Bridging Lean to Agile Production Logistics Using Autonomous Carriers in Pull-Flow

Transcription

1 Bridging Lean to Agile Production Logistics Using Autonomous Carriers in Pull-Flow Since early 90s the lean manufacturing system has become popular for industries. Following that, agility in production has got a great attention. Exploration of any new techniques for bringing these strategic concepts closer to each other has become advantageous for pioneer industries. Accordingly, the new paradigm of individual control, with the progressive interpretation of autonomy, can contribute to the objectives of the lean and the agile concepts in production and logistics environments. To explain the contributions of the addressed thesis the study describes it in theoretical and empirical forms. The compatibility of these leading-edge concepts to realize the notion of continuous material-flow through supply chains and production floors is examined. Simultaneously, the factors of efficiency, effectiveness, and responsiveness are considered. This study covers a quick review on the lean and the agility techniques and highlights some specific contributions of autonomous control to their targets. The purpose is to clarify the role of the autonomy in compliance with the lean and agility goals. This is inspected through development of a discrete-event simulation with some scenarios in a supply network. Keywords: Supply network; lean and agile production; Conwip; autonomous control; fuzzy logic 1 Introduction Appearance of new constraints and complexities on manufacturing systems, competitions in the market, customer fulfilment issue, and scarce resources have brought on several paradigms and techniques for keeping industrial enterprises alive. Following the deployment of the lean concept by pioneer industries (Womack et al., 1991), it was distinguished that sometimes leanness approaches, including zero inventory, are not capable enough to achieve the objectives. This holds particularly true, when demand is lumpy and highly varying (Xian et al., 2010), (Pool et al., 2011) as a result of the new business environments. For this reason new production systems have been introduced to manufacturing industries, which are more flexible and capable to meet fluctuating demands on time and with close adherence to the desired products. Consequently, the flexible manufacturing system, the reconfigurable manufacturing system, and some other implications of agility concept have been developed and widely deployed (Mehrabi et al., 2000). Nowadays, it is known by businesses that one of their major competitive advantages depends on their supply chain s (SC) structure and on how they manage it (Gimenez and Ventura, 2003). Two prominent approaches in such environments are

2 lean and agility which distinct their performances in manufacturing policies. From some points of view, on a broad scale, the lean concept can be accomplished for some special production and supply strategies, like make-to-forecast (MTF) or make-to-stock (MTS), under a reliable demand forecast. On the other hand, the agile system can be usually employed for make-to-order (MTO) strategies with quick response ability, when demand is uncertain and difficult to forecast (Xiamomei et al., 2008). Both lean and agile paradigms are accompanied with several characteristics and principles which can be complied with several tools and techniques. For the lean manufacturing, because of its precedence, some adequate methods and tools have been introduced that guide companies to the achievements. However, the core of the lean concept is independent of its tools (e.g., levelling and sequencing, one piece flow, JIT, Kanban, continual improvement, Kaizen, flexible capabilities, value stream mapping, and automation (Cowton and Vail, 1994), (Wadhwa et al., 2007)). In the following sections, some of the tools are explained briefly. Indeed, the lean systems emphasize on efficiency and cost reduction, although there are some additional indirect benefits in lean (e.g. dissemination of lean culture). Nevertheless, the core concept of agile systems deals with increasing flexibility that makes the SC more responsive to oscillating demand with reduced lost-sales. Still several research studies are conducted on methods and tools to make production systems agile. These all contribute to realizing the main principles of the agile concept, i.e., virtual manufacturing, agile production design, and knowledge management (Wadhwa et al., 2007). Meanwhile, individualization of all engaged objects in a logistics symphony progresses the trend of continuous improvement in a highly flexible material flows and control. Smart individual entities configure a prospective framework of production logistic systems with superior agility and adaptation, while elaborating the efficiency and the effectiveness. Smart objects with the capability of self decision-making and control lead to autonomy in a generic context. In this respect, the autonomous control in operational functions and decisions can be considered as a practical method for more flexibility, agility, and lean targets. Along with the introduction of new methodologies and concepts, the autonomy paradigm has become an attractive approach for industrial scientists to tackle the complexities and dynamics embedded in the supply chains with their respective processes under uncertain circumstances (Windt and Hülsmann, 2007). According to Scholz-Reiter et al., as a general term, autonomy means: the independence of a system in making decisions by itself without external instructions and performing actions by itself without external forces (Scholz-Reiter and Freitag, 2007). As a favourable paradigm for expanding leanness and agility, the autonomy issue is supposed practical to elaborate the targets of the two mentioned concepts. The complementary performance of these three concepts will be manifested, when they are applied and experimented in a supply chain or production network, working in a dynamic environment. To illustrate the claimed thesis, firstly, a brief introduction to the lean principle was already given, which was accompanied with some discussions about the agility concept and the autonomous control (AC) in production and logistics. In the following sections some collaborative aspects of lean-agile strategies and AC are theoretically identified, to improve the performance of supply chains and shop-floors in terms of some specific logistic performance measures, i.e., responsiveness, reliability, throughput time (TPT), utilization, and work-in-process (WIP) (Laure, 1999), (Wiendahl and Schneider, 2002) (Nyhuis and Wiendahl, 2006). Secondly, some of the logistic measures and a specific strategy of pull-push variations are explained in order to

