Improving distribution operations: Implementation of material handling systems

ARTICLE IN PRESS Int. J. Production Economics 122 (2009) 89–106

Contents lists available at ScienceDirect

Int. J. Production Economics journal homepage: www.elsevier.com/locate/ijpe

Improving distribution operations: Implementation of material handling systems Satya S. Chakravorty  Department of Management and Entrepreneurship, Michael J. Coles College of Business, Kennesaw State University, 1000 Chastain Rd., Kennesaw, GA 30144-5591, USA

a r t i c l e in fo

abstract

Article history: Received 27 June 2008 Accepted 27 December 2008 Available online 27 May 2009

Academic research has primarily focused on the technical factors of material handling systems, with little or no discussion of human factors. In order to improve the performance of distribution operations, we found that the implementation of material handling systems involves both human and technical factors. These human and technical factors interact over time and go through three somewhat overlapping transitional stages. In the first stage, both human and technical problems exist; however, human problems dominate, and require conflict management skills to resolve. In the second stage, human problems improve, but technical problems persist, requiring formal problem-solving methods to resolve. Finally, in the third stage, both human and technical problems improve. It is important to recognize these transitional stages because they must be effectively managed in order for the material handling functions to perform at the optimal level. Implications of this research, including directions for future research, are also provided. Published by Elsevier B.V.

Keywords: Material handling system Distribution operations Human resources

1. Introduction In the present environment of intense global competition companies spend millions of dollars each year on the latest material handling systems with the goal of improving the performance of their distribution operations. Examples of these material handling systems include automatic storage and retrieval system (ASRS), voice activated pick modules, or sortation systems. Technical aspects of these systems are showcased in international conferences,1 endorsed in popular magazines,2 and promoted on a plethora of websites3 around the world. It is well known that implementation of these systems involves both technical factors (e.g., the size of ASRS system) and human factors (e.g., work groups). However, do we know how these two factors interact over time?  Tel.: +1770 423 6582; fax: +1770 423 6606.

E-mail address: [email protected] www.promat.com or www.cies.com 2 www.mmh.com or www.dcvelocity.com 3 www.dematic.com or www.knapp.com 1

0925-5273/$ - see front matter Published by Elsevier B.V. doi:10.1016/j.ijpe.2008.12.026

What problems are encountered? What kind of training needs to be provided to the workers to prepare them for the change? And, more importantly, do these systems deliver the promised results? In short, we lack an understanding of how the human and technological factors interact, and the stages that they go through before they begin to perform at an optimal level. The purpose of this study is to discuss the implementation of material handling systems at a Fortune 100 home furnishing company in the southeastern United States. While improving distribution operations, we found that both human and technical factors impacted the successful implementation of the material handling systems being implemented. These human and technical elements went through overlapping, transitional stages before they began to perform at the optimal level. It is important to recognize these transitional stages because they must be effectively managed in order to reap the maximum benefits of a material handling system implementation. In the first stage, both human and technical problems exist; however, human problems dominate, and in order to resolve them, conflict management skills are required.

ARTICLE IN PRESS 90

S.S. Chakravorty / Int. J. Production Economics 122 (2009) 89–106

In the second stage, human problems improve, but technical problems persist, requiring formal problemsolving methods to resolve. Finally, in the third stage, both human and technical problems improve, and the distribution system begins to perform at the optimal level. We discuss the importance and implications of this research to both practitioners and academicians, and provide direction for future research. In the next section, we provide the theoretical foundation of transitional stages of material handling system implementation. In section three, we discuss our case study approach in implementing material handling systems to improve distribution operations. In section four, we describe our implementation experience. In section five, we provide implications of our implementation experience. Finally, we provide conclusions and directions for future research.

2. Literature review The theoretical foundation of the transitional stages of a material handling system implementation is developed in three steps. First, technical factors are discussed, along with recent research on distribution operations and material handling systems. Second, the importance of human interface is delineated in implementing these systems. There is a paucity of literature on human factors relating to material handling systems. The research involving implementations of new technology or information systems is used to highlight the importance of human interface. Third, implementation of material handling systems involves group activities and it is well known that groups go through transition or developmental stages before they perform optimally. The research from group development literature is utilized in order to understand how human and technical factors interact to provide the basis for transitional stages.

2.1. Technical aspects According to Tompkins et al. (2002) there are many steps of material handling system design. These steps include: defining the objectives; establishing the scope; analyzing the requirements of moving, determining storage, protecting and controlling materials; generating and evaluating alternatives; selecting the preferred material handling system; and implementing system. According to Gu et al. (2007) these steps touch essential warehousing functions such as storage, order picking or handling, receiving or shipping. In order to improve overall warehouse operations, the performance of each of these functions needs to be optimized. Over the years, the majority of academic research has focused on mathematical or simulation models of warehouse functions. These studies involve restrictive assumptions and provide limited insights for real-world implementations. It is beyond the scope of this study to present an exhaustive discussion of previous research on all areas of warehouse operation; therefore, only a sample of the

latest research is included. Storage system related research includes that by Fukunari and Malmborg (2009), Arruda and do Val (2008), He and Luo (2009), Dooly and Lee (2008), Roodbergen and Vis (2009), Hua and Zhou (2008), Hsieh et al. (2007), Chang et al. (2007), and Fukunari and Malmborg (2007). Order picking or handling system related research includes that by Lee and Kuo (2008), Tsai et al. (2008), Zhang et al. (2008), Alemany et al. (2008), Papachristos and Katsaros (2008), Alarco´n et al. (2008), Sujono and Lashkari (2007), Bozer and Kile (2007), and Sari et al. (2007). Receiving or shipping activities related research includes that by Liao (2008), Wamba et al. (2008), Wooyeon and Egbelu (2008), Kreng and Chen (2008), Cetinkaya et al. (2008), Xu and Leung (2009), Jaber and Goyal (2008), Persona et al. (2007), and Wong and Hvolby (2007). It is important to note that the studies cited are narrowly focused on the technical details of material handling systems, with little or no discussion of human factors. While many of the human-centered issues have been adopted in a number of technology implementations, these issues are largely ignored, or considered outside the scope of the material handling system implementation. 2.2. Human aspects Implementation of material handling systems also involves many decisions on human factors. These factors include the work groups, the assignment of operators to specific work groups, the training needed for workers, and the role of supervisory personnel and the warehouse leadership. According to Witt (2006) material handling or new technology systems should be designed with an awareness of the impact of changes on the human component. Fiske (2007) suggested that technology implementation has a human component that must be managed to reap the full benefits of such an implementation. Many authors (e.g., Majchrzak et al., 2000; Orlikowski, 2000) argue that technology and human factors are interdependent; each impacts the other to achieve a successful design and implementation. Human-centered design takes a socio-technical view (Emery and Trist, 1960), and emphasizes managing the interaction of technical and human factors (Hedberg and Mumford, 1975; Heller, 1989). The main points of human-centered design perspective are: Human-centered design promotes flexible work design which allows people to define (or fine tune) work and manage their work (Gill, 1991; Kapoor, 1996; Lehaney et al., 1999). Technology expectations influence social expectations, which in turn influence technology expectations (Mackenzie and Wajcman, 1999). Human-centered design advocates a systemic view, which considers technology and its environment together.

