Strategic ramp-up planning in automotive production networks

In many industries, the frequency of production ramp-ups has increased due to shorter product life cycles and increased product variety. Production often takes place at globally dispersed facilities for customers in multiple markets, for example, in the automotive industry. Therefore, ramp-up planning must be carried out for a network. The planning problem at hand is to simultaneously optimise the allocation of products of a common product platform to plants, the timing of ramp-ups and ramp-downs, ramp-up duration and shape of capacity and production and transportation volumes with respect to the net present value of the profit. We develop a hierarchical planning framework and place strategic ramp-up planning therein. We provide a mixed integer programming model for this planning problem. Following the example of a German car manufacturer in the premium segment, we conduct a numerical analysis to obtain insights into optimal ramp-up and ramp-down decisions. The results show that it could be beneficial to use both steep and flat ramp-up curves. The impact of flexibility in the choice of ramp-up slots and the number of ramp-up curves provided on the objective value is substantial. The strategic ramp-up planning model outperforms sequential planning approaches.     

Modelling of multi-period multi-product production planning considering production routes

The multi-period multi-product (MPMP) production planning problems, generally, deal with matching production levels of individual products with fluctuated demands over planning horizon. The conventional MPMP optimisation models suffer from insufficient utilisation of available capacity of machines. This fallacy is due to inappropriate formulation of machine capacity and material handling constraints. In this study, a novel mathematical model is proposed to simultaneously optimise production quantities and provide information about managerial decisions such as subcontracting, carrying inventory/backordering, and also hiring/layoff personnel. The problem is then formulated as a mixed integer linear programming (MILP) model by applying appropriate linearisation of non-linear components. The objective is to minimise production costs comprising of production, storage, shortage, subcontracting costs and costs associated with hiring/dismissing labourers. Superiority of the proposed model over existing ones, has been initially evaluated by solving the case presented by Byrne and Bakir [Byrne, M.D. and Bakir, M.A., 1999. Production planning using a hybrid simulation-analytical approach. International Journal of Production Economics, 59 (1), 305–311], and then evaluated by comparing the results obtained from solving both the proposed and the conventional MPMP production planning models using a 100-randomly-generated-test-problem.     

Production planning in automotive powertrain plants: a case study

Based on a real case study from the automotive industry, this paper deals with production planning in powertrain plants. We present an overview of the production planning process and propose a mixed integer linear programme to determine the production quantities of each product over a planning horizon of several days. Then, using real data of an engine assembly line, we simulate the performance obtained through the proposed model within a rolling horizon planning process. We perform multiple tests in order to evaluate the impact of two parameters involved in this process: planning frequency and frozen horizon length. Furthermore, in order to illustrate the value of improving coordination between engine plants and their customers, we evaluate the impact of the quality of demand information (orders and forecasts). We analyse the simulation results and provide insights and recommendations in order to achieve a good trade-off between service level, inventory, and planning stability.     

An integral planning model that includes production,selling price,cash flow management and flexible capacity

Many authors consider that production and marketing decisions should be integrated. In this paper, we discuss an aggregate planning problem that includes production, selling price, cash management and flexible capacity (by means of hiring and firing and with the possibility of unlimited production subcontracting). The demand is considered to be a nonlinear function of the product selling price. The problem, which is modelled as a mixed integer linear program, can be solved using standard optimisation software. The results of a computational experiment and a numerical example are shown to illustrate the performance of the proposed model and obtain some managerial insights.     

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.     

Robust optimisation approach applied to the analysis of production / logistics and crop planning in the tomato processing industry

The soluble solids content in the tomato fruit, also known as ‘brix’, and the crop yield are the most relevant uncertain parameters to determine technical and economic performance in the tomato processing industry. This paper presents a linear programming model and three robust optimisation models to deal with data uncertainty in the analysis of crop, logistics and industrial tactical planning in this industry. We focused the analysis on the production and logistics costs due to the impacts of unfavourable disturbances on the amount of soluble solids and the quantity of tomatoes processed in the system. A typical industry in this sector collaborated with this study by providing real data of its production, logistics and crop plans and with in-depth discussions. From the results, some general conclusions were outlined and we discuss the benefits of adopting the robust optimisation approach instead of a deterministic one. The robust approach proved to be a powerful tool for elaborating scenarios for uncertainty analysis in medium-term decisions, as described in this study, and clearly has potential to be employed in real contexts.     

