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Operations Scheduling

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1 Operations Scheduling
35E00100 Service Operations and Strategy #5 Fall 2015

2 Topics Principles of scheduling Practices in Finnish companies
Objectives and constraints Problem types Practices in Finnish companies Setting lead times and order priorities Lead time quoting Basic dispatching rules Advances scheduling heuristics Key points Useful material in the textbook and course package: Hopp, W. & Spearman, M. (2000), Factory Physics, Chapter 15 Vepsalainen, A. & Morton, T. (1987) “Priority Rules for Job Shops with Weighted Tardiness Costs”, Management Science

3 Production Scheduling
Principle Concerns the allocation of limited resources to tasks over time (Pinedo, 1999). Determines in what sequence orders are processed on available resources. Justification Allows better matching of customer requirements and service. e.g. response times and delivery accuracy Supports coordination of material flows. Machine a Machine b Machine k ai ri Ci pi1a dij pi2b di2 pijk dij di Operation 1 Operation 2 Operation j

4 The Definition of Scheduling
A scheduling system dynamically makes decisions about matching activities and resources in order to finish jobs and projects needing these activities in a timely and high-quality fashion while simultaneously maximizing throughput and minimizing direct operating costs. (Morton & Pentico 1993, 10) Basic scheduling decisions typically include Routing Sequencing Timing / release

5 A Two-Operations Process A Flow Shop
1 2 Input Output Buffer (WIP)

6 Most scheduling environments are extremely complex..
B E Raw materials Finished products C F Queue Decision Work center

7 Types of Scheduling Models (1)
Project Scheduling Objective is to minimize total time Unlimited number of machines Precedence constraints  critical path to be identified Important in large multi-stage projects Job Shop Models Jobs with a number of operations Machine configuration / job routing One or more objectives Both manufacturing and service industries Production Systems with Automated Handling Jobs with a number of operations Material handling and conveyor systems control the movement of jobs and timing of their processing on various machines Objective is to maximize throughput Applied for example in FMS and in automotive industry

8 Types of Scheduling Models (2)
Lot Scheduling Medium- and long-term production planning and control Objective is usually to minimize total inventory and changeover costs Continuous production & demand processes Handle a variety of different products Process and retail industries Workforce Scheduling Different, although often intertwined with machine scheduling Reservation and Timetabling Models Reservation systems Objective is to process as many jobs as possible Starting and completion times of jobs are fixed Timetabling models Objective is to process all jobs and minimize makespan Starting and completion times are not fixed Two jobs that require same tool cannot be processed at the same time Facility scheduling (classrooms, vehicles)

9 Order Management Decisions
Markets / Customers Material requirements Orders Demand forecasts Order acceptance Production planning Master scheduling Shop status Schedule performance Scheduling constraints Capacity status Due date assignment Schedule Shop orders Release dates Job loading Material requirements planning, Capacity planning Order release Detailed scheduling Scheduling and Rescheduling Order sequencing Dispatching Job dispatching Shop floor management Modified from Pinedo and Chao 1999, 7

10 Practices in Finnish Industrial Firms
Illustrative Examples Practices in Finnish Industrial Firms Order processing Via worldwide sales network Cooperation between mfg and centralized sales support Chain-wide IT integration in progress A part of orders electronically Decision-makers Order acceptance: sales (market) LT estimation: sales (mill) Final schedule: sales and production planning Dispatching: shop floor Main criteria in use Order acceptance: profit, relationship Lead-times: standard estimates Schedule: delay penalties Case 1: Machinery Order processing Via sales unit network in Europe Centralized sales support IT integration within the firm; there are efforts to share information openly through the supply chain Orders via fax and Decision-makers Order acceptance: sales (market) LT estimation: sales (market) Final schedule: production planning Dispatching: --- (not needed) Main criteria in use Order acceptance: capacity Lead-times: standard estimates Schedule: no priorities or penalties Case 2: Metal Products

11 Practices in Finnish Industrial Firms
Illustrative Examples Practices in Finnish Industrial Firms Case 3: Electronics Case 4: Paper Order processing Directly to manufacturing units IT systems integrated partly in the chain: limited information sharing A part of orders electronically Decision-makers Order acceptance: sales (mill) Lead-time estimation: production planning Final schedule: production planning Dispatching: shop floor Main criteria in use Order acceptance: capacity Lead times: order-specific estimates Schedule: some implicit priorities, penalties occasionally considered Order processing Via sales units and agents Centralized sales support System integration in-progress; currently order and schedule information is shared only internally Decision-makers Order acceptance: sales (mill) Lead-time estimation: sales (mill) Final schedule: sales (mill) & production planning Dispatching: --- (not needed) Main criteria in use Order acceptance: capacity Lead-time: standard estimates Schedule: no priorities or penalties