3 evaluate the performance of a network scenario, which is set up by a discrete-event simulation package, called Plant-Simulation. Thirdly, the simulation results are presented and following them the conclusion of the paper is explained. 2 Lean Logistics After several progresses in production field, the lean concept has expanded its application to other fields of supply chain management (SCM) and logistics (in this paper, logistics and supply chains are used alternatively) (Jones et al., 1997). Indeed, lean thinking seeks for numerous improvements that have direct or indirect effects on the performance of logistics. The value-adding and waste elimination activities, as well as the zero inventory desire are the prominent approaches that can be promisingly employed throughout SC (Moyano-Fuentes and Sacristán-Díaz, 2012). In order to reflect the role of lean concept in improving logistic operations some outstanding techniques of that are briefly explained in the following: 2.1 Value added activities and wastes Lean thinking is a special philosophy which reflects doing more with less. In this context, waste and non-value added activities within a system can be identified. Generally, in lean philosophy value added activities imply those activities which customers (internal or external) are willing to pay for. Unreliable schedules, extra WIP, long TPT, and under capacity utilization (also as logistics performance measures) are some evidences of non-value added activities with in a production system. Overproduction, transportation, extra motion, inventory and WIP, defects, waiting, unnecessary processing (known as seven wastes) are the main activities, which bring no value to a lean system and must be eliminated as much as possible. 2.2 House of Toyota Since the lean concept is extracted from the Toyota Production System (TPS), the principles of this system provide the structure of the lean. There are three main parts to make up the house of Toyota: Heijunka as the foundation of the house (i.e. levelling and sequencing approaches), besides, the two pillars of JIT (i.e., pull production, one piece flow, and Takt time) and Jidoka (i.e., stop at an abnormality and automation) (Tapping, 2007) Levelling and Sequencing: This technique is used to balance production systems. It aims at producing or dispatching materials according to demand sequence as well as distributing production tasks between different workstations, concerning queues and capacities, to avoid bottlenecks. As a collaborative feature, this technique is an implication of flexibility and agility, when it can be instantaneously realized under dynamic demands and real-time capacity changes. Although real-time decisions seemed almost impossible before the introduction of state of the art in communication technology, this task can be perfectly achieved by AC at the current exercises of industries. Moreover, just in time (JIT) and just in sequence (JIS) contexts are two contributing techniques to the attainment of the

4 levelling and sequencing of material flows as well. Therefore, this case of contribution between lean, agility, and autonomy paradigms reflects several benefits in the target. Indeed, the importance of Heijunka in every efficient manufacturing industry is undeniable. Once the material flow lines e.g., production line, follow the aim of levelling and sequencing many of logistic targets automatically get fulfilled, i.e., utilization increases, TPT converges to its minimum, WIP decreases and consequently responsiveness rises. By means of AC any kind of required tradeoffs between local bottlenecks on the way of martial flows can be accomplished without bothering the other effective decision points. Specifically, when demand is uncertain or fluctuating, this lean technique can be still followed via AC to give rise to an agile material flow control. This agility realizes instantaneous reactions (in real-time) to current demand rate and available capacity One-piece flow and Pull: Lean philosophy persuades material flow systems to approach the flow of one piece at any event throughout production lines and even outbound logistics. The supporting idea of that is to go toward the zero-inventory and continuous flow theories as outstanding targets in the lean. Despite the fact that practices have shown pure zero inventory cannot be yet achieved, it can be partially realized with the use of a competent pull concept and JIT. Material pull is executed upon the demand of customer (internal or external) that prevents overproduction and consequently results in less WIP. Correspondingly, JIT method follows the concept of fulfilment at the time of request (Lee et al., 1997), (Aghazadeh, 2004). On the one hand, JIT technique is exploited to decrease flow wastes, and, in part, to increase flexibility (Injazz, 1994) to meet customized demands through less inventory and MTO strategy. However, not always flexibility and waste elimination are equivalent to each other. Making a bridge between these two necessities upon the current business environment can be satisfied via awareness of lean and agile principles as well as concurrent reactions of autonomous agents to specific demands. Furthermore, to overcome the Forrester or bullwhip effect (Forester 1961), JIT technique may fail because of inventory shortage or stock out situation. Thus, a combination of JIT technique with intelligent and autonomous agents can trigger a damping effect versus amplification in material flow, caused by oscillating demands, via decentralized and distributed accomplishments and proactive decisions (McCamish et al., 2010), (Mouaddib and Jeanpierre, 2011) Jidoka: To make a feasible JIT technique, it is vital to aim at zero-defect in production systems. Jidoka guides such systems how to make their stations capable to detect abnormalities and stop the operation automatically. Nevertheless, stopping affects not only the defective machine or operation, but the whole production line is met until the problem get fixed. This task is usually done in cooperation with human that inspires a kind of autonomation (autonomy + automation) to processes (Ohno, 1988), (Simons and Sokaei, 2005). This type of autonomy has been carried out for several years by lean manufacturers. However, autonomy (making equipments, products and processes autonomous) is a suitable approach to cope with sudden events and disturbances throughout processes. By upgrading the decisive processes and objects in a logistic system to self-organized ones, recognition of defects and dissemination of their effects

5 can be authorized in an agile way. Trade-offs between agents about their current situations, operating quality, and even maintenance requirements (Debenest et al., 2008) are the privileges of autonomy toward agility with lean targets. Despite the achievements in the lean logistics, global competition, growth in existing dynamics (e.g., fluctuating demand, uncertainty), and rise in material handling and flow complexities are some causes to make the lean system unable to meet its targets under dynamics circumstances. However, as a constructive suggestion, integration between the lean, agile and autonomy paradigms seems to be a responsive solution for the current and prospective problems in logistics. In fact, autonomy paradigm can be considered as a technique to bridge the lean targets to the specification of agility under highly oscillating conditions (Alves et al., 2012). To use the advantages of both lean and agile principles a twofold approach exists. Firstly, the option is to employ concurrently both concepts in an entire supply chain. Secondly, both can be employed but separately in two positions, called leagility strategy. Leagility is emerged from both the lean and the agile systems by configuring a decoupling point (DC) between them (Hoek, 2000). Although in literature the lean approach is considered for predictable demands with low variety and high volume (Christopher, 2000), lean principles have some flexibility methods and characteristics like: pull system, one-piece flow that enables shorter TPT and consequently quick response to changing market demand (Nyhuis and Vogel, 2006). In addition, single minute exchange of die (SMED) technique which brings flexibility to set-up new product types (i.e., quick changeover is one aspect of agility (Christopher, 2000)) is an advantageous method to bring lean systems closer to flexibility. 3 Agile logistics Nowadays, it is well known to enterprises that turbulent demand and volatility in material flows are the inseparable conditions in global markets. Market uncertainty and shortened product-life-cycles are faced in such a competitive environment. These enforce a trade-off between the paradigms of economies of scale and economies of scope in confronting the market demand as well as efficiency and effectiveness targets of suppliers. Agility is an organizational oriented and business-wide capability with the targets of greater responsiveness, customization and flexibility, that embraces organizational structures, information systems, logistics processes, and in particular mindsets (Christopher, 2000). In other words, Agility can be considered as a need to encourage the enterprise-wide integration of flexible and core competent resources so as to offer value-added product and services in a volatile competitive environment (Wadhwa et al., 2007). Whereas lean principles can be part of agile systems, there are some circumstances that lean fails facing the customer requirements at the right time, volume, and variety or even its effectiveness targets in supply chains, like the vehicle manufacturing case introduced by (Christopher, 2000). By evolving the agile manufacturing, several tools and techniques have been launched as practical ways to realize agility throughout supply chains. Network management, knowledge management, mass customization, dynamic enterprise reconfiguration, virtual enterprises, interoperable systems, agile human resources, value chain integration, concurrent engineering, and agile technologies (e.g., RFID) are some instances of those techniques (Wadhwa et al., 2007), (Vázquez-Bustelo, 2007).