ARTICLE IN PRESS S.S. Chakravorty / Int. J. Production Economics 122 (2009) 89–106

Explicit, rule-based knowledge required to implement technology is useless without tacit and skill based knowledge (Cooley, 1987; Rosenbrock, 1988). Humancentered design promotes the need for informal or formal communication (e.g., problem solving) to enable to use of implicit knowledge (Land, 1992). We should avoid the propensity of ‘‘planning’’ versus ‘‘doing’’ tasks. Doing tasks implies that people are not familiar with problem solving and are ill-equipped for effective decision-making (Cooley, 1987). Humancentered design supports a socio-technical view and promotes effective decision-making (Gill, 1991; Lehaney et al., 1999). 2.3. Human and technical interaction Considering both technical and human factors, implementation of material handling systems involves work design (Sinha and Ven de Van, 2005) which consists of group activity. Literature points out that a group goes through specific developmental stages (e.g., Schermerhorn, 2007). According to Mennecke et al. (1992, pp. 526–527) there are three categories of group development models. Those categories are linear, cyclical and hybrid. Linear models of group development exhibit ‘‘an increasing level of maturity and performance over time.’’ Cyclical models are defined as models that ‘‘imply a recurring sequence of events.’’ Hybrid models are models that ‘‘do not imply a specific sequence of events; rather, the events that occur are assumed to result from contingent actors that change the focus of the group activities.’’ Linear models are perhaps the best known, most widely used group development models, and are most applicable in this study. Over the years many authors (e.g., Bennis and Shepard, 1956; Mills, 1964; Tuckman, 1965; Sarri and Galinsky, 1974; Braaten, 1974/75; Heinen and Jacobson, 1976; Tuckman and Jensen, 1977; Caple, 1978; Lacoursiere, 1980; Kormanski and Mozenter, 1987; Maples, 1988) have described various stages of the linear models. These stages are characterized by unique patterns of behavior and have been observed in many different environments (e.g., Wheelan and Burchill, 1999; Banker et al., 1996). Gibbard et al. (1974) emphasized that linear models are sequential in time, encounter transitional stages, and must follow a definite order of progression. According to Smith (2001) an example of transitional stages would be a flight of stairs. In order to reach the top (representing optimal performance) groups must walk up individual steps or deal with successive stages. In Stage 1, or the early stages of development, there is a need for group member to understand what is expected of them, and how they fit into the rest of the group. The members are polite to each other and there is general respect for both the formal and informal leadership.

Following this, group members begin to compete for leadership roles, creating interpersonal conflict. One reason for interpersonal conflict is due to functional differences that exist among group members (e.g., Chakravorty and Franza, 2005). At this stage of group development effective conflict management skills become very important. Improving communication, reinforcing positive behavior, and clarifying procedures are all foundations of good conflict resolution (e.g., Shani and Lau, 2005). Conflict resolution clarifies the roles of group members. Members then develop formal and informal procedures, and begin to work together cohesively. In Stage 2, once group members understand their roles, they are better able to resolve conflicts encountered when following the established procedures, become taskoriented, and begin to solve technical problems. Relationship orientation switches to task orientation, groups set clear goals and objectives, and begin to show progress (Gratton et al., 2007). Group development reaches a pinnacle stage, and at that point problem-solving skills become essential in order to continue work group development. There are many approaches to problem solving, most consisting of a four to six step process (Chakravorty et al., 2008). The processes include identifying a problem, gathering information, generating solutions, evaluating solutions, and implementing solutions. As groups become effective working units they emphasize attaining the group’s goals and objectives, which helps reach the optimal level of performance, Stage 3. In short, before becoming an effective working unit, group members experience interpersonal conflict, establish their respective roles, apply problem-solving skills, and finally, coordinate their work to reach optimal performance (Fig. 1). 3. Methodology 3.1. Case study A case study approach was used to document implementation of a material handling system designed to

Stage 3 Optimal Performance Performance

Human-centered design jointly optimizes questions like, ‘‘what can be produced?’’ and ‘‘what should be produced?’’ The first question deals with what is technically feasible, and the second question deals with what is socially desirable (Kuhn, 1996; Lehaney et al., 1999).

91

Stage 2 Technical Problems Problem Solving Skills

Stage 1 Human Problems Conflict Management Skills

Time Fig. 1. Transitional stages.

ARTICLE IN PRESS 92

S.S. Chakravorty / Int. J. Production Economics 122 (2009) 89–106

improve distribution operations for a home furnishing company. According to Yin (2008) there are three reasons why a case study research methodology is appropriate for our study. First, when ‘‘how’’ and ‘‘why’’ questions are investigated, a case study approach provides a description of linkages among events, rather than their frequencies or occurrences. Our study is an exploratory study developing an understanding of how material handling systems are implemented. Second, a case study approach is preferred when a real-world event is examined. Since many companies are actively engaged in implementing material handling systems, it is a natural way, in a real-world environment, to document insights from such an implementation. In addition, many authors (e.g., Chakravorty et al., 2008) emphasize the need for real-world based research in order to help practicing managers stay competitive and to improve operations. Third, a case study approach is appropriate because this approach uses multiple sources of evidence, such as documents, archival records, interviews, and direct observation. According to Meredith (1998, p. 443), y the importance of direct observation [first source (seeing it oneself) rather than second (speaking or writing to someone who saw or experienced it) or third, or sometimes no source at all], the role of the context in which the phenomenon is occurring, and the dynamics of the temporal dimension through which the events of the phenomenon unfold help to understand the how and why elements of the phenomenon. 3.2. Data collection As suggested by Yin (2008) we used multiple sources of evidence to collect data. First, qualitative data were collected through documentation obtained in the form of letters, memoranda, minutes of meetings, progress reports, strategic planning reports, and other similar data. Second, quantitative data were collected in the form of archival records of financial data, customer complaint reports, order processing records, quality control reports, purchase orders, operational data (e.g., pick line utilization), and performance measurements (e.g., annual sales, and lead-time performance). Third, qualitative data were collected through extensive interviews with participants, including sales executives, managers, and workers. We conducted the interviews in an open-ended manner, which increased the probability that respondents provided objective opinions of the events, as well as insights into certain occurrences. We followed the general guidelines provided by Fontana and Frey (2000). Fourth, qualitative data were also collected by direct observation of all decision processes during the implementation. Fifth, the researcher was involved in the implementation process, so it was possible to collect qualitative data in a participation-observation mode. Sixth, quantitative data were collected, recording errors such as those made in picking orders and in merging and sorting, which resulted in multiple handling of orders, and were reasons for long lead-time and poor delivery performance. During the study the researcher kept a research log and documented

each problem encountered during the implementation, as well as thoughts and insights gained during the process. 3.3. Data analysis The data analysis was performed using Kolb’s learning cycle for experiential learning (Fig. 2). According to O’Sullivan and Dooley (2009) this learning cycle is a well known theoretical model for experiential learning. As the model is specific to experiential learning, this should be helpful to the researchers engaged in implementing material handling systems. According to Kolb (1984) there are four steps of the model: concrete experience, reflective observation, abstract conceptualization, and active experimentation. Concrete experience was realized when the researcher identified patterns and common themes by analyzing his own experiences and those of other participants. Content analysis worked well for identifying possible root causes and for prioritizing alternative solutions. In essence, content analysis is the counting of words, sentences, or ideas within categories of interest. In this case, we used it to collect ideas on how the material handling system was implemented. While a number of variations exist when using content analysis, three general guidelines are applicable to all. First, two judges were used for performing the analysis so that the consistency of the results could be estimated. Second, the categories of interest had to be applicable to the research objectives. In this case, we collected data specifically on the implementation and use of the material handling system. Third, the units of analysis must be appropriate for representing the topic under examination (Rosenthal and Rosnow, 1991). In this case, the unit of analysis was the operational level of distribution where the material handling system was designed and implemented. Reflective observation was performed by examining the flow of material through the distribution operations; we employed value stream mapping. Each activity in a value stream map was shown on a two-dimensional scale. The map was then used to identify value added and non-value added activities. These were then connected with arrows

Concrete Experience

Reflective Observation

Active Experimentation

Abstract Conceptualization

Fig. 2. Learning model. Source: adapted from Kolb (1984).

