Zhang, Y.; Hua, G.; Wang, S.; Zhang, J.; Fernandez, V. International journal of production economics Vol. 196, p. 56-67 DOI: 10.1016/j.ijpe.2017.10.001 Data de publicació: 2018-02-01 Article en revista
Demand variability is prevailing in the current rapidly changing business environment, which makes it difficult for a retailer that sells multiple substitutable products to determine the optimal inventory. To combat demand uncertainty, both strategies of inventory substitution and probabilistic selling can be used. Although the two strategies differ in operation, we believe that they share a common feature in combating demand uncertainty by encouraging some customers to give up some specific demand for the product to enable demand substitution. It is interesting to explore which strategy is more advantageous to the retailer. We endogenize the inventory decision and demonstrate the efficiency of probabilistic selling through demand substitution. Then we analyze some special cases without cannibalization, and computationally evaluate the profitability and inventory decisions of the two strategies in a more general case to generate managerial insights. The results show that the retailer should adjust inventory decisions depending on products' substitution possibility. The interesting computational result is that probabilistic selling is more profitable with relatively lower product similarity and higher price-sensitive customers, while inventory substitution outperforms probabilistic selling with higher product similarity. Higher demand uncertainty will increase the profitability advantage of probabilistic selling over inventory substitution.
The mainstream servitization literature mostly describes the success of manufacturing firms in integrating services for their corporate clients. However, the literature is relatively silent on how territories capitalize on the potential interconnectedness between manufacturing firms and the knowledge-intensive business service (KIBS) sector. The analysis of the outcomes that result from the mutually dependent associations between manufacturing businesses and KIBS firms, a process that we call Territorial Servitization, is of great relevance for academics and policy makers. This research hypothesizes that there is a positive symbiotic and bidirectional link between the growth in KIBS activity and employment generation by manufacturing sector start-ups. Furthermore, we scrutinize the mediating role over this relation of relevant industry characteristics, in our case the stock of manufacturing firms and the total number of freights transported. The empirical application considers a unique dataset created from multiple sources—the Global Entrepreneurship Monitor (GEM), the Spanish Institute of Statistics and Eurostat—for the 17 Spanish regions during the period 2006-2012. The results support the view that territorial servitization contributes to employment creation in manufacturing sectors. Territories with a vigorous manufacturing base benefit from a virtuous circle in which KIBS start-ups and newly formed manufacturers are connected through the economic activity of incumbent manufacturing firms. The study offers valuable insights for scholars and policy makers on how to implement specific policies—e.g., the development of digital infrastructures—that facilitate the interaction between manufacturing and KIBS businesses, thus fuelling territorial development.
Bautista, J.; Alfaro, R.; Batalla, C. International journal of production economics Vol. 161, num. March 2015, p. 83-95 DOI: 10.1016/j.ijpe.2014.11.018 Data de publicació: 2015-03-02 Article en revista
In this paper, it is presented an extension of the mixed-model sequencing problem with work overload minimization (MMSP-W) for production lines with serial workstations, parallel homogeneous processors, and variable operation processing times. This extension is intended to consider that the processing times of the operations can be prolonged or shrunk with respect to the established standard processing times depending on the work pace of the workers. To do this, the activity of workers is set by means of functions which take into account the periods of adaptation and fatigue of the beginning and end of the workday, respectively. Thus, two mathematical models and four functions for the work pace factor are presented and their performances are analyzed through a case study of the Nissan powertrain plant in Barcelona, using the Gurobi solver. The results show that the work overload can completely be either eliminated with an increase of the activity of operators of 5% over their normal work pace or reduced by 88% with an increase of 3.33%. Consequently, the losses due to the uncompleted work or the hiring costs of auxiliary operators can be avoided by demanding a greater effort to workers at certain moments of their workday, but always respecting the limits set by collective agreement.