Cellular manufacturing systems design with routing flexibility,machine procurement,production planning and dynamic system reconfiguration

This paper investigates the problem of designing cellular manufacturing systems with multi-period production planning, dynamic system reconfiguration, operation sequence, duplicate machines, machine capacity and machine procurement. An important aspect of this problem is the introduction of routing flexibility in the system by the formation of alternate contingency process routings in addition to alternate main process routings for all part types. Contingency routings serve as backups so as to effectively address the reality of part process routing disruptions (in the main routings) owing to machine breakdowns and allow the cellular manufacturing system to operate in a continuous manner even in the event of such breakdowns. The paper also provides in-depth discussions on the trade-off between the increased flexibility obtained versus the additional cost to be incurred through the formation of contingency routings for all parts. Some sensitivity analysis is also performed on some of the model parameters. The problem is modelled and solved through a comprehensive mixed integer programming formulation. Computational results presented by solving some numerical examples show that the routing and process flexibilities can be incorporated within the cellular manufacturing system design without significant increase in the system cost.     

Convex optimisation for aggregate production planning

This paper presents a new solution approach to the problem of aggregate production planning (APP). As identified by many researchers, the APP cost function is convex and piecewise. Thus, the convex optimisation approach can be applied to the APP problem. Solving the APP problem using convex optimisation is attractive since it leads to an improved solution over the classical solution methods and it can be applied to a wider range of functions. The classical Linear Decision Rule model of APP is solved using convex optimisation and the resulting solution is compared to three solution approaches which have been historically used to solve this model. The results suggest that convex optimisation may be an effective approach for solving certain types of planning models.     

Supply chain planning in the German automotive industry

Following the evolution in the computer industry, quite a lot of car manufacturers currently intend to move from a built-to-stock oriented production of standardized cars towards a customized built-to-order (BTO) production. In the premium segment of Germanys automotive industry, the share of customized BTO cars traditionally is comparatively high. Nevertheless, German car manufacturers have spent a lot of efforts in recent years to further increase this share in order to realize short delivery times, high delivery reliability and a fast responsiveness. Surprisingly, comprehensive overviews of the short- and mid-term planning landscape of car manufacturers cannot be found in the scientific literature. Thus, the first part of the paper discusses supply chain planning, as traditionally established in the premium segment of the German automotive industry, and reviews methods of Operations Research (OR) that are able to support the various planning tasks involved. In the second part, the major change in strategy, currently to be observed in the German automotive industry, is briefly summarized in order to derive its impacts for the planning system and for the respective planning methods. In this way, challenges for a future application of OR methods in the automotive industry can be identified.     

A data-driven approach to multi-product production network planning

The clearing function models the non-linear relationship between work-in-process and throughput and has been proposed for production planning in environments with queuing (congestion) effects. One approach in multi-product, multi-stage environments has been to model the clearing function at the bottleneck machine only. However, since the bottleneck shifts as the product release mix changes, this approach has its limitations. The other approach is the Alternative Clearing Function formulation, where the clearing function is first estimated at the resource level using piecewise linear regression from simulation experiments, and then embedded into a linear programme. This paper develops an alternative to the Allocated Clearing Function formulation, wherein system throughput is estimated at discrete work-in-process points. A mixed integer programming formulation is then presented to use these throughput estimates for discrete release choices. The strength of the formulation is illustrated with a numerical example and the new approach is compared with the ACF.     

Model for flexibility evaluation in manufacturing network strategic planning

In response to market pressures, manufacturers have adopted different approaches to provide flexibility regarding several aspects. In this paper, we suggest a model for the evaluation of the flexibility of the manufacturing supply chain, based on graph theory techniques. This model defines maximum excess demand that may be met using flexibility. Recourse to flexibility enablers is determined based on cost minimisation. Such enablers are volume flexibility, mix flexibility and safety stocks. The proposed model is solved using a two-step Mix Integer Linear Programme; the first step consists in defining maximum demand that may be met while the second step concerns minimising cost. The main benefit of our model is to deal with realistic problems in a rather short time. Therefore, it can be used in a wide ‘what-if’ design process. It means evaluating various contemplated flexibility configurations in multiple demand scenarios in order to choose the best option. It can be also used during operational supply chain planning in order to face to an unbalanced situation. This paper ends with a numerical example illustrating our model’s efficiency.     