ARTICLE IN PRESS S.S. Chakravorty / Int. J. Production Economics 122 (2009) 89–106

showing the direction of material flows. Non-value added activities included average waiting times, mistakes in order processing, defect rates, and other measurable factors. Value added activities include the order processing times estimated for each stage of the material flow. These maps also helped identify where material was processed through redundant or unnecessary steps. Abstract conceptualization was carried out each evening when the researchers reviewed that day’s information and data. They spent several hours with managers and supervisors brainstorming ideas and reflecting on what had happened during the day’s events. They grouped the ideas using content analysis, and mapped the order processes to be addressed the next day. The ideas with the greatest potential for success, and with the most support from executives, were prioritized for implementation. Results of the ideas implemented during the current day were reviewed for performance. Those that provided improvement were noted, as well as the preliminary insights into why they worked. Active experimentation was performed the following day where the order process could benefit from the same solutions. Each morning, based on consensus, management and supervisors chose the next problems to attack. The planned solutions were implemented during the company’s first shift operations. During this period, the researcher focused solely on assisting with the implementation efforts and documenting the process. 3.4. Strengths and weaknesses Case study research has both strengths and weaknesses. A strength of this method is that it provides a rich explanation of ‘‘how’’ and ‘‘why’’ phenomena occur, an explanation that cannot be expressed through simulation or statistical models based on survey. Second, the phenomenon is studied in a natural setting which would be expensive, difficult, or impossible to replicate in a laboratory experiment. According to Stake (2000), realworld studies are valuable for refining theory and for suggesting complexities for further investigation. A weakness of the case study method is that it fundamentally assumes that an espoused theory should adequately specify action, which is rarely the case (Kemmis and McTaggart, 2000). Rather, the best theories are parsimonious and do not claim to replicate reality. Second, the participants are exposed early on to the ideas of the researcher, which can create bias when identifying root causes or explaining events. Finally, conclusions from a single study may have limited generalizability, and therefore, participants’ ideas may have little validity in developing or forming a theory. Other researchers are invited to test the extent that these findings can be generalized by conducting further research in other environments. 3.5. The company We conducted this study in a Fortune 100 home furnishing company located in the southern United States.

93

This company provides home furnishing products for consumers, has a number of manufacturing facilities in the southern United States, and has distribution centers located throughout the United States. This implementation was initiated in their distribution operations because there was a dramatic increase in lead-time of delivery performance, resulting in poor customer service. The distribution operations were experiencing all time low performance levels. In the previous three years, inventory levels increased dramatically, causing a rapid proliferation in the number of distribution warehouses. At the time of this study implementation, the company held an estimated 43% of the home furnishing products market in the United States.

4. Implementation experience The materials handling system implementation was initiated to improve the company’s distribution operations’ worsening performance. Poor customer service due to long lead-time and inadequate delivery performance was forcing many of the company’s customers to move their business to competitors. Because theirs was a highly competitive environment with only two major market players, this was a serious problem for the company. The Vice President of Distribution and his management team, consisting of the Senior Manager of Sales and Marketing, the Senior Manager of Production, the Manager of Purchasing, the Manager of Human Resources, and the Accounting Manager, deliberated, and concluded that they needed to completely redesign material flow through the warehouse. The redesign effort was a significant change requiring modification in work-design practices which would impact all warehouse employees. In the past, the team had worked well with the warehouse employees and felt confident in their organizational skills and in their ability to manage the human aspects of the change. The team knew, however, that they lacked sufficient skills to design the technical elements of the change, so a consulting firm, specializing in material handling systems, was hired to handle the technical aspects of the implementation. The team and the consulting firm deliberated and identified two distinct phases: design and implementation. The entire project was projected to take 18 months, consisting of 4 months for the design (Phase I), and 14 months for the implementation (Phase II). The design phase was an iterative process, with active participation from the management team, and involved three major activities. First, existing operations were studied, using a variety of tools, including value stream mapping, labor efficiency histories, existing layout, and shipping analysis. The objective was to develop a thorough understanding of the existing operations before design options were explored. An in-depth analysis was performed to complete the study; however, due to the proprietary nature of work, only representative examples are included (see Appendices A–D). Second, many different material handling technologies (e.g., ASRS versus order storage and retrieval (OSR) system) were

ARTICLE IN PRESS 94

S.S. Chakravorty / Int. J. Production Economics 122 (2009) 89–106

considered, and several high-level design options were conceptualized and presented to the management team. With their input, three alternatives (i.e., design options A–C) were fully developed. These alternatives included high to low technology applications (see Appendices E–G). The management team, supervisors, and many workers deliberated with the consulting firm to finalize an alternative. After many days of discussions, and after considering all pros and cons, an agreement was reached to finalize design option B. Using option B, an engineering design, complete with equipment specifications and control systems, was developed. In order to receive approval from upper management (e.g., Chief Financial Officer), a business case was developed which included the material handling system design, a description of operations, the final layout, and financial justifications. The implementation phase was initiated by the management team by forming a steering committee responsible for the transition plan and for the execution of the plan. The committee began their transitional plan by examining work skills in warehouse functions including receiving, put-away, storage, order picking, sortation, and shipping. The committee reclassified many functions to remove all redundancies in the functions. In doing so, the committee planned for a 30% reduction in the number of warehouse workers. Many of these workers were retrained and absorbed into the company’s manufacturing operations; others took voluntary retirement. Using the reclassified warehouse functions, the committee formed workers into 24 work groups. Depending on the warehouse function, the work groups consisted of 8–12 workers. Some workers were not included in any work group because they were in support roles (e.g., maintenance, custodian, cafeteria workers, or administrative personnel). The work groups consisted of members with varying degrees of experience and skills, ranging from high to low. The committee thought that the use of work groups would take advantage of the varying skill levels of the workers by balancing the workload, facilitating implementation of equipment, and allowing highly skilled supervisors to oversee the entire implementation. Once work groups were identified, the next step was to select supervisors or champions for managing the work groups during implementation. The steering committee spent inordinate amounts of time selecting supervisors and champions. The committee believed that the success of the implementation was dependent on the champions and their ability to manage the work groups. While technical skills of champions were required, their human skills were more important for this role. Without performing any formal psychological profiles, the committee looked for warehouse employees who had been passionate about their work and committed to showing positive results. These employees stayed focused and inspired others to work together as a group. They worked hard, took full responsibility, and found creative ways to attack and solve problems. In other words, they followed their passion with a ‘‘Just Do It’’ spirit and encouraged others to do the same. They came with varying levels of experience (2–20 years), professional qualifications (e.g., information technology or maintenance), and education

(e.g., no college degree to Bachelors in Industrial Engineer). After many days of deliberations, the committee selected 8 champions or supervisors to manage 24 work teams (the average was 3 work groups per champion) which were to implement material handling systems throughout the warehouse operations. The next step for the steering committee was to determine which individual workers to assign to which work groups. This task also required a great deal of time and effort. The committee deliberated with the managers and the newly selected champions for their opinions before forming the work groups. In choosing individual workers, both technical and human factors were considered. Technical factors considered included an individual’s number of years of experience, and knowledge of different warehouse functions. Since the proposed material handling involved more sophisticated technology applications than their existing operations, superior technical competency was a very desirable attribute. Human factors considered were an individual’s ability to work in a group and their ability to quickly adjust to the changing environment. These factors were important because the plan was to install a material handling system in one area as a prototype for the entire implementation. The prototype helped to fine-tune transitional plans and to determine the initial number of workers necessary in each work group. As soon as the prototype work group was formed and the first group of material handling systems was installed in their area, the supervisors went to work. There was excitement among the workers in that area and throughout the warehouse. The committee and management closely observed the functioning of the area. They gathered suggestions for improvement from the workers and from the supervisor of the area. They entered the suggestions into a log book, grouped them into different categories, and analyzed each entry for possible root causes. The possible root causes were later reviewed, and those deemed appropriate were included in the final detailed transitional plan. The involvement of management was vital at this point. Workers noted how managers reacted to the process and began to believe that the new material handling system could improve the company’s performance. Using the information gained from the prototype work group, the steering committee developed a detailed transitional plan consisting of major activities. The financial impact of each was meticulously tabulated. This plan was reviewed and approved by management, supervisors, and workers. The plan included a sequence of execution activities such as work group composition, training schedules, movement of existing equipment, and installation of new material handling systems in order to improve material flow through the warehouse operations. Several aspects of the plan were executed simultaneously. For example, as soon as work group composition was finalized, each member went through several hours of training on how to properly utilize the new material handling system, as well as problem-solving skills training. While the training sessions were taking place, necessary equipment was being physically moved into