12 HSE Study on Priority Scheduling and Customer Order Management
Research question Do priority rules provide a protocol for coordinated order management? Objectives Identify robust and well-performing priority rules or families of such rules for order mgmt. Examine different technical specifications and tolerances for information and communication necessary to implement the scheduling rules. Demonstrate the sensitivity of system performance to different conventions in order handling. The scheduling problem analyzed Job shop problem with standard assumptions Non-delay scheduling Weighted tardiness problem Heterogeneous lead time expectations Different costs for inventory, expediting, and tardiness 35E00100 Service Operations and Strategy HSE/Logistics

13 Order Management and Scheduling Practices in Finnish Manufacturing
Explorative multi-case study 16 manufacturing companies with operations in Finland Leading players in their field electrical equipment and components, heavy machinery, metal & paper Objective To review current intra- and inter-firm order management and scheduling practices Data collection process Personal interviews based on a questionnaire in total about 160 questions mostly structured questions Data includes detailed descriptions on organization structure and culture operations within the order-delivery process and supply chain firm and supply chain performance Companies interviewed Contract manufacturing industry Elcoteq: terminal products and electronics Flextronics: industrial electronics Tellabs: network components Machinery industry ABB Industry: engines and generators KCI Konecranes: lifting equipment and cranes Kone: elevators Metso Automation (Neles): industrial valves Metso Paper (Valmet): paper machines Metal manufacturing industry Outokumpu Drawn: copper rods and profiles Outokumpu Rolled: rolled copper products Outokumpu Tubes: tubular copper products Rautaruukki Metform: welded steel tubes and tubular products Paper industry M-Real: publishing and printing paper Myllykoski Paper: printing paper Stora Enso: fine paper UPM-Kymmene: newsprint paper 35E00100 Service Operations and Strategy HSE/Logistics

14 Descriptions of the Selected 16 Cases
Companies interviewed Contract manufacturing industry Elcoteq: terminal products and electronics Flextronics: industrial electronics Tellabs: network components Machinery industry ABB Industry: engines and generators KCI Konecranes: lifting equipment and cranes Kone: elevators Metso Automation (Neles): industrial valves Metso Paper (Valmet): paper machines Metal manufacturing industry Outokumpu Drawn: copper rods and profiles Outokumpu Rolled: rolled copper products Outokumpu Tubes: tubular copper products Rautaruukki Metform: welded steel tubes and tubular products Paper industry M-Real: publishing and printing paper Myllykoski Paper: printing paper Stora Enso: fine paper UPM-Kymmene: newsprint paper 35E00100 Service Operations and Strategy HSE/Logistics

15 Categorization of Production Planning and Control Approaches
Type of Logistics System Contingent Projects Standard Deliveries Routine Shipments Customer-driven Planning & Control Job Shop Priority Scheduling Type of Production System Flow Balancing in Planning & Control Assembly/ Batch Line Capacity-driven Planning & Control Continuous Flow Production 35E00100 Service Operations and Strategy HSE/Logistics

16 Positioning of the 16 Cases Framework of Operational Systems
Type of Logistics System Contingent Projects Standard Deliveries Routine Shipments Case F Case H Group 1 Case E Case G Group 2 Job Shop Case D Group 3 Type of Production System Case A Case B Case C Group 4 Case J Case K Case L Group 5 Assembly/ Batch Line Case I Case M Case N Case O Case P Group 6 Continuous Flow Production 35E00100 Service Operations and Strategy HSE/Logistics

17 Categorization of Production Scheduling Methods
Objective of Scheduling Sales-oriented, Customer Requirements Production-oriented, Productivity Requirements 1 Order-based scheduling 3 Production and materials planning Adjustable Capacity Capacity Constraint 2 Sales budgeting / Capacity allocation 4 Product sequencing for capacity Fixed Capacity 35E00100 Service Operations and Strategy HSE/Logistics

18 A Generic Framework of Order Management Decisions
(modified from Pinedo 1995) Suppliers Customers Orders Forecasts Material requirements Order acceptance Production planning, Master scheduling Due date assignment Capacity status Material requirements planning, Capacity planning Order release Shop orders Scheduling constraints Release dates Scheduling and Rescheduling Order sequencing Detailed scheduling Schedule performance Schedule Dispatching Job dispatching Shop status Job loading Shop floor management 35E00100 Service Operations and Strategy HSE/Logistics

