Lean Cables - A Step towards Competitive ...

Lean Cables – A Step towards Competitive, Sustainable and Profitable Processes Parminder Singh Kang1, Alistair Duffy1, Nigel Shires2, Trevor Smith3 and Mike Novels2 1

Advanced Manufacturing Processes and Mechatronics Centre, Faculty of Technology, De Montfort University, Leicester, UK, +44-116-257-8089 · [email protected], [email protected];

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Preactor International Ltd – A Siemens Company, Bumpers Way, Chippenham, Wiltshire, SN14 6RA. +44-1249650316 – [email protected], [email protected]

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Plessey Semiconductors Ltd, Tamerton Road, Roborough, Plymouth, PL67BQ – [email protected]

Abstract

The main focus of this research is to illustrate the applicability of Lean scheduling in different manufacturing sectors; this paper exemplifies the concept of Lean scheduling using a semiconductor manufacturing case study, and further identifies how this approach could be used in a cable manufacturing environment. This paper is organized as; the first section, briefly introduces the Lean concept as philosophy, waste and value. The second section exemplifies the scheduling from a lean philosophy perspective.in the third section, problem description and methodology is provided based on the current implementation in a semiconductor manufacturing environment and the fourth section provides the key benefits of implementing the Lean scheduling. The fifth segment, exemplifies how this methodology could fit with cable manufacturing scheduling problems, and finally conclusion is derived to describe the findings and future work.

In the business world, one of the key challenges is how to survive in ever changing business environments and outperforming the competitors, while keeping the operational cost at minimum and profits at maximum level. In other words, this can be described as the problem of improving operational efficiency and reducing cost. Over the past few years due to global financial challenges, it has become even more important to improve the operational efficiency and reduce costs to survive through these tough conditions. Like other successful implementations of Lean philosophy, especially in automobile, aerospace, steel and healthcare, it can be applied in cable manufacturing to achieve competitive, sustainable and profitable processes. The main focus of Lean philosophy is to create value for the stakeholders and end customers by reducing the nonvalue added activities or waste, and laying a strong foundation towards sustainable growth, by focusing on continuous process improvement. The overall philosophy provides a focused approach for continuous process improvement and the targeting of a variety of tools and methods to bring improvements. This paper presents case studies for Lean scheduling of manufacturing processes (Semiconductor and cable industry) and exemplify how Lean scheduling processes allow organisations to maintain the continuous improvement philosophy. This paper also addresses the key elements of rescheduling processes i.e. how to schedule and when to schedule. Further, the paper will exemplify key benefits of Lean scheduling and how the Cable industry could benefit from the proposed approach.

1.1 LEAN – Philosophy

Lean originates from Toyota’s manufacturing philosophy, called “The Toyota Production System” introduced in 1940, which further matured in to Lean Manufacturing System. The whole concept is based around the idea of producing in a continuous flow of material and information which does not rely on long production runs to be efficient. Lean philosophy emphasizes the fact that only a small fraction of the total time and effort to process a product adds value to the end customer. Lean philosophy started with the automotive industry and there is a whole framework of methods, principles, tools and techniques to eliminate the waste [1, 2, 3, 4, 5 and 6]. The idea of Lean starts with customer and value definition. From the process perspective, the idea of Lean philosophy is applicable to any process within the organization. For instance, a given process in the organization acts as a customer for the preceding process and supplier in the succeeding process, which can be applied to any process within the organization [7].

Keywords: Lean Philosophy, Continuous Process Improvement, Sustainability, Lean Waste, Scheduling.

1. Introduction

Certainly, Lean philosophy and continuous process improvement techniques (CPI) are not a new concept however, these are still relevant and have been applied extensively, as organizations seek to attain and sustain the competitive advantages. In fact, newly developed manufacturing paradigms and systems are always examined in relation to leanness. One of the main goals of these CPI is to find and eliminate the causes of defects/mistakes in business processes by focusing on the organizational/process key performance indicators (KPI’s). Most of the process improvement methodologies consist of more than one process improvement technique i.e. a multidimensional construct. This research adopts a similar approach; the proposed methodology uses the underlying concept of advanced planning and scheduling (APS) integrated with a closed loop controlled methodology to implement the concept of Lean-scheduling. A Closed controlled loop system provides the mechanism for continuous process improvement and allows the system to cope with the changes that could occur at an event.

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“Lean Thinking: Banish Waste and Create Wealth in Your Corporation” [8] demonstrates that Lean philosophy is not only applicable to the automotive industry but is also applicable to many other industries, such as healthcare, aerospace, food-processing, hitech, service, logistics and construction. The underlying principles however, remain the same. These are [8 and 9]: • • •

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Identify value; determine the features that create value in the product from the perspective of internal and external customer. Identify the value stream; identify the activities required to generate the value, and eliminate the activities which do not add any value for the end customer. Generate flow; once the value added activities are identified, allow uninterrupted flow of product/service from system to customer.

