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Rough-Cut Capacity Planning in SCM EIN 5346 Logistics Engineering Fall, 2015.

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Presentation on theme: "Rough-Cut Capacity Planning in SCM EIN 5346 Logistics Engineering Fall, 2015."— Presentation transcript:

1 Rough-Cut Capacity Planning in SCM EIN 5346 Logistics Engineering Fall, 2015

2 Rough-Cut Capacity Planning in SCM Theories & Concepts

3 APICS-Standard Planning Framework APICS - American Production and Inventory Control Society

4 Production Process (review) Requirements for Production Planning: 1) to meet the demand, 2)to consider the resource capacities and the material availabilities, 3)to improve utilisation of the resources, 4)to lower the setup time, 5)to minimize the inventory levels, 6)to minimize the work in process (WIP), and 7)to improve stability of the plan.

5 SOP and Production Plan in SAP

6 Rough-cut Capacity Planning Main goal in rough-cut capacity planning is to identify where overloading or under-loading of production capacity occurs and revise the MPS as required. Overloading means that too much production of products has been planned in the facility and insufficient capacity exists to produce planned quantities of products required in MPS. Under-loading means that not enough production of products has been planned to fully load the facility.

7 Order Life Cycle for Make-to-Stock Original Revised DEMANDS SUPPLIES

8 Order Life Cycle for Make-to-Order 0 0 0 0

9 Forecast Consumption Mode and Horizon (Backword consumption of 4 days and a forward consumption of 3 days)

10 Forecast Consumption Mode and Horizon (Backword consumption of 4 days and a forward consumption of 3 days)

11 Forecast Consumption Mode and Horizon (Backword consumption of 4 days and a forward consumption of 3 days) Order 70

12 Forecast Consumption Mode and Horizon (Backword consumption of 4 days and a forward consumption of 3 days) Order 70 0

13 Transactional Data for Transferring Starting from a demand plan, SNP checks the resource capacities and delivers a medium/long-term plan for the estimated sales volumes. The plan includes 1) quantities to be transported between locations (e.g., DC-customer, or plant-DC) and 2) quantities to be produced (and procured), taking available capacity into consideration. SNP creates planned orders, purchase requisitions, and stock transfers that can be transferred directly to the connected OLTP systems.

14 Capacity Levelling Capacity leveling supports the following resource categories: Production resources in APO (Work centers in ERP) Transportation resources

15 Capacity Levelling Profile The main settings in the capacity leveling profile are scheduling direction, prioritization, and method. Scheduling Direction controls whether Forward, Backward or Combined scheduling is used. Prioritization for the heuristic run defines how leveling determines the sequence of orders. The two possible choices for prioritization (to be sorted by ascending or descending order) are by order size or by product priority. Three Method choices are Heuristic, Optimizer or Badi (Business Aided-in).

16 Time-based Capacity Levelling

17 Capacity Levelling

18 Heuristics-based Capacity Levelling Heuristic-based capacity leveling compares: 1) period by period, and 2) capacity load on a resource with the requested load, either from the beginning or from the end of the planning horizon – depending on which scheduling direction is selected (forward or backward scheduling). If the resource is found overloaded, the system 1) selects all the activities or orders that cause the overload in this period, 2) sorts these orders according to the priority one by one into subsequent or previous periods until the required maximum resource utilization is reached.

19 Operation Research (OR) Operation research refers to the application of quantitative methods and techniques to business problems in order to best utilize a company’s resources. OR is used by many leading companies in recent years to optimize their limited resources in order to maximize their profits or minimize their costs. Linear programming (LP) is one of the most important tools of operation research.

20 Linear Programming (LP) Linear Programming (LP)

21 Linear Programming (LP) Five common types of LP problems: Product mixed Ingredient mix Transportation Production plan Assignment

22 Five common types of LP problems

23 Five common types of LP problems Five common types of LP problems

24 Steps in Formulating LP Problems 1.Define the objective 2.Define the decision variables 3.Write the mathematical function for the objective (objective function) 4.Write a one- or two-word description of each constraints 5.Write the right-hand side (RHS) of each constraint, including the unit of measure. 6.Write = for each equation 7.Write all the decision variables on the left-hand side of each constraints 8.Write the coefficient for each decision variable in each constraint.

25 Formulating LP

26

27 Formulation LP

28 Formulation of Problem

29 Objective and Constraints

30 Steps in Graphical Solution Method

31 Graphical Solution

32

33

34

35 Transportation (Network) Problem

36 Requirement Assumption

37 Feasible Solutions Property

38 Cost Assumption

39 Parameter Table for Transportation Problem Supply S 1 S 2. S m n

40 Transportation Problem Modeling Any problems (whether involving transportation or not) fits the model for a transportation problem if it can be described completely in term of a parameter table like Table 8.5 and it satisfies both the requirements assumption and cost assumption. The objective is to minimize the total cost of distributing the units. All the parameters of the model are included in this parameter table.

41 Objective Function & Constraints

42 Software for Solving LP Programs 1.Lingo: to download software and access user menu at http://www.lindo.com/index.php?option=com_content&view= article&id=35&Itemid=20 2. Excel with Add-ins

43 Solving LP Models with Lingo – Download software for free

44 Solving LP Models with Lingo - Modeling LP Example 1:

45 Solving LP Models with Lingo - Solution to LP example 1:

46 Solving LP Models with Excel - to include Solver Addin

47 Solving LP Models with Excel - Solver Addin included

48 Solving LP Models with Excel - Modeling LP Example 1 in Excel Sheet

49 Solving LP Models with Excel - Modeling LP Example 1 in Solver Addin

50 Solving LP Models with Excel X1X2 Optimal solution:1,0002,000 Z:2,100,000900600 400021 500012 3000113500 - Solutions to LP Example 1 using Excel Add-Ins

51 1. Please solve the following LP problem. Objective:Min Z = 10,000 X 1 + 15,000 X 2 S.T. X 1 + 2X 2 >= 4 X 1 + X 2 >= 2.5 X 1, X 2 >= 0 1) Draw a graph 2) Plot the constraint function 3) Outline the feasible solution 4) Circle the optimal solution point. Questions 1 and 2 by individual (Due date 12/12/2015)

52 2. The Green Up Fertilizer Company ships fertilizer from three manufacturing plants to four distribution centers (DC). The shipping cost per truckload of fertilizer from each plant to each DC is: PlantDistribution Center (DC) ABCD 1$464$513$654$867 2$352$416$690$791 3$995$682$388$685 Plant 1 has a monthly capacity of 75 truckload, Plant 2 has a monthly capacity of 125 truckload, and the Plant 3 has a monthly capacity of 100 truckload. The monthly DC demand is A = 80 truckload, B = 65 truckload, C = 70 truckload, and D = 85 truckload. Please formulate an LP problem to determine how much truckload of fertilizer should be shipped from each plant to each DC per month to minimize monthly shipping cost. 1) Define the objective. 2) Define the decision variables. 3) Write the mathematical function for the objective. 4) Write the constraints. 5) Solve the LP problem using Lingo or Excel Addin. Questions 2 (Optional) by individual


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