This paper offers an up-to-date review on strategic capacity planning in manufacturing companies, with
two main objectives: (1) to describe and analyze the strategic capacity planning problems; and (2) to
review the mathematical programming models proposed in the literature for dealing with these
problems. The main search was conducted in the Web of Science using critical keywords and was
complemented by using other search engines. Cross checking of citations of all the articles was also
carried out. Papers were selected that have formulated discrete time, finite horizon, multi-period
models. The major decisions addressed and the main conditioning factors of the strategic capacity
problem in the literature are identified and described. A structured overview of the main strategic
capacity planning mathematical programming models is given. A classification of the models is proposed
and their main characteristics, solution procedures and industrial applications are identified. Based on
the review of the existing studies, a framework for capacity planning is presented, consisting of three
main phases: problem definition (considering context, characteristics of the manufacturing system and
specific factors that could influence the decision-making process), model design and solution procedure.
Closing the paper, some future lines of research are suggested. The review should help both practitioners
and academic researchers in developing useful models and processes to aid decision-making in strategic
Chica, M.; Cordón, O.; Damas, S.; Bautista, J. International journal of production economics Vol. 145, num. 2, p. 761-772 DOI: 10.1016/j.ijpe.2013.05.030 Data de publicació: 2013-10-01 Article en revista
The time and space assembly line balancing problem (TSALBP) is a realistic multiobjective version of assembly line balancing industrial problems involving the joint optimization of conflicting criteria such as the cycle time, the number of stations, and the area of these stations. However, the existing problem formulation does not consider the industrial scenario where the demand of a set of mixed products is variable and uncertain. In this work we propose to introduce novel robustness functions to measure how robust the line configuration is when the production plans demand changes. These functions are based on the stations overload under future demand conditions and are used as additional a posteriori information for the non-dominated solutions found by any multiobjective optimization method. The values of the robustness functions are put together with a novel graphical representation to form a generic model that aims to offer a better picture of the robustness of the set of Pareto-optimal solutions.
Real data from the assembly line and production planning of the Nissan plant of Barcelona is considered for the experimentation. This information is also employed to develop a new TSALBP instance generator (NTIGen) that can generate problem instances having industrial real-like features. The use of the robustness information model is illustrated in an experimentation formed by a set of instances generated by NTIGen. Results show how the use of this robustness information model is necessary for the decision maker as it allows her/him to discriminate between different assembly line configurations when future demand conditions vary.
Ferrer-Martí, L.; Pastor, Rafael; García-Villoria, A. International journal of production economics Vol. 119, num. 1, p. 46-54 DOI: 10.1016/j.ijpe.2008.12.017 Data de publicació: 2009-05 Article en revista
The annualisation of working hours (i.e., the irregular distribution of the total number of working hours over the course of a year) makes it possible to adapt production capacity to fluctuations in demand. The required capacity, which is an essential data for the optimal planning of working time, usually depends on several complex factors. Often, it is impossible to reliably predict the required capacity or it is unrealistic to adjust it to a probability distribution. In some cases, it is possible to determine a set of required-capacity scenarios, each with a related probability. This paper presents a multistage stochastic optimisation model that provides a robust solution (i.e., feasible for any possible scenario) and minimises the expected total capacity shortage.
Annualised hours—the irregular distribution of working hours over a year—allow companies to adapt capacity to demand, thus reducing overtime, the number of temporary workers and inventory costs. To avoid a significant deterioration in working conditions, many laws and agreements constrain the distribution of working time. One way of doing this is by specifying a finite set of weekly working hours and bounding the annual number of weeks of each type. Although this set has a great impact on the solution, it is usually agreed without taking all the available data (demand, costs, etc.) into consideration. This paper proposes a method for selecting the most appropriate set of weekly working hours and establishing an annual plan or working time for each worker as a way of optimising service level. To this end, two mathematical programming models are proposed. By means of a computational experiment, it is shown that one of the models can be solved in short computing times and can thus be used as a decision-making tool.