6 However, some of these techniques and contributions are still in development phase, while others have already been installed. Furthermore, the contribution level of those techniques to agility is yet alternative and may be ambiguous. Several research studies have been run over practical techniques to obtain relevant capabilities for making manufacturers resilient enough in performances (Zhang and Sharifi, 2000), (Vázquez- Bustelo, 2007). Nevertheless, complexity of implementing those techniques for inspiring flexible behaviours and real-time decisions on logistics processes necessitates a practical strategy. (Duffie et al., 2002), (Scholz-Reiter1 et al., 2005), (Windt et al., 2010), and (Mehrsai1 et al., 2013) have recommended the autonomy approach to reduce the complexity of supply chains and manufacturing systems; by employing a decentralized control mechanism for logistics objects instead of the conventional hierarchical control of material flow. Figure 1 represents a recommended strategy to span lean- and agilitycontributions closer to each other. This figure is the resultant of some relevant studies, like (Christopher and Towill, 2000), (Stratton and Warburton, 2003), (Wang et al., 2012), and (Stump and Badurdeen, 2012). In this figure, the principle of the four strategies are studied and recommended before by researchers. Nonetheless, the strategy of devising autonomous control, as a connector mechanism, for putting together the advantages of both push and pull, respectively lean and agility, performances is initiated by the authors. Regarding new experiments and studies of autonomy in logistics and production system, it can positively contribute to all four sections of this figure. In other words, if the efficiency factor of implementing autonomy in such systems can be moderated the performance of these strategies reflects improvements in general. However, the target of employing autonomous control, concerning volatility and dynamics in both sides of supply and demand, should be located in the extreme case that both demand and supply are dynamic and agility is preferred to lean. This condition explains the best the feasibility of using autonomy in production and logistics networks. Nevertheless, under this extreme condition employment of push, pull, or both strategies is not yet clear in literature. Therefore, more precise explorations are required to find the best-fit location of autonomy and flow strategy in the extreme case, shown in figure 1. #####Figure 1: Bridging lean to agile efforts by means autonomy under volatile demand.###### 4 Autonomous control for carrier objects AC for material flow objects in logistics can be described as: decentralized coordination of intelligent logistic objects and the routing through a logistic system by the intelligent parts themselves (Scholz-Reiter and Freitag, 2007). Logistic objects include staffs, products, machines, transport objects, and any other means that carries goods in logistic operations. However, the studied object in this paper is limited to the transport means. Generally, AC seeks for decentralized decision makings by having a heterarchical controlling structure (Duffie et al., 2002) which bears flexibility and robustness to any dynamic system. In addition, AC has shown its capabilities in virtual experiments and mathematical calculations which can reflect the required flexibility and real-time performance under dynamic circumstances. This aptitude is quite desirable for agile systems (Scholz-Reiter et al., 2006), (Scholz-Reiter1 et al., 2007). On the other hand, its

7 contributions to lean targets, like enhancement in utilization and reduction of WIP, make AC quite compatible with both the lean and the agility paradigms in a favourable manner (Mehrsai et al., 2012). Originally, several AC methods have been introduced for routing material transporters between logistic stations, e.g., production lines, roads, plants, etc. For example, queue length estimator (QLE), pheromone, earliest due-date, (Scholz-Reiter2 et al., 2005), (Scholz-Reiter and Freitag1, 2007), (Scholz-Reiter2 et al., 2007), heuristic methods (Vogel et al., 2002), (Mehrsai and Scholz-Reiter1, 2011), fuzzy control (Mehrsai1 et al., 2013), and artificial neural network (Mehrsai2 et al., 2011), (Mehrsai2 et al., 2013) are some experimented methods of them. Among all, here it is decided to employ QLE as an AC method because of its easier and understandable functionality. It is noticeable that AC is introduced versus conventional scheduling and flow control systems (Conv), which are centrally planned and executed regarding available forecasts. For the sake of simplicity, the Conv scheduling in this paper represents first-in-first-out (FIFO) dispatching rule by taking into account the material replenishment and the least processing time for every product type on each production line. Generally, QLE method performs based on local decision-making for routing among parallel and alternative stations. Thanks to QLE, carriers get the capability of finding their own route to proceed through production networks. Carriers by perceiving the situation (the length of the queue) of their successive stations can compare their waiting times in front of each station, so that the one with the least waiting time can be chosen to avoid any unnecessary idleness (wastes). Each carrier can derive a unique single routing-decision by the local estimation capability of the next queue length plus the processing time of each successive station. See (Scholz-Reiter et al., 2009) for the limitation of QLE. The perception of waiting time may be given to the autonomous carriers by various methods spanning from simple comparative calculation to complex model-base and model free ones for learning the conditions, e.g., artificial intelligence methods (Mehrsai2 et al., 2013). This competence may be used for autonomous rescheduling of operations-order in an open/job-shop problem as well (Mehrsai and Scholz-Reiter1, 2011). The data transmission and processing between stations and autonomous carriers has to be occurred by employing state-of-the-art information and communication technology (ICT), e.g., wireless, RFID, cloud computing. Furthermore, the performance results of QLE as well as other autonomous techniques directly contribute to the lean targets by smoother flow of material, less processing time, more machine utilization, and avoidance of idleness in queues (waste). This contribution shows itself specifically when a breakdown happens in a station and a local rescheduling is required. On the other hand, agility of such production systems can be reflected by the flexible structure and the prompt decisions of single objects, which perceive local obstacles and deal with them to globally enhance the logistics performance measures (Von Cieminski and Nyhuis, 2007). For more information about pure QLE see (Scholz-Reiter1 et al., 2005), (Scholz-Reiter2 et al., 2005), (Scholz-Reiter et al., 2006), and for learning capability in using QLE see (Mehrsai1 et al., 2013). 5 Logistics performance measures In general, logistics operations of industries have very crucial roles in becoming successful at the global market with growing competitions. Nonetheless, satisfaction of all logistics objectives in supply networks is accompanied with full of conflictions and contradicted desires. Fulfilment of some targets may lead to abortion of the other ones;