ARTICLE IN PRESS S.S. Chakravorty / Int. J. Production Economics 122 (2009) 89–106

the appropriate area. Many workers decided to leave the company because they were not willing to adopt the new material handling system. The details of the transitional plans, including the supporting financial plans, are provided in Appendices H and I. 5. Transitional stages Following the implementation, there was cooperation within the work groups, and members generally worked in a cohesive manner. Although there were occasional technical problems, performance improvements were noticeable. However, minor disagreements began to increasingly occupy the supervisor’s time. Initially, these disagreements were infrequent and confined to one or two work groups; however, with the passage of time, these problems started to occur more frequently among all work groups. Increasingly, disruptions began to affect work group performance and endanger earlier gains in lead-time. Although there was variation from group to group, the human and technical issues soon became evident throughout the warehouse. We documented the event and will later explain the phenomenon as the first stage in implementing material handling systems. 5.1. Stage 1: both human and technical problems become apparent—two months During this stage of implementation, the work groups encountered both human and technical problems; however, human problems were dominant. The workers did not enjoy social interaction. Experienced workers felt a loss of control in handling ordering through the voice activated system, and inexperienced workers were feeling peer pressure to learn the system and perform. There were problems in many steps of the material handling system. The first step of order handling was coordinating picking activities among the work group members, using a voice activation system. In the existing system, one worker was responsible for all picking activities needed to complete an order, so each followed a slightly different picking order, which was not a problem. Now, with voice activation and the new pick module, each order was fulfilled within a work group. With many experienced and inexperienced workers in each work group, serious disagreements ensued. While the supervisor was able to resolve the issues, there was time lost in order picking, negatively impacting performance. After work group formation, both higher skilled and lower skilled workers were processing more orders, but the accuracy of order picking suffered. The reduced order picking accuracy created re-picking, resulting in additional difficulties in the sortation system, ultimately delaying the completion and shipping of the order. Higher skilled workers felt at a loss; they were not able to mentor the lower skilled workers because they were learning the voice activation system and were feeling peer pressure to improve performance. There was considerable difficulty with the pick module location (or layout in the warehouse); however, formal problem-solving methods were rarely

95

applied. For example, although the voice activation system was working fine, workers slow in picking orders frequently created situations where two people would need overlapping zones, creating serious conflicts. Here again, the supervisor had to ultimately resolve the issue, but at a loss of processing time. Order picking errors made in previous steps were frequently not discovered until after the order passed through the sortation system, resulting in shipping delays while the orders were re-picked. Complete orders were frequently scrapped because incorrect items were picked in the previous step and not discovered until the end of line content inspection or weight check. Instead of employing formal problem solving techniques, workers blamed each other for making obvious mistakes, and also accused the new material handling system of being the cause of all problems. Based on the average number of complaints per week, we calculated that 90% were human problems (requiring conflict management skills to resolve), while only 10% were technical problems (requiring problem-solving skills). As workers competed for leadership and other roles, interpersonal conflicts were leading to serious disruptions in work group functioning. Constant arguments and open disagreements hampered the smooth functioning of work groups. As tensions grew, so did vocal confrontations. These confrontations soon extended to the supervisor who was trying to play the role of peacemaker. For example, instead of immediately intervening, the supervisor used counseling in an attempt to get the two workers to resolve their issues among themselves. If a work group member complained about another member, the supervisor, after listening to the problem and providing suggestions, would usually ask the work member to return and work out a mutual solution. If no agreement was reached, the supervisor would call a meeting with the entire work group (not with an individual member) and act as facilitator to develop a working solution acceptable to all members. If this did not work, the supervisor acted unilaterally, switching the members to other work groups. In short, while both human and technical problems were present, human problems dominated this stage of work group evolution, and often disrupted the functioning of work groups, erasing earlier performance improvements.

5.2. Stage 2: human problems improve, but technical problems persist—four months During this stage of implementation, supervisor intervention and coaching revised worker assignments. Establishment of work group leadership and the resolution of conflicts were leading to the stabilization of the work group. We later classified this in our transitional analysis as ‘‘Stage 2.’’ As each work group was gaining experience in the new system, it also developed informal and formal procedures to deal with recurring and annoying problems. For example, meetings with work group members contributed greatly to relieving tension among the workers, as well as to establishing informal leadership roles. These meetings were held, with encouragement from the

ARTICLE IN PRESS 96

S.S. Chakravorty / Int. J. Production Economics 122 (2009) 89–106

supervisor, at a minimum of twice weekly (in some instances daily) during lunch hours and break times. The meetings became a forum for open dialogue, where workers openly expressed their points of disagreement to each other. In some instances, two or three work groups held their meetings simultaneously. As they shared their problems they found that all groups were encountering similar problems. With some degree of order and stability restored, work groups began to apply problem-solving skills to technical problems. The average number of complaints per week was about the same as in the previous stage; however, based on content analysis, we calculated that 85% were technical in nature, while only 15% were human conflict related. Although technical problems persisted at each step of processing, resolution of the human issues allowed the focus to switch, thereby accelerating technical improvements. An unexpected observation at this stage was that workers were receiving higher job satisfaction from a healthy level of challenge and from working through many of the technical issues. Unlike Stage 1, where ‘‘turfs’’ were protected, the higher skilled members learned many aspects of the material handling system and began to mentor the less skilled. The less skilled enjoyed learning new aspects of the job. Work groups developed formal procedures to handle the problems associated with order picking. Typically, the workers with the highest skills clearly played the leadership role. This member was designated to determine resolution with order picking errors, with the supervisor providing support for this member’s solution. There were additional problems with the conveyor system and control systems of sortation system, but those were gradually eliminated over time by technicians. Several changes to the material flow within each work group reduced many problems associated with the pick module layout. Conflicts in zone picking were resolved as team members gained experience in voice activation and started to work together as a group. There were also signs of improvements in many other steps of order processing. As work groups processed more orders, workers gained experience. This facilitated fewer mistakes, yielding less discarded orders. At the same time, lower skill workers were gaining work experience as well, which was gradually improving the quality of their work. The work groups were spending a great deal of time in applying formal problem-solving skills. For example, once a picking error was detected, group members would spend time in gathering relevant information. Initially, the problem was to identify the source of the problem or the root cause. For example, upon gathering relevant information, the source of a problem could be faulty voice activation, a poor conveyor control system, failures in the sortation system, or jamming of a retrieval system in ASRS, or other similar issues. The work groups would get together to generate possible solutions ideas. They kept an open mind, and refrained from blaming each other. Time permitting, work group members from other work groups, as well as supervisors and managers, would participate in these sessions. Once large a number of solution ideas were generated, the work groups evaluated these ideas conceptually, and sometimes performed mini experiments to

further evaluate the best ideas. Many times, the evaluations reveled that sources of the problems identified earlier were not correct. Additional information, therefore, was necessary and the group would begin the process again. This was time consuming and frustrating at times; however, once the true source of the problem was clearly identified, the solution was speedily implemented. In short, work groups routinely applied problem-solving skills, and although technical problems persisted, there was improved performance of the material handling systems. 5.3. Stage 3: both human and technical problems improve Technical problems appeared to improve during this stage of material handling system implementation. Informal procedures established in the previous stages helped work group members resolve their interpersonal conflicts, resulting in the emergence of effective work units. This promoted the enthusiasm that the workers experienced during the prototype-testing period for the new material handling system. As more orders were processed, accelerated learning of technical skills took place. Formal procedures established to handle problems associated with previous steps (‘‘order picking’’) worked very well. The frequent jamming of parts associated with sortation systems virtually disappeared. Work groups’ order picking accuracy and the sortation system, along with shipping of packages to complete an order, progressed at a record pace. There was a significant reduction in the number of delays in shipping orders. In other words, there was smooth functioning of work groups, and group performance was at its optimal level, exceeding the performance achieved during the prototypical period. The average number of complaints per week was one third that of previous stages. Based on content analysis, we calculated that 55% of the complaints were technical in nature (requiring problem-solving skills), and 45% were human conflict related (requiring conflict management skills). 5.4. Performance Delivery lead-time, inventory reduction, and order accuracy levels of the company’s distribution operations during the transitional stages are summarized in Table 1. In Stage 1, when both human and technical problems were apparent, the performance of the distribution Table 1 Performance: during transitional stages. Measures

Stage 1 (2 months)

Stage 2 (4 months)

Stage 3 (2 months)

Lead-timea Inventory reductionb (%) Order accuracyc (%)

18.7 weeks 23.4 80

16.4 weeks 53.9 91

14.9 weeks 78.9 94

a b c

Progressive assembly. Finished goods inventory. Average of two pick lines and two shifts.

