Inventory Management Lecture Notes PDF

◆ I = P65,000 ◆ Turns = 0.02 per day INVENTORY MANAGEMENT P1,000 /P65,000 ➔ Example: convert into a year Measuring the average inventory...

◆ I = P65,000 ◆ Turns = 0.02 per day INVENTORY MANAGEMENT P1,000 /P65,000 ➔ Example: convert into a year Measuring the average inventory ◆ (1,000 x 365)/65,000 ➔ In pesos R x 365/ I ➔ In days-of-supply ◆ 365,000/65,000 ➔ In turns ◆ = 5.62 per annum DAYS-OF-SUPPLY INVENTORY TURNS (USING THE DATA IN FINANCIAL REPORTS) ➔ The average amount of time (in days) it takes for a unit to flow through the ➔ Note: we will use the cost of goods sold system as the rate ➔ Formula: I = R x T ➔ Example: Vintage industries reports ➔ Where: annual sales of P160 million, cost of ◆ I = inventory goods sold of P120 million, inventory of ◆ R = flow rate P20 million, and net income of P5 ◆ T = time million. What is its annual inventory ➔ Example: an instrument manufacturer, turns? the P65,000 worth inventory would be ◆ R = 120,000,000 depleted in 65 days ◆ I = 20,000,000 ◆ I = P65,000 ◆ 120,000,000/20,000,000 ◆ T = 65 days ◆ Turns = 6 per annum ◆ R = P1,000 per day P65,000 / 65 days FORECASTING The average number of inventory flowing ➔ The process of creating statements about through a system in 65 outcomes of variables presently days is P1,000 per day uncertain and will only be realized in the future INVENTORY TURNS ◆ Variables would include profit ➔ The number of times the average DEMAND FORECASTING inventory flows through the process in a designated interval of time ➔ The process of creating statements about ➔ Formula: Turns = R/I future realization of demand ➔ Where: ◆ R = flow rate ◆ I = inventory TIME SERIES ANALYSIS ➔ Example: using the data in the previous examples ➔ The process of analyzing the old ◆ R = P1,000 per day (demand) data  ➔ A time series-based forecast is a forecast ➔ Long-term forecasts that is obtained based on nothing but old (demand) data. SHORT-TERM FORECASTS ◆ Example: forecasting from the volume of sales which can be seen in the income statement. ➔ Used to support decisions that are made ◆ Example: patterns in stock for short periods of time ranging from market data daily to the monthly level ➔ Example: during holidays, there is a need to have more restaurant serves EXTAPOLITION during lunchtime ➔ Estimation of values beyond the range MID-TERM FORECASTS of the original observation by assuming that some patterns in the values present within the range will also prevail outside ➔ Forecasts that drive capacity-related the range decisions. These are made from the monthly level to the yearly level ➔ Example: forecast for the entire flu REGRESSION ANALYSIS season so that enough amount of vaccine can be produced ➔ A statistical process of estimating the relationship of one variable with LONG-TERM FORECASTS multiple variables that influence this one variable ➔ Forecasts that are made for multiple years REGRESSION ANALYSIS VARIABLES ➔ Help with strategic decisions such as entering new markets, and launching ➔ Dependent variable (outcome variable) new products – the variable that we try to understand ◆ You must have a strong basis ➔ Independent variable – a variable that when forecasting influences the dependent variable EVALUATING THE QUALITY OF A EXPERT PANEL FORECASTING FORECAST ➔ Forecasts generated using the subjective FORECAST ERROR opinions of management ➔ The difference between a forecasted THREE TYPES OF FORECASTING value and realized value APPLICATION IN BUSINESS ➔ Formula: forecast - true demand ◆ After doing the formula, get the ➔ Short-term forecasts average of all the forecasts as ➔ Mid-term forecasts well as all the forecast error  QUEUE GROWTH RATE Unbiased Forecast ➔ A forecast that is correct on the average ➔ The average must be zero ➔ If the demand exceeds capacity, then the queue grows Biased Forecast ➔ Formula: queue growth rate = demand - ➔ A forecast that is wrong on the average capacity ➔ The average must not be zero, either negative or positive. Example: Customers per minute MEAN SQUARED ERROR (MSE) Demand 1.5 ➔ A measure evaluating the quality of a Capacity 1.25 forecast by looking at the average ➔ 1.5 - 1.25 =.25 customers/minute squared forecast error. ◆ Madadagdagan ng.25 ➔ Formula: average of (forecast error)2 customers per minute ◆ Lower MSE, more desirable LENGTH OF QUEUE AT TIME MEAN ABSOLUTE ERROR (MAE) Formula ➔ Measure evaluating the quality of a ➔ Length of queue at time (T) = T x Queue forecast by looking at the average growth rate absolute value of the forecast error ➔ = T x (Demand - Capacity) ➔ Formula: average of the absolute value ➔ Importance: kapag mahaba na yung of the forecast error queue, may mga customers na aatras ◆ Lower MAE, more desirable ➔ MSE and MAE do not always agree on Example: same given in the queue growth rate which forecast is better, which measure After 120 minutes… of forecast quality you use is really up to ➔ 120 x (1.5 - 1.25) = 30 customers you ◆ Ganyan na kahaba yung pila ng customers after 120 minutes QUEUES WHEN DEMAND EXCEEDS SUPPLY TIME TO SERVE CUSTOMERS THINGS TO CONSIDER DURING RUSH Formula HOUR ➔ Qth = specific customer that must be served ➔ Time to serve the Qth customer in the ➔ Length of the queue (demand) queue = Qth / Capacity ➔ How long does a customer have to wait ➔ Time to serve the customer arriving at to be served (capacity) time (T) = T x [(Demand / Capacity) - 1] ➔ What you can do to improve performance. For time to serve the Qth customer in the queue; Example: same given in the queue growth rate For the 30th customer… ➔ 30 / 1.25 = 24 minutes ◆ 24 minutes ang kailangan antaying ng 30th customer in line For time to serve the customer arriving at time; Example: same given ➔ 120 x [(1.5 / 1.25) - 1] = 24 minutes ◆ Specific time kung kailan dumating yung customer after a particular time AVERAGE WAITING TIME Formula ➔ Average time to serve a customer =.5 x T x [(demand / capacity) - 1] Example: same given ➔.5 x 120 x [(1.5 / 1.25) - 1] = 12 minutes ◆ Kung ilan yung waiting time per customer