Fundamentals of Industrial Plants Prof. Andrea Sianesi academical year 2011/2012

1. Fundamentals of Industrial Plants Prof. Andrea Sianesiacademicalyear 2011/2012 Push Techniques: Sales & Operations Plan

2. Approaches to plan make PUSH Systems Traditional PULL Systems JustInTime

3. Approaches to plan manke MANUFAC. MANUFAC. ASSEMBLY COMPON. SUBASS.Y FINISHED P. PULL SYSTEM MANUFAC. MANUFAC. ASSEMBLY COMPON. SUBASS.Y FINISHED P. PUSH SYSTEM

4. Finished product inventory Manufacturing LT Re-order point r3 r2 r1 Re-order time

5. Semi-finished product (Work In Progress) inventory r3 r2 r1 time

6. Raw material inventory r2 time

7. The objectives of the planning process RM RM RM Supply network RM FP FP FP FP WIP Production system Distribution system Commercial demand Objective: “To make the supply chain move” in the best way depending on the “signal” (commercial demand: orders and/or forecast) that arrives from the market Plan = Set of information that drives the definition of the operative capacity and the execution of activities in the supply chain

8. Output of Planning process Buy Produce Stock Ship WHAT HOW MUCH WHEN WHERE SUPPLY DEMAND

9. Methodology Horizon • Planning is usually done by time bucketing, based on a rolling horizon: Horizon Horizon M0 M1 M2 M3 M4 M5 M6 M7 M8 current M-1 M0 M1 M2 M3 M4 M5 M6 M7 current M-2 M-1 M0 M1 M2 M3 M4 M5 M6 current

10. Methodology Planning and scheduling of material flows and of resources capacity is decomposed into activities… …DEMAND …STOCK CLIENTS SUPPLIERS …DISTRIBUTION …and in “phases” that correspond to different planning problems that are solved in different temporal horizons …PRODUCTION …PROCUREMENT time

11. Demand planning (1) • It has the following objectives: • Forecast future demand through all the available information • Influence demand by means of specific actions (e.g. promotions, price policies,…) aimed at increasing it and making it more regular • Manage commercially possible limitations to the production capacity and/or to the distribution capacity etc.

12. Demand planning (2) • Input: acquired orders, time series of past demand, underway negotiations, clients withdrawal plans, market experience, general and finished product market economic trends, competitors actions, casual factors,…. • Activities: data gathering, cleaning and combination, application of forecasting techniques, checking, presentation and sharing of the forecast plan, forecast error measurement, .. • Output: forecast plan (with defined detail level and temporal horizon) + plan accuracy measurement, ...

13. Inventory planning • Objectives: • To define what, how much, when and where to keep stock in the logistic-production system and so: • To choose for each item and for each warehouse the most suitable management technique (that is the material flow management logics) • To define the principal operational parameters (e.g. safety stock, refilling frequency, lots sizes, ….)

14. Distribution planning (1) • Objectives: • To define the plan of refilling activities (what, how much and when ship from node x to node y ) – the plan controls the flow of materials from the end of the production lines to the transfer to final users trough the distribution system. • To identify the shipping modes between the nodes of the system (means of transport, routes, frequencies, times, vectors,…. )

15. Distribution planning(2) • Input: • Demand forecast plan • Inventory planning choices in the different “nodes” of the system • Physical structure of the logistic system (number, type, structure, geographical position and potentiality of the nodes of the network) • Operative capacity and elasticity of the system resources (structures that receive the goods, transport, stock, etc.) • Cost objectives and cost structure

16. Distribution planning(3) • Activities: use of techniques DRP-like (Distribution Resource Planning) to generate finite capacity plans • Output: distribution activities plan (picking, transport, receiving)

17. Production planning(1) • Objectives: • To define the production plan of the different units of the production system (what, how much and when produce in each unit) • To identify the ways to realize in the defined time the desired volumes of the identified items (e.g.: demand anticipation, overtime shifts, temporary workers, external production, etc.) • Activities: use of techniques MPS and MRP-like (Master Production Schedule, Material Requirements Planning) and scheduling to generate finite capacity plans

18. Production planning(2) • Input: • Demand forecast plan • Material distribution planning choices in the different “nodes” of the system • Physical structure and elasticity of the production system (number, type, structure, geographical position, capacity and capability of the nodes of the network) • Cost-Service objectives and cost structure

19. Procurement planning • Objectives: • To define the procurement plans for the different key-suppliers (what, how much and when order from each supplier) • To anticipate and solve possible problems coming form the suppliers • Activities: use of techniques MRP-like (Material Requirements Planning) to generate procurement plans

20. Demand forecast and stock objectives Formulation of Sales & Operations Plan - Master Production Schedule (SOP-MPS) Material planning (MRP) Operative planning (scheduling) Control Performances measurement Phases (referred to Production Plan.) time

21. Synthesis

22. Production plan development Demand Model / Algorithm Constraints Objectives Production and Resource Plan

23. Planning “objects” • Item: • single code • Family: • set of products that can be grouped according to some similarities (e.g. resource consumption) • Kind: • set of families that have similar production costs and demand patterns with the same seasonality (or shape)

24. Hierarchical structure Number of constraints to take into consideration Economic impact Considered Horizon Strategical Planning Sales & Operations Plan Master production Schedule