8 this conflict is called by Gutenberg dilemma of operation planning (Nyhuis and Vogel, 2006), (Von Cieminski and Nyhuis, 2007). Therefore, in order to optimize logistics operations in industries, the performance of operations must be evaluated. In doing so, several measures and criteria have been considered to estimate the results of different practices (Colledani and Tolio, 2011); some of these factors contribute to the lean and some others to the agility concept. Here, just some factors are considered to estimate the performance of autonomous and conventional control in material flows as: throughput time (a factor of responsiveness in agility), utilization (a factor of waste elimination and value-adding in lean), WIP (a factor in lean), schedule reliability (a factor of responsiveness and agility). Employment of these measures and their interactions with each other demonstrate the performance of the proposed control strategies for each focused paradigm. Accordingly, an agile supply chain strategy aims at increasing flexibility and enabling velocity to adjust promptly to volatile market conditions and to unpredictable sources of supply. Thus, an agile supply chain strategy focuses on fast market responses and quick new product development, while minimizing supply disruptions by streamlining information flows across the supply chain (Roh et al., 2013). 6 Push vs. pull A prominent contribution of lean in logistics is material flow control and optimization of flow performance. In doing so, two strategic approaches to material flow as push towards customer and pull by customer has been analyzed. For each of which strategy some control methods has been already introduced, e.g., among them MRP for push and Kanban for pull are well-known. In MRP system every material-transport between stations is imperatively fed-forward regarding a prepared plan based on demand forecast and nominal capacity of workstations. Thus the material-flow control is centralized and any unforeseen changes in the system may cause malfunctions and requires a rescheduling within a time-window. Nonetheless, when demand fluctuation is relatively high or demand forecast is reliable push system is more responsive and effective. In contrast, in pull strategy any movement of material is triggered by a direct order from downstream of the system. For each type of material a specific number of card/cart exists that are sent to upstream for making signals of production and/or fulfilment. The number of cards/carts is always constant between the two points of supply (source) and demand (sink); so that the number of existing products within this system stays always constant as well. In other words, direct demand moves upwards and in contrast material proceeds forward to avoid any extra WIP. Therefore, the limited WIP between workstations stays always in constant situation and the threat of product obsolescence drastically drops. Moreover, the flow control in pull is totally decentralized by means of individual cards/carts, while information sharing and flexibility of production system are essential too (Roh et al., 2013). This is advantageous in case of local malfunctions and solving-decisions. In this regard, the performance of pull is more consistent with lean objectives. For more information about the advantages and drawbacks of push and pull strategies see (Riezebos et al., 2009). Universally, the industrial application of pull systems has been commenced by the use of Kanban technique in TPS (Toyota Production System). Following that novel flow, some other general techniques have been configured, which use more hybrid features. For instance, constant work in process (Conwip) is recognized as a production activities control mechanism with maximum throughput via control of WIP (Marek et al., 2001), (Framinan et al., 2006), (Satyam and Krishnamurthy, 2011). Or Polca

9 (paired-cell overlapping loops of cards with authorization) is a push-pull mechanism which is subsequently introduced to exploit both push and pull control advantages (Suri and Krishnamurthy, 2003). The two latter mechanisms are usually treated as hybrid systems between push and pull. However, a disadvantage of CONWIP is that inventory levels are not controlled at the individual stages, which can result in high inventory levels building up in front of bottleneck stages (Geraghty and Heavey, 2005). Kanban (=card in Japanese) is a pure pull mechanism that takes place between two adjacent stations (one predecessor and one successor), while in Conwip the pullcards/-carts move between the source and the sink of an assembly system by following the push mechanism in between. However, an assembly line may encompass several workstations in linear or in parallel forms. In Conwip, once an order is issued by the sink (customer) for a specific part, a corresponding cart will be released to the source for carrying that order. And from there it will proceed throughout the entire production system by approaching back the sink with a finalized order. Nevertheless, the journey of a cart throughout the system follows the push principle that meanwhile may result in high inventory levels. Thus, an exact control mechanism is required there. Consequently, Conwip seems more suitable for the purpose of AC. Indeed, the similarity of Conwip to the original Kanban system with inherent decentralized control mechanism is the advantage of Conwip to make a favourable combination with AC. Thanks to the given AC to each player in Conwip, the push mechanism can take place in a fully decentralized manner by relying on carts themselves. In other words, the constant number of material flows as well as the entrance events of carts to the system is controlled by Conwip mechanism, whilst the push-flows of carts are autonomously controlled. The decentralized flow-control in Conwip uses smart decisions to enhance the overall performance by avoiding unnecessary local waiting and idleness. The constant number of cards/carts in a production system to achieve the control of WIP can be calculated by several specified equations in alternative cases. For example, equation (1) shows a simple calculation for number of cards (Huang et al., 1998). It is utilized for defining an initial assumption about the number of carts. For complete calculation of Conwip performance in complex multi-class networks, see (Satyam and Krishnamurthy, 2011). Average _ number _ cart Average _ Throughput Average _ flow _ time (1) Basically, there are two general material-flow control strategies in production environments. Firstly, the push strategy which normally supports make-to-stock (MTS) and comparable systems with dependency on demand forecasting. This strategy is more efficient and easier to realize for the mentioned systems and has been used for a long time. Additionally, capacity utilization is quite high in push systems; because of in advance planning (predefined logistics operations) and transfer of materials as soon as they are available. It is more suitable for mass production with guaranteed demand. Nevertheless, this system suffers from some shortcomings confronting with logistics targets. It works in contrast to some lean targets; because of higher WIP and inventory level, blind production, and less flexibility in the predefined schedule. Secondly, the material-pull which triggers the flow of materials by (internal or external) customer orders and is based on the MTO/assembles to order (ATO) systems. Normally, in M/ATO strategy customer orders are customized, hence, final production is always postponed to the respective individuals orders. The later system is more

