A Model-Driven Decision Support System for the Master Planning of
Ceramic Supply Chains with Non-uniformity of Finished Goods
I. MUNDI1, M. M. E. ALEMANY2, A. BOZA2, R. POLER2
1 Escuela Técnica Superior de Ingenieros Industriales,
Universitat Politècnica de València
Camí de Vera s/n, 46022 València, Espanya
2 Centre d’Investigació de Gestió i Enginyeria de Producció (CIGIP),
Universitat Politècnica de València
Camí de Vera s/n, 46022, València, Espanya
email@example.com, firstname.lastname@example.org, email@example.com
Abstract: In this paper, a Model-Driven Decision Support System (DSS) for the Master Planning of ceramic Supply Chains characterized by producing units of the same finished good in a specific lot that differ in the aspect (quality), tone (colour) and/or gage (thickness) is proposed. The DSS is based on a mathematical programming model reflecting these non-uniformity characteristics. Through the different DSS functionalities, Decision Makers can generate different scenarios by means of changing any data. Optimal solution of each scenario can be evaluated for robustness under other scenarios. The Decision Maker can compare different solutions and finally choose the most satisfactory one for being implemented. To demonstrate the validity of the DSS, a realistic example is described through the generation of different scenarios based on the degree of finished goods uniformity in lots.
Keywords: Model-Driven Decision Support System, Master Planning, Ceramic Supply Chains, Lack of Uniformity.
CITE THIS PAPER AS:
I. MUNDI, M. M. E. ALEMANY, A. BOZA, R. POLER, A Model-Driven Decision Support System for the Master Planning of Ceramic Supply Chains with Non-uniformity of Finished Goods, Studies in Informatics and Control, ISSN 1220-1766, vol. 22 (2), pp. 153-162, 2013. https://doi.org/10.24846/v22i2y201305
Supply chains (SCs) operations planning is a complicated task due to the existence of a huge number of decisions, constraints, objectives (sometimes conflictive), possible alternatives to be evaluated and the presence of uncertainties. For the case of ceramic SCs, this planning task becomes even more complex due to the appearance of the so called Lack of Homogeneity in the Product (LHP) .
LHP in ceramic SCs implies the existence of units of the same finished good (FG) in the same lot that differ in the aspect (quality), tone (color) and/or gage (thickness) [1,2] that should not be mixed to serve the same customer order. The usual consideration of three qualities, two tones and three gages causes the existence of thirteen different subtypes of the same FG. This fact increases the volume of information and makes the ceramic system management more complex. Additionally, the customers from this type of companies tend to request quantities of different FGs in one same order, and they also require that the units of one same FG in the order are homogeneous.
LHP systems should face with a new kind of uncertainty : the uncertainty in the future homogeneous quantities in production lots. Due to the inherent LHP uncertainty, the real homogeneous quantities of each subtype in a FG lot will not be known until their production was finished. Not knowing the homogeneous quantities available of the same FG to be promised to customers proves to be a problem when customers’ orders have to be committed, reserved and served from homogeneous units available derived from the planned production. Furthermore, not accomplishing with this homogeneity requirement can lead to returns, product and company image deterioration, decreasing customer satisfaction and even lost of customers.
The order promising process (OPP) plays a crucial role in customer requirements satisfaction  and, also, in properly managing the special LHP characteristics. The OPP refers to the set of business activities that are triggered to provide a response to customer order requests . This process requires information about available-to-promise (ATP) quantities, i. e. the stocks on hand or projected inflows of items stocked at the customer order decoupling point (already in transit or planned by the master plan) that has not yet been allocated to specific orders and thus can be promised to customers in the future. Because the master plan is a fundamental input to the OPP, one of the objectives and contributions of this paper is to define a master plan that considers LHP features and can provide this process with reliable information about future available homogeneous quantities.
Up to our knowledge there is no DSS that takes into account LHP features. Therefore, in this paper, we propose model-driven Decision Support System (DSS) for the operations planning of ceramic supply chains with diversity in qualities, tones and gages. Model-driven DSS are designed so a user can manipulate model parameters to examine the sensitivity of outputs or to conduct a more ad hoc “what if?” analysis .
Thus, DSS functionalities are designed to allow the definition of several scenarios by changing input data, generating, evaluating and comparing different solutions through a series of interactive steps. Hence, dealing with assumptions is one of the main DSS roles . Another important advantage of the DSS is that the Decision-Maker (DM) does not require understanding the complexities of the mathematical modeling, reducing the gap between theoretical contributions by researchers and the expectations of managers responsible for implementing the plans .
