Toward Sustainable Operations of Supply Chain and Logistics Systems

A geographical concentration of nodes within a supply chain or supply chain density is one of the supply chain network characteristics that may affect the resiliency of a supply chain to disruption. Some major cases include the disruption of the global PC supply chain due to the 1999 earthquake in Taiwan, the disruption of automotive supply chain due to the 2011 earthquake in Japan, and the disruption of hard disk supply chain due to the 2011 massive floods in Thailand. These disruptions were resulted from the high geographical concentration of suppliers and manufacturers. In this chapter, the optimal design of a supply chain is discussed with the objectives of maximizing the total profit and the density of the supply chain. A bi-criteria mixed-integer linear programming model (MILP) is formulated to determine the optimal locations of the facilities and the distribution of flows between facilities in the supply chain. A four-stage supply chain network model is developed, which consists of suppliers, manufacturing plants, warehouses, and retailers. The model is illustrated using a realistic example, and the results are discussed.

In this chapter, the cash-to-cash cycle time (C2C) and financial performance metrics of seven selected industries (integrator, apparel, electronics, automobile, airline, logistics, and retail) are investigated with data taken from their annual reports from 2008 to 2012. The purpose is to study C2C whether it is appropriate to measure supply chain performance or not. The C2C characteristics are identified industry by industry with a focus on supply chain management (SCM) and logistics. The relationship between C2C and financial performances is evaluated using measurements of gross, net profit rate, revenue growth, current ratio, and return on asset (ROA). It is found that C2C is very powerful metric to understand supply chain strategies, but it is not a predictor of financial performance.

Performance measurement or benchmarking in healthcare logistics requires right components and meaningful key performance indicators (KPIs), but having them is very challenging since it involves factors both outer and inner dependence among those factors. This chapter presents a model, based on analytic network process (ANP) model, for sorting and prioritizing the meaningful and a sufficient number of components with individually unique KPIs in healthcare logistics. They are used to benchmark the healthcare logistics performance in different countries in ASEAN such as Singapore, Malaysia, Thailand, Myanmar, and Lao. Those components and their KPIs are identified based on the literature review, and checked and weighted by experts. ANP model is used to prioritize each logistics component and its KPIs. Super Decisions software is used to do all related computations in ANP model such as supermatrix (limit matrix) to get synthesized priorities. The research result offers a good and implementable healthcare logistics performance measurement framework from which managers or researchers in healthcare industry can use to carry out the healthcare logistics performance benchmarking within particular organizations in the same or different countries.

One of the main challenges in supply chain management is to manage uncertainties within its environment, which yields increased total operational costs. Hence, there is a need to consider all the aspects that are responsible for uncertainties in supply chain management environment. There has been increasing dependence on supplier’s side that triggers companies more prone to risk and uncertainties. This chapter considers all the uncertainty factors from supplier’s perspective in supply chain management. The major factors are production, transportation, inventory, penalty and discounts. Thus, a supply chain model is established that explicitly incorporates all the factors responsible for the uncertainty from supplier’s side in supply chain environment. The problem objective is to minimize overall cost considering demand satisfaction and profit. The chapter also highlights the root causes of uncertainty and various modelling approaches used in supply chain under uncertainty.

The purpose of this chapter is to analyze the changing structure of retail logistics in Japan through the comparison with that from the UK. The grocery retail supply chain in Japan and UK is quite advanced, and the focus is on the retailers of the grocery. This chapter consists of four sections. In the first section, the research on the theory of retail logistics and marketing is presented. In the second section, the logistical transformation at Tesco is explained. Tesco is a biggest supermarket in the UK and had changed the logistics strategy and adopted a strategy to sell private brand goods (PB). In the third section, the logistics and marketing strategy are described about Japanese retailer, Daiei, Seven-Eleven, and AEON. In the fourth section, a description is given on how the retailer of the grocery has the power over the manufacturers by using IT and selling PB. The conclusions of the study are given in the last section.

