Optimal concurrent product design and process planning based on the requirements of individual customers in one-of-a-kind production

One-of-a-kind production is a new manufacturing paradigm for producing customised products based on the requirements of individual customers while maintaining the quality and efficiency of mass production. This research addresses the issues in optimal concurrent product design and process planning based on the requirements of individual customers. In this work, a hybrid AND-OR graph is developed to model the variations of design configurations/parameters and manufacturing processes/parameters in a generic product family. Since different design configurations and parameters can be created from the same customer requirements, and each design can be further achieved through alternative manufacturing processes and parameters, co-evolutionary genetic programming and numerical optimisation are employed to identify the optimal product design configuration/parameters and manufacturing process/parameters. A case study is introduced to identify the optimal design configuration/parameters and manufacturing process/parameters of custom window products of an industrial company to demonstrate the effectiveness of the developed method.     

Identification of the optimal product configuration and parameters based on individual customer requirements on performance and costs in one-of-a-kind production

One-of-a-kind production (OKP) aims at manufacturing products based on the requirements from individual customers while maintaining the high quality and efficiency of mass production. This research addresses the issues in identifying the optimal product configuration and its parameters based on individual customer requirements on performance and costs of products. In this work, variations of product configurations and parameters in an OKP product family are modeled by an AND-OR tree and parameters of the nodes in this tree. Different product configurations with different parameters are evaluated by performance and cost measures. These evaluation measures are converted into comparable customer satisfaction indices using the non-linear relations between the evaluation measures and the customer satisfaction indices. The optimal product configuration and its parameters with the maximum overall customer satisfaction index are identified by genetic programming and constrained optimization. A case study to identify the optimal configuration and its parameters of window products in an industrial company is used to demonstrate the effectiveness of the introduced approach.     

ExPlanTech: Applying multi-agent systems in production planning

This paper presents work carried out within the 'ExPlanTech' project (IST-1999-20171) funded in part by the European Commission's Information Technologies Programme. The mission of the ExPlanTech technology transfer project is to introduce, customize and exploit the multi-agent production planning technology (ProPlanT multi-agent system research prototype) in two specific industrial enterprises. An agent-driven service negotiations and decision process, based on usagecentred knowledge about task requirements, substitutes the traditional production planning activity. We introduce a methodology for integration of the projectdriven production planning based on agent-based engineering within the existing enterprise resource planning system. This novel production planning technology will facilitate optimization of resource utilization and supplier chain while meeting the customer demands. This paper describes a FIPA-compliant implementation of the ExPlanTech technology at the LIAZ Pattern Shop manufacturing company. We describe the structure of the agent community, types of agents, implementation of the planning strategy and its incorporation within the real production environment.     

Unpacking IT use and integration for mass customisation: a service-dominant logic view

Significant changes in customer demands for individualised offerings are causing firms to move away from mass production strategies toward customisation. Many firms struggle in this migration because the requirements for mass customisation (MC) differ greatly from those needed for mass production. As firms strive to optimise their interactions with customers and suppliers to produce highly customised offerings at near mass production prices, IT use along with customer and supply integration are important organisational competencies. Research studies explore many facets of MC but, in regard to IT use for customer and supplier integration, most focus on specific activities within the product design and development process. This study extends the IT-enabled organisational capabilities line of research, using data collected from 220 manufacturers, to explore IT use in a comprehensive sense for planning, infrastructural, and operational activities in various business processes intended to achieve MC. These findings suggest that comprehensive IT use can enhance a firm's integration efforts with customers and suppliers. These factors, in turn, drive operational performance and MC, which lead to firm performance. Grounded in service-dominant logic (SDL) theory, these findings provide theoretical and empirical support to explain why customer integration may be a driving force in MC.     

Product design resources optimization using a non-linear fuzzy quality function deployment model