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2. Scheduling – Lean Perspective

Pull value from manufacturer; once the above three steps are completed allow customer to pull service/product through the system. Pursue perfection; the above four steps need to be repeated continuously to eliminate the newly identified layers of waste.

Scheduling is defined as production planning and control techniques used to sequence and prioritize production quantities across operations in a job shop. There are several factors, which are essential to produce a schedule such as; status and priority of each order on the shop floor, machines and resources required to process these orders, when the resources are required and what is their availability, etc. [10].

Lean principles provide a systematic methodology to implement the process of continuous improvement. Lean philosophy allows eliminating the waste under CPI, as new layers of waste appear following the five principles in an iterative manner. These five principles represent the core of the lean philosophy.

This section epitomizes the two key aspects of this research paper; i.e. scheduling process - which answers the key questions, when to schedule and how to schedule and Lean perspective – how scheduling and Lean are linked together.

1.2 LEAN –Value and Waste

The main aim of Lean philosophy is to eliminate waste and focus on value as a process of continuous improvement; in fact, Lean philosophy starts from the refusal to accept the waste. Waste is anything, which is not required to produce the product or service. For instance, if something doesn’t add value or there is an alternative way of doing, then the current process/activity is waste. Therefore, the first step is to understand what value is, and identify the resources that are absolutely necessary to create that value [11, 12 and 13]. According to [3], value is created if;

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2.1 Scheduling – How and When

Scheduling is one application domain where the implementation of manufacturing philosophies is challenging due to the inherent complexity and combinatorial nature of scheduling problems [17]. This makes production planning and scheduling one of the most critical activities in manufacturing, where the main concern is the distribution of scarce resources and machines, to tasks over time. Efficient scheduling is of utmost importance, when only finite capacity is available and availability of resources is just enough or insufficient at a given instance, and there are a number of objectives or needs to be optimized (fulfilled). For most organizations increasing the plant capacity or other pseudo-fixed resources is not a feasible option. In this case, production planning and scheduling should be at its optimum level. According to [18], scheduling is a decision making process, used on a regular basis in both manufacturing and service organizations to allocate resources to tasks, and the goal is to optimize one or more objectives. Some of the key objectives revolve around cost, quality, delivery and quantity, and justifies the reason why scheduling is so important. Due to the combinatorial nature of most scheduling problems it is not feasible to find the best solution in a reasonable time frame; resource/activities can take different forms depending on the system state and constraints at the given time. Scheduling problems therefore, are classified as NP-hard (Non-deterministic Polynomial-time hard), due to their high complexity and combinatorial nature. These represent the close approximations of real-life systems. For instance, the large number of input parameters, their interdependencies and the stochastic nature of many of these parameters all adds to the complexity of the scheduling problems [17]. This makes scheduling problems very complex and difficult to solve. Heuristic methods therefore, are developed to find near optimal solutions in shorter period of time [21]. However, most of the time the developed heuristic methods implemented in practice are the dispatching rules, which are easy to implement and have lower computational complexity.

Internal waste is reduced, i.e. the overall value proportion for the customer is increased as the associated costs and wasteful activities are reduced. Additional features or services are offered, which adds value for the customer such as shorter delivery period.

Therefore, the starting point is to identify the value from a customer point of view, and anything that doesn’t add value from the end customer perspective is regarded as waste. However, some of the waste cannot be removed completely, it can only be minimized. For instance, changeover time is waste from the end customer perspective, but, is essential from the manufacturing process perspective. In this case, setup time can be reduced by adopting the sequence optimization but cannot be eliminated. From the Lean perspective, there are seven types of waste (Table 1) [14, 15 and 16]; Table 1 (Lean Waste) Waste Over production Waiting Transportation

Over processing Excess Inventory Defects Excess Motion

Description Producing anything more than customer demand or specifications Queuing or downstream process is waiting for upstream activities to finish Unnecessary movement of material either between the processes or point of use to the process non-value added processing due to the rework, testing, sampling, inspection etc. Frozen assets or value beyond the customer demand, such as raw material, WIP and finished products Errors during the production process or service i.e. product or service doesn’t meet the customer specifications Excess motion of resources across the production facilities

Also, in most real world production, a production schedule never will be executed precisely. Manufacturing operations can face a wide range of uncertainties, and scheduling methods need to be advanced enough to accommodate these changes either by accommodating them in advance, or by reacting to the situation. These uncertainties could occur due to uncertain demand (arrival of new orders and order cancellation), changes in order priority, processing delays, changes in release dates, machine breakdowns, unavailability of raw material/machine/personnel or organizational constraints/priorities etc. Rescheduling of operations is required due to these uncertainties; [19 and 20]. [22 and 23] has reviewed different approaches to scheduling under uncertainties; these can be classified into three main groups;

Benefits of reducing the waste can be enormous by identifying the non-value added activities in the early stages of the process. As the processes continually improve, the waste reduction will be more incremental as the company strives to achieve a waste free process.