25. Strategic capacity planning • Horizon: 2-5 years • Objectives: to reach stratecial objectives • Decisions: adjustment capacity/volume allocation production to plant levels of focalization - automation strategy MTO, ATO,MTS ... • Time unit: year • Frequency: annual • Tools: experience curve, decisions tree, equivalent units

26. Strategic capacity planning • Phases: • Sales estimation (Revenues/Kind) • Overall Planning Factors computation (Revenues/Kind): • STEP 1: for the entire plant in terms of aggregate volumes • STEP 2: for the different typologies of resources (lines, shops, workforce) • Actual conditions comparison • Corrective actions planning (increase or decrease capacity)

27. Aggregate planning / S&OP • Horizon: • 1 year • Objectives • Satisfy demand at the lowest cost • Objects: • Products family • Decisions: • when to produce in each period, make or buy tactical, etc. • Time unit: • Month (week) • Frequency: • Monthly-quarterly –semestral

28. Master Production Schedule Demand Model / Algorithm Constraints (from the budget) Objectives (min. costs) MPS • It is a version of Aggregate Planning dis-aggregated for product and period

29. Aggregations S&OP MPS Product aggregation Time aggregation

30. Aggregate planning decisions • Match demand and capacity • inventory planning; • use of sub-contractors; • Incremental little investments; • workforce shifts allocation; • overtime work; • internal mobility of workforce • anticipate structural situations of delays in delivery

31. Aggregate planning decisions Standard Capacity Plan that uses inventory as a capacity “flywheel” Plan that exploits workforce or sub-contractors flexibility Planning strategies: - “Chase” - “Stable-variable” - “Level” demand

32. An example of match demand capacity ?

33. An example of match demand capacity

34. Aggregate planning - MPS • The problem has many feasible solutions • It is fundamental to evaluate the decisions in terms of total cost of the production plan : • Stock holding costs • Setup • Overtime • Sub-contracting • Stock-out

35. Mathematical representation of the problem by simple LP • Applicability field • Seasonal demand deterministically predictable • Single-product • Single-machine; critical resource: workforce • Possibility to use overtime

36. Legend • INV(t) inventory at the end of period t • X(t) quantity to be produced in t • W(t) hours of workforce (wkf) used in t • S(t) hours of overtime used in t • D(t) demand in period t • h(t) hours of wkf per unit of product • MaxW(t) hours of wkf available in t • MaxS(t) max hours of overtime in t • i(t) stock holding cost per unit • r(t) wkf cost per hour • s(t) wkf cost per hour of overtime • m(t) variable cost (materials/energy)

37. LP Model • Objective: minimization of costs connected to the plan (cost of workforce in regular working hours and overtime, stock holding costs, variable production costs)

38. Model • Constraints (for every t)

39. Mathematical representation of the problem by integer LP • Applicability field • Seasonal demand deterministically predictable • Multi-product • Single-machine; critical resource: machine hours • Setup is modelled as the cost a firm sustains every time a product is produced, i.e. for setting up the production of that product

40. Legend • X(i,t) quantity of i to be produced in t • Cp(t) pieces that can be produced in t • D(i,t) demand of product i in period t • e(i,t) production excess vs demand till period t • c(i,t) stock holding cost per unit • a(i,t) setup cost of i at period t • k(i,t) = 0 if quantity produced is zero, 1 if quantity produced is more than zero

41. Model • Objective: minimize the costs connected to the plan (setup costs, inventory holding costs)

42. Constraints

43. Karni-Roll model • Hypothesis • Multiproduct • Demand of whatever shape • Demand known deterministically • There are limits to the production capacity • Setup are considered like costs • No backlog

44. Karni-Roll model • The analytical formulation is similar to integer LP, but the solving method is heuristic • It does not provide the best possible solution, given the objective and the constratins, but a solution that is “reasonably good”… • … on the other side it can be applied more easily to real situations

45. Karni-Roll model • The working procedure: • The starting point is the solution of Wagner-Whitin algorithm (dynamic EOQ) • This solution is the inferior limit of cost, as it does the loading with infinite capacity • If the solution is feasible, the algorithm ends • If the solution is unfeasible… • The capacity constraint is violated in one or more periods • … the algorithm looks for a feasible solution by shifting (“shift”) the quantity from one period to another in the time horizon

46. Karni-Roll model • The concept of shift: Posticipating shift (right shift) Anticipating shift (left shift) Capacity Limit Time peridos

47. Karni-Roll model • Warning: If unfeasibility appears in the first period, the problem is a prioriimpossible to solve, but in the case in which backlog is accepted. Time periods

48. Karni-Roll model • Shift characteristics: • dimension, i.e. the quantity that should be moved • direction, i.e. the number of periods (to the left or to the right) of the shift • Shift objectives: • unfeasibility elimination at minimum cost • total plan cost reduction … • total setup costs and total stock holding costs • … through changes in the plan matrix

49. Karni-Roll model • Shift rules: • shift the minimum quantity in order to eliminate unfeasibilities • shift the maximum quantity in order to reduce stock holding costs • shift all the possible quantities in order to: • eliminate a setup • reduce stock holding cost without generating new setup • shift to the right (posticipate) the maximum possible quantity without generating unfeasibbilities • shift to the left (anticipate) the minimum quantity without generating unfeasibilities

50. Karni-Roll model • The effect of each shift is evaluated in terms of reduction of plan cost (ridC) and overlapping the effects. • In symbols, considering the single shift … • so, referring to a specific product, that is the object of the shift (for which the index has been omitted):