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Push and Pull Production Control Systems

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					Fall, 2007
                                                                                     1 of 28




                                    Chapter 7
                             Push and Pull Production
                                 Control Systems


Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics

                                                                  www.izmirekonomi.edu.tr
Fall, 2007
                                                                                     2 of 28


  Basic Definitions

              MRP. (Materials Requirements Planning). MRP is the basic
               process of translating a production schedule for an end
               product (MPS or Master Production Schedule) to a set of
               requirements for all of the subassemblies and parts needed
               to make that item.

              JIT. Just-in-Time. Derived from the original Japanese
               Kanban system developed at Toyota. JIT seeks to deliver the
               right amount of product at the right time. The goal is to
               reduce WIP (work-in-process) inventories to an absolute
               minimum.



Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics

                                                                  www.izmirekonomi.edu.tr
Fall, 2007
                                                                                     3 of 28


  Why Push and Pull?

              MRP is the classic push system. The MRP system computes
               production schedules for all levels based on forecasts of
               sales of end items. Once produced, subassemblies are
               pushed to next level whether needed or not.

              JIT is the classic pull system. The basic mechanism is that
               production at one level only happens when initiated by a
               request at the higher level. That is, units are pulled through
               the system by request.




Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics

                                                                  www.izmirekonomi.edu.tr
Fall, 2007
                                                                                     4 of 28


       Comparison
               These methods offer two completely different approaches to basic
               production planning in a manufacturing environment. Each has
               advantages over the other, but neither seems to be sufficient on its
               own. Both have advantages and disadvantages, suggesting that
               both methods could be useful in the same organization.

              Main Advantage of MRP over JIT: MRP takes forecasts for end
               product demand into account. In an environment in which
               substantial variation of sales are anticipated (and can be forecasted
               accurately), MRP has a substantial advantage.

              Main Advantage of JIT over MRP: JIT reduces inventories to a
               minimum. In addition to saving direct inventory carrying costs, there
               are substantial side benefits, such as improvement in quality and
               plant efficiency.



Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics

                                                                  www.izmirekonomi.edu.tr
Fall, 2007
                                                                                     5 of 28


  MRP Basics
        The MRP system starts with the MPS or Master
         Production Schedule. This is the forecast for the
         sales of the end item over the planning horizon.
         The data sources for determining the MPS
         include:
              Firm customer orders
              Forecasts of future demand by item
              Safety stock requirements
              Seasonal variations
              Internal orders from other parts of the organization.



Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics

                                                                  www.izmirekonomi.edu.tr
Fall, 2007
                                                                                     6 of 28


  Schematic of the
  Productive System (Fig. 7.1)




Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics

                                                                  www.izmirekonomi.edu.tr
Fall, 2007
                                                                                     7 of 28

  The Three Major Control
  Phases of the Productive System (Fig. 7.2)




Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics

                                                                  www.izmirekonomi.edu.tr
Fall, 2007
                                                                                     8 of 28


     The Explosion Calculus
      The explosion calculus is a set of rules for converting the
       master production schedule to a requirements schedule for
       all subassemblies, components, and raw materials necessary
       to produce the end item.
      There are two basic operations comprising the explosion
       calculus:
       • Time phasing. Requirements for lower level items must
           be shifted backwards by the lead time required to
           produce the items
       • Multiplication. A multiplicative factor must be applied
           when more than one subassembly is required for each
           higher level item.



Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics

                                                                  www.izmirekonomi.edu.tr
Fall, 2007
                                                                                     9 of 28


  The Product Structure Diagram

   The product structure diagram is a graphical
    representation of the relationship between
    the various levels of the productive system. It
    incorporates all of the information necessary
    to implement the explosion calculus. Figure
    7-3 (next slide) depicts an end item with two
    levels of subassemblies.



Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics

                                                                  www.izmirekonomi.edu.tr
Fall, 2007
                                                                                     10 of 28


  Typical Product Structure
  Diagram (fig. 7-3)




Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics

                                                                  www.izmirekonomi.edu.tr
Fall, 2007
                                                                                     11 of 28


  Trumpet and Subassemblies (Fig. 7-4)




Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics

                                                                  www.izmirekonomi.edu.tr
Fall, 2007
                                                                                     12 of 28


  Product Structure Diagram
  for Harmon Trumpet




Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics

                                                                  www.izmirekonomi.edu.tr
Fall, 2007
                                                                                     13 of 28


  Explosion Calculus

      Rules for translating gross requirements at one level to production
      schedule at that level and requirements at lower levels.