10 suitable for lean principles; however, the agility targets may partially fail if the pull strategy prolongs the customer lead time (CLT). In pull systems storage of WIP and inventory volume is normally constant and low (supermarkets instead of WIP inventory), obsolete material is avoided, and mass customization approach is facilitated (Lee et al., 1997). Moreover, the required flexibility to meet demand fluctuations may be hit by a fully pull system, which can be increased by a hybrid strategy of push and pull. In an idealistic lean logistic system, the JIT technique can eliminate extra efforts for inventory keeping or even demand forecasting. However, pull systems have some drawbacks too which include: increased risks in material procurement, higher lead times for customer orders, fragile continuous material flows, in case of sudden disturbances (e.g. machine breakdowns), and stock-out situation. In a broad scale, one competent hybrid strategy out of both systems can be reflected in the form of leagility strategy, which divides supply chains into two parts of push and pull. The upstream part of DC has push system and the other part in downstream of DC applies pull system. This strategy uses the benefits of both lean and agile principles, respectively, push and pull systems (Hoek, 2000). In the current study the considered logistic network uses leagility strategy in general context by employing the pull technique of Conwip. 7 Logistic network scenario In order to reflect the usability of the suggested strategy, a logistic network scenario is modelled by simulation, see Figure 2. In this network the performance measures define the suitability of the recommended alternative strategies to the lean and agility targets. A special leagile supply chain scenario has been assumed with fully coupled plants that resembles a realistic production network. It is developed to show the characteristics of AC in logistics against simple flow controls, concerning some waste aspects (less WIP, high utilization) as well as agility factors (less TPT and less CLT). #####Figure 2: The exemplary logistic network with flexible DC point. ##### Three stages are supposed for this network: one source plant in the first stage, two parallel assembly plants with identical capabilities in stage two, and one original equipment manufacturer (OEM) in stage three. The flow of products from stage one is equal for either plant in stage two. For making the model more understandable and undertaking the postponement concept (Hoek, 2000) a standard DC point is located at the entrance of OEM with displacement possibility to one prior step (alternative DC). Thus, demand-signal penetrates up to this point, i.e., from the source up to DC the push principle is used and from there up to the end customer the pull principle is employed. The Conwip system pulls the semi-finished materials from DC. For the Conwip system three types of pallets are assumed each for every product type (there are three types of products to produce). Under this mechanism, for each single order one pallet will be triggered to the exit of DC (entrance of OEM) to carry the respective piece of product. If the principle of one-piece flow is followed, the superiority of that is compared against batch flow in the following section. It should be mentioned that number of pallets, product types, and pallet-size (lot-size) each has a different influence on logistic performances. Notice that these assumed values have empirically been identified to simplify the illustrations and are left constant in the experiments. #####Figure 3: The flexible Production system in the form of a (3 3) line matrix.

11 ##### Inside the source plant and OEM flexible shop-floors are embedded which each is configured by a (3 3) production line matrix (see Figure 3). Every of the three stations in a column is connected to its three successors (stations) in the next column and every column of stations has to be met once by each product. Table 1 shows the processing times of each workstation inside every plant. For OEM the processing times are considered stochastic with the normal distribution, where there the standard deviation equals to (σ = µ/10). Besides, the mean of processing times is tried to be similar to the mean intervals of products replenishment. These values are extracted from several simulation runs toward smoother and coordinated flows. The simulation is run for 80 days each 24 hours. Moreover, the performance measures inside each plant are considered as local factors and the measures for the entire network are used to describe global behaviour. Each two plants with the distance of 140 km are connected via a truck with the velocity of 70 km/h. It takes almost four hours for each round trip and the trucks do not pause during their journeys. Thus, the carrying load of the truck varies each time (between 3 to 6) concerning the delivery rate, i.e., the trucks may be not full in every trip. It should be mentioned that the trucks return empty from their journeys and products can meet each plant only once. #####Table 1: Processing times of production lines in each plant of the exemplary logistics network. ##### 8 Simulation experiments To show the compatibility of the three discussed paradigms (lean, agility, and autonomy) some criteria of each are selected to be presented on the resulting figures. Some decisive measures like material sequencing and levelling, TPT, and utilization as lean factors versus standard deviation (STD) and CLT (as schedule reliability factors) in agility are chosen here. These performance elements are the most representative performance measures logistics that are separately assigned to both lean and agility. Different sort of experiments are simulated to analyze the performance of AC and Conv flow control under dynamic circumstance (i.e., fluctuating demand with and without sequenced loads). As explained before, the chosen AC for logistic objects is QLE that enables the pallets to make real-time decisions for their routes, according to their successor queues prior to each line. To represent a dynamic (volatile) circumstance for material flow, throughout the network, oscillations in replenishment s and demand s rates (known as fluctuating or seasonal effect too) are considered. This can be reflected in case of push vs. pull material flow. In doing so, four flow scenarios with several variants are assumed to unfold the performance of the developed material flow control systems, which are as follows: (1) Sinusoidal push and pull, by use of equation (2), for both load and demand rates, which are represented as fluctuating or seasonal effect. The three types of products are similarly released sinusoidal, but with a (1/3 φ) phase shift for each type (i.e., φ=0, 2π, 4π). Here, the amplitude equals to 0.15 and the mean value of the sine equation (its rates) is 2:30 h. This scenario completely reflects the well-known bullwhip effect in supply chains and consist of two variants: Variant 1: with 25 pallets for each type and lot-size 1 (representing one-piece flow) in pull side.

