Detailed Production Planning & Shop-Floor Control

Detailed Production Planning&

Shop-Floor Control

Dealing with the Problem Complexity through Decomposition

Aggregate Planning

Master Production Scheduling

Materials Requirement Planning

Aggregate UnitDemand

End Item (SKU)Demand

Corporate Strategy

Capacity and Aggregate Production Plans

SKU-level Production Plans

Manufacturingand Procurementlead times

Component Production lots and due dates

Part processplans

(Plan. Hor.: 1 year, Time Unit: 1 month)

(Plan. Hor.: a few months, Time Unit: 1 week)

(Plan. Hor.: a few months, Time Unit: 1 week)

Shop floor-level Production Control(Plan. Hor.: a day or a shift, Time Unit: real-time)

Disaggregation and Master Production Scheduling

(MPS)

The (Master) Production Scheduling Problem

MPS

Placed Orders

Forecasted DemandCurrent and PlannedAvailability, eg.,•Initial Inventory,•Initiated Production,•Subcontracted quantities

Master ProductionSchedule:When & How Muchto produce for eachproduct

CapacityConsts.

CompanyPolicies

EconomicConsiderations

ProductCharact.

PlanningHorizon

Timeunit

CapacityPlanning

MPS Example: Company Operations

Mashing(1 mashing tun)

Boiling(1 brew kettle)

Fermentation(3 40-barrelferm. tanks)

Filtering(1 filter tank)

Bottling(1 bottling

station)

Grain cracking(1 millingmachine)

Fermentation Times:Brew Ferm. TimePale Ale 2 weeksStout 3 weeksWinter Ale 2 weeksSummer Brew 2 weeksOctoberfest 8-10 weeks

Example: Implementing the Empirical Approach in Excel

# Fermentors: 1 Unit Cap: 200 Shelf Life: 20

Microbrewery PerformanceWeek 0 1 2 3 4 5 6 7 8 9 10# Fermentors Req'd 0 0 0 0 0 0 0 0 0 0Feasible Loading?Min # Fermentors Req'd 2 2 2 2 2 2 2 2 2 2Fermentor Utilization 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%Total Spoilage 0 0 0 0 0 0 0 0 0 0

Pale Ale Fermentation Time: 2Week 0 1 2 3 4 5 6 7 8 9 10Demand 45 50 40 40 40 40 40 40 40 40Scheduled Receipts 200Fermentors Released 1Inventory SpoilageInventory Position 100 255 205 165 125 85 45 5 -35 -40 -40Net Requirements 35 40 40Batched Net ReceiptsScheduled ReleasesFermentors SeizedTotal Fermentors Occupied

Stout Fermentation Time: 3Week 0 1 2 3 4 5 6 7 8 9 10Demand 35 40 30 30 40 40 40 40 50 50Scheduled ReceiptsFermentors ReleasedInventory SpoilageInventory Position 150 115 75 45 15 -25 -40 -40 -40 -50 -50Net Requirements 25 40 40 40 50 50Batched Net ReceiptsScheduled ReleasesFermentors SeizedTotal Fermentors Occupied

Computing Inventory Positions and Net Requirements

Net Requirement:

NRi = abs(min{0, IPi})

Inventory Position:

IPi = max{IPi-1,0}+ SRi+BNRi -Di

(Material Balance Equation)

iDi

IPi

(IPi-1)+

SRi+BNRi

Problem Decision Variables: Scheduled Releases



Pale Ale Fermentation Time: 2Week 0 1 2 3 4 5 6 7 8 9 10Demand 45 50 40 40 40 40 40 40 40 40Scheduled Receipts 200Fermentors Released 1Inventory SpoilageInventory Position 100 255 205 165 125 85 45 5 165 125 85Net RequirementsBatched Net Receipts 200Scheduled Releases 200Fermentors Seized 1Total Fermentors Occupied 1 1

Stout Fermentation Time: 3Week 0 1 2 3 4 5 6 7 8 9 10Demand 35 40 30 30 40 40 40 40 50 50Scheduled ReceiptsFermentors ReleasedInventory SpoilageInventory Position 150 115 75 45 15 -25 -40 -40 -40 -50 -50Net Requirements 25 40 40 40 50 50Batched Net ReceiptsScheduled ReleasesFermentors SeizedTotal Fermentors Occupied

