Analyzing the benefits of lean manufacturing and value stream mapping via simulation: A process sector case study

ARTICLE IN PRESS

Int. J. Production Economics 107 (2007) 223–236 www.elsevier.com/locate/ijpe

Analyzing the benefits of lean manufacturing and value stream mapping via simulation: A process sector case study Fawaz A. Abdulmaleka, Jayant Rajgopalb, a

Industrial and Management Systems Engineering Department, Kuwait University, Kuwait Department of Industrial Engineering, University of Pittsburgh, Pittsburgh, PA 15261, USA

b

Received 1 November 2005; accepted 1 September 2006 Available online 28 November 2006

Abstract The ‘‘lean’’ approach has been applied more frequently in discrete manufacturing than in the continuous/process sector, mainly because of several perceived barriers in the latter environment that have caused managers to be reluctant to make the required commitment. We describe a case where lean principles were adapted for the process sector for application at a large integrated steel mill. Value stream mapping was the main tool used to identify the opportunities for various lean techniques. We also describe a simulation model that was developed to contrast the ‘‘before’’ and ‘‘after’’ scenarios in detail, in order to illustrate to managers potential benefits such as reduced production lead-time and lower work-in-process inventory. r 2006 Elsevier B.V. All rights reserved. Keywords: Lean manufacturing; Value stream mapping; Simulation; Process industries; Steel

1. Introduction Lean manufacturing is one of the initiatives that many major businesses in the United States have been trying to adopt in order to remain competitive in an increasingly global market. The focus of the approach is on cost reduction by eliminating nonvalue added activities. Originating from the Toyota Production System, many of the tools and techniques of lean manufacturing (e.g., just-in-time (JIT), cellular manufacturing, total productive maintenance, single-minute exchange of dies, production smoothing) have been widely used in discrete Corresponding author. Tel.: +1 412 624 9840; fax: +1 412 624 9831. E-mail address: [email protected] (J. Rajgopal).

manufacturing. Applications have spanned many sectors including automotive, electronics, white goods, and consumer products manufacturing. On the other hand, applications of lean manufacturing in the continuous process sector have been far fewer (Abdullah and Rajgopal, 2003). It has sometimes been argued that in part, this is because such industries are inherently more efficient and have a relatively less urgent need for major improvement activities. Managers have also been hesitant to adopt lean manufacturing tools and techniques to the continuous sector because of other characteristics that are typical in this sector. These include large, inflexible machines, long setup times, and the general difficulty in producing in small batches. While some lean manufacturing tools might indeed be difficult to adapt to the continuous sector,

0925-5273/$ - see front matter r 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.ijpe.2006.09.009

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F.A. Abdulmalek, J. Rajgopal / Int. J. Production Economics 107 (2007) 223–236

this does not mean that the approach is completely inapplicable; for example, Ahmad et al. (2005), Melton (2005), Radnor (2000), Cook and Rogowski (1996), and Billesbach (1994). Abdullah et al. (2002) and Abdelmalek et al. (2006) examine aspects of continuous production that are amenable to lean techniques and present a classification scheme to guide lean implementation in this sector. The objective of this paper is to use a case-based approach to demonstrate how lean manufacturing tools when used appropriately, can help the process industry eliminate waste, maintain better inventory control, improve product quality, and obtain better overall financial and operational control. A large integrated steel mill is used to illustrate the approach followed. Since some of the information is confidential, the company is referred to as AB steel (or ABS) throughout this paper. In our approach, value stream mapping (VSM) is first used to map the current operating state for ABS. This map is used to identify sources of waste and to identify lean tools for reducing the waste. A future state map is then developed for the system with lean tools applied to it. Since the implementation of the recommendations is likely to be both expensive and time-consuming, we develop a simulation model for the managers at ABS in order to quantify the benefits gained from using lean tools and techniques. 2. Background We begin by providing a brief overview of the principles used in this work, followed by some background information on the company where the work was conducted. 2.1. Overview of lean manufacturing and its tools After World War II Japanese manufacturers were faced with vast shortages of material, financial, and human resources. These conditions resulted in the birth of the ‘‘lean’’ manufacturing concept (Womack et al., 1990). Kiichiro Toyoda, the president of Toyota Motor Company at the time, recognized that American automakers of that era were out-producing their Japanese counterparts by a factor of about ten. Early Japanese industrial leaders such as Toyoda, Shigeo Shingo, and Taiichi Ohno responded by devising a new, disciplined, process-oriented system, which is known today as the ‘‘Toyota Production System,’’ or ‘‘Lean Man-

ufacturing.’’ The system focused on pinpointing the major sources of waste, and then using tools such as JIT, production smoothing, setup reduction and others to eliminate the waste. A very brief description of the most common lean tools is given below (Monden, 1998; Feld, 2000; Nahmias, 2001); the interested reader is referred to one of the many books on lean manufacturing for more details: Cellular manufacturing: Organizes the entire process for a particular product or similar products into a group (or ‘‘cell’’), including all the necessary machines, equipment and operators. Resources within cells are arranged to easily facilitate all operations. Just-in-time (JIT): A system where a customer initiates demand, and the demand is then transmitted backward from the final assembly all the way to raw material, thus ‘‘pulling’’ all requirements just when they are required. Kanbans: A signaling system for implementing JIT production. Total preventive maintenance (TPM): Workers carry out regular equipment maintenance to detect any anomalies. The focus is changed from fixing breakdowns to preventing them. Since operators are the closest to the machines, they are included in maintenance and monitoring activities in order to prevent and provide warning of malfunctions. Setup time reduction: Continuously try to reduce the setup time on a machine. Total quality management (TQM): A system of continuous improvement employing participative management that is centered on the needs of customers. Key components are employee involvement and training, problem-solving teams, statistical methods, long-term goals, and recognition that inefficiencies are produced by the system, not people. 5S: Focuses on effective work place organization and standardized work procedures.

