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Inventory Management: Traditional And Modern Approaches
I. Overview
Traditional inventory management.
- Purchase costs.
- Carrying costs.
- Ordering costs.
- Stockout costs.
(Average daily demand * Lead time in days) + safety stock.
- Expected stockout cost.
- Carrying cost.
- Economic order quantity (EOQ).
Formula=√2*Fixed cost per purchase order *periodic demand/Per unit carrying cost
Just-in-time (JIT).
- Limits output to the demand (pull) of the subsequent operation.
- Reduction in inventory results in less investment in idle assets.
- Reduction of storage space requirements.
- Lower inventory taxes, pilferage, and obsolescence risks.
- Focus on quality control to prevent quality problems to achieve zero machine breakdowns and zero defects goals.
Combines purchasing, production, and inventory control.
Because many inventory-related activities are nonvalue-added.
Carrying inventory.
Improving a process is best done not by maximizing efficiency in every part of the process but by focusing on a few constraints.
Note: Increasing the efficiency of processes that are not constraints merely creates backup.
- Identified the bottleneck operation.
- Determines the most profitable mix given the constraint.
- Maximizes product flow through the bottleneck.
- Increase capacity at the bottleneck.
- Redesigns the process.
Short-term profit maximization requires maximizing the contribution margin.
Throughput costing.
Note: All other manufacturing costs are ignored because they are fixed in the short run.
Drum-buffer-rope (DBR) system.
The bottleneck operation.
It is the minimal work-in-process input to the drum.
The activities preceding and including the bottleneck must be coordinated.
II. Inventory Fundamentals
- Knowledge of the behavior of business cycles.
- Information on the seasonal variations in demand.
- Economic modeling.
Time series analysis.
- Divides inventory into two quantities (first bin and second bin).
- Inventory is reordered when the items in the first bin are used up.
- The second bin provides a safety stock plus the quantity used during the lead time before receiving the re-order.
- The amount of the inventory in the first bin is a constant quantity.
- The two-bin procedure is a constant order-quantity system.
- Clerical processing.
- Credit checking.
- Packing.
- Shipping.
- Collecting payments are the phase of the order-filling cycle.
- Market research.
- Competitive analysis.
- Promotion policies.
- General economic conditions.
- All related policies and decisions affecting the organization up to the time of the order.
- Storage costs.
- Warehousing costs.
- Property taxes.
- Spoilage.
- Handling costs.
- Insurance costs.
- Interest on capital invested.
- Obsolescence.
- Purchasing costs.
- Shipping costs.
- Setup costs for a production run.
- Quantity discount lost.
III. Economic Order Quantity (EOQ)
Minimize the sum of the order costs and the holding costs.
To identify the maxima or minima of curvilinear functions.
- Points of revenue or profit maximization (maxima).
- Cost minimization (minima).
- Specific given demand.
- The cost per order or production run.
- The periodic cost of carrying one unit in stock.
Regression analysis.
Markov process.
Queuing theory.
Linear programming.
- The cost of a transaction is independent of the dollar amount of the transaction.
- Interest rates are constant over the short run.
- An opportunity cost is associated with holding cash, beginning with the first dollar.
- The total demand for cash is known with certainty.
The production rate is greater than the demand rate.
- Periodic demand for the good is known.
- Unit carrying cost is constant.
- The costs of placing an order are assumed to be constant.
- The cost of the item being purchased is assumed to be constant.
IV. Other Inventory Control Methods
To reduce carrying costs.
By eliminating inventories and increasing the deliveries made by suppliers.
- A reduction in the number of suppliers.
- More deliveries from suppliers.
- A lesser need for inspection of goods as the goods arrive.
- Greater need for communication with the vendor.
- Assume receipts of goods are perfect.
- Relies heavily on good quality materials.
- Materials are delivered directly to the production line, ready for insertion in the finished product.
- Items are pulled through production by current demand, not pushed through by anticipated demand.
- Increase in inventory turnover.
- Decrease in inventory percentage to total assets.
- Decrease in cost per purchase order.
- Increase in inventory unit carrying costs.
- Increase in stockout costs.
- Decrease in carrying costs.
- Decrease in economic lot size.
- Decrease in relevant costs.
- Decrease in inspection costs.
- Reduction in the costs of spoilage, reworked units, and scrap.
- Increase flexibility.
- Shorter manufacturing lead time.
- Quicker development of new products.
- Better product delivery and service.
- Faster response to market changes.
- Improved competitiveness.
- Translate a production schedule into requirements for each component needed to meet the schedule.
- Implemented in the form of a computer-based information system designed to plan and control materials used in production.
- Output based on forecasted demand is pushed through to the next department or to inventory.
- It assumes that forecasted demand is reasonably accurate and that suppliers can deliver based on this schedule.
V. Reorder Points, Safety Stock, Stockout Cost
It equals the sales per unit of time multiplied by the time required to receive the new order (lead time).
The time needed for delivery, rate of inventory usage, and safety stock.
Safety stock.
- Translate a production schedule into requirements for each component needed to meet the schedule.
- Implemented in the form of a computer-based information system designed to plan and control materials used in production.
- Output based on forecasted demand is pushed through to the next department or to inventory.
- It assumes that forecasted demand is reasonably accurate and that suppliers can deliver based on this schedule.
Stocking time.
A backorder.
Monte Carlo simulation.
- Often is used to generate individual values for random variables.
- Performance under uncertainty can be investigated by randomly selecting values for each of the variables in the model (based on the probability distribution of each variable) and calculating the value of the solution.