The system under our study can be considered as a Large Complex System (LSS). Filip and Leiviskä  indicate that LSS are characterized by their high dimensions (large number of variables), constraints in the information structure and the presence of uncertainties. The complexity of systems designed nowadays is mainly defined by the fact that computational power alone does not suffice to overcome all difficulties encountered in analyzing, planning and decision-making in presence of uncertainties. Thus, when human intervention is necessary, DSSs can represent a solution. These systems can help the decision-maker to overcome his/her limits and constraints he/she may face when approaching decision problems that count in the organization  and this is the objective of the DSS proposed in this paper.
The rest of the paper is structured as follows. Section 2 describes the problem under consideration and reviews the more closely related literature. Section 3 presents the mixed integer linear programming model proposed for the centralized master planning of ceramic SCs that explicitly takes into account LHP. Section 4 describes the DSS architecture. Section 5 shows the functionalities and practicability of the DSS through its application to a ceramic SC by means of realistic case. Finally, section 6 states the conclusions derived from the obtained results and future research lines.
- ALARCÓN, F., M. M. E ALEMANY, F. C. LARIO, R. F. OLTRA, La falta de homogeneidad del producto (FHP) en las empresas cerámicas y su impacto en la reasignación de inventario, Boletín de la Sociedad Española de Cerámica y Vidrio, vol. 50, no.1, 2008, pp. 49-58.
- DAVOLI, G., S. A. GALLO, M. W. COLLINS, R. MELLONI, A Stochastic Simulation Approach for Production Scheduling and Investment Planning in the Tile Industry, International Journal of Engineering Science Technology, vol. 2, no. 9, 2010, pp 107-124.
- ALEMANY, M. M. E., F. C. LARIO, A. ORTIZ, F. GOMEZ, Available-To-Promise Modeling for Multi-plant Manufacturing Characterized by Lack of Homogeneity in the Product: An Illustration of a Ceramic Case, Applied Mathematical Modelling, Available: http://dx.doi.org/10.1016/j.apm.2012.07.022 (in press).
- ALEMANY, M. M. E., F. ALARCÓN, A. ORTIZ, F. C. LARIO, Order Promising Process for Extended Collaborative Selling Chain, Production Planning & Control, vol. 19, no. 2, 2008, pp. 105-131.
- POWER, D., R. SHARDA, Model-driven Decision Support Systems: Concepts and Research Directions, Decision Support Systems, vol. 43, 2007, pp. 1044-1061.
- SEOK, H., S. Y. NOF, F. G. FILIP, Sustainability Decision Support System based on Collaborative Control Theory, Annual Reviews in Control, vol. 36, 2012, pp. 85-100.
- GOMES DA SILVA, C. G., J. FIGUEIRA, J. LISBOA, S. BARMAN, An Interactive Decision Support System for an Aggregate Production Planning Model based on Multiple Criteria Mixed Integer Linear Programming, Omega, vol. 34, 2006, pp. 167-177.
- FILIP, F. G., K. LEIVISKÄ, Large-Scale Complex Systems, Part D, Automation Design: Theory and Methods for Integration. Springer Handbook of Automation, Springer, 2009, pp. 619-638.
- FILIP, F. G., Decision Support and Control for Large-scale Complex Systems, Annual Reviews in Control, vol. 32, 2008, pp. 61-70.
- ALEMANY, M. M. E., J. J. BOJ, J. MULA, F. C. LARIO, Mathematical Programming Model for Centralised Master Planning in Ceramic Tile Supply Chains, International Journal of Production Research, vol. 48, no. 17, 2010, pp. 5053-5074.
- MUNDI, I., ALEMANY MME, BOZA, A., POLER, R., The Effect of Modeling Qualities, Tones and Gages in Ceramic Supply Chains’ Master Planning, Informatica Economică vol. 16, no. 3, 2012, pp.5-18.
- MEYR, H., Customer Segmentation, Allocation Planning and Order Promising in Make-to-Stock Production, OR Spectrum, vol. 31, no. 1, 2009, pp. 229-256.
- SPRAGUE, R. H., A Framework for the Development of Decision Support Systems, MIS Quarterly, vol. 4, no. 4, 1980, pp. 1-26.
- POWER, D., S. KAPARTHI, Building Web-based Decision Support System, Studies in Informatics and Control, vol. 11, no. 4, 2002, pp. 291- 302.
- ZARATÉ, P., Decision Making Process: A collaborative Perspective, Studies in Informatics and Control, vol. 17, no. 2, 2008, pp. 225-230.
* Complementary versions of this paper were presented in the “6th International Conference on Industrial Engineering and Industrial Management”, Vigo, July 2012, with the title “Managing qualities, tones and gages of Ceramic Supply Chains through Master Planning” and published in Informatica Economică, vol. 16, no. 3, pp.5-18, (2012) with the title “The Effect of Modeling Qualities, Tones and Gages in Ceramic Supply Chains’ Master Planning”. The current paper provides significant additional content including a Decision Support System and additional results from different solution scenarios dealing with LHP uncertainty.