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Reactive; jobs are dispatched in real time based on the available information at a given time. For example, at a given instance the next job is selected from a given set of candidate jobs by sorting and filtering according to the predefined criteria and system state at given instance [24 and 25]. Robust; this adds the next level of sophistication on to reactive scheduling approaches. Robust scheduling follows the underlying mechanism of reactive scheduling. However, the main objective remains to minimize the effect of uncertainties on the primary performance measure of schedule. Predictive-Reactive; scheduling is done as a two phase process, a predictive schedule is generated for a given time horizon and represents the desired behavior of the system. The Predictive schedule, then is modified (if required) to accommodate any uncertainties, which represents the reactive component. These uncertainties are event driven changes, for instance machine breakdowns, new orders introduced in the system, etc.

Table 2: Scheduling Approaches Approach

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Fixed Time Intervals Variable Time Intervals

At one or some events, such as; • • • • • •

Event-Driven

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Changes to Due Date Job/Order Finished Resource Events New Orders/Jobs Changes to Job/Order Priority Deviation of Performance Measures Machine Failure, etc.

Special case of Event-Driven on all possible events defined under Event-Driven

2.2 Lean Perspective

Lean principles are not limited to the basic tools and techniques but are applied at different levels of advanced planning and scheduling (APS) systems to reduce the non-value added activities and process waste. This forms the second main aspect of this section, to implement scheduling from the perspective of Lean philosophy i.e. how two key aspects of scheduling, which are policy and frequency fit with the Lean philosophy. According to [22 and 23], from the Lean perspective, the main idea is to smooth out the uncertainties (peaks and valleys) from the production schedule. Smoothing out the uncertainties will allow a consistent production rate and balancing of available resources. From the capacity available and resource utilization perspective, resources and plant capacity should be used at its optimal level to achieve the required production rate with minimal waste and operational cost. Wellorganized and carefully executed work, routing, scheduling and dispatching are essential to bring the production through in the required quantity, of the required quality, at the required time and at the minimal cost [28].From a Lean perspective therefore, scheduling should be able to accommodate and anticipate uncertainties and have the ability to react to the event based changes [22].Scheduling how and when exemplifies the need for correct decision making when choosing the criteria for the rescheduling process. The rescheduling process will add no value other than waste if customer and scheduling objectives are not met. For instance, event driven approaches might provide an advantage over the periodic and continuous approaches by reacting to the situation as needed; however, selection of correct rescheduling event is of utmost importance to make the scheduling process effective. Similarly, continuous scheduling allows rescheduling on every event, even though on some events rescheduling may not be required, and could deteriorate the quality of final schedule. Also, over processing is a waste and will not add any value from the end customer perspective. In summary, from lean scheduling perspective, how and when needs to be considered carefully when developing a scheduling methodology. This may, however, vary with respect to problem domain and organizational requirements.

Periodic; the manufacturing environment is monitored continuously, but the necessary actions are taken periodically based on the unscheduled operations and the current system state, where the scheduling period can be either fixed or variable. Both variable and fixed periods have their advantages and disadvantages; however, it is important that period length is optimal so that schedule changes are made at the right time. The main advantage of periodic scheduling over continuous scheduling is that fewer schedule changes are required, which reduces the system nervousness and computational time. However, the scheduling performance might be compromised as scheduling changes can only be made after the defined period i.e. events occurs within the rescheduling points, which may yield poor schedules [22]. Event-Driven; under this policy rescheduling only occurs when an event happens and there is a change in current system state. For instance, machine breakdown, new order arrival, order cancelation, changes in constraints/priorities etc. are all examples of events that possibly could trigger the rescheduling process. Studies have shown that event-driven scheduling tends to perform slightly better than the fixed periodic scheduling. For instance, rescheduling of an event (Machine failure or a machine becomes available after repair) can improve the system performance, however; on the downside it can increase the rescheduling frequency [28 and 29]. The rescheduling frequency can be minimized by choosing the events important for the given problem. Continuous; this is the special case of event-driven scheduling, where the system is rescheduled on every event. Clearly, the continuous rescheduling poses the risk of rescheduling even if the event doesn’t cause any significant disruption; this can increase the computation time significantly and, potentially, can cause unnecessary changes in the schedule [22 and 29].

3. Case Study – Semiconductor Manufacturer

It is important to note that this section discusses the fundamental aspect of different scheduling policies and scheduling frequency. These policies can be implemented programmatically or using different tools and techniques based on heuristic, mathematical and evolutionary approaches, which is out of scope of this paper.