2.2. Overview of VSM A value stream is a collection of all actions (valueadded as well as non-value-added) that are required to bring a product (or a group of products that use the same resources) through the main flows, starting

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with raw material and ending with the customer (Rother and Shook, 1999). These actions consider the flow of both information and materials within the overall supply chain. The ultimate goal of VSM is to identify all types of waste in the value stream and to take steps to try and eliminate these (Rother and Shook, 1999). While researchers have developed a number of tools to optimize individual operations within a supply chain, most of these tools fall short in linking and visualizing the nature of the material and information flow throughout the company’s entire supply chain. Taking the value stream viewpoint means working on the big picture and not individual processes. VSM creates a common basis for the production process, thus facilitating more thoughtful decisions to improve the value stream (McDonald et al., 2002). VSM is a pencil and paper tool, which is created using a predefined set of standardized icons (the reader is referred to Rother and Shook, 1999 for details). The first step is to choose a particular product or product family as the target for improvement. The next step is to draw a current state map that is essentially a snapshot capturing how things are currently being done. This is accomplished while walking along the actual process, and provides one with a basis for analyzing the system and identifying its weaknesses. The third step in VSM is to create the future state map, which is a picture of how the system should look after the inefficiencies in it have been removed. Creating a future state map is done by answering a set of questions on issues related to efficiency, and on technical implementation related to the use of lean tools. This map then becomes the basis for making the necessary changes to the system.

these. As a result, management decisions on implementing lean manufacturing often come down to their ‘‘belief’’ in lean manufacturing, reported results of others who have implemented lean techniques, and heuristic rules of thumb on the expected payback. For many managers this is insufficient justification, and lacks the quantifiable evidence needed to convince them to adopt lean (Detty and Yingling, 2000). This raises the question of how we can make lean and VSM more viable. While in some situations the future state map can be evaluated with relatively modest effort, it is not as easy to do so in many others. For example, predicting inventory levels throughout the production process is usually impossible with only a future state map, because with a static model one cannot observe how inventory levels will vary for different scenarios (McDonald et al., 2002). In general, we need a complementary tool with VSM that can quantify the gains during the early planning and assessment stages. An obvious tool is simulation, which is capable of generating resource requirements and performance statistics whilst remaining flexible to specific organizational details. It can be used to handle uncertainty and create dynamic views of inventory levels, lead-times, and machine utilization for different future state maps. This enables the quantification of payback derived from using the principles of lean manufacturing, and the impact of the latter on the total system. The information provided by the simulation can enable management to compare the expected performance of the lean system relative to that of the existing system it is designed to replace (Detty and Yingling, 2000), and assuming that this is significantly superior, it provides a convincing basis for the adoption of lean.

2.3. Simulation in support of VSM

2.4. Company and process background

For companies that have long relied on traditional approaches to their manufacturing systems, it is often difficult to gain from management the commitment required to implement lean manufacturing. Doing so is hard because of differences in a number of aspects including raw material procurement, inventory management, employee management, and production control. For traditional manufacturers, the reluctance to implement many lean ideas arises because their distinctive requirements often make it hard to predict the magnitude of the gains that can be achieved by implementing

ABS produces several grades of steel that are used primarily in appliance manufacturing. The focus of this VSM is on one product family: annealed products, of which there are three types produced; open coil annealed, hydrogen batch annealed, and continuous annealed. Average customer demand was estimated as 76,500 tons per month, and the distribution by product is as follows: 8500 tons per month of open coil (OCA), 10,000 tons per month of continuous (CA), 58,000 tons per month of hydrogen batch (HBA).



The processes for this product family start with a blast furnace where on a daily basis raw material including skips of iron ore, coke, and limestone are charged at the top of the furnace. The melted raw material is then poured into sub-ladles (essentially, large bins for holding liquid iron) from the tap hole at the bottom of the furnace. The liquid iron travels in the sub-ladle to the basic oxygen process (BOP) where scrap is added and oxygen is blown in to burn off excess carbon and obtain the initial form of liquid steel. Depending on the grade of the final steel to be produced this initial liquid steel can go either to a ladle metallurgical facility (LMF) or a Degasser to further refine and remove impurities from the liquid steel. The refined liquid steel then goes to a dual-strand continuous caster where steel slabs are cast in accordance with specific customer widths. The hot slabs are then shipped on railroad and rack cars from the continuous caster process to the finishing mill facility for further refining processes, which include the hot strip mill (HSM), pickling, cold reduction (CR), annealing (OCA, HBA or CA), temper mill (TM), and finally, shipping. At ABS the business planning department receives demands from two types of customers: repeat and spot business (open market). The repeat demand is received on a weekly basis, where major ABS customers call or send through EDI their requirements for the weeks ahead. Since these are committed customers the quantity and the order delivery time are more or less fixed. On the other hand, spot customers generate daily schedules. There are currently two separate scheduling groups: one is for the hot end liquid steel, which usually includes the blast furnace and caster, and the second is for the finishing mill, which handles the product from the HSM through shipping. When an order arrives, business planning enters it into the planning system, estimates the date by which they think they can complete it, and rough-schedule orders on the production units on a weekly basis. Next, they affix a routing on the order and assign a ‘‘plan week’’ to it. This schedule on the operating side becomes the basis to monitor day-by-day and week-by-week increments against how closely they are in accordance with the schedule. The schedules can then be updated further on an as-needed basis to daily or even bi-daily schedules. ABS uses three types of transportation modes: truck, rail, and barge. The shipments go to different customers on a daily or weekly basis. The plant works on a continuous basis for 24 h a day all year long except for major

