In this chapter, the following content will be discussed: Material requirement planning, nature of demand, inputs to MRP, bill of material, planned orders, net requirement plan, MRP and JIT, lot sizing techniques, maintenance and reliability, reliability, product failure rate, providing redundancy, maintenance cost, total productive maintenance.
LECTURE 32 LSM733-PRODUCTION OPERATIONS MANAGEMENT By: OSMAN BIN SAIF CHAPTER : MATERIAL REQUIREMENT PLANNING (Contd.) Lot-Sizing Techniques ỵ Lot-for-lot techniques order just what is required for production based on net requirements ỵ ỵ ỵ May not always be feasible If setup costs are high, lot-for-lot can be expensive Economic order quantity (EOQ) ỵ EOQ expects a known constant demand and MRP systems often deal with unknown and variable demand Lot-Sizing Techniques ỵ ỵ Part Period Balancing (PPB) looks at future orders to determine most economic lot size The Wagner-Whitin algorithm is a complex dynamic programming technique ỵ þ Assumes a finite time horizon Effective, but computationally burdensome Lot-Sizing Summary For these three examples Lot-for-lot EOQ PPB $700 $730 $490 a l p a d e d l e i ey v a h d l u o w itin h W r e n g 55 a $ W f o t s o c l with a tota n ERP and MRP Figure 14.11 CHAPTER : JIT, Lean Operations Just-In-Time, TPS, and Lean Operations ỵ ỵ ỵ JIT is a philosophy of continuous and forced problem solving via a focus on throughput and reduced inventory TPS emphasizes continuous improvement, respect for people, and standard work practices Lean production supplies the customer with their exact wants when the customer wants it without waste Eliminate Waste ỵ ỵ Waste is anything that does not add value from the customer point of view Storage, inspection, delay, waiting in queues, and defective products not add value and are 100% waste Ohnos Seven Wastes ỵ ỵ þ þ þ þ þ Overproduction Queues Transportation Inventory Motion Overprocessing Defective products Queuing System Designs Some college registrations Queue Arrivals Phase service facility Channel Phase service facility Channel Phase service facility Channel Phase service facility Channel Multi-channel, multiphase system Figure D.3 50 Departures after service Queuing Models The four queuing models here all assume: ỵ ỵ ỵ Poisson distribution arrivals FIFO discipline A single-service phase 51 Poisson Distribution e- x P(x) = x! where time for x = 0, 1, 2, 3, 4, … P(x) = x = probability of x arrivals number of arrivals per unit of = average arrival rate e = 2.7183 (which is the base of the natural logarithms) Poisson is A statistical distribution showing the frequency probability of specific events when the average probability of a single occurrence is known E.g What is the probability that more than 600 people will come for the dinner at a specific restaurant 52 ADDITIONAL CHAPTER: CAPACITY AND CONSTRAINT MANAGEMENT 53 Planning Over a Time Horizon Options for Adjusting Capacity Long-range planning Add facilities Add long lead time equipment Intermediaterange planning Subcontract Add equipment Add shifts Short-range planning * Add personnel Build or use inventory * Schedule jobs Schedule personnel Allocate machinery Modify capacity Use capacity * Difficult to adjust capacity as limited options exist Figure S7.1 54 Tactics for Matching Capacity to Demand Increasing/decreasing employees and shifts Adjusting equipment u u Purchasing additional machinery Selling or leasing out existing equipment Improving processes to increase throughput Redesigning products to facilitate more throughput Adding process flexibility to meet changing product preferences Closing facilities 55 Bottleneck Analysis and Theory of Constraints u u u Capacity analysis determines the throughput capacity of workstations in a system A bottleneck has the lowest effective capacity in a system A bottleneck is a limiting factor or constraint 56 Break-Even Analysis u u u Technique for evaluating process and equipment alternatives Objective is to find the break-even point in dollars and in units at which cost equals revenue Requires estimation of fixed costs, variable costs, and revenue 57 Expected Monetary Value (EMV) and Capacity Decisions -$14,000 Market favorable (.4) rg La e nt a l p Market unfavorable (.6) -$90,000 $18,000 Market favorable (.4) Medium plant Sm all pla nt Do no th in g $100,000 Market unfavorable (.6) $60,000 -$10,000 $13,000 Market favorable (.4) Market unfavorable (.6) $40,000 -$5,000 $0 58 Phases of Quality Assurance Inspection of lots before/after production Acceptance sampling Inspection and corrective action during production Process control Quality built into the process Continuous improvement The least progressive The most progressive 59 Statistical Process Control • Variations and Control – Random variation: Natural variations in the output of a process, created by countless minor factors – Assignable variation: A variation whose source can be identified 60 SPC Errors • Type I error – • Concluding a process is not in control when it actually is Type II error – Concluding a process is in control when it is not 61 Use of Control Charts • At what point in the process to use control charts – X chart – R chart – c chart – p chart • What size samples to take • What type of control chart to use – Variables 62 Process Capability • Tolerances or specifications – • Process variability – • Range of acceptable values established by engineering design or customer requirements Natural variability in a process Process capability – Process variability relative to specification 63 THANK YOU ... (Contd.) Lot-Sizing Techniques ỵ Lot-for-lot techniques order just what is required for production based on net requirements ỵ ỵ ỵ May not always be feasible If setup costs are high, lot-for-lot can... Market $200,000 -$ 180,000 $100,000 -$ 20,000 $0 $0 Maximax decision is to construct large plant 33 Example - Maximin States of Nature Minimum in Row Market Market $200,000 -$ 180,000 -$ 180,000 Alternatives... Outcome A-31 Decision Making Under Uncertainty - Criteria • • • Maximax - Choose alternative that maximizes the maximum outcome for every alternative (Optimistic criterion) Maximin - Choose alternative