One of the emerging concepts that gained popularity in many countries is virtual supply chain (VSC). Since the end-customer is a key player in the success of integrating the VSC in any business, this chapter focuses on examining the end-customers’ acceptance of using technology through the technology acceptance model (TAM) to investigate the potential application of VSC in the grocery retailers’ sector in Egypt. A survey-based questionnaire was used to assess the Egyptian customers’ acceptance of virtual shopping. The questionnaire consisted of 44 questions that focused on the five variables of TAM: perceived usefulness, perceived ease of use, attitude toward using the virtual shopping, intention to use virtual shopping, and the actual use of the virtual shopping. The structural equation model (SEM) was applied to test the research hypotheses. The data analysis revealed a strong relationship between the five TAM variables which concludes the acceptance of the Egyptian customers’ for VSC. The data only focused on customers in the city of Alexandria which might limit the generalizability of the research results.

This chapter applies stewardship theory to explain the effects of top managers’ postures toward green supply chain management (GSCM). It combines institutional theory and stewardship theory to examine the relationship among institutional pressure, top managers’ posture, and GSCM. The method of questionnaire investigation is adopted for electrical and electronic industries in Taiwan. A total of 1000 questionnaires was mailed, and the 180 valid questionnaires were returned. There are some main findings. First, institutional pressures have significant positive effect on the GSCM. Second, institutional pressures have significant positive effect on top managers’ posture. Third, top managers’ posture has a significant positive effect on GSCM. Finally, institutional pressures through top managers’ posture have indirect effect on GSCM.

This chapter discusses a green logistics optimization problem associated with production method of manufacturers and green attitude of consumers. A novel hierarchical method is developed for a three-echelon logistics network to optimize the production methods with different structures regarding cost and emission of carbon dioxide (CO

2

) at production sites (PSs), the available collection center, the paths between members of the logistics network, and circular routes over consumers. First, the problem is aimed at minimizing either the total cost or CO

2

emission through controlling prone and aversion behaviors on sustainability of each member. Then, as a promising glue to integrate these individual problems and to evaluate them on the same basis, an economic mechanism known as the emission trading rate on CO

2

is introduced. Furthermore, to discuss the sustainability in a broader logistics system, the modal shift in transportation is noted. To show the significance of the proposed approach, a case study is provided to explore some prospects for green logistics in whole society based on the computational results.

This chapter presents a method for aggregation of different indicators, which are relevant for sustainability assessment. The originality of the procedure relies on the use of Averaging Quality Rating method that was used on the level of indicators analysis for making them comparable despite different measurement method (qualitative or quantitative). It is suitable for small- and medium-sized companies (SMEs) where the information system is rather undeveloped.

Due to significant economic and environmental benefits, remanufacturing has been deployed and enhanced in various market sectors. Determining product parts whether they should be reused, repaired, or disposed is an important task in the remanufacturing. There are a variety of criteria such as cost optimization, minimizing disposed items, and so on. Many researches considered cases with one or two criteria while it is possible to have more factors that should be considered in an industrial case. In this chapter, highlighting on sustainability, available decision-making criteria are reviewed and categorized into groups, based on the objective in the literature, if they are economical or environmental focused. Additionally, the decision-making criteria are classified if they are related to products, processes, and people in the system. Decision makers can select the criteria based on the sections which they concentrate on. The decision criteria are covered by various aspects to support decision makers to set product recovery objectives easily and quickly. Criteria can be conflicted with each other to be minimizing cost and maximizing product quality, simultaneously. This chapter investigates conflicting criteria to assist decision makers in selecting a decision method to determine product recovery option. In future work, the decision-making criteria will be identified to develop a framework by integrating an intelligent decision-making agent system.

This chapter presents a framework for sustainable operations of food supply chains. A case study approach is used. The data were collected using exploratory interviews and through a structured questionnaire. The sustainable food supply chain involves operational levers of robustness, transparency, traceability and information flow and performance measure for monitoring and control of day-to-day operations for efficiency, flexibility, responsiveness and product quality. New product development, research and development, productivity improvement, entrepreneurial orientation, quality control and conducive policy support across key stages of production, procurement, processing, distribution and consumption of the dairy supply chain emerge as key elements of the sustainable framework.