shutdowns and runs a three-shift operation in all production departments except for continuous annealing, which runs two shifts. Each shift is 8 h long. 3. VSM: current state map All data for the current state map were collected according to the approach recommended by Rother and Shook (1999). Data collection for the material flow started at the shipping department, and worked backward all the way to the blast furnace process, gathering snapshot data such as inventory levels before each process, process cycle times (CTs), number of workers, and changeover (CO) times. Fig. 1 shows the current state map that was constructed; the small boxes in the map represent the process and the number inside the box is the number of workers at each process. Also, each process has a data box below, which contains the process CT, machine reliability (MR), the number of shifts, and the CO time. It should be noted that this data was collected whilst walking the shop floor and talking to the foreman and operators at each workstation. The processing and set-up times are all based on the average of historical data. Note that there are two inventory triangles ahead of some processes, one for annealed products and one for all other products. This simply indicates that other products could be scheduled to use the process in addition to the annealed products considered herein, so that the total inventory is actually higher than what is shown. After collecting all the information and material flows, they are connected as indicated by arrows in the map, representing how each workstation receives its schedule from business planning. The timeline at the bottom of the current state map in Fig. 1 has two components. The first component is the production waiting time (in days), which is obtained by summing the lead-time numbers from each inventory triangle before each process. The time for one inventory triangle is calculated by dividing the inventory quantity into the daily customer requirements. For example, the lead-time for the inventory triangle ahead of pickling is 17.65 days; this is calculated by dividing 45,000 tons (the total inventory ahead of the pickling) by 2550 (the daily average demand rate for the annealed product). The total observed value for the waiting time is around 46 days. Other than about three days that are required for the coils to


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Fig. 1. Current state map.

cool down after processing at the HSM, the rest of this time is non-value added time. Note that we do not consider the amount of raw material at the beginning of the production, since ABS owns the mines and raw material sources, so that and raw material is not an issue for them. The second element of the timeline is the processing (or value-added) time, which is about two days. This time is calculated by adding the processing time for each process in the value stream. The CT for each process is the average CT, which was determined by using actual data from the company. Thus the total lead time is around 48 days. If we are conservative and include the approximately three days required for the coils to cool down after processing at the HSM we get a total of about five days (429,030 s) of value-added time; this works out to slightly over 10% of the total production lead time. 4. VSM: future state map The process of defining and describing the future state map starts while developing the current state

map, where target areas for improvement start to show up. Looking at the current state map for ABS several things stand out: (a) large inventories, (b) the difference between the total production lead-time (around 51 days) and the value added time (5 days), which is under 10% of the total, and (c) each process producing to its own schedule. Inventory and lead time may be viewed as two related issues since the more the inventory, the longer any item must wait for its turn and thus, the longer the lead time. In creating the ideal future state map we try to identify lean manufacturing tools to drive both of these down, while looking at the schedule across the entire value stream. We follow a systematic procedure where we try to answer a series of structured questions; this allows us to come up with an ideal future state map that will help in eliminating or at least reducing different types of waste in the current manufacturing system. Question 1. What is the takt time? ‘‘Takt time’’ refers to the rate at which customers are buying products from the production line; i.e., the unit production rate that is needed to match



customer requirements. It is calculated by dividing the total available time per day by the daily customer demand. The throughput required for the annealed products is an average of 76,500 tons per month. Assuming 30 days per month, the average daily requirement is thus 2550 tons per day. With an average coil weight of 20 tons, this translates into approximately 127 coils per day. ABS continuously runs three shifts per day, which translates to 1440 working minutes per day, so that the takt time is thus approximately ð1440=127Þ ¼ 11:3 min per coil. Question 2. Will production be directly to shipping or to a finished goods supermarket? A ‘‘supermarket’’ is nothing more than a buffer or storage area located at the end of the production process for products that are ready to be shipped (Rother and Shook, 1999). On the other hand, producing directly to shipping means that only the units that are ready to be shipped are produced. Currently ABS produces all the annealed products and sends them to a holding area where they are stored with other products waiting to be shipped. However, this is done based on a push system, and coils of steel can wait a long time in this area before being shipped. Our recommendation was that ABS produce to a supermarket (warehouse) and move the coils based on a kanban system. Whenever the supermarket inventory is below a certain level this would trigger the TM (the last production stage) to schedule the annealed products to replenish the supermarket according to the pitch, which is addressed in more detail under Question 7. Question 3. Where will ABS need to use pull system supermarkets inside the value stream? The hot end at ABS is a continuous flow process by design, so that a supermarket at this end does not make sense. The introduction of supermarkets is necessary only at the finishing end where large amounts of inventory exist between different workstations. In addition to the shipping supermarket recommended in Question 2, six additional supermarkets are needed to create a continuous flow at the finishing mill (cold end): one before the pickling line, one before the CR process, one before each of the three annealing processes (HBA, OCA, CA), and one before the TM (the reader is referred to the flow displayed in Fig. 1). Once a shipment of coils is withdrawn from the shipping supermarket, the corresponding kanban is sent to the TM where it

