Optimization of production plans according to estimates of the probability of future orders

Authors:
V.Ya. Zaruba1
1. National Technical University «Kharkiv Polytechnic Institute» (Kharkiv, Ukraine)
Section:
Problems of Management of Innovative Development
Pages:
222 - 232
Language:
Russian
DOI:
10.21272/mmi.2017.2-21


Annotation

The aim of the article. The aim of the article is to analyze the description ways of not completely certain demand, production volume choice criteria in the conditions of not completely certain demand as well as to develop a conceptual model to optimize production volumes according to estimates of probability of future orders.

The results of the analysis. The account of the likely future orders in short-term planning of production is one of the areas of reduction of terms of their performance and, thus, increases the demand for the company's products. The planned volume of production and the corresponding quantity of the finished products must ensure maximization of operational effect, which is the difference between the profits derived from the production and sales, and the magnitude of losses, including lost profits. This choice of the quantity of the finished product will be based on the expected economic results of the enterprise activity and this quantity may be different from the volume of the expected demand.

Managers use subjective interpretation of probability for estimates of the demand on the basis of their fuzzy knowledge. Indexes, which have main influence on managers’ risk acceptance in the choice of the finished products volume, include risk coefficient, which is obtained by dividing the amount of expected losses from overproduction on the expected profits value, and also the probability of losses arising in connection with the demand realization in a smaller amount than the planned volume of finished products.

A set of volumes of finished products, which to ensure acceptable risks, is determined by the maximum risk coefficient value and the maximum value of loss probability. If this set comprises several elements, it is necessary to select a value of the finished product, which corresponds to the maximum value of expected total effect.

Worthy of attention is the situation when the fuzzy knowledge on future demand are described in estimates of volumes and of probability of receiving orders from buyers of products. Conceptual model to optimize production volumes, which is designed for this situation, includes algorithms for constructing the probability distribution function for the quantities of demand and effect. The article includes a numerical example, used to consider options for the choice of the finished products volume, taking into account the different preferences in relation to the risks.

Conclusions and directions of further researches. The article represents conceptual model to optimize short-term planning of production volumes in the conditions of not completely certain demand. Prospects for future research include in the clarification of the production volumes optimization model, taking into account industrial characteristics of the company. In particular it is planned to identify the factors that are useful to take into account for the probability assessments of orders receipt and to develop a model of the production process to optimize work for performance of orders, which will come after the main reception.


Keywords
short-term planning of production, not entirely certain demand, subjective assessment of probability, risks of losses, preferences in relation to risks


Links
  1. Kaplan, Robert S., & Norton, D.P. (2006). Sbalansirovannaia sistema pokazatelei. Ot stratehii k deistviiu [Balanced Scorecard. From strategy to action]. Moscow: ZAO «Olimp-Biznes» [in Russian].
  2. Zaruba, V.Ya. (2013). Sistemno-resursnii pidkhid do upravlinnia diyalnistu pidpriemstva [The system approach to resources in management of enterprise]. Modeli otsenki i analiza slozhnikh socialno-economicheskikh sistem – Models of evaluation and analysis of complex socio-economic systems. (pp.364-371). Kharkov, ID «INSHEK» [in Ukrainian].
  3. Klaus, H., Rosemann, M., & Gable, G.G. (2000). What is ERP? Information systems frontiers, 2, 141-162. 
  4. Aloini, D., Dulmin, R., & Mininno, V. (2007). Risk Management in ERP Project Introduction: Review of the Literature.  Information & Managcment,  44(6), 547-567.
  5. Afanasev, E.V. (2005). Ekonomiko-matematchne modeluvannia riziku velikih promislovih pidpriemstv z monoproduktovim virobnitstvom [Economic-mathematical modeling risk of large industrial enterprises, producing a single product]. Dnipropetrovsk: Nauka i osvita [in Ukrainian].  
  6. Vitlinskiy, V.V., & Velikoivanenko, V.V. (2004). Ryzykolohia v economitsi ta pidpryiemnictvi [Ryzykolohiya in economics and entrepreneurship]. Kyiv: KNEU [In Ukrainian].
  7. Dubrov, A.M., Lagosha, B.A., & Khrustalev, E.Yu. (2003). Modelirovanie riskovikh situatsyi v ekonomike i biznese [Modeling risk situations in economics and business]. Moscow: Finansy i statistika [in Russian]. 
  8. Slabinskiy, S.V. (2011). Podhodi i printsipi formirovania indikatorov riska na promishlennom predpriiatii [Approaches and principles of risk indicators in an industrial plant]. Rossiyskoie predprinimatelstvo, 6-2(186), 88-93 [in Russian]. 
  9. Suleiymanov, R.F. (2012). Priniatie resheniyi v sfere marketinga v usloviiakh riska i neopredelennosti [The adoption of the marketing decisions in conditions of risk and uncertainty]. Rossiyskoe predprinimatelstvo, 12, 63-69 [in Russian].
  10. Risk Management Basics – ISO 31000 Standard. (2009). secureworldexpo.com. Retrieved from http://www.secureworldexpo.com/2011/detroit/Louis_Kunimatsu.pdf.
  11. Risk management – Vocabulary. (n.d.) husdal.com/2010/11/21/risk-management-vocabulary. Retrieved from http://www.husdal.com/2010/11/21/risk-management-vocabulary/.
  12. Goncharov, D.C. (2008). Kompleksniy podkhod k upravleniu riskami dlia rossiyskikh kompaniyi [A comprehensive approach to risk management for Russian companies]. Moscow: Vershina [in Russian]. 
  13. Rogachev, A. (2007). Postanovka sistemi risk menedgmenta v kompanii [Implantation of the system of risk management in the company]. Finansovyi direktor, 5, 17-22 [in Russian]. 
  14. Utkin, E.A., & Frolov D.A. (2003). Upravlenie riskami predpriatia [Enterprise risk management]. Moscow: TEIS [in Russian]. 
  15. Zaruba, V.Ya. (2016). Modeli imovirnisnogo predstavlennia nechitko viznachenogo popitu [Models of probabilistic representation of fuzzy demand]. Ekonomiko-matematichne modelyuvannia – Economic-mathematical modeling: Proceeding of the 1 scientific conference. (pp. 138-140). Kyiv: KNEU [in Ukrainian].