This chapter presents application of material flow cost accounting (MFCA) analysis technique in meatball production. MFCA was used to analyze 6 processes that focused on the cost of positive and negative product occurred along the target production line. The results of MFCA analysis for existing process showed that the main negative product cost was material cost (MC). Thus, the improvement solution was proposed to reduce the cost of negative product of MC. The main material waste occurred at the first process of raw material preparation when melted ice occurred during the temperature reduction during raw material preparation step. To reduce this wastewater from melted ice, the proposed solution was to add a refrigerator instead of using ice for temperature reduction. By this method, the results from MFCA analysis showed that negative material quantity at raw material preparation step was reduced from 47.77 to 3.62 % that gave effects on negative MC reduction from 4.98 to 4.20 %. Consequently, this solution can help in saving production cost as 67,406.67 Baht per year when considering only this product.

This chapter analyzes the current issues and proposes an effective reverse logistics system for discarded tires, which considers collection, transportation, and intermediate treatment enterprises, elements that are not involved in forward logistics. The possibility of constructing a reverse logistics network over a wide area is examined from a viewpoint based on previous studies and practices concerning the construction of a forward logistics network. The result revealed that these improvement measures for the reverse logistics can be effectively functioned.

This chapter attempts to raise the awareness of the recycling-based society through the creation of efficient strategy for reverse logistics by searching for the optimal location for collecting centers of the intermediate treatment facilities. Generally, the efficiency of collecting center can be improved by relocating the facilities and by changing the collection and transport routes. The optimal location of collecting centers for treatment facilities are found by the density method. The case study searches for the optimal location of the collecting centers for the intermediate waste treatment facilities in Chiba prefecture. The results show that the efficiency of waste collection can be improved with leading collection routs from the collecting center to all other treatment facilities.

This chapter aimed to assess performance measurement related to lean manufacturing and net profit of small and medium enterprises (SMEs) in Thailand, by interviewing 100 SME entrepreneurs, particularly in the manufacturing sector, and analyzing data by applying factor analysis and multinomial logistic regression. The results found that performance measurement for SMEs in the manufacturing sector in Thailand agreed with most variables at a high level, except for product usage and service at a medium level. When considering each entrepreneur individually, they had medium net profit at 11–15 %. When comparing performance measurements related to lean manufacturing, it was found that factors affected a high net profit of 15 % and they should continually work on it in order to ensure product quality, process quality, waste, defects, suppliers, work-in-process, output, lead time, delivery lead time, and inventory.

Remanufacturing of automotive components is a sector in which a big number of small- and medium-sized companies (SMEs) operate. The challenges of remanufacturing process are described in the literature, but the empirical studies in this domain are still limited. This chapter compares the theoretical findings with the empirical results. The overview of the literature analysis on remanufacturing process challenges is presented. The results of the pilot surveys in Polish small-sized companies that are involved in automotive part remanufacturing are presented along with the characteristics of the respondents, the main problems which appear by remanufacturing of automotive parts.

This chapter presents an investigation of a scheduling problem of unrelated parallel machines with sequence-dependent setup time from hard disk drive industries. It specifically focuses on the testing process and setup changes of the testing program according to the product type required. When the demand of customers is greater, the setup of the testing program increases, causing a reduction in production efficiency. The objective is to sequence and allocate the jobs for the unrelated parallel machines to minimize the makespan. The problem is formulated as a mixed integer linear programming (MILP) model, and a heuristics algorithm is developed to find near-optimal solution. The results from the heuristics are compared with the optimal solutions to evaluate the effectiveness of the heuristics algorithm.