Quality Function Deployment (QFD) is a well-known customer-oriented methodology that is widely used to assist decision-making in product design and development in various types of production including highly customized One-of-a-Kind Production (OKP), batch production as well as continuous/ mass production. Determining how and to what extent (degree) certain characteristics/technical attributes (TA) of products are to be met with a view to gaining a higher level of overall customer satisfaction is a key to successful product design and development. Most of the existing approaches and models for QFD planning seldom consider the resource constraints in product design, nor do they normally take into account the impacts of the correlation among various TA. In other words, most of the existing QFD applications assume that the resources committed fully to attaining the design target for one TA have no impacts on those for other TA. Hence, the costs/resources required are usually worked out individually by linear formulation. In practice, design resource requirements should be expressed in fuzzy terms to accommodate the imprecision and uncertainties innate in the design process, such as ill-defined or incomplete understanding of the relationship between a given set of customer requirements (CR) and TA, the complexity of interdependence among TA, etc. A non-linear fuzzy model is proposed here to offer a more practical and effective means of incorporating the resource factors in QFD planning. The impacts of the correlation among TA are also considered. In the model, the resources for achieving the design target for a certain TA are expressed in a non-linear formulation of its relationship, correlation as well as interdependence with other customer requirements or TA. The concepts of the achieved attainments and planned attainments for TA, and the corresponding primary costs, planned costs and actual costs are introduced. Solutions to the non-linear fuzzy model can be obtained using a parametric optimization method or a hybrid genetic algorithm. A case study is also given to illustrate how the proposed fuzzy model and the optimization routine can be applied to help decision-makers in a company deploy their design resources towards gaining better overall customer satisfaction.     

Operational tactics and tenets of a new manufacturing paradigm ‘instant customerisation’

An increasing trend towards a new manufacturing paradigm of ‘instant customerisation’ is occurring due to customers demanding their orders for customised products or services be fulfilled under zero customer lead time and at a price near the price of mass production. By means of the manufacturing paradigm innovation model (MPIM), the operational tactics, operational tenets, and an operational framework of instant customerisation are elicited and described. An important new tactic within all the operational tactics is finalise-to-individual demand forecasting, meaning end products are finalised according to the forecasting of individual customer's needs. The knowledge and information, which is the primary input of individual demand forecasting, is gleaned and understood by total customer participation and value fusion. Total customer participation and value fusion are developed through customer participation and customer relationship management, which have critical distinctions. The operational framework, which is termed manufacturing paradigm tree of instant customerisation, can characterise the operational tenets, the relationships between different operational tactics and the objectives, and the relationships among different operational tactics. With the proposed framework, instant customerisation can be understood more effectively and holistically.     

Lot sizing with bounded inventory and lost sales

In production planning, there can be situations where the ability to meet customer demands is constrained by inventory capacity rather than production capacity. This situation often happens in petrochemical manufacturing, food processing, and glass manufacturing. Only a few studies can be found in the literature for this situation, and among these lost sales usually are not considered. In this paper, we consider the lot sizing problem with bounded inventory. We further consider that (1) lost sales are allowed; (2) production cost functions are non-increasing with respect to the time period; and (3) inventory capacity is non-decreasing with respect to the time period. With these considerations, we present a model as well as an algorithm which has a polynomial time complexity. An illustration is given to demonstrate both the application of our model and the algorithm.     

Simulation and performance evaluation of partially and fully integrated sales and operations planning

This article presents rolling horizon simulation models and performance analysis of partially and fully integrated sales and operations planning (S&OP) against traditional decoupled planning in a multi-site make-to-order (MTO) based manufacturing supply chain. Three simulation models are developed illustrating, respectively, the fully integrated S&OP model, which integrates cross-functional planning of sales, production, distribution, and procurement centrally; the partially integrated S&OP model, in which the joint sales and production planning is performed centrally while distribution and procurement are planned separately at each site; and the decoupled planning model, in which sales planning is carried out centrally while production, distribution, and procurement are planned separately and locally. A solution procedure is provided for each model so that a more realistic planning process can be simulated. Performances of rolling horizon simulation models are evaluated against those of the fixed horizon deterministic models. The results demonstrate that while deterministic models are important for theoretical studies, they are insufficient for decision support and performance evaluations in a real business environment. A rolling horizon simulation model is required to provide more realistic solutions. The effects of demand uncertainties and forecast inaccuracies are incorporated in the evaluation. The study is carried out based on a real industrial case of a Canadian-based oriented strand board (OSB) manufacturing company.     

Multi-objective master production scheduling in make-to-order manufacturing

In this paper a multi-objective, long-term production scheduling in make-to-order manufacturing is considered and a lexicographic approach with a hierarchy of integer programming formulations is proposed. The problem objective is to allocate customer orders with various due dates among planning periods with limited capacities to minimize the number of tardy orders as a primary optimality criterion. Then, the maximum level of the input and output inventory is minimized as a secondary criterion, and finally the aggregate production is leveled over the planning horizon as an auxiliary criterion. A close relation between minimizing the maximal inventory and the maximum earliness of customer orders is shown and used to simplify the inventory leveling problem. Numerical examples, modeled after a real-world make-to-order flexible flowshop in a high-tech industry, are provided and some computational results are reported. The paper indicates that the maximum earliness of customer orders is an important managerial decision variable, and its minimum value can be applied to control the inventory of purchased materials and finished products to maximize the customer service level and minimize production costs.     