Lean versus agile production: flexibility trade-offs within the automotive supply chain

Given the recent dynamics of the automotive industry in the UK, the ability for a firm to be flexible has often taken priority over other performance indicators. Using the notion of distinct business models and trade-offs as our theoretical lens, the purpose of this study was to: (1) Distinguish lean and agile firms based upon production methods; and (2) Compare lean and agile levels of external flexibility (EF) and supply chain flexibility (SCF). Data was obtained from 140 automotive firms in the Midlands (UK) via a survey which was sent by emails. Findings supported the theoretical notion of trade-offs, as firms implementing agile production methods were found to be more flexible in comparison with firms implementing lean production methods. More importantly, the agile firms that possessed high EF levels and SCF levels were predominantly positioned at the lower end of the automotive supply chain, whereas the lean firms were largely found to be operating at the top of the supply chain. First, we provide an innovative way in which lean and agile firms can be conceptualised. Second, as flexibility levels were assessed on actual numerical values, as opposed to using opinion based Likert Scale questions, a methodological contribution is made. Third, as flexibility is in its infancy stage of theoretical development we make an empirical contribution by developing a taxonomy that distinguishes each production concept. Finally, given the supply chain position to where lean and agile firms were found, we invoked a power perspective better understand this phenomenon.     

Modeling and optimizing of strategic and tactical production planning in the automotive industry under uncertainty

This work considers the strategic flexibility and capacity planning under uncertain demands in production networks of automobile manufacturers. We present a deterministic and a stochastic model, which extend existing approaches, especially by an anticipation scheme for tactical workforce planning. This scheme is compared to an extended formulation of the deterministic model, which incorporates workforce planning via detailed shift models. The stochastic model is efficiently solved by an accelerated decomposition approach. The solution approach is integrated into a decision support system, which calculates minimum-cost product allocations and capacity plans. Our numerical results show that, in spite of the considerably increased complexity, our approach can efficiently handle hundreds of scenarios. Finally, we present an industrial case study.     

Production planning problems with joint service-level guarantee: a computational study

We consider a class of single-stage multi-period production planning problems under demand uncertainty. The main feature of our paper is to incorporate a joint service-level constraint to restrict the joint probability of having backorders in any period. This is motivated by manufacturing and retailing applications, in which firms need to decide the production quantities ex ante, and also have stringent service-level agreements. The inflexibility of dynamically altering the pre-determined production schedule may be due to contractual agreement with external suppliers or other economic factors such as enormously large fixed costs and long lead time. We focus on two stochastic variants of this problem, with or without pricing decisions, both subject to a joint service-level guarantee. The demand distribution could be nonstationary and correlated across different periods. Using the sample average approximation (SAA) approach for solving chance-constrained programs, we reformulate the two variants as mixed-integer linear programs (MILPs). Via computations of diverse instances, we demonstrate the effectiveness of the SAA approach, analyse the solution feasibility and objective bounds, and conduct sensitivity analysis for the two MILPs. The approaches can be generalised to a wide variety of production planning problems, and the resulting MILPs can be efficiently computed by commercial solvers.     

A membership function approach for aggregate production planning problems in fuzzy environments

This paper proposes a novel approach for the aggregate production planning (APP) problem with fuzzy parameters. Different from the results of previous studies, in this paper the membership function of the fuzzy minimal total cost is constructed based on Zadeh's extension principle and fuzzy solutions are provided. A pair of mathematical programs parameterised by possibility level α is formulated to calculate the lower and upper bounds of the fuzzy total cost at α. By enumerating different values of α, the membership function of the fuzzy total cost is constructed. To illustrate the validity of the proposed approach, the example studied by Lai and Hwang (1992 Lai, YJ and Hwang, CL. 1992. Fuzzy mathematical programming: methods and applications, Berlin: Springer.  [Google Scholar]) using Chanas's approach is investigated. Since the objective value is expressed by a membership function rather than by a crisp value, the proposed approach can represent APP systems more accurately, thus obtained solutions which contain more information can offer more chance to achieve the feasible disaggregate plan, and it is beneficial to the decision-maker in practical applications. The proposed approach can also be applied to APP problems with other characteristics.     