12 Variant 2: with 25 pallets for each type and lot-size 2 (not one-piece flow) in pull side. (2) All product types are triggered stochastically at the source plant following the normal distribution with (μu=50 min, σ=5 min). This represents a normal condition of a constant demand (see figure 8). However, each product is sequentially generated (representing Heijunka in house of Toyota). It has three variants: Variant 1: to pull products at OEM with unlimited pallets and lot-size one, demands proceed with negative exponential distribution (Neg-Expo) by (ß=2:50 hour), independently for each type, see equation (3). This demand behaviour simulates a fully stochastic and volatile demand with no constancy (see figure 8). Yet the pallets are released to the entrance by pull signals and not at the supply time. DC is located before OEM and just after assembly plants which models a not strong postponement strategy (demand volatility signals penetrated to upstream that causes fluctuations in pulling materials). Variant 2: to pull products at OEM with unlimited pallets and lot-size one, demands proceed with Neg-Expo by (ß=2:50 hour). DC is located at the entrance of OEM that models a strong postponement strategy (closer to end customer). Variant 3: products are pushed to the end customer (with unlimited pallets) throughout the network and OEM. The pallets are ready at the entrance once the supply takes place and push the products to the exit. (3) Every product type is triggered stochastically at the source plant by the use of the normal distribution. On the other hand, demands proceed with sinusoidal manner as stated before in equation (2). This simulates a condition that supply rate is relatively constant but demand is seasonal (oscillating). At OEM the pull principle is conducted with 10 pallets for each type and lot-size 1. Its variants are following: Variant 1: at the source plant, raw materials for each product type are separately (independently) released according to the normal distribution with (μu=2:30 hour, σ=10 min). This represents no sequencing condition in upstream of material flow. DC is located before OEM and just after assembly plants that models a not strong postponement strategy. Variant 2: like variant 1 but DC is located at the entrance of OEM that models a strong postponement strategy (closer to end customer). Variant 3: at the source plant raw materials are released in sequence according to the normal distribution with (μu=50 min, σ=5 min). This represents sequencing condition as a matter of lean flow in upstream. DC is located before OEM just after transportation from assembly plants. Variant 4: like variant 3 but DC is located at the entrance of OEM. (4) All product types are triggered stochastically at the source plant following the normal distribution with (μu=2:30 hour, σ=10 min), however, independently. This holds true also for stochastic demands to pull the products at OEM with (μu=2:30 hour, σ=10 min). This scenario almost models a chaotic system that AC should react to that positively. The three variants of it are:

13 Variant 1: DC is located at the entrance of OEM that models a strong postponement strategy (closer to end customer). Variant 2: DC is located before OEM just after transportation from assembly plants that models a not strong postponement strategy. Variant 3: DC is located at the entrance of OEM and with consideration of in average 20% failure probability for two stations out of nine at OEM. This variant deals with unpredicted conditions (internal uncertainty) in general production system in addition to the supply and demand behaviour (external uncertainty). Note that the number of pallets in Scenario 3 is approximately calculated based on (1): the average throughput rate equal to 1.06 and average flow time (ALTPT) equal to approximately 9.4 hour which achieves the value of (10= ). For the sake of simplicity and understanding the equations all notations used in this paper are presented in Table 2. #####Table 2: Applied notations in the paper. ######## The numerical values in scenarios are all empirically distinguished out of several simulation runs to configure an entire smooth flow. In Conv system products just get processed on the lines with the predefined least processing times for the corresponding type, see Table 1. This method totally follows the predefined schedule to control the flows without any rescheduling in between. t sint (2) f x 1 x exp max P EP max U AU min T ALTPT AGTPT ACLT min W WIP (3) (4) s.t: 1 (5) Generally, the multi-objective of the selection problem for the flow strategy is to maximize the number of end products and average utilization of the resources as well as minimizing the average local / global throughput time and customer lead times for every strategy, see equation (4). In the following results, it is shown that every specific flow mechanism is suitable for special demand and fulfilment condition. Accordingly, since the considered coefficients are subjectively selected by decision makers regarding their own assumptions and priorities, the weights of importance can be bias for lean or agility. Decision makers of these values are usually production and logistics (middle- and strategic-level) mangers, who define and plan the preferences of their systems regarding lean and agility performances. The decisions concerning coefficients are usually made

14 thanks to fuzzy compromises between demand and supply situation, and the importance criteria of performance from their point of view. Because it is usually impossible to enhance positively all performance measures, since some of them are controversial and this is the art of mangers and planning engineers to find the optimum trade off among all objectives. Local optimization is always simple; nonetheless, its influence on the global performance is critical. Managers in each plant of a supply network like to optimize their own system regarding higher income and less cost. However, this is not possible because by increasing the profit of a member of a supply network the profit of others will be negatively influence. Therefore a global optimization is necessary for the sustainability and robustness of a network. For instance, ß, δ, and θ underline the agility aspect of a supply chain, whilst ε, α, and γ rather highlight the lean factors. However, at the current study three aspects of lean, agility, and the combination of both (as leagility) are considered. Alternatively, leagility focuses on less throughput time and more output (quantity of finished products). Therefore, the assumed weighting values for each paradigm are selected as in Table 3. The subjective selected values for the coefficients in this table are according to the importance of them to the respective strategies: lean, agility, and leagility. #####Table 3: Weight coefficients for the objectives of the problem. ##### However, this multi-objective is not homogeneous; therefore, requires becoming normal for optimization solution. In order to normalize this multi-objective with heterogeneous units and different optimization targets (minimization and maximization), an application of fuzzy sets is employed. In other words, the value of every objective is subjectively rescaled to [0 1] as a satisfaction degree (SD), denoted by μ. In order to calculate the SD for each of the minimization and maximization objective the following equations (6)-(7) are employed, based on the triangular fuzzy numbers in Figure 4: q 1 x1 if h1 x1 q1 q1 h1 if a 0 x1 q1 if 1 x1 h1 (6) x2 h2 if h2 x2 q2 q2 h2 if 0 x b 2 q2 if 1 x1 h2 (7) #####Figure 4: Triangular satisfaction function for maximization (a) and minimization (b). ##### In this tactic, the optimization problem aims at maximizing each SD and consequently the aggregated form of all SD, as the maximum of all achieved satisfaction degrees. This solution is competently applied by (Petrovic et al., 2008), see equation (8):