is placed in a load-leveling (heijunka) box. This in turn, triggers the production and movement of material from the earlier stages as described below. The first supermarket recommended is ahead of the pickling area after the HSM. The latter currently pushes coils to pickling, which causes inventory to accumulate in two lines in front of the latter. Both of these lines are shared resources (i.e., other products can use them), and a kanban pull system was recommended to regulate the replenishment of this supermarket. A pull signal from the shipping area is eventually transmitted to the HSM to replenish the supermarket in front of the pickling area, whenever the number of coils in the latter drops to a trigger point. The second supermarket is recommended to stabilize the CR process for the annealed products. The inventory after pickling and before CR is large and both workstations are shared resources. Also, ABS runs its schedule in batches according to coil width, gauge, and product, so that it is necessary to set up a supermarket to accommodate schedule changes. Again, a kanban pull system can be used to regulate the replenishment of this supermarket. Note that whenever this supermarket is full, the pickling process could run other products (nonannealed products) so that it is not idle. Also, pickling will no longer receive a schedule from business planning for the annealed products. The third, fourth, and fifth supermarkets are recommended after CR and ahead of each of the three annealing workstations. Thus the supermarket in front of the HBA process will be used for coils that are ready to be placed in the HBA furnaces. The same thing will apply for the supermarkets ahead of CA and OCA. Once again, the CR mill that supplies annealing will no longer need to receive a schedule for the annealed products from business planning and can run other products types when those supermarkets are at their capacities. The last recommended supermarket is ahead of the TM. Since 96% of the products that go to the TM come from annealing, this supermarket area can for all intents, be completely dedicated to the annealed products. The kanbans at the supermarkets follow the standard rules of a pull system. For example, the pickle line (supplier) is allowed to process the next coil in line as long as there is an empty coil spot in the supermarket for the coil before CR (customer). By definition, if the supermarket is at its capacity then this means that CR does not need another coil. In this case there are two things that can be done;


either the pickle line can slow its production rate to match that of CR or it should be halted. The first option is costly in a steel mill, while the second is almost always infeasible. Therefore if the supermarket at the pickle line is full our recommendation is that it be switched to satisfy other product types until the time of the next order for the annealed product is reached. In doing so we prevent producing more than the capacity of the supermarket and also satisfy requirements for other product types, while avoiding shutting down the pickle line. In the following questions we will address how a production order will be released and the time increment at which those orders will be released. Question 4. Where can continuous flow be used? Manufacturing assets in the steel industry are such that they cannot easily be moved into the classical cellular arrangement, and batch sizes are typically fixed. However, the steel industry does have a significant amount of continuous flow manufacturing at the hot end. For example, starting from the blast furnace through the BOP, the degasser/LMF, and finally the continuous caster, the flow is continuous since the liquid steel moves in a ladle in a batch size of one. At the finishing mill however, the slab can move through one of many possible routings using expensive general purpose reduction equipment, which precludes cellular flow. Different CT and down times of the workstations also make it difficult to introduce continuous flow, and many of the workstations are restricted to different schedules depending on width, gauge and product type, so that it is unrealistic to join these workstations at the finishing mill to obtain a continuous flow. Therefore, the focus at the cold end should be on developing a system to enable pull by the customer, rather than continuous flow. In most steel mills, the hot end (liquid steel) and the finishing mill (solid steel) are located in the same area; however, at ABS the two are actually about nine miles apart, purely based on historical circumstances. This actually enables a natural decoupling of the two phases and allows a pull system to be incorporated. The introduction of supermarkets that are controlled by a kanban system forces the entire cold end to pace every workstation to the speed of the bottleneck, which as the current state map indicates is between the pickling line and the CR mill. Thus the mill begins to take on the characteristics of an

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assembly line where every product starts to flow rather than stop and start. Question 5. What single point in the production chain (the ‘‘pacemaker’’ process) should ABS schedule? To stop overproduction at any workstation in the value stream, only one point in the supplier-tocustomer value stream needs to be scheduled. This point is called the pacemaker process, because this point sets the pace of production for all the upstream processes and ties the downstream and upstream processes together. Every workstation upstream produces by a pull signal from the next downstream process and flows downstream from the pacemaker must occur in a continuous manner. The pacemaker is typically the continuous flow process that is farthest downstream in the value stream, so there should be no supermarket (other than finished goods) downstream of it (Rother and Shook, 1999). For ABS, since the hot end is located in a different facility than the finishing mill, the scheduling of a single process is unrealistic. For this reason one schedule will be released to the continuous caster to set the base for the hot end production area and the pacemaker process for the finishing mill is the TM; this is the final process and sets the base for the entire production at the finishing mill. Question 6. How should ABS level the production at the pacemaker process? The basis for addressing this question is to distribute the production of the three annealing processes uniformly over the production time at the pacemaker process. This means that several batches of the same sequence must be scheduled. This will allow ABS to avoid long lead-times, large amounts of in-process and finished goods inventory, quality problems, and in general, help them avoid wastes related to overproduction. We consolidate the scheduling width and gauge for the coils so that we deal with only three different products; adjustment for width and gauge within a particular type can be made as required. The key idea is for ABS to send a schedule to the pacemaker process (TM) that ensures that each product is produced at a constant rate. We use a simple formula (Monden, 1998) to determine the product sequence that levels the mix and has a constant rate for the three different