ARTICLE IN PRESS S.S. Chakravorty / Int. J. Production Economics 122 (2009) 89–106

Table 2 Performance: before and after the implementation. Measures

Before implementation After implementation a

Lead-time 21.4 weeks Inventory reductionb (%) 10 c 74 Order accuracy (%) a b c

14.3 weeks 82.4 97

Progressive assembly. Finished goods inventory. Average of three pick lines and one shift.

operations was at its worst, manifesting itself in poor lead-time, low inventory reduction, and low order accuracy. In Stage 2, when human problems improved, but technical problems persisted, there were considerable improvements in lead-time, inventory reduction, and order accuracy. In Stage 3, when both human and technical problems improved, we observed significant improvements in lead-time, inventory reduction, and order accuracy. Compared with Stage 1, Stage 3’s leadtime dropped by 20%, inventory reduction increased more than 300%, and order accuracy increased by about 18%. This implementation was successful because the performance of the distribution operations improved (see Table 2). As summarized in Table 2, performance data were collected before the implementation began, and follow-up data were collected one year after the implementation. One year after the implementation, the lead-time performance of the company’s distribution operations had been reduced by more than 33%. This improvement made the company’s performance better than that of their direct competitors. Improvement also occurred in inventory reduction, in significant decreases in the number of warehouses, and in improvements in order accuracy, all of which implies a significant decrease in customer complaints.

6. Implications of implementation There are three important points worth discussing regarding the material handling system implementation stages. In Stage 1, workers did not enjoy social interaction, felt a loss of control, and encountered enormous peer pressure to perform. Although the nature of human problems may differ from one environment to the other, human problems will persist, as documented in group development literature. Conflict management skills were crucial to resolving conflict among the work group members by opening communication channels and by reinforcing positive behaviors of other work group members. The time dimension associated with this stage will be proportional to the size and the number of work groups. Our implementation experience shows that supervisors or champions played an important role in reducing the length of Stage 1. This is a significant finding because, given the capital intensive nature of material handling

97

systems, this could potentially save tremendous amounts of time and money for companies. There are two areas of future research. First, the steering committee had lengthy deliberations before selecting the supervisors or champions, but no formal psychological profiles were considered. There are many such tests available. See, for example, Stevens and Campion’s (1994) knowledge, skill, and ability test, Kembel’s (1996) rational, organized, loving, and energized test, and Kolbe’s (1994) measure for instinctive behavior of individuals, which could assist in making decisions earlier. While theoretical research (e.g., Askin and Huang, 2001) provides strong argument for the consideration of psychological factors when implementing new technology, additional insights are necessary in this area. Second, the committee used its experience to assign an average of three work groups to each supervisor. The committee did not find any research to suggest how to benchmark the number of work groups to assign to each supervisor. We believe that many factors, such as the business environment, the complexity of implementation, and the skill levels of work groups will play an important role in determining the optimal number of work groups per supervisor. More research is necessary to support this claim. Previous research (e.g., Chakravorty and Hales, 2004) has emphasized the need to conduct research on the role and the appropriateness of supervisors, especially for new technology implementation. In Stage 2, we observed that human problems improved; however, technical problems then became the dominate issues. The group development literature suggests that when groups work cohesively they become task-oriented and engage in problem solving. We encountered technical problems with voice activation, pick module layout, conveyor systems, and sortation systems in almost all area of implementation. It is important to note that the nature of technical problems may differ from one company to another. Problem-solving skills were helpful in resolving many technical problems. Again, as in the previous stage, the time dimension associated with this stage may vary from one environment to another. The time dimension associated with this stage is directly proportional to the size and the number of work groups. We believe that teaching work group problem-solving skills considerably reduced the length of Stage 2. As the literature on problem solving suggests, there are many models of problem solving; however, there is a difference between what these models describe and what actually happens in the real world (Chakravorty et al., 2008). More research in this area should be beneficial for practicing managers. In addition, problem-solving skills are at the core of many improvement strategies, Six Sigma being one example (Chakravorty and Franza, 2009). Typically, implementation of improvement strategies and material handling systems are considered separate initiatives in distribution operations. As our implementation shows, it is beneficial for companies to implement improvement strategies and material handling systems simultaneously. More research is necessary to determine how to synergistically blend the implementation of improvement strategies and material handling systems to further shorten the transitional stages.

ARTICLE IN PRESS 98

S.S. Chakravorty / Int. J. Production Economics 122 (2009) 89–106

In Stage 3, both human and technical problems improved. Work groups became effective working units. At this point, there was accelerated learning of technical skills, and work groups began to perform at the optimal level. We do not have a complete understanding of the long-term sustainability of optimal performance of material handling systems. We find that a complex set of issues, such as market dynamics, process variability, technology innovation, and human skill or knowledge, will interact in a complex manner over time, but how that interaction will look, we simply do not know. More research is necessary to provide insights into sustainability issues of material handling systems.

7. Conclusions We conclude that implementations of material handling systems go through three, somewhat overlapping, transitional stages before they begin to perform at the optimal level. These stages must be managed in order to reap the maximum benefits of the implementation efforts. In the first stage, both human and technical problems existed; however, human problems were dominant, and conflict management skills were critical at this stage. In the second stage, human problems improved; however, technical problems persisted, and problem-solving skills were critical at this stage. In the third stage, both human and technical problems improved, and the material handling system performance reached the optimal level. In this company, the material handling system reached the optimal performance in six months. Based on the size and the complexity of material handling systems, the time to reach optimal performance will be different in different business environments. These findings suggest that in each stage of material handling system implementations there are many areas for research. In the first stage, we found that the group supervisor’s or champion’s role was important in reducing the transition time. In order to select the supervisors, we

informally evaluated many characteristics, but we did not apply formal psychological profiles. We believe that selecting supervisors using psychological profiles could further shorten the transition time associated with the first stage. More research is necessary to support this claim. In addition, the steering committee used its experience to assign three work groups per supervisor. There is a need to determine which factors to use in order to decide the optimal number of work groups to assign to each supervisor. We believe that optimality in the number of groups per supervisor could reduce the transition time associated with the first stage. More research is necessary in this area. In the second stage, we found that work groups applied problem-solving skills in reducing the transition time. In other words, we found that providing the work groups with basic problem solving training was beneficial. While there are several models of problem solving, research is emerging in terms of how these models work in the real world. More research is necessary to understand how problem solving really works in different operating environments. The findings of this research could further reduce the transition time associated with the second stage. In addition, we find that problem solving methodology is the cornerstone of many improvement strategies (e.g., Six Sigma). Typically, implementation of improvement strategies and material handling systems are considered separate initiatives. More research is necessary to determine how to synergistically blend the implementation of improvement strategies and material handling systems to further shorten the transitional time associated with the second stage. In the third stage, we know that there was accelerated learning of technical skills; however, we do not have a complete understanding of how to sustain optimal work group performance. We do not have a complete understanding of the long-term sustainability of the optimal performance of material handling systems. More research providing insights into this issue should be beneficial for both practitioners and academicians.