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Periodic

Once the rescheduling approach is selected, the second main aspect to be considered is the frequency of rescheduling i.e. changes in system are detected now the decision needs to be made when to reschedule the jobs. According to [23, 26 and27] there are three main approaches to determine the frequency of rescheduling (Table 2 – Scheduling Approaches); •

Associated Events (When to Schedule)

This section exemplifies the scheduling problem from a semiconductor manufacturer perspective. This section is divided into two main sections i.e. problem description, which gives a brief description about the problem involved in the case study,

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and the proposed approach discusses the closed loop controlled approach integrated with the APS mechanism.

selection, since there are a number of priority based dispatching rules.

3.1 Scheduling – Problem Description

From the current research perspective, two rules are developed for this case study, Due Date Performance Rule and Customer Priority Rule having associated KPI’s as number of orders missing due date and number of remaining orders with high priority respectively. One of the main aspects of this research therefore, is to select the right rule at the right time in order to improve the efficiency and effectiveness of the produced schedule, and reduce the overall process waste, and thus facilitate Leanness as a shop-floor control enabler. The given problem adds another layer of complexity to the classic scheduling problems to have an effective rule selection mechanism.

The selected case study represents a semiconductor manufacturing environment; 6” wafer fabrication line (Fab) used to manufacture LEDs. •

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The manufacturing line works in both Make to Order (MTO) and Make to Stock (MTS) setups with additional runs for engineering and development sample devices. Depending on the job specifications, a job could require more than 100 different machine visits to complete the processing of 1 wafer. At full capacity, the key reactor starts 3 batches a day and each batch could have up to 20 days of lead time i.e. at any given time there may be more than 60 batches in the production line. However, the future plan is to install 10 reactors i.e. at any given time there will be more than 600 batches in the line. Currently, no daily work plan is produced; prioritised dispatch lists are re-calculated and created dynamically every time an operator needs to decide what batch of work to process next. One of the key challenges therefore, is the dynamic and stochastic nature of the production environment i.e. long term shop-floor schedules cannot be used since random events occur that alter the state of the system affect the validity of the schedule. This means that visibility when a batch is likely to be finished is uncertain, as it is very difficult to define capacity. There are no routing alternatives, each job has a set routing, however between jobs the number of processes may vary. Machine qualification and planned maintenance will always be done; currently there is some degree of negotiation between equipment engineering and production to find the optimum time slot for maintenance. Current production uses the priority based dispatching rules, which are derived from the lot processing state. Jobs are processed based on following priorities; o o o

3.2 Scheduling – Proposed Approach

The proposed approach uses the underlying concept of advanced production and scheduling systems (APS). APS provides advantages over the finite capacity scheduling approaches (FCS). For instance, FCS approach generates a schedule by taking into account the availability of resources only. FCS provides an understanding about how much work can be produced in a certain time period by taking the resource constraints into consideration. On the other hand, APS approach generates the production schedule by considering both capacity of production resources and availability of material. The APS functionality is provided by Preactor International – A Siemens Company’s APS software called 500 APS. The model is developed using 500 APS based on the resource, product, orders, constraints and routing information. Figure 1 describes the schematic for the proposed approach. Figure 1; Proposed Approach

Priority 1 Lots: Customer Priority Priority 2 Lots: Due Date Based (behind and ahead of schedule) Priority 3 Lots: Critical Ratio Based (expected lead time versus customer set due date)

However, if there is rework then the rework lot takes the highest priority. •

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Two machine types are identified as key bottlenecks, GAN Reactors and the Bonders. One of the key determinants is maximizing the utilization of GAN Reactors and the Bonders to achieve required throughput. Key metrics used for schedule evaluation are throughput, lead time and on time delivery.

This research adopts the closed loop controlled response approach. According to controlled response policy a scheduling decision is triggered following a pre-determined decision from the original schedule. For instance, when KPIs go out of the defined limits, then schedule change occurs. From figure 1, the key components of the proposed approach are;

The high number of changes in WIP and equipment states as well as the variability in the process means that re-scheduling may be required every few minutes. Thisraises three immediate questions; • • •

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When to schedule; frequency of scheduling. How to schedule; scheduling technique i.e. predictive, predictive-reactive or robust. Once the above two are decided the most important question is to implement a mechanism for correct rule

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Orders Information; Order information is generated from the manufacturer’s ERP system. Order information contains the data required to generate the production schedule. The key attributes used for scheduling process are; Order No, Part No, Product, Quantity, Earliest Start Date, Due Date, Priority, Operation No, Resource

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Required, Setup Time, Processing Time and Product Attributes. Preactor 500 APS; Preactor 500 APS is installed in a real time environment, where orders information is read in on a periodic basis. The order information is further passed to the Autoplan Controller for the scheduling rule selection decision. Preactor 500 APS simulates the production environment based on the resource, product, orders, constraint and routing information. The current implementation reads orders information every 15 minutes. Performance Measures; Performance measures indicate the current system state and the effectiveness of the selected scheduling rule. Preactor 500 APS updates the performance measures on a continuous basis i.e. on each event;for instance, Order completion data; early, late, completed, started, incomplete and lead time and Resource data, working, waiting, idle, utilization and setup percentage. However, from the case study perspective additional performance measures are derived i.e. number of orders missing due date to determine the due date performance and number of remaining high priority orders. Autoplan Controller; the Autoplan controller implements the rule selection mechanism. The best scheduling rule is selected for an event based mechanism (Operation Finished Event) from the rule library based on the system state, performance measures and user defined decision variables and tolerance limits. Autoplan controller links with the scheduling rule library, which contains the rule specific to the case study. In the current case study, two rules are developed, which are due date performance, to minimize the number of orders missing due date and customer priority rule, to schedule jobs according to the customer priority. The working mechanism of Autoplan controller can be given as;