19 Operations Control Framework Typical Order Management Procedures with Different Production Scheduling Methods Objective of Scheduling Sales-oriented, Customer Requirements Production-oriented, Productivity Requirements Customer orders Material requirements planning, Capacity planning Production planning, Master scheduling Shop floor management Dispatching Scheduling, Rescheduling Due date assignment Order release acceptance sequencing Job dispatching Customer orders Shop floor management Material requirements planning, Capacity planning Production planning, Master scheduling Dispatching Scheduling, Rescheduling Due date assignment Order acceptance Job dispatching sequencing release 1 Order-based scheduling 3 Production and materials planning Adjustable Capacity Capacity Constraint Customer orders Material requirements planning, Capacity planning Production planning, Master scheduling Shop floor management Dispatching Scheduling, Rescheduling Job dispatching Order release assignment Due date acceptance sequencing Customer orders Material requirements planning, Capacity planning Scheduling, Rescheduling Dispatching Shop floor management Production planning, Master scheduling Order release Due date assignment sequencing Job dispatching acceptance 2 Sales budgeting / Capacity allocation 4 Product sequencing for capacity Fixed Capacity 35E00100 Service Operations and Strategy HSE/Logistics

20 Positioning of the 16 Cases Framework of Scheduling Methods
Objective of Scheduling Sales-oriented, Customer Requirements Production-oriented, Productivity Requirements 1 Order-based scheduling Case companies E, G, H, J, K, L 3 Production and material planning Case companies B, C, D, F Adjustable Capacity Capacity Constraint 2 Sales budgeting / Capacity allocation Case companies I, M, P 4 Product sequencing for capacity Case companies A, N, O Fixed Capacity 35E00100 Service Operations and Strategy HSE/Logistics

21 Positioning of the 16 Cases Decision-makers in Order Management
Order Management Decision Order acceptance Lead-time estimation Final production schedule H Sales organization E I F D O G Sales at mill P A K M Decision-making Responsibility J L L G L G Production planning at mill B B B C C C N N N Order-based scheduling Production and materials planning Sales budgeting / Capacity allocation Product sequencing for capacity Shop floor management 35E00100 Service Operations and Strategy HSE/Logistics

22 Characteristics of Scheduling Models Machine Configurations
Single-Machine Models Parallel-Machine Models Single bottleneck in a multiple-machine environment Bottleneck determines the schedule Up- and downstream operations scheduled after the bottleneck Reduction of the problem or a decomposition approach Optimal solutions can be found e.g. by applying dispatching rules EDD, SPT Single stage with a number of machines in parallel Jobs can be processed on any one of available machines Important for the same reason as the single-machine model: one workcenter may determine the performance of an entire system Machines are not necessarily identical e.g. skills of operators, machine age Specific subsets

23 Characteristics of Scheduling Models Typical Production Environments
Flow Shop Models Job Shop Models Routes of all jobs are identical Machines set up in a series Whenever completed a job joins the queue of the next machine Job sequence may vary due to resequencing between machines Some flow shops allow bypassing of machines Generalization: flexible flow shop Different routings are possible Jobs can visit a machine either once or several times Jobs do not need to be processed on every machine Models can be extremely complex!

24 Illustrations of Flow Shop and Job Shop
Flow Shop Manufacturing Machine a Machine b Machine k ai ri Ci pi1a dij pi2b di2 pijk dij di i Operation 1 Operation 2 Operation j Job Shop Manufacturing Machine c pijc Machine f pijf ai ri Machine a pija Ci di Machine d pijd Machine b pijb 35E00100 Service Operations and Strategy HSE/Logistics

25 Service-Related Performance Measures
Processing time related objectives Throughput is determined by the output rate of the bottleneck machine. Ensure that the bottleneck machine is never idle (jobs in queue) Sequence jobs so that the sum of setup times or average setup time is minimized Makespan is the total processing time of a set of jobs. Important, if the number of jobs is finite Due date related objectives Lateness Number of tardy jobs Average tardiness  Measure the level of customer service and external efficiency  Weighted versions of indicators can be used. e.g. Hopp and Spearman 2000, 489

26 Measures on the Fulfillment of Confirmed Delivery Dates
Service level MTO systems The part of orders, which is produced/delivered by given due dates. Fill rate ( service level) MTS systems The part of demand, which is fulfilled from the stock without shortage Lateness Shop floor control Difference between due date and completion date Variance of lateness Tardiness If job is late same as lateness, otherwise 0 Important measure for the average delay e.g. Hopp and Spearman 2000, 489

27 Due Date Related Measures
Li Cost function in practice di Ci The lateness Li of job i Ti di Ci di Ci Ci = completion time of job I di = due date of job i The tardiness Ti of job i