ARTICLE IN PRESS S.S. Chakravorty / Int. J. Production Economics 122 (2009) 89–106

99

Appendix A. Value stream mapping

Note: Some information from value stream mapping is concealed due to its proprietary nature.

Deck Boards Batch Size = 100 Colors = 24

Material Handling 5 days (40 Hours)

Receiving 14 hrs

1hr

Queue (Wait)

8 hrs

1hr

Queue (Wait)

Cutting

Labeling + Binding

Cutting Value Added Activities

8 hrs

2 hrs 5 hrs 1 hrs Queue (Wait)

Queue (Wait)

Beveling

Assembly

Cycle Time = 40 Hours Value Added Time = 5 Hours Value Added = 12.5 %

Non-Value Added Activities Contract Folders Batch Size = 300 Colors = 12

Material Handling 5 days (40 Hours)

Receiving

2 hrs 4 hrs 3 hrs

19 hrs Queue (Wait)

Queue (Wait)

Cutting

Cutting Value Added Activities

10 hrs Queue (Wait)

2hrs

Beveling

Cycle Time = 40 Hours Value Added Time = 7 Hours Value Added = 17.5%

Non-Value Added Activities Deck Boards Contract Folders

Shipping 5 days (40 Hours)

8 hrs

4 hrs

8 hrs

Queue (Wait)

16 hrs

4 hrs

Queue (Wait)

Printing Value Added Activities

Non-Value Added Activities

Picking + Kitting + Shipping

Sorting

Cycle Time = 40 Hours Value Added Time = 0 Hours Value Added Activities = 0%

ARTICLE IN PRESS 100

S.S. Chakravorty / Int. J. Production Economics 122 (2009) 89–106

Appendix B. Labor efficiency

Note: This information was extracted from the first shift performance.

5000 4500 4000 3500 3000

1ST LINE A

2500

1ST LINE B

2000

1ST LINE C

1500 1000 500 0 1

11

21

31

41

51

71

81

91

101 111

18000 16000

Order Picking (Units)

14000 12000 10000 8000 6000 4000 2000 0 0

200

400

600

Labor (Hrs)

800

1000

ARTICLE IN PRESS S.S. Chakravorty / Int. J. Production Economics 122 (2009) 89–106

Appendix C. Existing Layout

Note: Partial existing layout is shown.

101

ARTICLE IN PRESS 102

S.S. Chakravorty / Int. J. Production Economics 122 (2009) 89–106

Appendix D. Shipping analysis

Note: Only a representative analysis is shown.

180000 160000 140000 120000

Boards/Folders

100000

Sets/Swatches Units

80000

Cases

60000 40000 20000 0 1

5

9

13

17

21

25

29

33

37

41

45

49

53

Comment: There is a high variability in number of cases/units shipped. Boards/Folders Average = 52383/Wk Std = 15968/Wk

Set/Swatches Average = 38842/Wk Std = 7640/Wk

Units Average = 98279/Wk Std = 22177/Wk

Cases Average = 53770/Wk Std=22190/Wk

100000 90000 80000 70000 60000

Outside Prod

50000

Inside Prod

40000

Total Prod.

30000 20000 10000 0 1

5

9

13

17

21

25

29

33

37

41

45

49

53

Comment: In order to meet customer demand, production orders areroutinely outsourced. Total Material Handling (Avg) = 59492/Wk Inside Material Handling (Avg) = 44428/Wk Outside Material Handling (Avg) = 15064/Wk

ARTICLE IN PRESS S.S. Chakravorty / Int. J. Production Economics 122 (2009) 89–106

Appendix E. Design Option A

Option A is the base proposal. Option A was developed as the most inclusive option to implement demand driven processing using the most efficient equipment and methods. The design parameters for Option A are: 90,000 units per week produced Cellular assembly response time ¼ 1 day Progressive assembly response time ¼ 14 days Option A includes: Receiving—Extendible unloading conveyors and conveyors to deliver to QC. Swatch Prep—Floor conveyors, vertical lifts, and overhead conveyors to transport WIP between processes. Picking Mezz—Conveyors for replenishment of product and empty totes, conveyors for transport of loaded totes. MTO Swatch area—Overhead conveyors from Picking Mezz to new packing/shipping. Cellular assembly—Overhead conveyors, vertical lift modules, discharge conveyors to individual cells. Conveyors from C.A. to shipping. Bulk storage—ASRS for storage and delivery of prepped materials for progressive assembly. Progressive assembly—Overhead conveyors, vertical lift modules, discharge conveyors to assembly lines. Finished goods—New mezzanine, revised storage racks, shipping conveyors.

Appendix F. Design Option B

Option B is similar to the base proposal, but eliminates some of the automation and replaces the automation with manual handling. The design parameters for Option B are: 90,000 units per week produced Cellular assembly response time ¼ 1–2 days Progressive assembly response time ¼ 14 days Option B includes: Receiving—Omit conveyors and use lift trucks, carts, and bins to deliver to QC. Swatch Prep—Eliminate most conveyors between process. Transport WIP in totes and on pallets. Use vertical lifts with minimal infeed and discharge conveyors when elevation changes are needed. Picking Mezz—Omit replenishment conveyors for product and empty totes, but keep conveyors for transport of loaded totes.

103

MTO Swatch area—Overhead conveyors from Picking Mezz to new packing/shipping (same as Option A). Cellular assembly—Overhead conveyors, vertical lift modules, discharge conveyors to individual cells (same as Option A). Eliminate conveyors to shipping and replace with carts and pallets. Bulk storage—Eliminate the ASRS and associate conveyors, and replace with a conventional rack system and turret trucks for storage and delivery of prepped materials for progressive assembly. Progressive assembly—Eliminate overhead conveyors, vertical lift modules, discharge conveyors to assembly lines; and replace with carts and staging conveyors at the assembly lines. Finished goods—New mezzanine, revised storage racks, but minimal shipping conveyors.

Appendix G. Design Option C

Option C is a ‘‘bare bones’’ approach which achieves much process improvement but sacrifices some of the throughput. The design parameters for Option C are: 60,000 units per week produced Cellular assembly response time ¼ 1–2 days Progressive assembly response time ¼ 14–21 days Option C includes: Receiving—Receiving is now all manual using existing equipment. Swatch Prep—Eliminate all conveyors and lifts, and manually transport WIP in totes and on pallets. Picking Mezz—Omit replenishment conveyors for product and empty totes, but keep conveyors for transport of loaded totes (same as Option B). MTO Swatch Area—Overhead conveyors from Picking Mezz to new packing/shipping (same as Option A and B). Cellular assembly—Overhead conveyors, vertical lift modules, discharge conveyors to individual cells (same as Option A and B). Eliminate conveyors to shipping and replace with carts and pallets (same as Option A and B). Bulk Storage—Eliminate the ASRS and associate conveyors, and replace with a conventional rack system and turret trucks for storage and delivery of prepped materials for progressive assembly (same as Option B). Progressive assembly—Eliminate overhead conveyors, vertical lift modules, discharge conveyors to assembly lines; and replace with carts and staging conveyors at the assembly lines (same as Option B, but simplify staging conveyors). Finished goods—New mezzanine, minimal revision of storage racks, and minimal shipping conveyors.