Combining customer needs and the customer’s way of using the product to set customer-focused targets in the House of Quality

An increasing number of products are equipped with software and sensors. This suggests that, in order to deliver more customised performance, future products will be developed to accommodate systems that supply information on how these products are used. Today, information on the customer’s way of using a product is seldom factored into product design, but the opportunities for making use of it are increasing dramatically due to the amount of available data that can be logged. The proposed methodology is to formulate Customer Needs at a detailed level to be able to link customer satisfaction with a clear interface to the Design Requirements. These links are obtained by combining information acquired by means of surveys, among other methodologies, as well as usage data from customer products. The method is based on the planning House of Quality and also takes cost and risk into consideration. Risk is estimated using the Analytical Hierarchy Process, whereby a hierarchy of the most relevant customer information is constructed to make designers aware of how customer-focused the design process is. To validate the proposed methodology an illustrative example is presented. Results show that the method provides valuable information that enables the company to remain customer-focused during the whole process but also when strategic decisions on price and product launch are made.     

Aggregate production planning for shipbuilding with variation-inventory trade-offs

Shipbuilding is a complex production system characterised by a complicated work and organisation structure, prolonged production lead time, and heterogeneous resource requirements. Thus, effectively planning all involved activities presents a challenging task and requires the timely coordination between the successive production stages at the plant level and effective resource allocation at the workshop level. With the work breakdown structure of all projects and their corresponding building strategies, the aggregate production planning (APP) is to address two important issues, namely, workforce level and inventory usage so that the fluctuating demands from downstream processes can be satisfied in a cost-effective manner. To achieve this, a novel APP model is proposed for ship production to minimise the variation of aggregate man-hour over the planning horizon and simultaneously minimise the logistic demands of the interim products. In view of the combinatorial nature and computational complexity, a directed genetic algorithm based solver has been developed to solve the two-conflicting-objective optimisation problem. The proposed approach has been applied to a case study and preliminary results have shown certain effectiveness in handling various situations with different planning strategies.     

Designing the closed loop elements of a. material requirements planning system in a low volume,make-to-order company (with case study)

This paper describes how material requirements planning (MRP) can provide a working solution to job shop scheduling problems. In particular, the critical factors relating to resource and capacity management are examined within a low volume, make-to-order company. A rough cut capacity planning (RCCP) module is described which, when used with a detailed capacity management methodology, has enabled the collaborating company to achieve substantial savings in work-in-progress (WIP), a marked reduction in machining lead times and viable control over customer lead times. Finally, an MRP production methodology is described, which allows such concepts as unit cost justification and machine utilisation to be re-examined.     

An on-time delivery improvement model for manufacturing organisations

The purpose of this research was to develop an on-time delivery (OTD) improvement model for make-to-order (MTO) manufacturing organisations, based on: (i) a business process model combining product development and customer order management processes; and (ii) an integrated database with basic data, transaction data and functional applications, for broader planning within manufacturing organisations. The business process model, as part of the overall model, was designed using event-driven process chain (EPC) methodology and incorporated both capacity and material requirements planning functionalities for estimating on-time delivery dates and times. The database associated with the model defines all the data including both basic and transaction data; and links with required functions from sales to service and field return. These functions integrate through the database, using basic data and generate various transaction data including sales orders with an accurate promised date, based not only on the available stock but also on procurement and distribution times of any raw materials from external sources. The proposed model was then implemented in a selected manufacturing organisation. A systematic investigation was carried out to find the major causes of OTD problems of that manufacturer, with a view to implementing and validating the proposed model. After implementing the model average OTD was increased from 10% to 65% in about 12 months of operation.     

Agility evaluation using the IF–THEN approach

The recent trend in the manufacturing sector is to develop highly customised products to satisfy niche needs of customers. In order to satisfy this requirement, a newer manufacturing technique has been deployed; this is called Agile Manufacturing (AM). AM principles are used to manufacture highly customised products within a shorter period of time. In this article, the IF–THEN rules approach has been used to evaluate the current agile position of the firm. The assessment has been carried out in an Indian modular switches manufacturing company. The assessment revealed that the organisation is fairly agile. Besides computing agility level, the gaps that impede agility have been identified, and proposals for agility improvement have been derived. The identified proposals are subjected to implementation in the case organisation.     