This chapter deals with a single machine system with job release times and sequence-dependent machine setup times. The objective of the problem is to minimize weighted sum of earliness and tardiness penalties. A mixed integer programming formulation for the problem is first presented, and using this formulation, one can easily find optimal solutions for small problems by using a commercial optimization software. Since the problem is NP-hard and the size of a real problem is large, a number of heuristic solution procedures are proposed including genetic algorithm to solve the practical big-sized problems in a reasonable computational time. To assess the performance of the algorithms proposed, a computational experiment was conducted and the results demonstrate that the heuristic algorithms show different performances as the problem characteristics are changed and a heuristic shows much better performance than genetic algorithm for the case when the number of jobs is relatively large.

This chapter deals with the design of relief network to improve efficiency and effectiveness of relief operation. About 80 % of disaster response operation involves logistics activities. The problem is modeled as a location-allocation problem in distributing relief supplies to disaster victims. The location of local distribution centers (LDCs) and the amount of relief supplies that will be delivered through the relief network are determined by minimizing logistics cost. Equity among demand points are considered by giving a penalty cost for unfair distribution. A numerical example is conducted to illustrate how the model operates. The results show the effect of unfairness cost on performance of relief system, particularly on logistics cost and demand satisfied.

This chapter presents a stochastic optimization model for disaster management planning. In particular, the focus is on the integrated decisions about the distribution of relief supplies and evacuation operations. The proposed decision-making approach recommends the best relief distribution centers to use as storage locations and determines their optimal inventory levels. The model also incorporates the priorities for the evacuation of particular communities, as well as specific disaster scenarios with estimates of the transportation needs and demand for aid. A case study is presented to determine the distribution of aid for a flood emergency in Thailand that uses a flood hazard map.

The chapter presents an evaluation of freight logistics performances and recommends policy on logistics facility and related transport infrastructure investment along the corridor that links between Laem Chabang Port in Thailand and Port of Vung Ang in Vietnam via Laos PDR. A dry port and its transportation links to the seaport have been proposed for investment. There are five nominal routes within the network where an origin is located over the dry port hinterland and Guangzhou Port in China PDR is a destination. The combination of simulation model and goal programming has been applied to evaluate the corridor in terms of freight logistics operational performance. A discrete event model visualizes the desirable system. The weighted goal programming is used to find an optimal route. Finally, the policy prioritizes that investment must be made for the dry port and the connection to Laem Chabang Port by direct rail link. In order to encourage the dry port to be part of the network, the desirable logistics system requires overall transit cost should be around US$1100. Quality of services for on time performance should be maintained around 320 h of total transit with 21 h of traveling time variance and 54 h of operation time variance.

This chapter explores time management of domestic express transportation services in Thailand. The express transportation services as known as courier that have significant roles in transportation industries. There are three key performance measures for customer satisfaction: time, speed, and prefer condition of the shipments, and they are related to the operation logistics in delivery measurement, delivery in-full on-time (DIFOT). The competition level in express transportation services is extremely high, and the core competitive advantage of this sector is time management and cost reduction. Almost all local express transportation services in Thailand only have the lower cost advantage. It means that they only compete on low price without the actual commitment on delivery date/time, proper speed, and good package. The main discussion is on the time management of express delivery service to improve service level. Data were acquired from four selected international transportation services companies in Thailand for a comparative study.

This chapter addresses the sugarcane mechanized harvesting problem. A mathematical model based on the vehicle routing problem (VRP) with time window (VRPTW) is formulated to obtain optimal harvesting plan for harvesters given specific time windows (TWs). The harvesters can operate only within the specific TW. The objective of the model is to determine the suitable sugarcane harvesting plan of a harvester. The model is applied to solve 18 generated sugarcane harvesting scenarios. Each scenario consists of 15 sugarcane fields and 20 harvesting periods. The solution comprises the harvesting sequences, traveling routes, harvesting periods, and harvest starting time. The model provides the optimal solutions which can be applied for sugarcane growers in Thailand and other similar regions.

This chapter considers a truck and trailer routing problem (TTRP) where the fleet consists of trucks and trailers, and some customers can be served by a truck pulling a trailer, while the other may be accessible by a truck only. A memetic algorithm is proposed by dividing the TTRP into two VRPs in the construction phase and then uses the alternating edge crossover with several local search methods. It is tested on TTRP benchmark instances in the literature and corresponding numerical results are presented.