ARTICLE IN PRESS 104

S.S. Chakravorty / Int. J. Production Economics 122 (2009) 89–106

Appendix H. Transition plan

Wks 1-12

Wks 13-24

Wks 25-36

Wks 37-48

Wks 61-72

Phase II

Phase I 1

Wks 49-60

16 Wks 2 Phase I 1. Finalize design parameters 2. Finalize layout 3. Engineering Bldg Upgrades 4. Qualify vendors 5. Solicit installed prices 6. Evaluate responses 7. Generate Phase 2 report

Phase II 1. Award Contracts 2. Vendor ?ramp ups? 3. Co-ordinate vendor activities 4. Perform any required building modifications 5. Supervise vendor installations 6. Co-ordinate startups

16 Wks 3 4

4 Wks 16 Wks 5

4 Wks 6

4 Wks

7

12 Wks 8 4 Wks 9

8 Wks 10

Activity Key 1 Phase I 2 Build New MTO mezzanine/Rework Finished goods rack 3 Move MTO packaging to new mezzanine 4 Expand picking mezzanine/Install conveyors to picking mezzanine and MTO packaging 5 Remove half of roll racks 6 Move cutting tables to new location/Add new cutting tables 13 7 Prepare floor for ASRS/Install half of new bulk storage 8 Remove remainder of roll racks/Move existing Baler(3)/Install new binding mezzanine 9 Move swatch prep to new area/Move equipment/Install transport conveyors 10 Install remainder of bulk storage/Install bulk to pick M/H system /Install receiving conveyors 11 Install cellular assembly/VLM and outfeed conveyors/M/H system feeding progressive assembly 12 Install progressive assembly line/M/H system feeding 13 WMS implementation

12 Wks 11 12 Wks 12 12 Wks 36 Wks

Appendix I. Financial plan

Wks 1-12

Wks 13-24

Wks 25-36

Phase I 1

16 Wks

Option A = $775,000 Option B = $775,000 Option C = $775,000

Wks 37-48

Wks 49-60

Option A = $100,000 Option B = $100,000 Option C = $100,000 2

16 Wks

Option A = $2,927,500 Option B = $2,131,326 Option C = $2,131,326

3 4

4 Wks

Option A = $945,700 Option B = $945,700 Option C = $532,850

16 Wks

5 Option A = $2,951,771 4 Wks Option B = $1,922,699 6 Option C = $1,922,699 Option A = $20,000 Option B = $20,000 7 Option C = $20,000

Option A = $450,000 Option B = $450,000 Option C = $50,000 Option A = $3,015,350 Option B = $2,567,950 Option C = $851,503

4 Wks

12 Wks 8

Option A = $2,463,645 Option B = $553,391 Option C = $553,391

Total Cost Option A = $20,552,250 Option B = $13,038,010 Option C = $10,025,266

Option A = $2,250,000 Option B = $2,182,500 Option C = $1,912,500 13

Wks 61-72

Phase II

4 Wks 9 10

Option A = $815,717 Option B = $563,772 Option C = $563,772 Option A = $1,044,750 Option B = $201,650 8 Wks Option C = $58,834 12 Wks

Option A = $2,792,817 11 Option B = $624,022 Option C = $553,391

36 Wks

12 Wks 12

12 Wks

ARTICLE IN PRESS S.S. Chakravorty / Int. J. Production Economics 122 (2009) 89–106

References Alemany, M.M.E., Alarcon, F., Ortize, A., Lario, F.C., 2008. Order promising process for extended collaborative selling chain. IIE Transactions 19 (2), 105–131. Alarco´n, F., Alemany, M.M.E., Ortiz, A., 2008. Conceptual framework for the characterization of the order promising process in a collaborative selling network context. International Journal of Production Economics, in press, doi:10.1016/j.ijpe.2008.07.031. Arruda, E.F., do Val, J.B.R., 2008. Stability and optimality of a multiproduct production and storage system under demand uncertainty. European Journal of Operational Research 188 (2), 406–427. Askin, R.G., Huang, Y., 2001. Forming effective worker teams for cellular manufacturing. International Journal of Production Research 39 (11), 2431–2451. Banker, R.D., Field, J.M., Schroeder, R.G., Sinha, K.K., 1996. Impact of work teams on manufacturing performance: a longitudinal field study. Academy of Management Journal 39 (4), 867–890. Bennis, W.G., Shepard, H.A., 1956. A theory of group development. Human Relations 9, 415–437. Bozer, Y.A., Kile, J.W., 2007. Order batching in walk-and-pick order picking systems. International Journal of Production Research 46 (7), 1887–1909. Braaten, L.J., 1974/75. Developmental phases of encounter groups and related intensive groups: a critical review of models and a new proposal. Interpersonal Development 5, 112–119. Caple, R.B., 1978. The sequential stages of group development. Small Group Behavior 9 (4), 470–476. Cetinkaya, S., Tekin, E., Lee, C.Y., 2008. A stochastic model of joint inventory and outbound shipment decisions. IIE Transactions 40 (3), 324–340. Chakravorty, S.S., Franza, R.M., 2009. Implementation of design for six sigma (DFSS): a development experience. International Journal of Product Development (special issue on: Transformations in Industrial Design: Emerging Trends, Approaches, and Challenges), in press. Chakravorty, S.S., Hales, D.N., Herbert, J.I., 2008. How problem solving really works. International Journal of Data Analysis Techniques and Strategies 1 (1), 44–59. Chakravorty, S.S., Franza, R.M., 2005. Enhancing cross-functional decision making: a simulation approach. Decision Sciences Journal for Innovative Education 3 (2), 331–337. Chakravorty, S.S., Hales, D.N., 2004. Implications of cell design implementation: a case study and analysis. European Journal of Operational Research 152, 602–614. Chang, T.H., Fu, H.P., Hu, K.Y., 2007. A two-sided picking model of M-AS/ RS an aisle-assignment algorithm. International Journal of Production Research 45 (17), 3971–3990. Cooley, M., 1987. Architect or Bee: The Human Price of Technology. Hogarth Press, London, UK. Dooly, D.R., Lee, H.F., 2008. A shift based sequencing method for twinshuttle automated storage and retrieval systems. IIE Transactions 40 (6), 586–594. Emery, F.E., Trist, E.L., 1960. Socio-technical systems. In: Churchman, C.W., Verhulst, M. (Eds.), Management Science Models and Techniques. Pergamon Press, Oxford, UK. Fiske, T., 2007. Automation change management risks and benefits. Hydrocarbon Processing April, 17. Fontana, A., Frey, J.H., 2000. The interview: from structured questions to negotiated text. In: Denzin, N.K., Lincoln, Y.S. (Eds.), Handbook of Qualitative Research, second ed. Sage Publications, Thousand Oaks, CA, pp. 645–672. Fukunari, M., Malmborg, C.J., 2007. An efficient cycle time for autonomous vehicle storage and retrieval systems. International of Journal of Production Research 46 (12), 3167–3184. Fukunari, M., Malmborg, C.J., 2009. A network queuing approach for evaluation of performance measures in autonomous vehicle and retrieval systems. European Journal of Operational Research 193 (1), 152–167. Gibbard, G.S., Hartman, J.J., Mann, R.D., 1974. Analysis of Groups. JosseyBass Publishers, San Francisco, CA. Gill, K.S., 1991. Summary of human-centered systems research in Europe, part I. Systemist: The Journal of The UK Systems Society 13 (1), 7–27. Gratton, L., Voight, A., Erickson, T., 2007. Bridging faultiness in diverse teams. MITSloan Management Review 48 (4), 22–29. Gu, J., Goetschalckx, N., McGinnis, L.F., 2007. Research on warehouse operation: a comprehensive review. European Journal of Operational Research 177 (1), 1–21. He, S.J., Luo, J., 2009. Deadlock control of autonomous vehicle storage and retrieval systems via coloured timed Petri nets and digraph tools. International Journal of Production Research 47 (12), 3253–3263.

