Managing Operations: Inventory Management Reflection and Analysis

This report is a reflection on managing operations, focusing on inventory management simulations for adjustable wrenches and rock salt over a 12-week period. The analysis considers ordering costs, lost opportunity costs, and holding costs. The simulation uses anticipation of customer demand to determine optimal reorder points and quantities. The report discusses the challenges of predicting demand, particularly for rock salt, and the application of economic order quantity models. It explores factors influencing order size, such as demand variability and minimum stock levels, and the impact of these factors on inventory costs. The reflection highlights the importance of balancing costs and the use of models to make informed purchasing decisions. The author reflects on the insights gained from the simulations, confirming the practical application of inventory management principles learned in class.

MANAGING OPERATIONS 1
Reflection on Managing Operations
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MANAGING OPERATIONS 2
This paper is a reflection report after the analysis of the performance order and reorders of both
the adjustable wrench and rock salt within 12 weeks. The simulations were performed on the
demand of both the adjustable wrenches and rock salt after conduction of inventory management
process through a single strategy known as the anticipation of the customers demand but differed
slightly on the approach of determining the minimum level (Park & Kyung 2014, p.1536). The
simulation represented a real-life situation of how ordering and reordering are taking into
account of the ordering cost, lost the opportunity and the holding cost as the major cost incurred
by every business organization to maintain the minimum level for maximum profitability.
My reflection about the adjustable wrench is that the necessity is straightforward and predictable
and has a standard deviation of one. According to the simulations results that I carried out during
the twelve weeks, the maximum demand of the adjustable wrench is twenty. On the other hand,
the low demand for rocks slat was 20 with a standard deviation of 9 that is on a hire side; this
means that prediction on the consumer's demand was difficult as the demand can either be higher
with nine or be lower with nine units (Shahi & Pukki 2015, p.1320). Before the simulation,
certain factors that I considered as the basics are the inventory at the beginning, the reorder point,
the quantity of the order, the cost of placing the order, the cost of lost opportunity and the cost
for holding per unit. Consequently, the variables in the simulation process are the order quantity
and the reorder point that have two components of probability. These are the demand and the
reorder lead time.
Using the anticipation demand of adjustable wrench, I realized that the ordering cost was fixed to
$ 6.3 irrespective of the quantity of the order; therefore in comparison of both the current
ordering data, I realized that the most economical quantity to order is 80 units for four weeks to
reduce the ordering cost(Jansen & Fransoo 2013,p.260)). With a holding cost of $0.4, the 80

MANAGING OPERATIONS 3
units will translate into about $3.2 that will be much cheaper than ordering per week .in reference
to per week the reorder of the adjustable wrench the inventory cost according to me is $ 32.74.
This because the safety stock is 2, and for 80 units per week will be economically viable as the
lost opportunity cost is reduced since the safety stock is constant throughout.
Using the customer anticipation demand on the second simulation of rock salt, I was challenged
with the method of determining the optimum level of replenishing so that I don’t run a loss on
the opportunity cost (Kuo 2011p.1249). Throughout the simulations, I faced either recurring
opportunity lost cost or high holding cost. These made me anxious taking any step such as
reducing the holding cost, incurring the lost cost or missing the opportunity cost, however I
adopted a bell curve that showed the probability of falling of the stock level . Applying the
economic order quantity of 60 units with reader level of 41units per weeks, the inventory cost
was $ 45.01 due the high standard deviation of the rock salt.
2. The two simulations on, Adjustable wrench and Rock Salt have just confirmed the knowledge
gained in class about the inventory and material models used by various organizations to keep
the products stock at an optimum level (Rădăşanu 2016, p.148).Models such economic order
quantity and uncertainty demand models help organizations to make proper purchases decisions
that do not lead to loss of opportunity cost, reduces or raises the holding stock depending on the
safety stock cost.
Various factors determine the size of orders in an organization, and these include: purchase unit
price, optimal ordered quantity, annual demand quantity
The demand product determines the size of the order being placed by the procurement
department. Products that such as the Adjustable Wrench has high demand with little standard

MANAGING OPERATIONS 4
deviation (Chakraborty & Giri 2012, p.718). This means that almost every day, the purchase
amount will be around 20 units, and therefore the rendering size must be able to cover the
available safety stock for quite sometimes.
The minimum stock level is also an essential factor in determining the size of the order being
placed. When the stock level is high, then the size of reordering must be low to reduce the cost of
holding and the same time avoiding the loss of opportunity cost. When the stock level is low, the
order size tends to be high to cover up the demand in future.

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List of References
Chakraborty, T, & Giri, B 2012, 'Joint determination of optimal safety stocks and production
policy for an imperfect production system', Applied Mathematical Modelling, 36, 2, pp. 712-722,
Academic Search Premier, EBSCOhost, viewed 7 May 2018.
Jansen, M, Kok, T, & Fransoo, J 2013, 'Lead time anticipation in Supply Chain Operations
Planning', OR Spectrum, 35, 1, pp. 251-290, Academic Search Premier, EBSCOhost, viewed 7
May 2018.
Kuo, T 2011, 'Simulation of purchase or rental decision-making based on product service
system', International Journal Of Advanced Manufacturing Technology, 52, 9-12, pp. 1239-
1249, Academic Search Premier, EBSCOhost, viewed 7 May 2018.
Park, K, & Kyung, G 2014, 'Optimization of total inventory cost and order fill rate in a supply
chain using PSO', International Journal Of Advanced Manufacturing Technology, 70, 9-12, pp.
1533-1541, Academic Search Premier, EBSCOhost, viewed 7 May 2018.
RĂDĂŞANU, AC 2016, 'INVENTORY MANAGEMENT, SERVICE LEVEL AND SAFETY
STOCK', Journal Of Public Administration, Finance & Law, 9, pp. 145-153, Academic Search
Premier, EBSCOhost, viewed 7 May 2018.
Shahi, S, & Pulkki, R 2015, 'A simulation-based optimization approach to integrated inventory
management of a sawlog supply chain with demand uncertainty', Canadian Journal Of Forest
Research, 45, 10, pp. 1313-1326, Academic Search Premier, EBSCOhost, viewed 7 May 2018.