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products: d ij ¼ ðj 0:5Þ ðT=Di Þ i ¼ 1; 2; . . . ; n and j ¼ 1; 2; . . . ; Di , where n is the number of different products to be made, Di the number of units demanded per day for product i. T ¼ D1 þ D2 þ þ Dn is the total number of units of all products to be made each day, j the index for the job (unit) of product i, d ij the ideal position index for job (unit) j of product i in the overall sequence. For our case n ¼ 3, while the Di values are: 98, 14 and 15 for HBA, OCA, and CA, respectively. Thus T is equal to 127. Ordering these jobs according to d ij sorted (shown in Table 1) one can see a pattern start to develop, yielding the following approximate sequence for smooth production (HBA-HBA-HBACA-HBA-OCA-HBA-HBA-HBA), (HBA-HBA-HBACA-HBA-OCA-HBA-HBA-HBA),yetc. We could just simplify the sequence to (HBA-HBA-HBAHBA-HBA-HBA-HBA-CA-OCA). Question 7. What increment of work (the ‘‘pitch’’) will be consistently released to the pacemaker process? Depending on the sequence determined by the last question, how often should we release and withdraw (the ‘‘pitch’’) the increment of production Table 1 Position index calculation for annealed products Product ðiÞ

Unit ðjÞ

d ij

d ij (sorted)

Product-unit

HBA

1 2 3 4 5 6 7 8 9 10 11 12 13 14

0.648 1.944 3.240 4.536 5.832 7.128 8.423 9.719 11.015 12.311 13.607 14.903 16.199 17.495

0.648 1.944 3.240 4.233 4.536 4.536 5.832 7.128 8.423 9.719 11.015 12.311 12.700 13.607 13.607

HBA – 1 HBA – 2 HBA – 3 CA – 1 HBA – 4 OCA – 1 HBA – 5 HBA – 6 HBA – 7 HBA – 8 HBA – 9 HBA – 10 CA – 2 HBA – 11 OCA – 2

OCA

1 2

4.536 13.607

14.903 16.199 17.495

HBA – 12 HBA – 13 HBA – 14

CA

1 2

4.233 12.700

from the pacemaker process? The pitch is the basic time unit of the production schedule for a product family. In other words, it is the material transfer interval at the pacemaker process. The pitch is calculated by multiplying the takt time by the finished-goods transfer quantity at the pacemaker process. Since there is no container size involved in the steel industry (we move one coil at a time), the number of kanbans will be the same as the current daily demand for OCA and CA. However one kanban will correspond to seven coils for HBA. Table 2 shows the number of kanbans required. Given a takt time of 11.3 min, and considering that the transfer lot size is nine coils, the pitch is approximately 1:40 h. Thus ABS will perform paced release of work instructions and a paced withdrawal of finished goods at the TM according to this pitch. This means that the material handler will arrive at the TM, remove the required kanbans from the heijunka (or load leveling) box of the TM corresponding to the next increment of work, and move the coils just finished from the previous pitch to the shipping area supermarket. The number of pitches required for every product is calculated as the daily requirement for every product divided by the transfer quantity, while the time interval required for every product to remove each kanban from the heijunka box is calculated by dividing the available daily time by the number of pitches for every product (Table 3). The heijunka box is thus divided into 14 columns, each equivalent to about 1:40 h that represent the frequency of introducing the kanban (work increment) to the TM. The column for each pitch interval will have three rows of kanban slots—one for each of the annealed products. Question 8. What process improvement will be needed to achieve the future state design? In order to accomplish the material and information flow envisioned by ABS, improvement and actions must take place to implement the future Table 2 Number of kanbans required by product Product

Daily demand Transfer lot (coils) size (coils)

Required number of kanbans

HBA OCA CA

98 14 15

14 14 15

7 1 1

ARTICLE IN PRESS F.A. Abdulmalek, J. Rajgopal / Int. J. Production Economics 107 (2007) 223–236 Table 3 Number of pitches and material transfer times Product

Pitches per day

Material transfer time

HBA OCA CA

98=7 ¼ 14 14=1 ¼ 14 15=1 ¼ 15

1440=14 ¼ 102 min 1440=14 ¼ 102 min 1440=15 ¼ 96 min

state. It is unrealistic to expect to obtain the benefits of the supermarkets, kanban control, takt time, the pitch, production leveling, continuous improvement, and other changes discussed in the previous questions without process improvement steps involving specific lean tools; these are described in the next section. 5. Tools for process improvement Since our goal was to identify the potential dynamic gains from implementing lean and to develop a desirable future state map, we focused on three lean manufacturing techniques that can be quantified and modeled objectively: a modified pulltype production system, setup reduction and total productive maintenance (TPM). To analyze and evaluate different scenarios for the future state map, a full factorial experimental design was planned for the simulation, with the three factors being the techniques just mentioned. Two levels were selected for each factor, thus resulting in 23 distinct combinations for each replicate. These factors are now discussed further. 5.1. Production system A push system and a hybrid push–pull system are the two levels for comparison that are used for the production system factor. The push system represents the current situation at ABS where coils are pushed through the system. The hybrid system however, is designed with the future state map in mind. In this system work will continue to be pushed through the hot end. However, the cold (finishing) end employs a pull system, starting with the HSM at the beginning of this subsystem. From the buffer area between the hot mill and the pickling line and all the way to the shipping area, the system will be based on a kanban pull system where the annealed products will be pulled from upstream workstations. The junction between the hot mill and the pickling line thus forms the push–pull boundary.