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(Performance Measures) and event-based (Autoplan Controller) mechanism. How; proposed approach adopts the reactive scheduling mechanism, as information is dispatched in real time and scheduling decision depends on the system state at a given instance. Scheduling Rule Selection; scheduling rule selection uses the event based reactive scheduling approach. For instance, scheduling rule can change at any event i.e. event based mechanism depending on KPI, tolerance limits and decision variable i.e. reactive approach.

The key to the proposed approach however, is to use advanced planning and scheduling (APS) techniques to optimize the schedules by integrating these with heuristic rules and evolutionary algorithms to get the best out of the available capacity.

4. Lean Scheduling – Benefits

Due to rising global competition and customer demands, one of the utmost priorities for organizations is to have flexible operations and lower inventories. Organizations have adopted/are adopting the Lean manufacturing principles in which lead time and cycle time are shortened, quality levels are increased and other waste is decreased [30].Adoption and careful implementation of Lean manufacturing philosophy can undoubtedly form the roadmap of global manufacturing excellence. The benefits of implementing the Lean philosophy can be seen at all levels of organisations [13]. For instance, the key benefits of implementation of Lean philosophy can be summarized as [13 and 14]; • •

Customer Benefits; reduced lead times and improved customer satisfaction. Organisational Benefits; reduced inventories and rework, increased process utilisation and understanding, flexible and robust processes, financial savings and flexible response to customer demand.

Figure 2 – Lean Scheduling and Proposed Approach

At an Operation finish Event1. Import the order information from the Preactor 500 APS. 2. Check the current value for Orders missing due date and number of remaining high priority orders i.e. KPIs. 3. Compare the KPIs Data with the user defined tolerance limits and decision variables. Based on the comparison results a scheduling rule is selected from the scheduling rule library. In addition to Preactor 500 APS’s existing rules, the scheduling rule library has two customized rules i.e. customer priority rule and dynamic critical ratio rule. 4. Based on the selected rule schedule the next feasible operation on the selected resource. Continue with this process until all operations are scheduled. •

Schedule Out; after the schedule is generated it is passed back to the shop floor dispatcher.

Going back to three key questions in problem formation therefore; •

When; system implements the periodic scheduling mechanism from the manufacturer’s perspective i.e. every 15 minutes. However, in order to generate the final schedule, sub functionalities are based on continuous

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The proposed approach rigorously applies the Lean philosophy in the process of schedule generation. Figure 2, exemplifies that how the proposed approach complies with Lean philosophy.

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The key benefits of the proposed scheduling approach therefore are; •

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Producing a schedule without or only minimal manual intervention. This is important from the current problem perspective, as the schedule needs to be generated at every 2 minutes (target) and a job could visit more than 100 machines, which makes manual scheduling infeasible. The proposed approach provides the ability to select different schedule rules based on the system state at a given instance, objectives and defined tolerance limits. It is essential that the correct rule should be selected in the first instance in order to improve the scheduling process efficiency and effectiveness, which adds value from both organizational and customer perspectives. Also, selecting the correct scheduling rule in the first instance will reduce waste in terms of unnecessary rescheduling of operations and data processing. This also implements scheduling rule selection as a pull system rather than a push. The rule selection process, as a closed loop controlled response approach, mimics the continuous improvement philosophy, which allows the system to choose the best scheduling rule based on the system state, KPI and tolerance limit at the given instance. The proposed scheduling approach has the ability to react to variability induced due to new jobs entered or jobs completed, scheduling parameters – changes in the dynamic critical ratio as time window moves forward, machine breakdown or scheduled maintenance, etc. Reduced waste in terms of rescheduling process. New orders are entered into system on periodic basis however, rule change will only be required once an order is finished as this will either be affecting the dynamic critical ratio and priority orders i.e. rule switching mechanism is only needed at the operation finished event. Integration of rule selection mechanism with 500 APS allows the advanced visualization scheduling process in real time.

