Forecasting and Optimization of Rohit Automobiles

In the current markets characterized by ever-changing market trends and consumer purchasing behaviors executive managers significantly depend on the forecasting of the markets especially in the fuel and gas industry(Dias, 2010). There is a myriad of forecasting techniques that can be employed by automobiles fuel companies such as Rohit Automobiles in developing coherent and sustainable business and operational plans for competitive advantage. These include futures-based forecasts which use historical data on the prices of traded assets such as stocks (Bajjalieh, 2010; Dias, 2010). Future prices of oil and gas can be predicted by analyzing the historical trends of the traded oil and gas stocks. Inventory-based forecast which is a structured-based forecasting model can be used topredict trends in the oil market as it uses relative inventory variables to identify and measure different imbalances between supply and demand. Lastly, Ronit Automobiles company can as well use time series forecasts which compare the structural-based forecast such as inventory models with the futures models. Bajjaleih (2010) provides a detailed description on how to use the futures-based, relative inventory-based, and time series forecasts in oil and gas industry.

Question 2

Economic Order Quantity (EOQ) model is cited as one of the most invaluable tools for inventory decision making. The model is based on two primary items that is how much products to ordered (order quantity) and when to order (reorder point) (Salunke & Verma, 2015). The reorder point is computed assessing thedemand lead time and determining the safety stock that shall help in ensuring there it meets the demand and supply trends. The reorder point is determined by calculating the lead time demand and safety stock; therefore, ROP is a summation of the safety stock and lead time demand over a set period (Salunke & Verma, 2015; Stephen &Gupte, 2016). However, lead-time is the product of the average daily unit sales by the lead time. The table below summaries the unit sales and the number of orders for petrol.The order quantity determined by commuting the costs of the total annual inventory so as to determine an appropriate order quantity that shall minimize the costs (Salunke & Verma, 2015). it is measured by calculating the yearly purchase costs, ordering costs, and holding costs as shown below:

Where TC = total cost, D = annual demand, C = unit cost, Q = order quantity, S = ordering cost, and H = holding cost. Holding costs increases with the number of products ordered hence the higher the order quantity the higher the holding inventory translating into higher holding costs. determining the appropriate order quantity helps in reducing the holding quantity while meeting the market demand. Basing on the above formula, EOQ model for order quantity shall be:

Co is the cost of placing one order

D is the annual demand

Ch is the annual cost of holding one unit of inventory

From the data

The annual demand for petrol is

The limitation of the method is that it assumes that the order demand to be constant across the year which is often very impossible. There are several variations and fluctuations such as disruptive technologies and currency devaluation in the markets that affect the consumers purchasing behaviors translating to changes in demand. This makes the method unrealistic and inapplicable in most instances.

Using a time series analysis, the following are the forecasts for Rohit Automobiles sales from June to September.

Petrol

Diesel

HSP

Forecasts 2016 (June-September)

Average Monthly Sales

January

3299.94

4262.77

349.87

February

3373.21

4613.14

312.55

March

3492.68

4522.71

342.77

April

3934.43

4769.00

238.60

May

3378.16

4609.16

243.81

June

3503.18

4501.01

248.95

July

3650.22

4956.90

192.34

August

3851.21

4943.99

177.98

September

4419.09

5302.06

100.94

Question 3

The operational practices employed by Rohit only help in monitoring and tracking the inventory of the fuels at the station but they do not provideinformed decisions on how to improve both demand and supply decision making. They should consider synchronization of the ordering cycles to reduce the lead times and the fuel stations. Using of relative inventory-based forecast models can significantly help the company to leverage its inventory capacity and competitive advantage. Inventory based forecast such as the EOQ model shall help in ordering sustainable ordering quantity that shall reduce the holding costs and total cost purchase hence reducing the operational costs.

References

Bajjalieh, J. W. (2010). Forecasting Diesel Fuel Prices (Master’s Thesis).Urbana, Illinois: theUniversity of Illinois at Urbana-Champaign.

Dias, R. M. (2010). Implementation of demand forecasting models for fuel oil - The case of the CompanhiaLogística de Combustíveis, S.A. Lisboa, Portugal: UniversidadeTécnica de Lisboa.

Salunke, H., & Verma, P. (2015). Optimization of inventory level at Oilfield services: Case Study. International Journal of Scientific & Engineering Research, 6(4), 308-313.

Stephen, A. G., & Gupte, J. (2016). Overview of the Classic Economic Order Quantity Approach to Inventory Management. The Business Age.