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In the simulation model, the portion of the system up to the HSM is the same as the current one at ABS. Starting with the pickling line the system was modeled as a pull system using kanbans to control the inventory between the workstations. This is done by modeling each kanban between a pair of workstations as a resource. An arriving entity seizes one kanban and one workstation at the same time. As soon as the workstation finishes processing the entity, the workstation is released; however, the kanban is retained. The entity then proceeds to the next workstation. At this point the entity seizes the workstation and a new kanban from the kanban set for this latter workstation, while simultaneously releasing the kanban from the previous workstation. Thus a kanban from one workstation is held until the entity receives a kanban from the subsequent workstation. This ensures that the former does not begin work until it gets a pull signal from the latter. In other words, the part retains the kanban from the former workstation until it receives the next kanban authorization movement to the following workstation (Marek et al., 2001). At the pull side of the hybrid system the total WIP is limited to the sum of the number of kanban cards across each kanban set, where the latter is represented by a supermarket as defined in Section 4. Since each coil in the supermarket has a kanban card attached to it, the average system WIP level may be found by calculating the sum of the average utilizations of the kanban resources in the simulation. The comparison of WIP inventory for the push and the hybrid system is based only on the inventory ahead of the pickle line and downstream to the TM. The reason for this is that the difference between the two systems in terms of WIP inventory will be after the push–pull boundary point; all earlier inventory levels are identical since the systems being compared are identical up to this point. 5.2. Total productive maintenance The two levels for the TPM factor are labeled ‘‘without’’ and ‘‘with.’’ The former identifies current maintenance procedures followed by ABS, while the latter models a proposed TPM procedure that splits the same scheduled maintenance time into smaller increments, i.e., it separates the maintenance process into smaller portions that are performed more frequently. Maintenance is planned in such a way that it cascades through the process so that the


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inventory shortages created by work stoppage for maintenance result in minimal disruption of flow. As an example, when the pickle line is maintained then the kanbans in its supermarket (the area ahead of cold rolling) would empty; therefore, the next maintenance operation is scheduled on the cold mill. This permits the pickle line to restock its supermarket, and so on. TPM can significantly reduce random machine breakdowns and in turn, inventory and lead-time. It is usually defined in terms of an increase in overall equipment effectiveness (OEE), which in turn is a function of down time and other production losses (Nakajima, 1989). Suehiro (1992) states that machine breakdowns and minor stoppages account for 20–30% of loss in OEE; Ljungberg (1998) also reports a 20% figure for the same. Volvo Gent reports that the OEE in the company increased from 66% to 69% before implementing TPM to 90% after TPM where most of the increase is a result of the elimination of machine breakdowns and minor stoppages (Ljungberg, 1998). Similarly, Avon Cosmetics report significant increase of OEE after TPM was implemented at its pump spray line (Ljungberg, 1998). Based on this experience we assume a 20% increase in OEE at ABS. Table 4 shows proposed TPM times at the finishing mill. The maintenance times were chosen based on optimistic but reasonable estimates after conversations with floor personnel. One issue that had to be taken into consideration with the proposed TPM program is that the time for each of the different maintenance tasks for a given process should not exceed the total proposed (reduced) maintenance downtime. This issue was discussed with ABS and it was confirmed that the proposed downtime should be feasible.

5.3. Setup time reduction The two levels for the setup reduction factor are also labeled ‘‘without’’ and ‘‘with.’’ The ‘‘without’’ level models the current situation at ABS with setup times the same as they are now, while ‘‘with’’ denotes reduced setup times. Again, the changeover reduction times were selected based on optimistic but reasonable estimates, with values that are realistic for ABS to drive their CO time down. These were based on extensive discussions with floor personnel including operators and engineers and are summarized in Table 5. For example, we analyze a setup time reduction for the HSM from 35 to 10 min for the backup rolls and 120–20 min for the work rolls. For CR the reductions analyzed were from 15 to 5 min for the backup rolls and 120–20 min for the work rolls. Other setup times reductions are as found in Table 5.

6. The simulation model To evaluate potential gains based on the implementation of the tools described in Section 5 and based on the questions analyzed in Section 4, a detailed simulation model was developed using System Modeling Corporation’s Arena 5 software. We began with a model for the current system, which was later modified to model the proposed future state. Before evaluating the future state considerable effort was expended to verify and validate the model for the current system. Verification is the process that ensures that the simulation model mimics the real system (Law and Kelton, 1991). Since this model is large with many types of entities (grades and products) in the system, verification required that every kind of product be traced and checked to ensure that it follows its required sequence. In order to see if the model Table 5 Proposed setup reduction times at ABS

Table 4 Proposed TPM times at finishing mill

Process Process

HSM 8400 Pickle 6400 Pickle CRM TM

Maintenance

Setup Times (min)

Day

Uptime (days)

Downtime (min)