105

Hedberg, B., Mumford, E., 1975. The design of computer systems: mans vision of man an integral part of the systems design process. In: Mumford, E., Sackman, H. (Eds.), Human Choice and Computers. North-Holland, Amsterdam, pp. 31–59. Heinen, J.S., Jacobson, E., 1976. A model of task group development in complex organizations and a strategy of implementation. Academy of Management Review October, 98–111. Heller, F., 1989. Human resource management and the socio-technical approach in new technology. In: Bamber, G., Lasnbury, R.D. (Eds.), New Technology: International Perspectives on Human Resources and Industrial Relations. Unwin Hyman Ltd., London, UK. Hsieh, Y.J., Chang, S.E., Chang, S.C., 2007. Design and storage cycle time analysis for the automated storage system with a conveyor and a rotary rack. International Journal of Production Research 45 (22), 5383–5395. Hua, W., Zhou, C., 2008. Clusters and filling-curve-based storage assignment in a circuits board assembly kitting area. IIE Transactions 40 (2), 569–585. Jaber, M.Y., Goyal, S.K., 2008. Coordinating a three-level supply chain with multiple suppliers, a vendor and multiple buyers. International Journal of Production Economics 116 (1), 95–103. Kapoor, M., 1996. A software design manifesto. In: Winograd, T.A. (Ed.), Bringing Design to Software. ASM Press, New York, pp. 1–9. Kemmis, S., McTaggart, R., 2000. Participatory action research. In: Denzin, N.K., Lincoln, Y.S. (Eds.), Handbook of Qualitative Research. Sage Publications, Thousand Oaks, CA, pp. 567–606. Kembel, J., 1996. ROLE Play—Personalities in Action. Life Change Partners, Boston, MA. Kolb, D., 1984. Experiential Learning. Wiley, New York, NY. Kolbe, K., 1994. Pure Instincts. Random House, New York, NY. Kormanski, C., Mozenter, A., 1987. A new model of team building: a technology for today and tomorrow. In: The 1987 Annual: Developing Human Resources, pp. 255–268. Kreng, V.B., Chen, F.T., 2008. The benefits of a cross-docking delivery strategy: a supply chain collaboration approach. IIE Transactions 19 (2), 229–241. Kuhn, S., 1996. Design for people at work. In: Winograd, T.A. (Ed.), Bringing Design to Software. ASM Press, New York, pp. 263–279. LaCoursiere, R.B., 1980. The Life Cycle of Groups: Group Development Stage Theory. Human Science Press, New York, NY. Land, F., 1992. The information systems domain. In: Galliers, R. (Ed.), Information Systems Research. Blackwell Scientific, Oxford, UK, pp. 6–13. Lee, S.D., Kuo, Y.C., 2008. Exact and inexact solution procedures for the order picking in an automated carousal conveyor. International Journal of Production Research 46 (16), 4619–4636. Lehaney, B., Clarke, S., Kiberlee, V., Spencer-Mathews, S., 1999. The human side of information development: a case of an intervention at a British visitor attraction. Journal of End User Computing 11 (4), 33–39. Liao, J.J., 2008. An EOQ model with noninstantaneous receipt and exponential deteriorating items under two-level trade credit. International Journal of Production Economics 113 (2), 852–861. Mackenzie, D.A., Wajcman, J., 1999. Introductory essay. In: Mackenzie, D.A., Wajcman, J. (Eds.), The Social Shaping of Technology. Open University Press, Milton Keynes, UK, pp. 3–27. Maples, M.F., 1988. Group development extending Tuckman’s theory. Journal of Specialists in Group Work 13, 17–23. Majchrzak, D.A., Rice, R.E., Malhotra, A., King, N., Ba, S., 2000. Technology adaptation: the case a computer-supported inter-organizational virtual team. MIS Quarterly 24 (4), 569–600. Mennecke, B.E., Hoffer, J.A., Wynee, BE., 1992. The implications of group development and history of group support system theory and practice. Small Group Research 23 (4), 524–572. Meredith, J.R., 1998. Building operations management theory through case and field research. Journal of Operations Management 16, 441–454. Mills, T.M., 1964. Group Transformation: An Analysis of Learning Group. Prentice-Hall, Englewood Cliff, NY. Orlikowski, W.J., 2000. Using technology and constituting structures: a practice lens for studying technology in organizations. Organizational Science 11 (4), 404–428. O’Sullivan, D., Dooley, L., 2009. Applying Innovation. Sage Publication, Thousand Oaks, CA. Papachristos, S., Katsaros, A., 2008. A periodic-review inventory model in a fluctuating environment. IIE Transactions 40 (3), 356–366. Persona, A., Battini, D., Manzini, R., Perschi, A., 2007. Optimal safety stock levels of subassemblies and manufacturing components. International Journal of Production Economics 110 (1–2), 147–159.

ARTICLE IN PRESS 106

S.S. Chakravorty / Int. J. Production Economics 122 (2009) 89–106

Rosenbrock, H.H., 1988. Engineering as an Art. AI & Society, 2(4), pp. 315–320. Roodbergen, K.J., Vis, I.F.A., 2009. A survey of literature review on automated storage and retrieval systems. European Journal of Operational Research 194 (2), 343–362. Rosenthal, R., Rosnow, R., 1991. Essentials of Behavioral Research. McGraw-Hill Publishers, Boston, MA. Sari, Z., Grasman, S.E., Ghouali, N., 2007. Impact of pickup/delivery stations and restoring conveyor locations on retrieval time models of flow-rack automated storage and retrieval systems. IIE Transactions 18 (2), 105–116. Sarri, R.C., Galinsky, M.J., 1974. A conceptual framework for group development. In: Glasser, P., Sarri, R., Vinter, R. (Eds.), Individual Change Through Small Groups. Free Press, New York, NY, pp. 71–88. Shani, A.B., Lau, J.B., 2005. Behavior in Organizations, eighth ed. McGrawHill/Irwin, New York, NY. Schermerhorn, J.R., 2007. Management. Wiley, New York, NY. Sinha, K.K., Ven de Van, A.H., 2005. Work designs within and between organizations. Organization Science 16, 380–408. Smith, G., 2001. Group development: a review of the literature and a commentary on future research directions. Group Facilitation 3, 14–45. Stake, R.E., 2000. Case studies. In: Denzin, N.K., Lincoln, Y.S. (Eds.), Handbook of Qualitative Research, second ed. Sage Publications, Thousand Oaks, CA, pp. 435–454. Stevens, M.J., Campion, M.A., 1994. The knowledge, skill, and ability requirements for teamwork: implications for human resource management. Journal of Management 20 (2), 303–530. Sujono, S., Lashkari, R.S., 2007. A multi-objective model of operation allocation and material handling system selection in FMS design. International Journal of Production Economics 105 (1), 116–133.

Tompkins, J.A., White, J.A., Bzer, Y.A., Tanchoco, J.M.A., 2002. Facilities Planning. Wiley, New York, NY. Tuckman, B.W., 1965. Developmental sequence in small groups. Psychological Bulletin 63, 384–399. Tuckman, B.W., Jensen, M.C., 1977. Stages of small-group development revisited. Group and Organization Studies 2 (4), 419–427. Tsai, C.Y., Liou, J.J.H., Huang, T.M., 2008. Using a multiple-GA method to solve the batch picking problem: considering travel distance and order due time. International Journal of Production Research 46 (22), 6533–6555. Wamba, S.F., Lefebvre, L.A., Bendavid, Y., Lefebvre, E., 2008. Exploring the impact of RFID technology and the EPC network on mobile B2B eCommerce: a case study in the retail industry. International Journal of Production Economics 112 (2), 614–629. Wheelan, S.A., Burchill, C., 1999. Take teamwork to new heights. Nursing Management April, 28–31. Witt, C.E., 2006. New technology introduction from the bottom up. Labor Management September, 33–36. Wong, C.Y., Hvolby, H.H., 2007. Coordinated responsiveness for volatile toy supply chains. IIE Transactions 18 (5), 407–419. Wooyeon, Y., Egbelu, P.J., 2008. Scheduling of inbound and outbound trucks in cross docking systems with temporary storage. European Journal of Operational Research 184 (1), 377–396. Xu, K., Leung, M.T., 2009. Stocking policy in a two-party vendor managed channel with space restrictions. International Journal of Production Economics 117 (2), 271–285. Yin, R., 2008. Case Study Research: Design and Methods. Sage Publications, Thousand Oaks, CA. Zhang, T., Ouyang, Y., He, Y., 2008. Traceable air baggage handling based on RFID tags in the airport. Journal of Theoretical and Applied Electronic Commerce Research 3 (1), 106–115.