28 Cost-Based Performance Measures
Setup costs Measures: total setup time, average setup time, total machine idle time Minimizing often pays off, if the goal is to maximize throughput and minimize makespan Not necessarily proportional to setup times Work-in-process inventory costs WIP ties capital, increases handling costs Older WIP easily becomes damaged, etc. Measure surrogating WIP is average throughput time (~avg # of jobs) Finished goods inventory costs In MTO, earliness costs In MTS, depends on setup and holding costs Personnel costs Regular  overtime costs Other measures: earliness, schedule robustness

29 Best-Known Problem Types
Minimizing average cycle time on a single machine Total time to complete all jobs does not depend on the ordering Apply SPT rule Minimizing average tardiness on a single machine Total time to complete all jobs does not depend on the ordering No sequencing rule is guaranteed to minimize this measure (NP hard problem) EDD is often a good heuristic Minimizing maximum lateness on a single machine Total time to complete all jobs does not depend on the ordering Sequence the jobs according their due dates = Apply EDD rule Intuitive solution Minimizing makespan on two machines Makespan no longer fixed but sequence dependent Certain schedules might induce idle time A simple algorithm proposed by Johnson (1954) e.g. Hopp and Spearman 2000,

30 Classic Scheduling Problems
One-, two- or three-machine problems Several simplifying assumptions All jobs are available at the start of the problem (no jobs arrive after processing begins) Process times are deterministic Process times do not depend on the schedule (no setups) Machines never break down No preemption: once a job starts processing it must finish Jobs are not cancelled Why? To reduce the problem to manageable proportions To allow restriction of attention to simplified schedules and sequences e.g. Hopp and Spearman 2000, 451

31 Basic Dispatching Rules
FCFS, FIFO (first-in-first-out) The order of arrival determines the sequence SPT (shortest processing time) The shortest job first Minimizes average processing time LPT (longest processing time) The longest job first EDD (earliest due date) Schedule prepared based on due dates Job with the closest due date first Minimizes maximum lateness ERD (earliest release date) Similar to the EDD rule In a sense minimizes waiting time CR (critical ratio) Ratio calculated by dividing time remaining with work remaining Process 1st the job with the smallest value When on-time, prioritizes jobs with long processing time When delayd, prioritizes short jobs (removes congestion) Other rules SIRO = service in random order WSPT = weighted SPT MS = minimum slack LWR = least work remaining FOR = fewest operations remaining LNS = largest number of successors SQNO = shortest operation at next queue SST = shortest setup time

32 Basic Sequencing Rules Test Setting
Example 2 Basic Sequencing Rules Test Setting 1 machine Empty at the beginning; No set-ups 5 jobs Release time: 0 for all jobs Weight: N/A 4 rules tested 4 performance measures Mean flow time, Maximum tardiness, Average tardiness, Number of tardy jobs

33 Schedule and Performance First Come First Served (FCFS)
Example 2 Schedule and Performance First Come First Served (FCFS) Mean flow time 268/5 = 53.6 Average tardiness 121/5 = 24.2 Number of tardy jobs 3

34 Schedule and Performance Shortest Processing Time (SPT)
Example 2 Schedule and Performance Shortest Processing Time (SPT) Mean flow time 135/5 = 27.0 Average tardiness 43/5 = 8.6 Number of tardy jobs 1

35 Schedule and Performance Earliest Due Date (EDD)
Example 2 Schedule and Performance Earliest Due Date (EDD) Mean flow time 235/5 = 47.0 Average tardiness 33/5 = 6.6 Number of tardy jobs 4

36 Schedule and Performance Critical Ratio (CR)
Example 2 Schedule and Performance Critical Ratio (CR) Mean flow time 289/5 = 57.8 Average tardiness 87/5 = 17.4 Number of tardy jobs 4

37 A Comparison of the Schedules
Example 2 A Comparison of the Schedules Measures = makespan = maximum tardiness = number of tardy jobs = total flow time = total tardiness = total weighted flow time = total weighted tardiness Avg flow time Avg tardiness

38 Different Types of Priority Rules
Dynamic rules (vs. static) Indices recalculated every time machine is loaded Examples CR (slack per remaining processing time pi) S/RO (slack per number of remaining operations) MOD (modified due date): Same principle as in EDD but a modified due date d’ = Max (di, t+pi) is used Composite rules Additive rules A ranking expression that combines basic dispatching rules Each basic rule has own scaling parameter Heuristics Combine good characteristics of basic rules ATC (apparent tardiness cost) COVERT (cost over time)

