Concepts for Safety Stock Determination under Stochastic Demand and Different Types of Random Production Yield

We consider a manufacturer\u27s stochastic production/inventory problem under periodic review and present concepts for safety stock determination to cope with uncertainties that are caused by stochastic demand and different types of yield randomness. Order releases follow a linear control rule. Taking manufacturing lead times into account it turns out that safety stocks have to be considered that vary from period to period. We present an approach for calculating these dynamic safety stocks. Additionally, to support practical manageability we suggest two approaches for determining appropriate static safety stocks that are easier to apply

Concepts for Safety Stock Determination under Stochastic Demand and Different Types of Random Production Yield

We consider a manufacturer's stochastic production/inventory problem under periodic review and present concepts for safety stock determination to cope with uncertainties that are caused by stochastic demand and different types of yield randomness. Order releases follow a linear control rule. Taking manufacturing lead times into account it turns out that safety stocks have to be considered that vary from period to period. We present an approach for calculating these dynamic safety stocks. Additionally, to support practical manageability we suggest two approaches for determining appropriate static safety stocks that are easier to apply.stochastic demand, random yield, lot sizing, safety stocks

Stochastic make-to-stock inventory deployment problem: an endosymbiotic psychoclonal algorithm based approach

Integrated steel manufacturers (ISMs) have no specific product, they just produce finished product from the ore. This enhances the uncertainty prevailing in the ISM regarding the nature of the finished product and significant demand by customers. At present low cost mini-mills are giving firm competition to ISMs in terms of cost, and this has compelled the ISM industry to target customers who want exotic products and faster reliable deliveries. To meet this objective, ISMs are exploring the option of satisfying part of their demand by converting strategically placed products, this helps in increasing the variability of product produced by the ISM in a short lead time. In this paper the authors have proposed a new hybrid evolutionary algorithm named endosymbiotic-psychoclonal (ESPC) to decide what and how much to stock as a semi-product in inventory. In the proposed theory, the ability of previously proposed psychoclonal algorithms to exploit the search space has been increased by making antibodies and antigen more co-operative interacting species. The efficacy of the proposed algorithm has been tested on randomly generated datasets and the results compared with other evolutionary algorithms such as genetic algorithms (GA) and simulated annealing (SA). The comparison of ESPC with GA and SA proves the superiority of the proposed algorithm both in terms of quality of the solution obtained and convergence time required to reach the optimal/near optimal value of the solution

Safety Stocks in Centralized and Decentralized Supply Chains under Different Types of Random Yields

Safety stock planning with focus on risk protection to cope with demand uncertainties is a very well researched topic in the field of supply chain management, in central as well as in local decision making systems. In contrast, there is only few knowledge about safety stock management in situations where supply risks have to be covered that are caused by uncertainties with respect to production yields. In this study, a two-stage manufacturer-retailer supply chain is considered in a single-period context that allows for an analytical study of the impact of yield randomness on safety stock determination. In order to concentrate the analysis on the effects of yield uncertainty demand will be assumed to be deterministic. We consider three basic types of yield randomness which represent different reasons for yield losses in production processes each, namely the stochastically proportional, binomial, and interrupted geometric yield type. It will be shown that these different yield risk specifications can bring about completely different properties with regard to the way safety stocks depend on various input parameters in supply chain planning. This holds especially for the impact of the demand size and for the influence of the level of product profitability in a supply chain. In an analytical model-based investigation it is demonstrated that these safety stock properties not only differ between the respective yield types, but also between systems of central and decentralized supply chain decision making. Thus, this study presents general insights into the importance of a correct yield type specification for an effective safety stock management and explains necessary differences in the stock distribution across supply chain stages in both centralized and decentralized settings

Operations research models and methods for safety stock determination: A review

In supply chain inventory management it is generally accepted that safety stocks are a suitable strategy to deal with demand and supply uncertainty aiming to prevent inventory stock-outs. Safety stocks have been the subject of intensive research, typically covering the problems of dimensioning, positioning, managing and placement. Here, we narrow the scope of the discussion to the safety stock dimensioning problem, consisting in determining the proper safety stock level for each product. This paper reports the results of a recent in-depth systematic literature review (SLR) of operations research (OR) models and methods for dimensioning safety stocks. To the best of our knowledge, this is the first systematic review of the application of OR-based approaches to investigate this problem. A set of 95 papers published from 1977 to 2019 has been reviewed to identify the type of model being employed, as well as the modeling techniques and main performance criteria used. At the end, we highlight current literature gaps and discuss potential research directions and trends that may help to guide researchers and practitioners interested in the development of new OR-based approaches for safety stock determination.This work has been supported by FCT – Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020, and by the European Structural and Investment Funds in the FEDER component, through the Operational Competitiveness and Internationalization Program (COMPETE 2020) [Project no. 39479, Funding reference: POCI-01-0247-FEDER-39479]

Managing uncertainty:financial, actuarial and statistical modelling.

present value; Value; Actuarial;