Predicting glass furnace output using statistical and neural computing methods

This paper describes the development of predictive models for glass production at a regional manufacturing company. The objectives of the models are to predict the actual batch tonnage produced per week from the glass furnace based on the planned production schedule. Four modelling methods were explored: (i) linear regression; (ii) nonlinear regression; (iii) artificial neural network using back-propagation; and (iv) radial basis function neural network. Using 175 cases of production schedule data and subsequent furnace output, the two neural network-based prediction models resulted in lower average absolute error and lower maximum absolute error than the linear or nonlinear regression models. Accurate neural network-based prediction models of furnace output will subsequently be used in the overall production planning system by utilizing estimates of furnace output to determine the necessary energy, raw material, repair and personnel requirements of the glass manufacturing facility.     

A primal decomposition method for the integrated design of multi-period production–distribution systems

We study the integrated design of strategic supply chain networks and the determination of tactical production-distribution allocations in the case of customer demands with seasonal variations. Given a set of potential suppliers, potential manufacturing facilities and distribution centers with multiple possible configurations, and customers with seasonal demands, the goal is to determine the configuration of the production-distribution system with the lowest sum of supply, production, transportation, inventory, and facility costs such that seasonal customer demands are met. We develop a mixed integer programming formulation and an integrated design methodology based on primal (Benders) decomposition. For a case study in the packaging industry, specialized acceleration techniques reduced the running times by a factor of 480. The company projects savings of 2% or $8.3 million by using the integrated rather than the optimal hierarchical configuration.     

A project scheduling approach to production planning with feeding precedence relations

In Manufacturing-to-Order or Engineering-to-Order systems producing complex and highly customised items, each item has its own characteristics that are often tailored for a specific customer. Project scheduling approaches are suitable for production planning in such environments. However, when we consider the production of complex items, the distinct production operations are often aggregated into activities representing whole production phases. In such cases, the planning and scheduling problem works on the aggregate activities, considering that, in most cases, such activities also have to be manually executed. Moreover, simple finish-to-start precedence relations no longer correctly represent the real production process, but overlapping among activities should be allowed. In this paper, a project scheduling approach is proposed for production planning in Manufacturing-to-Order systems. The Variable Intensity formulation is used to allow the effort committed to the execution of activities to vary over time. Feeding precedences are developed to model generalised precedence relations when the execution mode of activities is not known a priori. Two mathematical formulations of these precedence relations are proposed. The formulations are applied both to randomly generated instances and to an industrial system producing machining centres and are compared in terms of computational efficiency.     

Design and application of Internet of things-based warehouse management system for smart logistics

Warehouse operations need to change due to the increasing complexity and variety of customer orders. The demand for real-time data and contextual information is requried because of the highly customised orders, which tend to be of small batch size but with high variety. Since the orders frequently change according to customer requirements, the synchronisation of purchase orders to support production to ensure on-time order fulfilment is of high importance. However, the inefficient and inaccurate order picking process has adverse effects on the order fulfilment. The objective of this paper is to propose an Internet of things (IoT)-based warehouse management system with an advanced data analytical approach using computational intelligence techniques to enable smart logistics for Industry 4.0. Based on the data collected from a case company, the proposed IoT-based WMS shows that the warehouse productivity, picking accuracy and efficiency can be improved and it is robust to order variability.     

Towards self-adaptive production systems: Modular generic simulation models for continuous replanning and reconfiguration

Changing factors within markets, such as continually shortening product life cycles, increasing competition due to shorter development and order processing times, manufacturing in intercompany networks and the individualization of customer needs, create new requirements that manufacturing organizations are expected to fulfil. To be successful, manufacturing organizations and business processes have to be transformable to cope with these environmental changes. That is why the creation and implementation of self-adaptive production systems represent one vision of the near future. A first step to self-adaptive production systems is the transition from a non-recurring, static planning and operation cycle to a continuous replanning and reconfiguration of work systems, supported by new planning methods and tools. Starting with a definition of self-adaptive production systems, the requirements for planning support systems for a continuous reconfiguration and replanning are derived. The paper also presents a solution for an integrated planning tool based on object-oriented, generic simulation models. The architecture and planning procedure are described in detail. The paper finally shows the first test results of the implemented prototype.