15 maximize max p,,, U T W (8) Nevertheless, mathematical solution of the optimization problem is not taken into account here, but the values are derived from the simulation experiments. Thus, in every new experiment the higher average value of the satisfaction degrees, the better performance of the strategy is achieved. Eventually, the final objective, by means of average aggregation (sum) operator, becomes a unique satisfaction degree out of all single objectives to get maximized, see equation (9). 9 Simulation results EP AU WIP ALTPT AGTPT ACLT 6 (8) maximize(sd) maximize The results of the several experiment-scenarios, explained above, are expressed on Table 4. Each simulation is conducted three times with confidence level of 95% to assure the validity of the results. This table broadly exposes the involvement of AC in each of the prominent paradigm. As it can be seen, the selected logistic measures vary under each flow environment and control strategy. It should be mentioned that the desired circumstance in production is a robust system with similarities to lean environment. Moreover, QLE method, in one view, does the task of levelling the lines by dispatching the products to those stations with less waiting times in queues and prevents any configuration of static bottlenecks. This fact leads to some contributions to the lean system as is illustrated on the result table and at the Figure 5, with most biases towards lean SD. The star shape of this radar chart represents the performance improvement of QLE against Conv control in the agile and specifically in the lean system. For instance, as is expected the experiments with strong postponement strategy performance better than the others, or, one-piece flow represent better impacts. For better understanding of the scenarios influences on the performance see Table 4 and Figure 5 in detail. By means radar chart in figure 5, the outputs of the experiments are better interpreted in terms of lean and agility contributions. ####Table 4: Simulation results in terms of logistic performance measures for all scenarios and in alternative variants. #### In order to express the calculation way of each single SD for every measure Table 5 shows the all values of SD. The detailed calculations to achieve the values of SD for general QLE as well as QLE in favour of leagility in Scenario 1 Variant 1 are given there: #####Table 5: Individual satisfaction degrees for each logistic performance measure. ##### #####Figure 5: Weighted satisfaction degrees by use of two control systems facing alternative scenarios and variants. ##### Furthermore, some representative figures out of several measures are depicted on Figures 6-10 to assist the understanding of the performance results. For instance, Figure 10 displays the trend of customer lead times for more than 700 events under QLE and Conv flow controls for scenario 1. After the 200 events as warm-up phase for

16 simulation the next 500 events show a relatively stable behaviour of QLE systems while Conv systems has an uprising tendency. This effect is more obvious in case of more than one-piece flow. #####Figure 6: Cumulated work in processes of stations at a) Scenario 3 Variant 1, b) Scenario 3 Variant 4, c) Scenario 4 Variant 3 and, d) Scenario 3 Variant 3. ##### #####Figure 7: Percentage of stations utilization for a) Scenario 1 Variant 2 and b) Scenario 4 Variant 3. #####. #####Figure 1: Intervals between #####Figure 2: Configuration of two respective demand events in different supply (replenishment) rate at OEM in disciplines#####. different supply form at the source plant#####. #####Figure 10: Customer lead time in terms of Conv and QLE control#####. 10 Summary and conclusion In summary, the short review on the three paradigms of lean, agile, and autonomy in the beginning of the paper provided an adequate entrance to the study of combining them in a prominent production logistics. As stated before, shifting from the original culture of lean at successful companies in the 80s and 90s towards the recent approach to agility concept at industries requires several contributions and attempts (Forester, 1961), (Simons and Sokaei, 2005). Whereas the requirements and constraints of the lean paradigm at the current business environment may not be fully achievable, the agility concept is not practical apart from the privileges of the lean culture. Hence, there must be a collaborative approach to them with some tradeoffs between the strengths and weaknesses of both paradigms. This synthetic approach can be partially accomplished by the means of autonomy concept on manufacturing and logistics operations. On this basis, the current paper complied with this concern by reflecting the effectiveness of the claimed thesis (i.e., combination of lean and agility through autonomy) by means of simulation. Some criteria for both lean and agility were given to be later compared in several simulation experiments; to show the applicability of this strategy to comply with the requirements of lean, agility, and autonomy paradigms. In general, the simulation results have illustrated the supplementary and correlations aspects of the mentioned paradigms. For this purpose, some logistics performance measures were considered to show the tradeoffs between the employed strategies under alternative performances. For instance, TPT and CLT represented the quickness and responsiveness of each control method (Conv and QLE) with regard to agility. Also the percentage of utilization and the quantity of WIP (supermarkets) defined the efficiency and effectiveness of the experimented methods, as important factors in lean attempts. Just by these few criteria the contribution of the autonomy to the agility and the lean concepts could be justified. In this respect, the results out of several simulation scenarios and the represented figures have shown that the system with application of autonomous control (QLE here) has privileges to conventional ones. According to the general SD of the multi-objective in different scenarios and the weighted SD with more emphasises on lean and agility factors, in most cases the autonomous control in decentralized manner has overwhelmed

17 the conventional material flow system. The satisfaction degrees showed the happiness of the decision maker in the correlative manner to the other alternative situations. QLE as an autonomous control method could enhance the overall performance record in all scenarios and variants except the Scenario 4 Variant 1 and 2, which seems normal yet. In this scenario the push and pull (supply and demand) sides are fairly coordinated; as it can be seen in Figure 10-11, the normal distribution has produced relatively constant intervals between two events. Moreover, the coordination is also resulted from the synchronized intervals between the push and pull flows. Despite failures in Variant 3, the constant supply rate fulfils the entrance inventory so that the failures in station have no big eventual effect on Conv performance. Accordingly, in this scenario the performance of all systems does not differ significantly and, therefore, a conventional system is more efficient and practicable compared to QLE autonomy. The Scenario 4 has defined that in a relatively stable demand and supply situation conventional system has no deficits against the autonomous control; so conventional system is favourable regarding less cost. In all scenarios the performance of QLE was visibly better than the Conv one and this justifies the applicability of autonomous control (individual and distributed) in enhancing the respective logistic performance measures for lean and agility. The structure of the network and the constant transport events between the existing plants, to some extent, assimilates the supply rates at OEM. This is specifically distinguishable when the source plant releases the raw materials with no big gap in intervals (e.g., sinusoidal supply has big intervals because of the phase shift between the supply of product types). However, the postponement s contribution is defined in Scenario 2 Variant 1 and 2 which shows the better results by shifting DC closer to the final customer. Whereas previous studies illustrated the superior performance of AC methods in pure push systems (Scholz-Reiter2 et al., 2005), (Scholz-Reiter2 et al., 2007) the recent experiments results demonstrate the importance of improvement in AC methodologies in order to suit the autonomy paradigm to pull systems and closer to practice. All in all, since fluctuating demand penetrates in part to upstream of supply chains, it can be deduced from the simulation results that in a leagile network (push in upstream and pull in downstream), before DC point, a conventional production plan can be properly applied. This is because of the cost efficiency and less setup times (e.g., zero changeover percentage) factors of conventional systems, while downstream of DC must be flexible and agile enough to make the entire network responsive to the customized demands of end customers. This fact can be derived also from the example in Scenario 2 Variant 1 as pull and Variant 2 as push system. It is shown that SD in Variant 1 is higher than Variant 2. As a favourable conclusion, according to the logistic performance measures and the results of this work, the autonomy can be employed for making positive tradeoffs between the preferred performance measures in lean and agile systems. Furthermore, it has been justified that in a conventional system as in lean manufacturing is highlighted less lot-size in material flow (e.g., one-piece flow) brings privileges like less TPT; as it was apparently presented by the variants in Scenario 1. However, calculation of the optimum number of pallets is an important factor in pull system logistics, which can deeply be discussed in future works. Furthermore, the autonomy paradigm in manufacturing and logistics environment can also deal with flexible lot-sizes, flexible sequencing in alternative product types and other optimizing factors. The experimented AC method in this level of research has been regarded as routing autonomy with just one simple technique in supply chains. Nevertheless, other aspects of the autonomy