7 7 7 7 7

240 240 240 240 240

Monday Tuesday Wednesday Thursday Friday

Hot strip mill Pickling Cold reduction Temper mill

Work Rolls

Backup Rolls

Current

Proposed

Current

Proposed

120 15 120 90

20 5 20 20

35 – 15 7

10 – 5 5


represents the real system adequately, the first step was to check the code and verify the model logic and the experimental conditions; followed by a careful trace study where various entities were traced from the point of creation until the point of disposal from the system. Finally, a detailed animation was used to further verify that the model sufficiently replicated the real system. Validation of the model calls for comparing outputs of the simulation to those from the actual system. Measures that we included were inventory at the finishing mill and the total time in the system, for both of which actual data was available. The simulation model was run for a one-year period, which is equivalent to an expected 11,520 heats (furnace batches) out of the BOP, so that the model can be validated when it is in steady state. Table 6 shows the actual values and the simulation results that were obtained by running the model. It should be noted that the figures represent average values. From the table it is clear that the numerical outputs from the simulation are all within the range of the actual data. Our simulation is non-terminating (Law and Kelton, 1991) but initial conditions do influence the initial dynamics of the system. Starting with an empty system at time zero, a transient (warm up) period was used for the system to load itself with entities and subsequently reach steady state. The warm up period for our simulation model was established by carrying out five replications with each having a run length of 1 year (11,520 heats from the BOP). The five replications examined successive observations of various performance measures. For example, Fig. 2 shows the plot for the total work in process inventory in the system as a function of time. Based on this type of analysis, a warm up period of 60,000 minutes (42 days) was adopted. Table 6 Performance measures: actual vs. simulation Performance measure

Actual range

Simulation

Entity lead-time Hot strip mill inventory Cold mill inventory HBA inventory CA inventory OCA inventory Temper mill inventory Number of coils per month

[30–49 days] [1000–5000] [250–2000] [250–1750] [100–750] [100–750] [150–750] [9000–9800]

34 days 3703 slabs 1755 coils 620 coils 121 coils 636 coils 653 coils 9466 coils

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7. Simulation results and assessment Once the simulation model for the current system was verified and validated it was used to evaluate the future state map and assess the relative impact of adopting the lean approach detailed in the previous two sections. It is worth mentioning that there are other lean techniques like 5S and visual systems, the benefits from which are not directly quantifiable and cannot be modeled as part of a simulation model. These have therefore not been included in our analysis, but these techniques could easily be applied at numerous places within the production system at ABS, and can be expected to further increase the potential gains from the adoption of lean. Based upon our initial observations from the current state map and discussions with managers at ABS it was decided that two primary performance measures would be examined: production lead-time and work-in-process inventory. The latter is evaluated as the sum of the WIP starting at the pickling line and ending at the TM; only this portion of the WIP is considered because the systems are identical up to the push-pull boundary point at the pickling line. As mentioned earlier for the hybrid production system the WIP inventory is just the sum of the average utilizations of the kanban resources. Two sets of factorial design experiments were ran to study the effect of the three factors on the production lead-time and WIP inventory, respectively. Each factor had two levels, and for each levelfactor combination the experiment is replicated five times using the simulation model and is completely randomized. Thus, eight simulation runs were carried out, each with five different replications. Analysis of variance (ANOVA) was used to formally study the results and determine the significance and magnitude of all effects and interactions. The statistical analysis was done using Minitab with outputs displayed in Table 7. The p-values indicate that for lead-time the production system and TPM are significant, while setup reduction, and all two- and three-way interactions are not. For the WIP inventory, once again the main effects of the production system and TPM are significant. Interestingly, the table shows that the two-way interaction between the production system and TPM is also significant here. To better understand this interaction, Fig. 3 presents a plot of the production system-TPM interaction, the significance of which is indicated by the lack of


234 10 10 9 9 8 8 7

Avg WIP (X103)

7 6 Transient Period

6 5 5 4 4 3 3 2 2 1 1 0 0

10

20

30

40

50

60

70

80

90

100

110

Simulation Time

120

130

140

150

160

170

180

190

200

(X103)

Fig. 2. Transient period analysis for the average WIP inventory for five replications.

Interaction Plot (data means) for Inventory Table 7 p-Values for effects

Lead-Time Constant Prod Sys TPM Setup Red Prod Sys TPM Prod Sys Setup Red TPM Setup Red Prod Sys TPM Setup

80

p-value

0.000 0.000 0.000 0.815 0.000 0.632 0.783 0.815

70

Inventory 0.000 0.000 0.000 0.815 0.000 0.632 0.783 0.815

Mean

Term

90

Prod System

60

Hybrid

50

Push

40 30 20 10

With TPM

Without TPM

Fig. 3. Main effect and interaction plot for inventory.

parallelism of the lines. In the figure, the lower solid line represents the hybrid production system and the upper dashed line represents the push system. The interpretation of the interaction graph is that going from no TPM to TPM when the production system is a hybrid will not change the level of WIP

inventory, whereas going from no TPM to TPM when the production system is a push system will decrease the level of WIP inventory. An intuitive explanation for this is that the WIP inventory in the pull system is dependent on the number of kanban cards that is predetermined before the run. This


makes the change in WIP inventory for the hybrid production system (WIP inventory is the sum of the average utilization of the kanbans, which are modeled as resources) insignificant when using TPM. Even though TPM was found to be significant, the kanban pull system is so instrumental in reducing the WIP inventory that the effect of TPM is relatively small. Based upon our the simulation experiment the actual magnitudes of the improvements in the two selected performance measures were significant. The results indicate that using a hybrid production system and TPM could potentially reduce the total production lead-time from its current value of 48 days to under 15 days, a reduction of almost 70%. Setup reductions in the amounts listed in Table 5 does not seem to have any significant additional effect on lead-time. In terms of the WIP inventory, the experiment revealed that the new system could potentially drive down the current average inventory level across all stations between the pickle line and the TM from the current value of about 96 coils to around 10 coils; a reduction of almost 90%. In summary, the results indicate that a hybrid production system with TPM can have enormous

235

effects on reducing both lead-times and WIP inventory; for this particular instance setup reduction did not have a similar effect. However, this does not necessarily mean that it is not a valuable lean tool for ABS. Rather, the effect of the hybrid system and TPM outweigh the advantages of setup reduction in this particular case. 8. The future state map revisited The future state map for the annealed product for ABS is shown in Fig. 4. The results of the foregoing analysis are documented on the future state map, and the proposed lean tools are shown as kaizen bursts to highlight the improvement areas. Also shown are the supermarkets between each process after the HSM. As we can see in the map, ABS receives two schedules only; one at the continuous caster for the push system at the hot end and the other one at the TM for the pull system at the finishing end. With the new improvements at ABS the value added time (5 days) is up from approximately one-tenth of the production lead-time in the old system, to approximately one-third of the total production lead-time of slightly under 15 days

Fig. 4. Future state map.