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The increasing number of constraints and items being scheduled therefore make the scheduling process almost impossible to do manually or using simple spreadsheet based approaches. Also, the generated schedule might not be optimal, which will decrease the customer service level and unacceptable changeover scrap rates. In addition to the scheduling issues, this can increase the overall production cost, as multi-fold increase in the price of copper has made scrap more and more costly in the operation [32]. The problems faced by wire and cable manufacturers are similar to those discussed above for the semiconductor manufacturing case study; and the same methodology could be followed to implement the Lean scheduling. Preactor has used the APS rules to implement the cable manufacturing process for “32” and it has shown reduced finished goods stock (40%), changeover scrap (30%) and stock excess to orders (60%) and scheduling time is reduced from days to minutes. Similarly, for “31” the key benefits of implementing APS system 98% on time delivery adherence and five times reduction in delayed orders is achieved. The next phase therefore, can be implementation of Lean scheduling concept in order to cope with the sudden demand changes and especially when more frequent rescheduling is required.

6. Discussion and conclusion

The Lean philosophy is an idealizing improvement approach that has an enormous impact in the field of operations management. One of the common perceptions among organisations, that their business processes are already efficient, is all too often an illusion. Functionally, many business processes may appear very efficient as the process might be delivering overall service levels and achieving set target limits, however the application of Lean forces them to review these processes and overall supply chain to target the hidden bottlenecks and pockets of inefficiencies. The proposed methodology allows continuous review of the performance measures and schedule change is only triggered if and only if a KPI deviates from set tolerance limits. Otherwise, the same scheduling rule is used. This prevents un-necessary schedule changes and allows scheduling rule change on operation finished event (if required). Also, in the presented case study for semiconductor environment the manual or spread sheet based model cannot be used for scheduling, as in reality the schedule needs to be generated every two minutes. This paper formulates the problem and presents the methodology for the given problems. From the implementation aspect, the

In summary the proposed scheduling rule selection approach and methodology adds value from both a manufacturer and customer perspective, as getting the schedule correct in the first instance is essential to eliminate the waste due to rescheduling and unnecessary data processing required during scheduling process.

5. Cable/Wire Industry Perspective

Growing demand, process complexity and competition have also affected the cable industry. Commonly used techniques such as spreadsheet based models and employee’s intrinsic knowledge are not capable of dealing with increased complexity in the scheduling process. In these cases production planning and scheduling are mostly based on the bottleneck utilization and the rest of the factory is planned according to the demand [31]. This may work well for scenarios where bottleneck definition is easy; however, according to [31 and 32], it is not feasible to use simple models when there is; •

High product variety and the variable demand mix hinder the bottleneck identification, which changes dynamically. For instance, the cables produced could be of different

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wire thickness which is coated with PVC. A cable coating could consist of a single color or a main color and stripe. Mixed model production; some products are produced in a make to stock concept and some are produced in a make to order concept. Mixing these two production models create a trade-off between stock balances and delivery deadline adherence. Customer priorities; some of the urgent customer orders need to be accommodated within current schedule. This may require rescheduling of operations and some of the low priority jobs will be moved to the different time slots in future. Grouping of orders by wire size, color and stripe to minimize the changeover time. Selection of the correct extruder to run on for instance, one extruder can only do single colors, otherwise it is best suited to short runs.

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proposed approach (Figure 1) is integrated with the Preactor’s APS system which provides the basic APS rule library and advanced schedule and KPI visualization. Preactor APS allows adding advanced/problem specific scheduling rules, which may not exist in the rule library. Also, use of different time intervals at different levels can provide following advantages; • Optimal rule selection mechanism; operation finished event is used as a decision making point for the rule selection mechanism, since only the operation finished event affects the dynamic critical ratio and priority for the unscheduled orders. At each event, the best rule can be chosen based on the current system state (KPI) and user set control variables. • Improved scheduling process performance; one of the key advantages of having performance measures and the Autoplan controller as separate modules allows use of different timings mechanisms with each of the modules i.e. improved scheduling process performance as fewer decision points are required in event based than continuous mechanism. Performance is one of the key criteria for the scheduling process as in reality the schedule needs to be generated at interval of 2 minutes. • Decision making tool; Autoplan controller combined with Preactor’s APS package provides an advanced visualization for generated schedule and KPIs. Schedule can be revised and analyzed before publishing.

[3] P. Hines, M. Holweg and N. Rich, “Learning to Evolve: A Review of Contemporary Lean Thinking”, International Journal of Operations & Production Management, 24(10), p. 994 – 1011 (2004). [4] R. Shah and P. T. Ward, “Lean Manufacturing: Context, Practice Bundles, and Performance”, Journal of Operations Management, 21(2), p. 129–149, (2003). [5] F. A. Abdulmalek and J. Rajgopal, “Analyzing the Benefits of Lean Manufacturing and Value Stream Mapping via Simulation: a Process Sector Case Study”, International JournalofProductionEconomics,107 (1), p. 223–236, (2007). [6] A. M. Sanchez and M. P. Perez, “Lean Indicators and Manufacturing Strategies”, International Journal of Operations & Production Management, 21(11), p. 1433 – 1451 (2001). [7] P. S. Kang and L. M. Manyonge, “Exploration of Lean Principles in Higher Educational Institutes – Based on Degree of Implementation and Indigence”, International Journal of Scientific and Engineering Research, 5 (2), pp. 831 – 838 (2014). [8] J. P. Womack and D. T. Jones, “Lean Thinking: Banish Waste and Create Wealth in Your Corporation”, 2ndEdition, Free Press, New York, NY (2003). [9] J. Pettersen, “Defining Lean Production: Some Conceptual and Practical Issues”, The TQM Journal, 21 (2), pp. 127 – 142, (2009).