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21 entire manufacturing department logistic process operating curve (LPOC). Initiatives of Precision Engineering at the Beginning of a Millennium, Springer US, Windt, K., and Hülsmann, M., Changing Paradigms in logistics- Understanding the shift from Conventional Control to Autonomous Cooperation and Control. In K. Windt, M. Hülsmann, eds., Understanding Autonomous Cooperation & Control- The Impact of Autonomy on Management, Information, Communication, and Material Flow, Springer, Berlin, Windt, K., Becker, T., Jeken, O., and Gelessus, A., A classification pattern for autonomous control methods in logistics. Logistics Research, Springer Berlin / Heidelberg, 2 (2), Womack, P. J., Jones, D.T., and Roos, D., The Machine That Changed the World. Michigan: Gale Group Farmington Hills Xiamomei, L., Zhaofang, M., Guohong, X., and Fu, J., Study on manufacturing supply chain leagile strategy driven factors based on customer value. In IEEE 4th Int. Conf. on Wireless Communication, Tianjin Univ., Tianjin, 1-4. Xian, J., Zhong, F., Jing, F., Lian, Y., and Song, J., Study on Integrated Model of Lean and Agile Supply Chain Based on Multi-DPs. In: Y. Demazeau, et al., eds., Trends in Practical Applications of Agents and Multiagent Systems, Springer Berlin / Heidelberg, 71, Zhang, Z., and Sharifi, H., A methodology for achieving agility in manufacturing organizations. Int. J. of Operations & Production Management, 20 (4), Table 1. Processing times of production lines in each plant of the exemplary logistics network. PROCESSING TIMES [H:MIN] FOR EACH PLANT PLANT Source;P 21 ; P 22 P 3 (OEM) LINE DETERMINISTIC VALUE MEAN VALUE (µ) PRODUCT TYPE 1 2:00 3:00 2:30 2:00 2:40 2:20 TYPE 2 2:30 2:00 3:00 2:20 2:00 2:40 TYPE 3 3:00 2:30 2:00 2:40 2:20 2:00 Table 2. Applied notations in the paper. t Time intervals between two released raw materials in each source-plant The phase in Sine wave (each sinusoidal wave has a phase) Weighted number of finished products (the higher the better) P EP U AU T Number of finished (end) products (the higher the better) Weighted utilizations (the higher the better) Average utilization of stations (the higher the better) Summation of weighted times (the less the better)

22 ALTPT Average local throughput time for each product from entrance to exit of OEM (the less the better) AGTPT Average global throughput time for each product throughout the entire network (the less the better) ACLT Average customer lead time for each product, the gap between the issued time and customer fulfilment (the less the better) W Weighted stocks (the less the better) WIP Work in process (stocks) in OEM (the less the better),,,,, Importance weights (coefficients) chosen by logistics/production managers as decision makers Membership value of the fuzzy number a ~ h q x a ~ Lower limit of the triangular fuzzy number Upper limit of the triangular fuzzy number The incident value of each objective between the two interpreted boundaries of triangular fuzzy number Table 3. Weights (coefficients) for the objectives of the problem. Leagility Lean Agility Table 4. Simulation results in terms of logistic performance measures for all scenarios and in alternative variants. ALTPT [hour] AGTPT [day (hour)] ACLT [hour] AU% EP WIP SD Weighte d SD for Leagilit y Scenario1 Variant1 Weighte d SD for Lean QLE (85.44) Conv (89.04) Scenario1 Variant2 QLE (89.04) Conv (99.84) Scenario2 Variant1 QLE (96.24) Conv (110.16) Scenario2 Variant2 QLE (70.32) Conv Weighte d SD for Agility

23 (93.84) QLE (37.68) Conv (38.64) QLE (68.4) Conv (71.76) QLE (63.36) Conv (66.24) QLE (67.2) Conv (70.56) QLE (61.68) Conv (64.32) QLE (35.52) Conv (34.56) QLE (38.16) Conv (37.2) QLE (49.2) Conv (77.28) Scenario2 Variant Scenario3 Variant Scenario3 Variant Scenario3 Variant Scenario3 Variant Scenario4 Variant Scenario4 Variant Scenario4 Variant Table 5. Individual satisfaction degrees for each logistic performance measure. QLE 0.86 =[( )/( )] QLE_ 0.17= Weighted 86 SD for Leagility ALTPT AGTPT ACLT AU EP WIP Scenario1 Variant1 0.33=[( =[( =[( =[( )/( )/(87-70)] 1948)/( )/( )] 1948)] )] 0.08= = =[(47-17)/(47-13) 0.08= = = Conv

24 Scenario1 Variant2 QLE Conv Scenario2 Variant1 QLE Conv Scenario2 Variant2 QLE Conv Scenario2 Variant3 QLE Conv Scenario3 Variant1 QLE Conv Scenario3 Variant2 QLE Conv Scenario3 Variant3 QLE Conv Scenario3 Variant4 QLE Conv Scenario4 Variant1 QLE Conv Scenario4 Variant2 QLE Conv Scenario4 Variant3 QLE Conv Figure 1. Bridging lean to agile efforts by means autonomy under volatile demand.

25 Volatility in supply Push-Pull Push? Pull Autonomy Push-Pull Volatility in demand Figure 2. The exemplary logistic network, simulated with flexible DC point. Figure 3. The flexible production system of OEM in the form of a (3 3) matrix.