Table 8 Assessment of lean tools in the steel industry Lean tool

Applicability

Cellular manufacturing Setup reduction 5S Value stream mapping Just-in-time Production leveling Total productive maintenance Visual systems

Probably inapplicable Partially applicable Universally applicable Universally applicable Partially applicable Partially applicable Partially applicable Universally applicable

(12.84 in waiting plus about 2 in processing). Put another way, non-value added time is about 8.6 times the value-added time according to the current state map, but with the future state map the value of this multiplier drops to about 2. 9. Summary Applications of lean manufacturing have been less common in the process sector, in part because of a perception that this sector is less amenable to many lean techniques, and in part because of the lack of documented applications; this has caused managers to be reluctant to commit to the improvement program. This paper takes a casebased approach to address both issues. Many industries in the process sector actually have a combination of continuous and discrete elements, and it is in fact quite feasible to judiciously adapt lean techniques. We demonstrate this with the steel industry where several lean techniques can be suitably adapted. Table 8 summarizes this contention. Furthermore, for managers who might be considering implementing lean manufacturing but are uncertain about the potential outcomes, we demonstrate that a detailed simulation model can be used to evaluate basic performance measures and analyze system configurations. The availability of the information provided by the simulation can facilitate and validate the decision to implement lean manufacturing and can also motivate the organization during the actual implementation in order to obtain the desired results. References Abdelmalek, F., Rajgopal, J., Needy, K.L., 2006. A classification model for the process industry to guide the implementation of lean. Engineering Management Journal 18 (1), 15–25.

Abdullah, F., Rajgopal, J., 2003. Lean manufacturing in the process industry. Proceedings of the IIE Research Conference, CD-ROM, Portland, OR, IIE, Norcross, GA. Abdullah, F., Rajgopal, J., Needy, K.L., 2002. A taxonomy of the process industry with a view to lean manufacturing. Proceedings of the American Society for Engineering Management, Tampa, FL, pp. 314–321. Ahmad, M., Dhafr, N., Benson, R., Burgess, B., 2005. Model for establishing theoretical targets at the shop floor level in specialty chemicals manufacturing organizations. Robotics and Computer-Integrated Manufacturing 21 (4–5), 291–400. Billesbach, J.T., 1994. Applying lean production principles to a process facility. Production and Inventory Management Journal, Third Quarter 40–44. Cook, R.C., Rogowski, R.A., 1996. ‘‘Applying JIT principles to continuous process manufacturing supply chains. Production and Inventory Management Journal, First Quarter 12–16. Detty, R.B., Yingling, J.C., 2000. Quantifying benefits of conversion to lean manufacturing with discrete event simulation: a case study. International Journal of Production Research 38 (2), 429–445. Feld, W.M., 2000. Lean Manufacturing: Tools, Techniques, and How To Use Them. The St. Lucie Press, London. Law, A.M., Kelton, W.D., 1991. Simulation Modeling and Analysis, second ed. McGraw-Hill, New York. Ljungberg, O., 1998. Measurement of overall equipment effectiveness as a basis for TPM activities. International Journal of Operation and Production Management 18 (5), 495–507. Marek, R., Elkins, D.A., Smith, D.R., 2001. Understanding the fundamentals of Kanban and CONWIP pull systems using simulation. Proceedings of the 2001 Winter Simulation Conference, pp. 921–929. McDonald, T., Van Aken, E.M., Rentes, A.F., 2002. Utilizing simulation to enhance value stream mapping: a manufacturing case application. International Journal of Logistics: Research and Applications 5 (2), 213–232. Melton, T., 2005. The benefits of lean manufacturing: what lean thinking has to offer the process industries. Chemical Engineering Research and Design 83 (A6), 662–673. Monden, Y., 1998. Toyota Production System—An Integrated Approach to Just-In-Time, third ed. Engineering & Management Press, Norcross, Georgia. Nahmias, S., 2001. Production and Operations Analysis, fourth ed. McGraw Hill, New York. Nakajima, S., 1989. TPM Development Program: Implementing Total Productive Maintenance. Productivity Press, Cambridge, MA. Radnor, Z., 2000. Changing to a lean organisation: the case of a chemicals company. International Journal of Manufacturing Technology and Management 1 (4/5), 444–454. Rother, M., Shook, J., 1999. Learning to See: Value Stream Mapping to Add Value and Eliminate Muda. The Lean Enterprise Institute, Inc., Brookline, MA. Suehiro, K., 1992. Eliminating Minor Stoppages on Automated Lines. Productivity Press, Cambridge, MA. Womack, J.P., Jones, D.T., Ross, D., 1990, The Machine that Changed the World. Macmillan Publishing Company, Canada.

Analyzing the benefits of lean manufacturing and value stream mapping via simulation: A process sector case study

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