This chapter proposes two DE algorithms for solving multi-depot vehicle routing problem (MDVRP) with multiple pickup and delivery requests (GVRP-MDMPDR). Two modified DE algorithms based on subgrouping of vectors and strategy switching concepts are developed and evaluated. In the first proposed algorithm, subgrouping of vectors is applied in the crossover process of the classical version of DE algorithm. The vectors are divided into two subgroups. The first subgroup applies exponential crossover process, while the other subgroup applies binomial crossover process. Experiences from two different crossover approaches are shared by allowing a target vector to randomly select vectors from both groups during the mutation process. The other algorithm is based on strategy switching concept applied in the crossover process. Two different crossover processes, exponential and binomial crossover processes, are used alternately. The results obtained from these two proposed algorithms are compared to those obtained by the classical DE algorithm. The results show that both proposed DE-based algorithms outperform the classic DE.

Logistics network is increasingly crucial because of shortened product life cycles, increasing competition, and uncertainty introduced by globalization. The logistics network distribution involves a multistage supply chain that consists of the flexible forward directions (i.e., factories, distribution centers, retailers, and various customers) and the flexible backward directions (i.e., re-manufacturing and reuse). Customer demands fluctuate and are unpredictable, thereby causing an imprecise customer quantity demand in each period in the production distribution model, and increasing inventory and related costs. Most studies have addressed the traditional multistage forward directions problem with certain demands or a single period. To fill the gap, this chapter proposes the hybrid genetic algorithm approaches for solving flexible, multiple periods, multiple stages, and forward/reverse logistics network. In particular, triangular fuzzy demands are considered to minimize the total cost, including transportation costs, inventory costs, shortage costs, and ordering costs, in the multistage and multi-time-period supply chain. The experimental results demonstrated practical viability for the proposed approaches.

This chapter focuses on the inbound logistic section of the sugarcane industry, which is one of the important industries in Thailand. For inbound logistics, there are three major operation steps: cultivation, harvest, and transportation. From the three operations of inbound logistics, the highest inbound logistic cost is harvest. At present, there are three harvest patterns in Thailand: (1) labor cutting and loading, (2) labor cutting and trans-loader truck, and (3) mechanized harvester (cane harvester). The cane harvester usage tends to increase because of lack of labor and increasing cane production costs. In addition, the pattern of harvesting management depends on the experience and expertise of operators. So it is necessary to develop decision-making tools for high management efficiency and reduced risk of uncertainty. In this chapter, a genetic algorithm (GA) was developed to solve the harvest scheduling problem, which consists of 2 main parts: (1) sugarcane field clustering and (2) harvester routing. The objective is to reduce the harvesting cost by minimizing harvester travel distance. Experimental results show that the developed heuristic is quite effective and gives better result than the current practice from the real case of the sugarcane industry.

The lack of growth in Europe is one of the reasons behind the dire economic situation facing many European countries today. The question is how to address this situation at the same time meeting the expectations of both the general public and enterprises. European countries and enterprises have to resist the crisis and prepare for the future. They need to reorganize themselves in order to improve their performance. The research of ICAM (School of engineers) in industrial organization is destined to enterprises, public establishments and departments. It is based on GRAI Methodology, one of the three main methodologies for enterprise modeling. GRAIMOD is a software tool being developed in ICAM for supporting GRAI Methodology. The approach used is composed of the modeling of the existing system, the diagnosis, and the analysis of the obtained models for detecting inconsistencies and the design phase for improving the system. A general typology is proposed for enterprises and general public entities, then for facilitating the improvement during the design phase a reference model is proposed for each enterprise, public establishment, or department domain. The performance criteria used for improving countries and enterprises are quality (of products, system, process, supplying), cost, lead time, carbon management, social, societal, and environmental management (including energy management). According to the climate and energy package adopted in December 2008 by the European Parliament in the current context of energy transition, the department of Vendée recently passed a plan to improve energy self-sufficiency of its territories. Through modeling of consumption and potential local renewable energy production, this chapter offers several energy scenarios and assesses the efforts needed to achieve objectives. An illustration is given as an example in one of its urban communities, a territory of 30,000 inhabitants. This example is used for elaborating a reference model according to the concerned domain. The proximity of ICAM with enterprises and general public facilitates the elaboration of these different reference models and the validation of concepts elaborated.