39 The Difficulty of Scheduling Problems
Many problems too “hard” for finding optimal solutions Class P: a polynomial solution exists Class NP: no polynomial solution Sequencing problems grow as n! Computation times How many jobs can we sequence optimally if The computer can examine sequences per second The response time of the scheduling system should be no longer than one minute? What if the capacity of the computer is 1000 times faster Polynomial algorithms can be used to obtain “good” solutions E.g. Simulated annealing, Tabu search, Genetic algorithms

40 Motivation for Improved Scheduling
Major changes in production Creeping networking and differentiation of roles Outsourcing increases the number of supply chain players involved Distances among decision-makers increase More uncertainty and variability Ever-increasing global competition Higher expectations for response times Pressure to improve cost efficiency Less slack in lead times, inventories, and capacities Advances in information systems Increasing computing power More sophisticated IT systems Use of planning and scheduling techniques Integration of inter-organizational systems Lack of real-life applications & proven benefits, yet high expenses Globalization and networking Distances among locations increase Outsourcing typically increases the number of supply chain players involved Advances in computers and information systems Significantly higher computing power More advanced systems Planning and scheduling techniques Integration of inter-organizational systems Increasing global competition Higher expectations for response times and level of total costs Less slack in lead times, inventories, and capacities Individual company Supply chain level 35E00100 Service Operations and Strategy HSE/Logistics

41 The Fundamental Issue in Order Handling
Which customer order is given a priority when… Production capacity and/or material are limited? Disturbances occur due to machinery problems or transport schedules? Only some customer orders can be delivered fast? How the value and price of the customer service offered is defined and communicated? Who takes the responsibility of the operating model before one must? What are the expected business benefits? How does the customer gain? If you don’t measure, you can’t manage… Can you really manage complicated scheduling tasks without measuring (or estimating) even the most relevant details of success? 35E00100 Service Operations and Strategy HSE/Logistics

42 Research on Scheduling Problems Has Been Fragmented
A classification of research by Jain & Meeran (1999, 393): 35E00100 Service Operations and Strategy HSE/Logistics

43 Researchers have focused on designing yet another dispatching rule
Dynamic pricing & due date mgmt Number of articles per decade 80 60 40 20 Optimal due date quoting Parametric analyses Simple rules Weighted criteria Composite rules Versatile rule testing Intelligent heuristics ??? Agent-based reasoning Selective order acceptance Lead-time estimation Order release mechanism Priority classes & pricing IJIE MS IJPR OR NRLQ Omega JORS IIE Transactions JOM DS POM CompsOR IJPE IJOPM PPC EJOR Major publication outlets 1960 1970 1980 1990 2000 35E00100 Service Operations and Strategy HSE/Logistics

44 How many rules must a scholar - or a manager - know?
There are over 300 dispatching rules defined in literature, perhaps 120 rules if you exclude situational variants – but mere 35 rules seem to span the whole scope of dispatching tasks. 35E00100 Service Operations and Strategy HSE/Logistics

45 Key Questions on Tardiness Rules
Why aren’t there business cases that one could learn from? researchers have developed ever new rules for different types of problems – not much academic glory in proving some rules useful managers have found most scheduling methods rather complicated to use, even to understand – half-hearted attempts are doomed some more advanced applications tend to be proprietary and limited to corporate-specific problems What priority rules are available in commercial planning and scheduling software? usually only the simple rules (FIFO, EDD, SPT) are readily defined managerial commitment required for appropriate data & use What would it take to utilize the best rules in practice? definition and use of an Order Scheduling Protocol minor adjustments in the information systems and relationship management 35E00100 Service Operations and Strategy HSE/Logistics

46 Open Questions Which company will be the first one to know?
What does a delayed delivery of an order to a customer really cost? Which company will be the first one to know? Who knows how long it takes to fulfill an order in a given business situation? How much would we gain from being able to predict order fulfillment times of different customer requests? 35E00100 Service Operations and Strategy HSE/Logistics

47 Theoretical Background Literature on Job Shop Scheduling
Research on scheduling problems has been fragmented Researchers have focused on designing yet another dispatching rule How many rules of the 300 must a manager know? One family of rules suffices! Classic scheduling books Carroll 1965, Conway et al. 1967, Baker 1974, French 1982, Morton and Pentico 1993, Pinedo 1995. Surveys of dispatching rules Panwalkar & Iskander 1977, Blackstone et al. 1982, Haupt 1989, Ramasesh 1990. Key publications on the weighted tardiness problem Vepsalainen & Morton 1987 Use of Apparent Tardiness Cost (ATC) rule in job shop environment Normalized performance measures and tardiness penalty as a key job attribute. Anderson & Nyirenda 1990 Two new combination rules (CR+SPT and S/RPT+SPT). Kutanoglu & Sabuncuoglu 1999 Comparison of the weighted priority index rules, use of inserted idleness, and resource pricing with the ATC rule. Jaymohan & Rajendran 2004 Use of both holding cost and tardiness penalty as order-specific weight information. Introduces a variety of new weighted composite rules. 35E00100 Service Operations and Strategy HSE/Logistics

