Safety Stock Influencers and Order Timing Decisions Analysis

This essay examines the impact of safety stock on order timing decisions within an organization. It discusses how safety stock, or buffer stock, is used to mitigate uncertainties in demand and supply. The analysis highlights that while rule-based approaches are common, they can be manipulated and may not always be optimal for determining inventory levels. The essay identifies four key rule-based approaches: protecting against unpredictable changes in raw materials, compensating for forecast inaccuracies, ensuring continuity in manufacturing and deliveries, and maintaining customer satisfaction by avoiding stock outs. Ultimately, understanding the influence of safety stock on order timing decisions is crucial for effective inventory management, cost minimization, and maintaining high levels of customer service. Desklib offers a range of study resources, including solved assignments and past papers, to aid students in their academic pursuits.

Running head: STOCK INFLUENCERS AND TIMING DECISIONS 1
How Safety Stock Influences Order Timing Decisions
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How Safety Stock Influences Order Timing Decisions
To deal with the uncertainties in demand and supply the safety of stock is executed by
certain measures and usually it’s through orders of timing decisions. The safety inventory stock
sometimes referred to the buffer stock by inventory managers when they are trying to describe an
additional layer of stock to cushion against risk. Just to mention, it is due to the shortfall of raw
or finished goods can change the uncertainness in the supply of demand. From my analytical
perspective, this article assesses how the influencers order timing decisions and the importance
to the organization.
Moreover, it is due to the imbalances in inventory from undelivered goods to suppliers
owing to the lack of raw materials that result to the excessive reduction in the amount in stock
inventory hence obstruct cash flow and cheap service levels. With this in mind, the executive
who is the major influences are obliged that this rule-based approach can be manipulated by
timing decisions Gaur et al (2017). I can pinpoint that; the rule-based approach is less than
accurate when management needs to determine the optimal levels of inventory in numerous
operations. Additionally, such a dependable method of calculation will not only justify the
organizational continuity but also give a mathematical rational to the leaders Kannan et al
(2013). Palpably, such an approach will balance the scale by minimizing inventory cost and
maximizing customer service.
Hançerlioğulları, et al. (2016) claims the influencer orders the timing decisions and are
done through four distinctive rule-based approaches. To begin with, the safety stock is
manipulated to protect against the unpredicted changes in raw materials that end up affecting the
supply and demand. Secondly, due to the unforeseen future inaccuracies that may arise in

STOCK INFLUENCERS AND TIMING DECISIONS 3
demand and can be compensated through this particular method.Just to mention, this approach is
viable only when the forecast is lower than the sudden increase in demand. As a third approach,
the timing decision that an influencer can safety stock is when it’s for continuity in the
manufacturing or deliveries and management will do anything to prevent such disruptions
Kannan et al (2013). Finally, business is profitable when the sole purpose of the organization is
customer satisfaction; therefore for these levels to maintain the influencer must avoid stock out
(Hançerlioğullarıet al.2016).
In summary, by describing how the timing decisions of stock influencers affect the safety
of the inventory stock we get to understand entirelyty of such approaches are fundamental to
both management and customer satisfaction.

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References
Hançerlioğulları, G., Şen, A., &Aktunç, E. A. (2016). Demand uncertainty and inventory
turnover performance: An empirical analysis of the US retail industry. International
Journal of Physical Distribution & Logistics Management, 46(6/7), 681-708.
Gaur, V., Osadchiy, N., Seshadri, S., & Subrahmanyam, M. G. (2017). Optimal timing of
inventory decisions with price uncertainty.
Kannan, G., Grigore, M. C., Devika, K., & Senthilkumar, A. (2013). An analysis of the general
benefits of a centralised VMI system based on the EOQ model. International Journal of
Production Research, 51(1), 172-188.