Ready-to-drink (RTD) tea achieves estimated sales of 6.7 billion in 2012 (according to Chicago-based market) (Zegler in Tea and RTD Teas—US—Chicago-based. Mintel Consumer Market Research Report,

2013

). The consumption of RTD tea is increasing rapidly in the USA (Cernivec in Food Chem 136, 1309–1315,

2015

) and also in Thailand (Katenil, Bottled Beverage Market in Thailand,

2014

), and the growth is projected to increase steadily for the next five years. The taste of beverage is the key success for achieving customer loyalty in which the flavor impact plays a crucial role to the taste. Traditional flavors such as lemon, peach, raspberry, citrus, and plain tea have survived the test of time; however, many beverage companies are seeking alternative flavors that are not typically associated with tea, such as pineapple, apple, mint, strawberry, chocolate, and herbal ingredients. In general, flavor consists of many compounds that make the odor notes. The chapter reports a study to assess which compound affects customer contentment for RTD tea flavor. The study selects the jasmine, lemon, peach, citrus, and plain tea flavors that are most wildly used in this market segment. In order to identify the hidden pattern of the customer’s contentment, the artificial neural networks (ANNs) have been applied to classify the key volatile compound of the flavors. According to the input data of the 4 customer groups and 5 key volatile compounds (5 flavors) as the output, the results show that the best structure of ANNs is 4-7-5 with 1.54

e−2

MSE and it can predict 75.5 % of accuracy. The compounds that carry the most effect on customer contentment are women–adult is jasmine; women–teen is lemon; men–teen is citrus; and men–adult is plain tea.

This chapter presents an approach based on particle swarm optimization (PSO) for minimizing total traveling distance in warehouse storage location assignment problems (SLAP). The traveling distance in an order-picking process is considered with three-axis traveling distance: two horizontal axes and one vertical axis. A mathematical model is first presented, and LINGO optimization program is used to find optimal solutions for a set of generated problems. As the problem size increases, LINGO could not find solutions within reasonable time. Thus, particle swarm optimization (PSO) is applied to solve SLAP. The proposed algorithm employs multiple learning terms and utilizes the random key representation to generate a solution. The numerical experiments show that the proposed PSO is able to generate good solutions with relatively shorter computing time.

This chapter presents the study of an integrated forward and reverse closed-loop supply chain (CLSC) network design problem with sustainable concerns in the solar energy industry. The focuses are in the logistics flows, capacity expansion, and technology investments of existing and potential facilities in the multi-stage CLSC. A deterministic multi-objective mixed integer programming model is formulated to capture the tradeoffs between the total cost and the carbon dioxide (CO

2

) emission and to tackle the multi-stage CLSC design problem from both economic and environmental perspectives. Due to the multi-objective nature and the computational complexity, a multi-objective particle swarm optimization (MOPSO) with novel flow assignment algorithms is designed to search for non-dominated/Pareto CLSC design solutions. Finally, a case study of crystalline solar energy industry is illustrated to verify the proposed multi-objective CLSC design model and demonstrate the efficiency of the developed MOPSO algorithm in terms of computational time and solution quality.

The requirement of large sample size and the strong subject knowledge to build suitable membership function restrict the applicability of probability and fuzzy theories in supplier selection problem. Due to this fact, the combination of grey system theory and uncertainty theory-based approach is applied for supplier selection problem, which requires neither any exact probability distribution nor membership function. The proposed approach is able to consider stochastic and recognitive uncertainties associated with supplier selection decision in supply chain management. This chapter develops framework for reducing the risks associated with suppliers. The novelty of this research is to consider proper uncertainty approaches in different stages of proposed framework instead of solving the whole selection problem using the same uncertainty theory or grey system theory. The proposed framework is based on three stages: identification of supplier selection criteria, defining weights to goals and ratings to supplier attributes as grey linguistic variables, and supplier selection and order allocation using uncertain-goal programming considering uncertain demand and lead time. The proposed model will help the practitioners to effectively evaluate and select suitable set of suppliers and optimal order allocation.