The proposed methodology is implemented in a real time shop floor environment for a semiconductor manufacturer and the research is in the process of data collection. The next steps for this research are; • Data analysis to investigate if the proposed approach (theoretical) benefits are feasible in a practical implementation. • Comparison of performance of proposed approach against the current system in use for scheduling. Proposed methodology needs to be performing better than the existing system in order to prove the concept and replace the existing system. Also, current system is capable of generating schedule at time interval of 2 minutes whereas, proposed approach currently generates schedule every 15 minutes. Proposed methodology needs to be tested against the current system’s rescheduling period of 2 minutes. Similarly, benefits can be obtained by implementing this methodology for cable/wire manufacturers. The advantages of using APS are already evident from the case studies presented by Preactor [31 and 32]. Cable/wire manufacturing operations further can be improved using the proposed approach especially when periodic rescheduling is required. This needs to be investigated through a relevant cable/wire manufacturing case study.

[10] J. H. Blackstone Jr. “APICS Dictionary – The Standard for Excellence in the Operations Management Profession”, APICS – The Association of Operations Management Dictionary, 12th Ed., p. 1 – 156, (2008). [11] J. K. Liker and T. Lamb, “A Guide to Lean Shipbuilding – Develop and Implement a ‘World Class’ Manufacturing Model for U.S. Commercial and Naval Ship Construction”, Lean Manufacturing Principles Guide – Version 0.5, University of Michigan (2000). [12] B. J. Hicks, “Lean Information Management: Understanding and Eliminating the Waste”, International Journal of Information Management, 27 (1), p. 233 – 249 (2007). [13] T. C. Papadopoulou and M. Ozbayrak, “Leanness: Experiences from the Journey to Date”, Journal of Manufacturing Technology Management¸16 (7), p. 784 – 807 (2005). [14] T. Melton “The Benefits of Lean Manufacturing What Lean Thinking has to Offer to Process Industries”, Transactions of IChemE – Institute of Chemical Engineers – Part A, 83 (A6), p. 662 – 673 (2005).

7. Acknowledgments

[15] M. Poppendieck, “Principles of Lean Thinking”, Technical Report, Poppendieck LLC, Minnesota, p. 1 -5 (2001).

8. References

[16] B. J. Hicks, “Lean Information Management: Understanding and Eliminating Waste”, International Journal of Information Management, 27 (4), p. 233 – 249 (2007).

The authors would like to thank Technology Strategy Board (Grant Reference NO: 5908-45002) for funding this research. [1] T. Melton, “THE BENEFITS OF LEAN MANUFACTURING What Lean Thinking has to Offer the Process Industries”, Journal of Chemical Engineering Research and Design, 83(A6), p. 663 – 673.

[17] T. C. Papadopoulou and A. Mousavi, “Scheduling of NonRepetitive Lean Manufacturing Systems under Uncertainty Using Intelligent Agent Simulation”, The 6th International Conference on Manufacturing Research (ICMR08), Brunel University, UK, p. 207 -215 (Sept. 2008).

[2] LERC, Lean Enterprise Research Centre, Cardiff Business School,www.cf.ac.uk/carbs/lom/lerc (2004).

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[18] M. Pinedo, “Scheduling: Theory, Algorithms and Systems”, Prentice-Hall, New Jersey (2002).

9. Pictures of Authors

[19] D. Petrovic and A. Duenas, “A Fuzzy Logic Based Production Scheduling/Rescheduling in the Presence of Uncertain Disruptions”, Journal of Fuzzy Sets and Systems, 157 (1), p. 2273 – 2285 (2006).

Dr Parminder Singh Kang is working as a Research Fellow at De Montfort University (DMU), Leicester, UK. He received B-Tech degree (Computer Science and Engineering) in 2006 form Punjab Technical University, India. He received M.Sc. degree (IT) in 2008 and completed his PhD (Improving Manufacturing Systems Using Integrated Discrete Event Simulation and Evolutionary Algorithms) in Manufacturing Science in 2012 at DMU, UK. He is member of institution of mechanical engineers (IMechE), Institution of Engineering and Technology (IET) and The American Society of Mechanical Engineers (ASME). He has over 5 years of experience working on externally funded collaborative industrial R&D crossdisciplinary project in the area of organizational operations optimization. His research interests are; evolutionary algorithms, combinatorial optimization, simulation modeling, Lean/Six Sigma and application of these techniques (integrated approaches) in industrial/service process improvement and operations optimization.

