to evaluate different options in order to make right business decisions. The paper also presents an industrial case where a pilot KTP system was developed for a semiconductor plant in Singapore. From information technology (IT) perspective, the KTP system can be considered as an application of utilizing data from ERP/MES systems to manage business process. In conclusion, the paper points out the needs for Business Intelligence (BI) software with both data analysis and predictive capabilities. ACKNOWLEDGMENT The authors would like to thank Dr Gary Hu of Cimtek Pte Ltd for sharing his knowledge and experience in the case study. The authors wish to express their sincere gratitude to Fairleigh Dickinson University in Vancouver for the support provided during this work. REFERENCES [1] Ball, M.O. et al (2004) Available to Promise in Handbook of Quantitative Supply Chain Analysis Modelling in the E-Business Era. Simchi-Levi, D. et al. (Eds.). Springer (2004) page [2] Lee, Y M. (2006) Simulating Impact of Available-to-Promised Generation on Supply Chain Performance. In Proceedings of the 2006 Winter Simulation Conference. Perrone L. F. et al (Eds) WSC 2006, Monterey, California, USA, December 3-6, page [3] Lee, Y M (2007) Analysing the effectiveness of Availability Management Process. In Trends in Supply Chain Design and Management: Technologies and Methodologies. Hosang Jung et al (Eds) Springer (2007), page [4] Zhao, Z (2005), Optimization-based Available-to-promised with multi stage resource availability. Annals of Operations Research 135, [5] Stadtler, H. et al (Eds) (2002) Supply Chain Management and Advanced : Concepts, Models, Software and Case Studies. Springer; 2 nd Edition (2002) 6

7 [6] Oracle Data Sheet: Oracle Global Order Promising Oracle Corporation Copyright 2006 [7] Babulak, E: E-Manufacturing for 21st Century, State-of-the-Art and Future Directions Keynote Address, the First International Conference on Management of Manufacturing Systems presentation, Presov, Slovakia, November 18th 19th, 2004 [8] Eduard Babulak: Interdisciplinarity and Ubiquitous Internet Technologies in Support of Automation, published for publication in the International Journal of Online Engineering ijoe, ISSN: , Vol. 2, No 1 (2006) in Austria [9] Eduard Babulak: Future Automation via Ubiquitous Communications Technologies published in the Journal of Telecommunications and Information Technology, RSS 2.0 or ISO , JTIT 1/2006, Warsaw, Poland. WEB SOURCES [1] SAP APO (Advanced Planner and Optimizer): b38f8cf/frameset.htm - last visited on October 24th 2007 [2] Oracle ATP (Available-To-Promise): last visited on October 22nd 2007 [3] I2: last visited October [4] Witness simulation software: last visited on October 18th 2007 AUTHORS Prof. Eduard Babulak MSc, PhD, PEng, Eur,Ing., C.Eng. SMIEEE, SMACM is currently Visiting Professor of Information Technology with the Fairleigh Dickinson University in Vancouver, 842, Cambie Street, Vancouver, BC, V6B 2P6, Canada ( ebabulak@fdu.edu) Ming Wang PhD, worked as an Adjunct Professor of Business with the Fairleigh Dickinson University 842, Cambie Street, Vancouver, BC, V6B 2P6, Canada ( wang604@fdu.edu) 7