Offering product variety is one of the strategies for today’s competitive advantage. In spite of the fact that greater product variety allows manufacturers to meet a larger share of customer’s satisfaction, it increases the complexity of the process and reduces efficiency of the operational performance in a supply chain. Increasing product variety also creates more wastes and more losses, and reduces efficiency of resources. In this chapter, a fuzzy multi-objective model is developed in an imprecise environment by using Material Flow Cost Accounting (MFCA) approach which concentrates on reducing resource consumption by reducing waste and losses as well as active response to environmental requirements. The cost objective function consists of waste management cost, system cost, material cost, and energy cost. The model considers different weights for each of the cost items. Due to uncertain seasonal market demand for textile productions, an imprecise production preparation time and vagueness of input data, a fuzzy weighted max–min multi-objective model is developed for this problem. In this model, the achieved level of objective functions will match the relative importance of objective functions. The model considers three objective functions: quality, product variety, and cost of negative products which focus on the appropriate variety of the products and help decision makers increase green productivity in a textile supply chain. A numerical example is developed to show the application of the model.

Private hospitals offer an advanced appointment program that allows patients to receive medical care services at their convenient time. While the amount of callers has increased, many hospitals face difficulties to determine the number of operators to promptly respond the calls. Long waiting time may cause some callers to abandon their lines, which leads to the loss of opportunity. This chapter focuses on how to determine the optimal number of operators and their assignment in a service time horizon. An integrated framework is proposed using mixed-integer nonlinear programming to solve the staff planning and allocation problem. The result shows that the framework is viable.

High-quality processes with very low rate of nonconforming items can be found in many industries nowadays. For these processes, the traditional Shewhart control charts such as

p

-chart,

u

-chart, and

np

-chart are not applicable. Due to low fraction of nonconforming, it is better to establish the control chart to monitor the time between successive defectives of the process. In fact, time between successive defectives of high-quality processes usually follows exponential distribution. Due to the fact that the exponential distribution is highly skewed, some transformation techniques should be applied to help developing the control charts with unbiased in-control average run length (ARL). In this chapter, two different approaches have been applied using Weibull transformation and Cornish–Fisher expansion to develop unbiased ARL control charts in such a way that the probability of false alarm is at acceptable value.

Workforce planning could be a critical process in call center operations. Due to arrival, calls are both time varying and uncertain, assigning the agents to the call center operations which responses to all calls might be not an easy task. This chapter demonstrates the workforce planning of a call center that services the number of calls required and satisfies the expected service level. The historical data are provided by a call center in a communication service company in Thailand. The data are forecasted by decomposition technique for the planning period. The queuing theory and the linear programming are used as the methodological tools. The call center operation is simplified to the

M/M/n

model and the Erlang C formulation is applied to solve for the number of agents required. In order to define shifts and assign agents to meet the needs, the model of Aykin is adapted to minimize the total of agents assigned. The results provide the number of agents to be assigned in the daily operations. The monthly workforce planning model is then formulated with the number of agents as decision variable and the objective is to minimize the total number of agent assignment. This problem can be solved by linear programming and the method could assist a manager to formulate monthly workforce plan that can satisfy the expected service level.

A quality control activity is one of the key elements in a food logistic system. It plays an important role to reduce cost and customer-claim due to unsatisfactory food products. Typically, the food inspection is performed by human, and this leads to inconsistent inspection results due to fatigue and tediousness. Therefore, an automated quality inspection program is required to ensure that the system is effective. This chapter presents an approach to improve consistency of sausage color inspection after packaging using an integrated framework of image processing techniques and high-dimensional control charts. The results indicate that sausage color consistency can be improved resulting in a more stable process.