[20] I. N. Pujawan, “Scheduling Nervousness in a Manufacturing System: A Case Study”, International Journal of Production Planning and Control, 15 (5), p. 515 – 524 (2004). [21] J. Y-T. Leung, “Handbook of Scheduling – Algorithms, Models and Performance Analysis”, Chapman & Hall/CRC Computer and Information Science Series – CRC Press LLC, Florida (2004). [22] H. Aytug, M. A. Lawley, K. McKay, S. Mohan and R. Uzsoy, “Executing Production Schedules in the Face of Uncertainties: A Review and Some Future Directions”, European Journal of Operational Research, 161 (1), p. 86 – 110 (2005).

Alistair Duffy is Professor of Electromagnetics at De Montfort University (DMU), Leicester, UK. He received his BEng (Hons) and MEng degrees in 1988 and 1989, respectively, from University College, Cardiff, Wales. He read for his PhD with professors Christopoulos and Benson at Nottingham University, graduating in 1993. He also holds an MBA from the Open University, UK, graduating in 2004. He is a Fellow of the Institution of Engineering and Technology (IET, formerly the IEE) and a Senior Member of the IEEE since 2004. He has published approximately 200 papers, mostly on his research interests of validation of computational electromagnetics; physical layer components, particularly communications cabling, and electromagnetic compatibility testing and technology management.

[23] I. Sabuncuogul and S. Goren, “Heading Production Schedules Against Uncertainty in Manufacturing Environments with a Review of Robustness and Stability Research”, International Journal of Computer Integrated Manufacturing, 22 (2), p. 138 – 157 (2009). [24] N. G. Hall and C. N. Potts, “Rescheduling for New Orders”, INFORMS – International Journal of Operations Research, 52 (3), p. 440 – 453 (2004). [25] V. Subramaniam, A. S. Raheja and K. Rama Bhupal Reddy, “Reactive Repair Tool for Job Shop Schedules”, International Journal of Production Research, 43 (1), p. 1 – 23, (2005).

Dr Nigel Shires (Head of Consulting); is one of the founders of Preactor, which he joined as Hawker Siddeley Factory Automation Systems in 1989. Previously he was a Manufacturing Systems Research Scientist with Asea Brown Boveri (ABB) based in Dattwil, Switzerland, having completed his Ph.D. at Loughborough in 1986. Nigel has been part of the long-standing success and development of the Preactor Scheduling System, which has been sold successfully around the world since 1993, and is now owned by Siemens Industry Software.

[26] L. K. Church and R. Uzsoy, “Analysis of Periodic and Event Driven Scheduling Policies in Dynamic Shops”, International Journal of Computer Integrated Manufacturing, 5 (1), p. 152 – 163 (1992). [27] I. Sabuncuogul and M. bayiz, “Analysis of Reactive Scheduling Problems in a Job Shop Environment”, European Journal of Operational Research, 126 (1), p. 567 – 586 (2000).

Dr Trevor Smith started work in the semiconductor industry in 1977, having completed a PhD in Physics from Leeds University. Initially I was involved in the design and development of power thyristors, but moved into process engineering for integrated circuits. I gradually changed focus and moved into manufacturing systems; initially involved in the set up and management of shop floor control systems in the semiconductor industry. Latterly I have been concentrated on software development for in house use, covering areas such as planning, scheduling, HR, engineering data and ERP systems.

[28] G. E. Vieira, J. W. Herrmann and E. Lin, “Analytical Models to Predict the Performance of Single-Machine System Under Periodic and Event-Driven Rescheduling Strategies”, International Journal of Production Research, 38 (8),p. 1899 – 1915 (2000). [29] G. E. Vieira, J. W. Herrmann and E. Lin, “Rescheduling Manufacturing Systems: A Framework of Strategies, Policies and Methods”, Journal of Scheduling, 6 (1), p. 39 – 62 (2003). [30] L. M. Sanchez and R. Nagi, “A Review of Agile Manufacturing Systems”, International Journal of Production Research, 39 (16), p. 3561 – 3600 (2001).

Mike Novels is the “father” of Preactor, having started as the CEO of a small consulting group within Hawker Siddeley called “Factory Automation Systems”, which became “The CIMulation Centre” and then became an international group of companies under “The Preactor Group”. One of the groups, Preactor International Ltd, is the author of the Preactor Scheduling System, which has been successfully sold around the world since 1993. Preactor was acquired by Siemens in 2013, and Mike is continuing to be part of the success of Preactor within Siemens Industry Software.

[31] Preactor International Ltd., “Furukawa Adopts Lean

Manufacturing Concepts and Improves Delivery Performance with Preacto”, Case Study, p. 1 – 3, (2012).

[32] Preactor International Ltd. “Walro Flex Gets Wired

up for Lower Costs Better Customer Service with Preactor”, Case Study, p. 1 – 2, (2008)

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