Testing the Schedule Feasibility


Microbrewery PerformanceWeek 0 1 2 3 4 5 6 7 8 9 10# Fermentors Req'd 0 1 1 1 0 1 2 1 1 0Feasible Loading? NOMin # Fermentors Req'd 2 2 2 2 2 2 2 2 2 2Fermentor Utilization 0% 100% 100% 100% 0% 100% 200% 100% 100% 0%Total Spoilage 0 0 0 0 0 0 0 0 0 0


Stout Fermentation Time: 3Week 0 1 2 3 4 5 6 7 8 9 10Demand 35 40 30 30 40 40 40 40 50 50Scheduled ReceiptsFermentors ReleasedInventory SpoilageInventory Position 150 115 75 45 15 175 135 95 55 5 155Net RequirementsBatched Net Receipts 200 200Scheduled Releases 200 200Fermentors Seized 1 1Total Fermentors Occupied 1 1 1 1 1 1

Fixing the Original Schedule




Stout Fermentation Time: 3Week 0 1 2 3 4 5 6 7 8 9 10Demand 35 40 30 30 40 40 40 40 50 50Scheduled ReceiptsFermentors ReleasedInventory SpoilageInventory Position 150 115 75 45 15 175 135 95 55 5 155Net RequirementsBatched Net Receipts 200 200Scheduled Releases 200 200Fermentors Seized 1 1Total Fermentors Occupied 1 1 1 1 1 1

Infeasible Production Requirements# Fermentors: 1 Unit Cap: 200 Shelf Life: 20


Pale Ale Fermentation Time: 2Week 0 1 2 3 4 5 6 7 8 9 10Demand 45 50 40 40 40 40 40 40 40 40Scheduled Receipts 200Fermentors Released 1Inventory SpoilageInventory Position 100 55 205 165 125 85 45 5 -35 -40 -40Net Requirements 35 40 40Batched Net ReceiptsScheduled ReleasesFermentors SeizedTotal Fermentors Occupied 1

Stout Fermentation Time: 3Week 0 1 2 3 4 5 6 7 8 9 10Demand 35 40 40 40 40 40 40 40 50 50Scheduled ReceiptsFermentors ReleasedInventory SpoilageInventory Position 150 115 75 35 -5 160 120 80 40 -10 -50Net Requirements 5 10 50Batched Net Receipts 200Scheduled Releases 200Fermentors Seized 1Total Fermentors Occupied 1 1 1

A feasible schedule with spoilage effects



Pale Ale Fermentation Time: 2Week 0 1 2 3 4 5 6 7 8 9 10Demand 45 50 40 40 40 40 40 40 40 40Scheduled Receipts 200Fermentors Released 1Inventory Spoilage 45Inventory Position 100 255 205 165 125 85 245 160 120 80 40Net RequirementsBatched Net Receipts 200Scheduled Releases 200Fermentors Seized 1Total Fermentors Occupied 1 1

Stout Fermentation Time: 3Week 0 1 2 3 4 5 6 7 8 9 10Demand 35 40 30 30 40 40 40 40 50 50Scheduled ReceiptsFermentors ReleasedInventory Spoilage 5Inventory Position 150 115 75 45 215 175 135 95 55 5 150Net RequirementsBatched Net Receipts 200 200Scheduled Releases 200 200Fermentors Seized 1 1Total Fermentors Occupied 1 1 1 1 1 1

Computing Spoilage and Modified Inventory Position

Spoilage:SPi = max{0, IPi-1-SRi-1+SRi-2+…+SRi-sl+1)

-BNRi-1+BNRi-2+…+BNRi-sl+1)}

Inventory Position:

IPi = max{IPi-1,0}+ SRi+BNRi -Di-SPi

(Material Balance Equation)

iDi

IPi

(IPi-1)+

SRi+BNRi

SPi

The Driving Logic behind the Empirical Approach

Demand Availability:•Initial Inventory Position•Scheduled Receipts due to initiated production or subcontracting

Future inventories

NetRequirements

Lot Sizing

ScheduledReleases

Resource (Fermentor)Occupancy Product i

FeasibilityTesting

Master Production Schedule

ScheduleInfeasibilities

ReviseProd. Reqs

Compute FutureInventory Positions

Manufacturing Resource Planning&

Scheduling

MRP II:

The “MRP Explosion” Calculus

BOM

MRPMPS

Current Availabilities

PlannedOrder Releases

PriorityPlanning

LeadTimes

Lot SizingPolicies

Example: The (complete) MRP Explosion Calculus

Item BOM:

Alpha

C(2)D(2)

B(1) C(1)

E(1)

E(1)

F(1)

F(1)

Item Lead Time Current Inv. Pos.Alpha 1 10

B 2 20C 3 0D 1 100E 1 10F 1 50

Gross Reqs for AlphaPeriod 6 7 8 9 10 11 12 13Gross Reqs. 50 50 100

Item Levels:

Level 0: Alpha Level 1: B Level 2: C, D Level 3: E, F

The “MRP Explosion” Calculus

Level 0

Level 1

Level 2

Level N

InitialInventories

ScheduledReceipts

External Demand

CapacityPlanning

Planned Order ReleasesGross Requirements

(borrowed from Heizer and Render)

Computing the item Scheduled Releases

Item CPeriod 1 2 3 4 5 6 7 8 9 10 11 12Gross Requirements 12 10 90 75Scheduled Receipts 20Inventory Position: 20 20 40 40 40 40 28 18 18 -72 0 -75 0Net Requirements 72 75Planned Sched. Receipts 72 75Planned Sched. Releases 72 75

Synthesizingitem demand

series

ProjectingInv. Positions

andNet Reqs.

Lot Sizing Time-Phasing

ParentSched. Rel.

Item ExternalDemand

Gross Reqs

ScheduledReceipts

InitialInventory

Safety StockRequirements

NetReqs

Lot SizingPolicy

Planned OrderReceipts

Lead Time

Planned OrderReleases

Lot Sizing• If affordable, a lot-for-lot (L4L) policy will incur the lowest inventory holding

costs and it will maintain a smoother production flow.• Possible reasons for departure from a L4L policy:

– High set up times and costs => need for serial process batching to control the capacity losses

– Processes that require a large production volume in order to maintain a high utilization (e.g., fermentors, furnaces, etc.) => need for parallel process batching

• Selection of a pertinent process batch size– It must be large enough to maintain feasibility of the production requirements– It must control the incurred

• inventory holding costs, and/or• part delays (this is a measure of disruption to the production flow caused

by batching)• Move or transfer batches: The quantities in which parts are moved between the

successive processing stations.– They should be as small as possible to maintain a smooth process flow

Some Lot Sizing Methods employed in the traditional MRP framework

• Main focus: Balance set-up and holding costs• Wagner-Whitin Algorithm for dynamic Lot Sizing• Economic Order Quantity (EOQ): Compute a lot size using the EOQ formula with the

demand rate D set equal to the average of the net requirements observed over the considered planning horizon.

• Periodic Order Quantity (POQ): Compute T = round(EOQ/D), and every time you schedule a new lot, size it to cover the net requirements for the subsequent T periods.

• Silver-Meal (SM): Every time you start a new lot, keep adding the net requirements of the subsequent periods, as long as the average (setup plus holding) cost per period decreases.

• Least Unit Cost (LUC): Every time you start a new lot, keep adding the net requirements of the subsequent periods, as long as the average (setup plus holding) cost per unit decreases.

• Part Period Balancing (PPB): Every time you start a new lot, add a number of subsequent periods such that the total holding cost matches the lot set up cost as much as possible.

Finite-Capacity Planning & Scheduling in the MRP II / ERP context:

Load Reports (Example)Availableresourcetime

Periods1 2 3 4 5 6 7 8

50

100

150

Finite-Capacity Planning & Scheduling in the MRP II / ERP context: More Systematic Approaches

• Bottleneck-based scheduling in a cellular manufacturing context (Goldratt’s Theory of Constraints approach):– Each part (family) has its own production cell with a well-defined bottleneck resource.– Production is scheduled on the bottleneck resource and the schedule for the other

resources are organized around this schedule by taking consideration the expected lead times.

– Typically, a “cushion” of extra workload is maintained at the bottleneck in order to prevent its starvation, in case of any disruptions in the upstream processes.

– If the bottleneck supports the production of more than one part types, a “single-machine” scheduling problem arises naturally. This is addressed by selecting an appropriate dispatching rule.