48 Is it possible to achieve all the goals of scheduling
Is it possible to achieve all the goals of scheduling... All at the same time? High utilization - low unit costs and On-time deliveries of important orders - premium service and Fast throughput of all orders - low inventory levels and Robust operations that are tolerant to faults - managerial errors in cost and processing time data and - disruptions in ordering and production process ? Yes – Provided there is a commitment to the Value of On-Time Delivery and a rational look-ahead rule. 35E00100 Service Operations and Strategy HSE/Logistics

49 The most important orders are delivered on-time if an appropriate rule is used
EDD 2x 3x 5x Look-ahead rules 50% 80% 90% 35E00100 Service Operations and Strategy HSE/Logistics

50 All orders flow through quickly if an appropriate rule is used.
Look-ahead rules (ATC, COVERT & CR+SPT) 35E00100 Service Operations and Strategy HSE/Logistics

51 Errors in order data can be managed if an appropriate rule is used.
35E00100 Service Operations and Strategy HSE/Logistics

52 Properties of the Look-ahead Rules
ATC has a natural approximation of look-ahead First day steep, second less, then linear. Local look-ahead is easier to use. Decision-makers benefit from the possible interpretation of cost index values. COVERT misses operation due dates Not a crucial limitation for one-stage operations. CR+SPT simple but hard to interpret and apply in practice? 35E00100 Service Operations and Strategy HSE/Logistics

53 The Logic of Apparent Tardiness Cost Rule
ATC: Priority index value Look-ahead Look-ahead t 35E00100 Service Operations and Strategy HSE/Logistics

54 The priority index of Apparent Tardiness Cost (ATC) rule
Priority index value ATC index: Two jobs: a (two operations) b (one operation) ATC ATC t 35E00100 Service Operations and Strategy HSE/Logistics

55 The logic of look-ahead rules such as Apparent Tardiness Cost and COVERT and CR+SPT
ATC: Priority index value COVERT: CR+SPT: CR+SPT CR+SPT ATC ATC COVERT ATC ATC COVERT t 35E00100 Service Operations and Strategy HSE/Logistics

56 Production scheduling decisions
An Illustrative Example – Final Sequencing Five Orders for Product Y (delay penalty=1) Confirmed delivery date to the customer (POD) Post Goods Issue (PGI) date (Scheduled Finish Date is – 1 day) Order data Arrival Order Processing Transport Due date OTDV Ex works # time quantity time (hrs) time (days) (at customer) (delay penalty) due date Production scheduling decisions Decisions are made on Oct 11, 7:00 AM. 2 production cells are available for total of 5+5=10 hrs work. There is material for each of the orders. If an order is not ready by PM, it may arrive late to the customer. In what sequence the orders should be made? ‘Due date’–‘transport time’ e.g – 4 days 35E00100 Service Operations and Strategy HSE/Logistics

57 What other criteria (or performance measure) could be used?
An Illustrative Example – Final Sequencing with OTDV=1 Performance in the Example Order data Arrival Order Processing Transport Due date OTDV Ex works # time quantity time (hrs) time (days) (at customer) (delay penalty) due date Results with different principles/rules #, %TJ avgTard Earliest arrival time first: 2, 40% (4-14:00, 5-14:00) 0.8 Smallest order quantity first: 2, 40% (1-13:00, 2-15:00) 0.8 Largest order quantity first: 3, 60% (4-13:00, 3-13:00, 5-15:00) 1.0 Shortest processing time first: 2, 40% (1-13:00, 2-15:00) 0.8 Largest processing time first: 3, 60% (4-13:00, 5-14:00, 3-14:00) 1.0 Using the EDD (=earliest due date) rule may not differentiate orders in the final sequencing. What other criteria (or performance measure) could be used? 35E00100 Service Operations and Strategy HSE/Logistics

58 Reduction rate per 3 slack hours
An Illustrative Example – Final Sequencing with Different Values for OTD Reduction rate per 3 slack hours 1.0, 0.7, ½, ⅓, ¼, … Order data Arrival Order Processing Transport Due date OTDV Ex works OTD index # time quantity time (hrs) time (days) (at customer) (delay penalty) due date (TAF*OTDV/PT) *1/4 = .25 *10/5= 2.0 *2/1= 1.4 *5/2= 1.75 *20/2=7.0 Results with different principles/rules %TJ avg weighted tardiness Earliest arrival time first: % tardy 10.0 Smallest order quantity first: 40% Largest order quantity first: 60% Shortest processing time first: 40% Largest processing time first: 60% (or 6.2 if #5 before #4) Consider also ‘weighted SPT’ & ‘highest penalty first’. Look-ahead OTD: 20% tardy, average weighted tardiness less than 0.1 - it gets the job (and jobs) done optimally! 35E00100 Service Operations and Strategy HSE/Logistics

