22 Production_Scheduling_in_MRP

22 Production_Scheduling_in_MRP - PRODUCTION SCHEDULING IN...

Info icon This preview shows pages 1–8. Sign up to view the full content.

PRODUCTION SCHEDULING PRODUCTION SCHEDULING IN MRP IN MRP
Image of page 1

Info icon This preview has intentionally blurred sections. Sign up to view the full version.

PRODUCTION SCHEDULING IN MRP PRODUCTION SCHEDULING IN MRP Once the netting is complete the production must be scheduled to satisfy them. In planning the production to satisfy the net requirements, we have the choices ranging from manufacturing all at once to manufacturing each bucket’s net requirement in the period needed. Various lot sizing models are proposed to help answer this question (collectively known as dynamic lot sizing models: Wagner-Within model, lot- for-lot, fixed order quantity, fixed order period, part period balancing, etc.)
Image of page 2
Dynamic Lot Sizing Models (DLS) Dynamic Lot Sizing Models (DLS) Dynamic lot sizing models come to bear when demand is lumpy, i.e., is not uniform during the planning horizon. We organize the discussion of “lumpy demand” models in three groups of solution techniques, as follows: Simple rules : are decision rules for the order quantity that are not based directly on “optimizing” the cost function but that have certain other merits. These simple methods are significant because they are widely used, especially in MRP systems.
Image of page 3

Info icon This preview has intentionally blurred sections. Sign up to view the full version.

Dynamic Lot Sizing Models (DLS) Dynamic Lot Sizing Models (DLS) Heuristic rules : aim at achieving a low-cost solution that is not necessarily optimal. Wagner-Whitin : is an optimization approach to lumpy demand. SIMPLE RULES : There are three rules that are common-fixed period demand, periodic order quantity, and lot for lot (nicknamed “L4L”).
Image of page 4
Dynamic Lot Sizing Models (DLS) Dynamic Lot Sizing Models (DLS) Fixed period demand . This approach is equivalent to the simple rule of ordering “m months of future demand.” For example, if we want to order “two months of demand,” we sum the forecasted demand for the next two months. This rule is different from the “months of supply” measure of effectiveness. The latter is an aggregate measure based on dollar value of all inventory items. Fixed period demand is for an individual item and is based on quantity.
Image of page 5

Info icon This preview has intentionally blurred sections. Sign up to view the full version.

Dynamic Lot Sizing Models (DLS) Dynamic Lot Sizing Models (DLS) Example: Fixed period demand. Consider the following two cases, where the forecasted demand is given in the table below. Solution: If we use a fixed period of six weeks, the order quantity is 60 for (a) and 72 for (b). The EOQ is preferred for constant demand. Fixed period demand (a) Uniform demand Week 1 2 3 4 5 6 Demand 10 10 10 10 10 10 (b) Lumpy demand Week 1 2 3 4 5 6 Demand 10 15 11 18 8 10
Image of page 6
Dynamic Lot Sizing Models (DLS) Dynamic Lot Sizing Models (DLS) Period order quantity (POQ): This is a modification of the previous rule, in which “structure” is used to select the fixed period. The average lot size desired (by whatever method) is divided by the average period demand, yielding the fixed period to be used. If the desired order quantity is 60, then the fixed period for (b) is five weeks, as the average weekly demand is 12.
Image of page 7

Info icon This preview has intentionally blurred sections. Sign up to view the full version.

Image of page 8
This is the end of the preview. Sign up to access the rest of the document.
  • Fall '14
  • Period

{[ snackBarMessage ]}

What students are saying

  • Left Quote Icon

    As a current student on this bumpy collegiate pathway, I stumbled upon Course Hero, where I can find study resources for nearly all my courses, get online help from tutors 24/7, and even share my old projects, papers, and lecture notes with other students.

    Student Picture

    Kiran Temple University Fox School of Business ‘17, Course Hero Intern

  • Left Quote Icon

    I cannot even describe how much Course Hero helped me this summer. It’s truly become something I can always rely on and help me. In the end, I was not only able to survive summer classes, but I was able to thrive thanks to Course Hero.

    Student Picture

    Dana University of Pennsylvania ‘17, Course Hero Intern

  • Left Quote Icon

    The ability to access any university’s resources through Course Hero proved invaluable in my case. I was behind on Tulane coursework and actually used UCLA’s materials to help me move forward and get everything together on time.

    Student Picture

    Jill Tulane University ‘16, Course Hero Intern