• Earliest Due Date (EDD) => minimizes maximum lateness (tardiness)• Least slack (LS), where slack = difference between job due date and expected

completion time => tend to reduce average tardiness• Shortest Processing Time (SPT) => minimizes average flowtime at the bottleneck,

and (by Little’s law) average WIP• Other heuristics addressing different problem variations including weighted

performance measures, non-zero release times, etc.

Finite-Capacity Planning & Scheduling in the MRP II / ERP context:

More Systematic Approaches (cont.)• Cases where the previous approach is not effective:

– There are more than one capacity-constrained resource– Bottlenecks are shifting depending on the product mix– There are operations involving parallel process batching– Process routes are non-linear (e.g., due to routing flexibility, re-entrance, extensive

need for rework)• Remark: The semiconductor manufacturing operational context is a typical example of

all of the above.• A more global view of the system operations is necessary in order to support effective

and efficient scheduling.• Possible approaches

– Employ a set of pertinently selected dispatching rules at the different (critical) resources, and assess its efficacy through simulation (possibly maintain a bank of such rules for different operational conditions – meta-heuristics)

– Generate efficient (not necessarily optimal) global schedules by employing an approach that searches for such a schedule in the space of feasible schedules

Typical approaches employed in the solution of the job shop scheduling problem

• Branch & Bound (B&B): Constructs all possible schedules incrementally, fathoming options that are clearly suboptimal to some other options. Can generate optimal schedules but it is very time consuming.

• Beam search: Similar to B&B, but it employs additional heuristics to increase fathoming.• Local search techniques: Given an initially constructed schedule, try to identify an

improved schedule that is obtained from the original one through a localized change (e.g., through the change of the order of two jobs on a single machine); repeat. Also, need a mechanism to avoid local optima.– Simulated annealing: Seeks to avoid local optima by maintaining a non-zero

probability for transitioning to an inferior schedule. However, this probability is reduced with the passage of time.

– Tabu search: Seeks to avoid local optima by pronouncing certain schedule changes as taboo (these changes are apparent improvements that might attract the schedule back to a local optimum)

– Genetic algorithms: Maintains an entire set of schedules at each iteration, and it updates this set by replacing schedules of inferior performance with new schedules resulting from the “combination” of the most efficient schedules currently available; the synthesis of such new schedules is known as “crossover”. Also, “mutation” provides additional schedules resulting from the local modification of some single schedules.

Typical approaches employed in the solution of the job shop scheduling problem

• The “shifting bottleneck” heuristic: Originally developed for minimizing the schedule makespan, but there are also additional versions, e.g., for minimizing total weighted tardiness (c.f. Pinedo, Scheduling: Theory, Algorithms and Systems, Prentice Hall, 2002)– Start with a simple schedule that observes only the precedence constraints imposed

by the job process routes and ignores completely the impact of the part contest for the various workstations.

– Repeat• Identify the “bottleneck” machine that causes the highest disruption (delay) to

the currently developed schedule, by solving a “single machine, maximum lateness with release times” problem, for each machine; the release times and the due dates for this max lateness problem are determined by the “critical path” in the currently available schedule.

• Enter the schedule of the identified bottleneck to the current schedule.• Reschedule all the previously scheduled machines to improve the overall

schedule efficiency; each of these rescheduling problems is another “single machine, maximum lateness with release times” problem, induced by the current schedule.

– until all machines have been introduced to the running schedule.

(borrowed from Heizer and Render)

Pegging and Bottom-up Replanning

Some Limitations of MRP-based Planning

• The employment of fixed nominal lead times– This problem is mitigated in case of a stable operational environment where past

experience and / or approximate formal models can provide insight for setting lead times

– Lead time assessment is also facilitated by a well-structured, cellular shop-floor• Lack of an inherent mechanism for detecting and managing shop-floor

congestion – a purely “Push” approach– However, it is possible to combine the planning visibility offered by the MRP

explosion calculus with more sophisticated production control mechanisms that take advantage of the existing technology of Manufacturing Execution Systems (MES).

• Possible system nervousness due to re-planning and the applied lot sizing policies– Potential remedies

• Firm orders• Time fences• L4L planning whenever possible

Documents

Detailed Production Planning & Shop-Floor Control