      Example
      Basic Equation:
      Net Req. = Gross req. - Scheduled Receipts - projected on hand
                 inventory

      Basic Algorithm
      1. Compute time-phased requirements
      2. Determine Planned Order Release (LS)
      3. Compute ending inventory
      4. Proceed to next level (if any)

Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics

                                                                  www.izmirekonomi.edu.tr
Fall, 2007
                                                                                     14 of 28


  Explosion Calculus
       Schedule for end item A:
       Week                  8 9 10 11 12 13 14 15 16 17
       Gross Req            77 42 38 21 26 112 45 14 76 34
       Sch Rpt              12       6 9
       Inv                  23
       Net Req              42 42 32 12 26 112 45 14 76 34
       Schedule for item B (1 unit/2 weeks)
       Week            6 7 8 9 10 11 12 13 14 15
       Gross          42 42 32 12 26 112 45 14 76 34
       Schedule for item C (2 units/4 weeks)
       Week     4 5 6 7 8 9 10 11 12 13
       Gross 84 84 64 24 52 224 90 28 152 68
Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics

                                                                  www.izmirekonomi.edu.tr
                         Lot Sizing For MRP Systems
Fall, 2007
                                                                                     15 of 28




      The simplest lot sizing scheme for MRP systems is
      lot-for-lot (abbreviated L4L). This means that
      requirements are met on a period by period basis as
      they arise in the explosion calculus. However, more
      cost effective lot sizing plans are possible. These
      would require knowledge of the cost of setting up for
      production and the cost of holding each item. This
      brings to mind the EOQ formula from Chapter 4,
      which can be used in this context. However, there
      are better methods.


Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics

                                                                  www.izmirekonomi.edu.tr
Fall, 2007

             Statement of the Lot Sizing Problem
                                                                                     16 of 28




                Assume there is a known set of requirements (r1,
                r2, . . . rn) over an n period planning horizon.
                Both the set up cost, K, and the holding cost, h,
                are given. The objective is to determine
                production quantities (y1, y2, . . ., yn) to meet the
                requirements at minimum cost. The feasibility
                condition to assure there are no stockouts in any
                period is:
                      j              j

                    y  r
                    i 1
                            i
                                   i 1
                                          i   for 1  j  n.

Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics

                                                                  www.izmirekonomi.edu.tr
Fall, 2007
                                                                                     17 of 28


  Methods

               One could apply the EOQ formula by defining
                  1 n      but there are better methods.
                  ri
                      n   i 1
                Property of the optimal solution: every optimal solution orders
                exact requirements: that is,

                 y1  r1 or y1  r1  r2 ,. . ., or y1  r1  r2  ...  rn
               One method that utilizes this property is the Silver Meal
                Heuristic. The method requires computing the average cost
                for an order horizon of j periods for j = 1, 2, 3, etc. and
                stopping at the first instance when the average cost function
                increases. The average cost for a production quantity
                spanning j periods, C(j), is given by:


                  C ( j )  ( K  hr2  2hr3  ...  ( j  1) hrj ) / j
Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics

                                                                  www.izmirekonomi.edu.tr
Fall, 2007
                                                                                      18 of 28


  Methods (continued)
      Another method that is popular in practice is part period
      balancing. Here one chooses the order horizon to most closely
      balance the total holding cost with the set-up cost.
      Finally, a third heuristic is known as the least unit cost heuristic.
      Here one minimizes the average cost per unit of demand (as
      opposed to the average cost per period as is done in the Silver
      Meal heuristic.) The average cost per unit of demand over j
      periods is given by:




                 C ( j )  ( K  hr2  2hr3  ...  ( j  1)hrj ) /( r1  r2  ...  rj ).

Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics

                                                                  www.izmirekonomi.edu.tr
Fall, 2007
                                                                                     19 of 28


  Methods (concluded)

   Experimental evidence seems to favor the
    Silver Meal Heuristic among the four
    discussed as the most cost efficient.
   Optimal lot sizes can be found by using
    backwards dynamic programming.
   A heuristic method for lot sizing subject to
    capacity constraints is discussed in this
    section.


Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics

                                                                  www.izmirekonomi.edu.tr
             Shortcomings of MRP
Fall, 2007
                                                                                   20 of 28




              Uncertainty. MRP ignores demand uncertainty, supply uncertainty,
                 and internal uncertainties that arise in the manufacturing process.
             Capacity Planning. Basic MRP does not take capacity constraints
                 into account.
             Rolling Horizons. MRP is treated as a static system with a fixed
                 horizon of n periods. The choice of n is arbitrary and can affect the
                 results.
             Lead Times Dependent on Lot Sizes. In MRP lead times are
                 assumed fixed, but they clearly depend on the size of the lot
                 required.
             Quality Problems. Defective items can destroy the linking of the
                 levels in an MRP system.
             Data Integrity. Real MRP systems are big (perhaps more than 20
                 levels deep) and the integrity of the data can be a serious problem.
             Order Pegging. A single component may be used in multiple end
Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics
                 items, and each lot must then be pegged to the appropriate item.
                                                                www.izmirekonomi.edu.tr
Fall, 2007
                                                                                     21 of 28


  Introduction to JIT

              JIT (Just In Time) is an outgrowth of the Kanban system
               developed by Toyota.
              Kanban refers to the posting board where the evolution of the
               manufacturing process would be recorded.
              The Kanban system is a manual information system that
               relies on various types of cards.
              It’s development is closely tied to the development of SMED:
               Single Minute Exchange of Dies, that allowed model
               changeovers to take place in minutes rather than hours.
               (The mechanics of a typical Kanban system are pictured in
                Figure 7-8.)


Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics

                                                                  www.izmirekonomi.edu.tr
Fall, 2007
                                                                                     22 of 28


  Kanban System for Two Production
  Centers (Fig. 7-8)




Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics

                                                                  www.izmirekonomi.edu.tr
Fall, 2007
                                                                                     23 of 28


  Features of JIT Systems
  Small Work-in-Process Inventories.

       Advantages:
           1. Decreases Inventory Costs
           2. Improves Production Efficiency
           3. Reveals quality problems (see Figure 7-10)

       Disadvantages:
           1. May result in increased worker idle time
           2. May result in decreased throughput rate



Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics

                                                                  www.izmirekonomi.edu.tr
Fall, 2007
                                                                                     24 of 28

  River/Inventory Analogy
  Illustrating the Advantages of Just-in-Time




Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics

                                                                  www.izmirekonomi.edu.tr
Fall, 2007
                                                                                     25 of 28


  Features of JIT Systems (continued)

  Kanban Information Flow System
       Advantages:
           1. Efficient tracking of lots
           2. Inexpensive implementation of JIT
           3. Achieves desired level of WIP


       Disadvantages:
           1. Slow to react to changes in demand
           2. Ignores predicted demand patterns




Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics

                                                                  www.izmirekonomi.edu.tr
Fall, 2007
                                                                                     26 of 28


  Kanban Information System vs
  Centralized Information System (MRP)




Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics

                                                                  www.izmirekonomi.edu.tr
Fall, 2007
                                                                                     27 of 28


  Features of JIT Systems (continued)

             JIT Purchasing System
                Advantages:
                    1. Inventory reduction
                    2. Improved coordination
                    3. Better relationships with vendors

               Disadvantages:
                   1. Decreased opportunity for multiple sourcing
                   2. Suppliers must react quickly
                   3. Potential for congestion
                   4. Suppliers must be reliable.

Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics

                                                                  www.izmirekonomi.edu.tr
Fall, 2007
                                                                                     28 of 28


  Comparison of MRP and JIT

             Major study comparing MRP and JIT in
              practice reveals:

                 JIT works best in “favorable” manufacturing
                  environments: little demand variability, reliable
                  vendors, and small set up times

                 MRP (and ROP based on Chapter 5 methods)
                  worked well in favorable environments (comparable
                  to JIT) and better in unfavorable environments.


Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics

                                                                  www.izmirekonomi.edu.tr

				
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