Productivity enhancement strategies in North American automotive industry

The aim of this study is to define a robust estimation model of the most dominant labour productivity measurement, Hours per Vehicle (HPV), in the auto industry. Data utilised in this study were from 10 different multinational North American carmakers from 1999 to 2007. Through a comprehensive literature review and practical consideration, 13 important variables that affect HPV were defined and developed. Several robust and advanced statistical methods were utilised to determine the best possible HPV regression equations. The MM estimator, multiple M-estimator, was defined as the best method to perform the data analysis and to derive the robust regression model to estimate HPV. Depending on the car class, the vehicle variety, model types, annual working days, car assembly utilisation and launching a new model penalise HPV; however, annual production volume, flexible manufacturing and year of production improve HPV. Moreover, Japanese plants are the benchmark regarding HPV followed by joint ventures, and American plants.     

An analysis of designing for quality in the automotive industry

The paper is based on the authors' experiences in devising and conducting continuing education courses on Designing for Quality for design, development and manufacturing engineers in a large automotive company. Participants in the courses brought forward a wide range of quality problems encountered in their day-to-day professional activities. Methods for classifying and defining these problems are developed as a prelude to examination of the product development process and its management to fulfil quality objectives. The technique of quality function deployment which has recently received wide publicity in automotive engineering is investigated in the context of established design theory. Conclusions are drawn relating to the practice of engineering design in industry and the need for the effective dissemination of the work of design researchers.     

Hierarchical scheduling of continuous casters and hot strip mills in the steel industry: a block planning application

This work addresses the joint scheduling of continuous caster and hot strip mill processes in the steel industry. Traditionally, slab yards are used to decouple these two stages. However, the rising importance of energy costs and reduced logistic effort gives motivation for a combined scheduling. For each of the processes, a mixed-integer linear optimisation model based on the block planning principle is presented. This approach develops production schedules that take technological sequences of steel grades and milling programmes into account. We consider the integrated steel plant of an international steel company as a case study. Numerical results demonstrate the practicability of this approach under experimental conditions, which reflect typical settings from an industrial application in the steel industry.     

Aggregate and detailed production planning integrating procurement and distribution plans in a multi-site environment

A monolithic mixed integer linear goal programming (MILGP) model that is developed in this paper produces a time and capacity aggregated production plan, a detailed production plan, a detailed procurement plan and a detailed distribution plan simultaneously to overcome the drawbacks of the hierarchical/sequential planning approaches of not yielding a feasible and/or an optimal plan. The model uses different time-grids and planning horizons for aggregate and detailed planning to reduce the computational burden. The limitations of storage space, raw material availability and production capacity at plants and a requirement of maintaining a minimum level of inventory buffer or forward cover have been modelled. Two heuristics that are proposed to solve the MILGP model gave good quality solutions with an average and the worse case optimality gap of 1.17% and 4.4% respectively when applied to one hundred randomly generated industry size problem instances.     

A case study on monitoring polynomial profiles in the automotive industry

In some statistical process control applications, the quality of a process or product can be characterized by a relationship between a response variable and one explanatory variable, which is referred to as profile. We give an example here of a profile that can be described using a polynomial model. This example comes from the automotive industry, where one of the most important quality characteristics of an automobile engine is the relationship between the torque produced by an engine and the engine speed in revolutions per minute. We find for this data set that a second‐order polynomial works well. In addition, we show that there is autocorrelation within each profile, thus an ordinary least‐square method that ignores the autocorrelation is inappropriate. We propose a linear mixed model method as an alternative approach. After the reduction of the data to a series of parameter estimates, we then conduct a step‐by‐step Phase I analysis of the polynomial profiles monitoring using a T²‐based procedure to check the stability of the process and whether or not there are outlying profiles. The remaining profiles are used to form the estimated mean vector and variance–covariance matrix to be used in Phase II studies. Finally, a brief discussion is presented to show how one can use these parameters in Phase II. Copyright © 2009 John Wiley & Sons, Ltd.