59 Three Parts of the OTD rule (=ATC rule)
Every order has three factors, which are necessary to establish consistent and rational expectations of the true value of on-time deliveries and reliable lead time estimates, and thereby use them accurately for marketing and internal incentives Processing Time (PT) On-time Delivery Value (OTDV) = cost of being tardy Tardiness Anticipation Factor (TAF) = f(slack) = f(due date - current date - processing time). (This factor increases from 0 to 1 as slack is reduced). The OTD index = (OTDV /PT) x TAF x slack €/time unit Notice: Waiting time estimates are needed for estimation of slack and evaluation of lead times (also in a one-stage problem) 35E00100 Service Operations and Strategy HSE/Logistics

60 On-time Delivery Value of an Order – What does a delayed delivery of an order to a customer really cost? Extra costs to the customer caused by lack of product and/or expediting and hustle (increased transaction costs). 5-20% of the value of the order -- € /day + € /day Late / tardy shipment causes harm and bad-will to the customer. 0-500% of the value of the order -- € /day (probability equivalent) OTDV depends on the importance and size of the order, and the number of hours or days tardy. What about lead time? What we mean by that? - Weight is constant w/ linear aggregate value Severity of tardiness of an order may increase OTDV as time progresses: Being late from the promised delivery date is a major problem. Potential cancellation of the customer order – probability increases? Inventory replenishment may have gradually increasing urgency. Pushing the standard processing and waiting times, including transfers, extra packaging etc. expediting costs, and the cost of faster transportation. 35E00100 Service Operations and Strategy HSE/Logistics

61 The Good News Due dates - We can set those! Job splitting
We can get smaller jobs by splitting larger ones. Single-machine SPT results imply small jobs “clear out” more quickly than larger jobs. Mechanics of Johnson’s algorithm implies we should start with a small job and end with a small job. Small jobs make for small “move” batches and can be combined to form larger “process” batches. Feasible schedules We do not need to find an optimal schedule, only good & feasible Focus on bottleneck We can often concentrate on scheduling the bottleneck process, which simplifies problem closer to a single machine case. Capacity management Capacity can be adjusted dynamically (overtime, floating workers, use of vendors, etc.) to adapt facility (somewhat) to schedule

62 Challenges of Some Advices Given for Order Management and Scheduling
How do you know when to switch from one rule type to another? Processing time based rules Due date based rules Time Capacity Utilization 100 % 80 % How to convince customers that lead times should be determined based on the work content of order? Lead time estimate Due date Job A [1] Job B [3] Job C [9] Constant (value 10) CON Total work content (multiplier 3) TWK 35E00100 Service Operations and Strategy HSE/Logistics

63 Lead Times and Rescheduling
Methods for estimating manufacturing lead times CON (constant) No separation between orders SLK (slack) Time allowance with equal waiting time or slack di = ai+ pi + fixed slack TWK (total work) A multiple of the processing time di = ai + bpi Priority-based Dynamic Waiting time proportional to relevant queue length Iteration-based Heuristic procedure Dynamic due date maintenance Can reduce total inventory level and simultaneously improve customer service Requires more efforts Risks? Filters of rescheduling decision Ability filters Only attainable due date adjustments Magnitude filters Threshold value = absolute value of the difference between the new and the old due date Horizon filters Filter out due date changes too far out in the planning horizon to be of immediate concern Partly from Vepsalainen & Morton 1988

64 Time when the new job will be filled:
Due Date Quoting “Emergency” positions New job c Rate Out rP, s Backlog b WIP w Completed Time when the new job will be filled: m = w + b + c Hopp and Spearman 2000,

65 Characteristics of Scheduling Models Processing Constraints
Routing Constraints Specify the route a job takes through a system Machine-Eligibility Constraints Jobs sometimes have to be processed on a specified subset of parallel machines Precedence Constraints A job can be started only after a given set of other jobs has been completed Precedence constraint graph may have a specific structure Tooling Constraints and Resource constraints e.g. training of operator Material Handling Constraints Refer to systems that convey jobs from one workcenter to another Depend on the automization of workcenters: if highly automated (roboticized), processing times are deterministic Often limit the amount of buffer space  WIP level Personnel Scheduling Constraints e.g. consecutive days off, shift types

66 Characteristics of Scheduling Models Processing Characteristics
Order Quantity Limits and Due Dates In MTS environment Jobs do not have tight due dates Inventory level triggers production of stocked items Amount produced depends on setup costs and inventory holding costs In MTO environment Jobs have specific due dates Quantities determined by customer MTO and MTS often combined Sequence-Dependent Setup Times and Costs Reconfiguration or cleaning of a machine; known as setup or changeover The length of a setup depends on the job just completed and on the one to be started Preemptions Take place for example due to breakdown or an arrival of high priority order Different forms Preemptive resume (work not lost) Preemptive repeat ( work lost)

67 Trade-off Curves in Job Shop Environments
Idle time Current system Improved system Work-in-process

68 Order Management Protocols
Justification of open protocols Increasing networking calls for integrative methods within one company and across independent players: Centralized decision-making – by whom? Real-time response - how? Could simple standardized protocol be more efficient than customized ones? Order management would be less expensive via protocols that are available to all potential users.  Rationale of open protocols Coordination of decentralized, postponed and localized decision-making Ease of use and accessibility Suggested three layers of protocols Rules of scheduling behavior Technical specifications and tolerances Conventions of usage 35E00100 Service Operations and Strategy HSE/Logistics

69 Layers of an Order Management Protocol
Screening rule – who, when, how Release rule – who, when, how Sequencing rule – who, when, how Conventions of usage OTD rule helps in estimating lead times, OTDVs and relative importance of orders. Scheduling rule Data tables for OTDV, TAF index, and lead time estimates Technical specifications and tolerances 35E00100 Service Operations and Strategy HSE/Logistics

70 The Order Management Protocol for the On-Time Delivery Rule (OTD Rule)
Rules of scheduling behavior Prefer look-ahead rules, especially decomposable ATC-rule Technical specifications and tolerances Many lead time estimation methods work, esp. w/look-ahead rules Moderate errors in the cost data and processing times estimates do not impact the ranking of the rules. Rough-cut priority classes can improve shop performance compared to not using any method but performance is still significantly worse than with the look-ahead rules. Data aggregation has limited benefits for most of the rules. Conventions of usage Priority rules can substitute for order release mechanisms The look-ahead rules improve tardiness performance of production systems, even if applied at one stage only. Predictability of tardiness behavior of orders over operations is easier with some given priority rules. 35E00100 Service Operations and Strategy HSE/Logistics

71 Business Potential of the Order Management Protocol and the OTD Rule
Streamlines the process Focus on the important issues – it is not necessary to maintain all options The real bottlenecks will be revealed and resources aligned Helps to learn the value of OTD to customers and fulfill your promises Develop customer relationships and sales Define and follow supplier contracts more accurately Design rational basis for a market-driven incentive system Enables the differentiation of service for satisfaction and better price Implementation requires training and minor changes in information system Running is free! Savings in personnel and shipping costs Enables Premium service w/o building new capacity 35E00100 Service Operations and Strategy HSE/Logistics

72 Using Order Priorities within Customer Order Management Process
Special indications from the sales / supply team. Orders are anonymous. Sales Office Manufacturing and Logistics Operations Supply Team OTD Order acceptance Order release Customer service Capacity Allocation & Demand-Supply Balancing Material requirements planning & supply mgmt Scheduling & Re-scheduling Final Sequencing Dispatching & Shop floor management Packaging & Shipping Transport & final delivery Factories are given the workload. MTO is a must! Demand peaks at the beginning of the week and at the end of period/quarter. Capacity allocations weekly: remaining (frame blocks) is released later. Expedited deliveries paid by the client The number of rush orders increases by the end of period/ quarter. Suppliers, Subcontractors, Partners and Logistics Service Providers 35E00100 Service Operations and Strategy HSE/Logistics

73 Main Challenges and Risks Can you accept the following?
Orders are different, situations / business cases are different.  Different lead times Some orders should go faster.  Would you trust simple rules? Control parameters & performance criteria can (and should) be quantified.  How else differentiate the service?  How else communicate the time value of an order?  How to manage if you can’t measure?! 35E00100 Service Operations and Strategy HSE/Logistics

74 Key Points Scheduling is challenging! Scheduling can be simplified
Flow simplification (sequencing in place of scheduling) Due date quoting Priority scheduling Easy to understand Flexible and perhaps not as myopic as claimed Finite capacity scheduling will emerge

75 Notation and Abbreviations Used
ATC = apparent tardiness cost COVERT = cost over time EDD = earliest due date FCFS = first come first served FMS = flexible mgg system MTO = make to stock MTS = make to stock SPT = shortest processing time WSPT = weighted SPT


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