Understanding Optimized Production Technology
Drum-Buffer-Rope (DBR) is the Theory of Constraints (TOC) production planning methodology originated by Eliyahu M. Goldratt in the 1980s. In fact, the concepts of DBR actually preceded the Five-Focusing-Steps and the notion of the “throughput world” in the development of the TOC paradigm.
While the DBR method is much simpler than the older Optimized Production Technology (OPT) algorithm and the recent Advanced Planning and Scheduling (APS) systems, for many production environments, especially those not currently- or consistently- dominated by an active internal bottleneck, an even simpler method can be adopted. We call this method S-DBR, to distinguish it from the traditional model, which we’ll refer to as traditional DBR.
S-DBR is based on the same concepts as traditional DBR and is certainly in harmony with TOC and the Five Focusing Steps. What distinguishes it from traditional DBR is its assumption of market demand as the major system constraint, even when an internal capacity constraint temporarily emerges.
S-DBR can be easily supported by traditional ERP/MRP systems and it is specifically intended to deal with fluctuating market demand.
Optimized Production Technology (OPT)
1. What is Optimized Production Technology?
2. What is the aim of Optimized Production Technology?
3. What are the main features of OPT?
4. How OPT can be developed?
5. How OPT is operated?
6. What are the benefits achieved from OPT?
Optimized production technology is proprietary scheduling system using, computer software which was originally developed by Dr. Eliyahu Galodratt and colleagues who recognized that one of the most complex problems facing manufacturing organizations was that of shop-floor scheduling.
The system is based on the concept that there are two fundamental manufacturing phenomena:
Dependent events. All processes rely upon the completion of preceding operations.
Statistical fluctuations. Process times fluctuate around an average.
The effect of these phenomena is that the capacity of a plant must be unbalanced and therefore bottlenecks are inevitable.
As defined by Johnson, the OPT method of scheduling dictates that material should only be launched on to the shopfloor at the rate at which it is consumed by the bottleneck.
Furthermore, a time buffer of work should protect the production in the bottleneck.
This means, that work scheduled for day three arrives on day one, creating a buffer of two days as protection against disruption in operations before the bottleneck.
AIM OF OPT
The aim of OPT is to schedule bottleneck capacity in an efficient way. This schedule is the master for the demand placed on other capacities.
MAIN FEATURES OF OPT
The main features of OPT are described by Fax as follows:
Balance flow not capacity.
The level of utilization of any part of the system, which is not a bottleneck, is dependent on other constraints in the system, not the potential of the worker.
The utilization and activation of a resource are not synonymous.
An hour lost at the bottleneck is an hour lost for the total system.
An hour saved at a non-bottleneck is just a mirage.
Bottlenecks govern both throughput and inventories.
The transfer batch may not, and many times should not be equal to the process batch.
The process batch should be variable, not fixed.
Schedules should be established by looking at all the constraints simultaneously. Lead times are the results of the schedule and cannot be predetermined.
The steps used to develop OPT consist of the following:
Preparation. Measuring performance, project planning and identifying hardware and software requirements.
Plant analysis. Analyzing the manufacturing processes and how they are managed.
Bottleneck analysis. (A bottleneck is defined as a resource ‘where capacity is equal to or less than the demand being placed upon it’.) This is conducted by analyzing work in progress and shortages vs. excesses (potential bottlenecks are those resources which appear on the shortage list but not the excess list).
Computer modeling. This is the process of developing the engineering network and instructing the OPT scheduler how to interpret details concerning the manufacture of products such as dependent set-ups, critical material, fixed batch quantities, maximum batch quantities, consumable tools, rework and uninterruptible processes. Data will be fed into the model concerning routines, bills of material and customer demand.
Data definition. Establishing what data is required to be fed into the system.
Defining outputs. The output will be a master production schedule (MPS), which is achieved by constraint capacity planning. This provides the basis for the process of demand management using the OPT software to carry out the scheduling – the OPT identifies the relevant demand and controls the build accordingly.
OPT is operated through OPT software which has been developed to control complex manufacturing processes. The software will model the process and produce the schedules in the shape ofmaterial and capacity plans using the OPT bottleneck forward-loading techniques. The shopfloor control system will then monitor progress against the schedule and initiate any action to overcome shortfalls.
The benefits claimed for OPT are that it will schedule finite resources in order to achieve maximum factory effectiveness.
The scheduling system:
Addresses the key problem of bottlenecks.
Improves profitability by simultaneously increasing throughput.
Reduces inventory and operating expenses.
– Part 6: Optimised Production Technology (OPT)
OPT is possibly the most radical of the 3 production strategies to be discussed as it requires a new way of thinking, not only about production but also about the basic accounting principles. In many areas this demands radical or revised thinking by our accountants and new approaches to the fundamentals of accounting.
OPT begins by stating that the goal of a manufacturing business is to make money both now and in the future. This might seem to be rather simple but it provides a framework for all the other decisions involved in the business.
The aim of OPT is to increase ‘throughput’ (the rate at which the company generates money through sales) whilst simultaneously decreasing inventory and operating expense. If an action does not directly improve one of the three measures then it is irrelevant at best and damaging at worst, do not do it.
The traditional approach has been to optimise each sub-system irrespective of its importance (i.e. to improve the output of the welder) but the OPT approach is to optimise the total system to maximise throughput (i.e. if the welder is not limiting your throughput then don’t work on it and put your efforts somewhere else). OPT states that the optimum of each sub-system is not necessarily the optimum of the whole system.
OPT defines a ‘bottleneck’ as any resource whose capacity is equal to or less than the market demand placed upon it. The bottleneck is thus the constraint that is preventing increased throughput from your factory. Improvements here will tend to optimise the whole system and have an increased payback by directly increasing throughput. Bottlenecks are easy to spot in the average factory – they are the operations that have lots of work in progress stacked up in front of them. In this sense a non-bottleneck is any resource whose capacity is greater than the market demand placed on it and improvements here will be irrelevant in terms of increased throughput.
Figure 1: Spotting the bottlenecks
Operation C is the obvious bottleneck for the factory. Running A at capacity will lead to a build up of inventory in front of B. Running B at capacity will lead to a massive build up in inventory in front of C. Investment or improvement in A, B or D will do nothing to improve throughput, the only meaningful investment area would be C where the ability of the plant to earn money can be rapidly improved.
Operation C must be protected from loss of output for any reason. It is the operation that controls the income of the factory. In reality the choice is never this clear and the important thing is to balance the flow and not the capacity.
The bottleneck concept is best explained in the hiking analogy from The Goal. The speed of a group of hikers needs to be maximised to get to the campsite by nightfall but the actual speed of the whole group is limited by the speed of the slowest hiker (the bottleneck). Placing the slowest hiker at the front of the group slows down the whole group and increases the time required i.e. reduces the throughput. Placing the slowest hiker anywhere else in the group still slows the whole group and also increases the length of the group (the inventory).
Thus the only way to reduce the length (the inventory) and achieve the fastest transit time the throughput) is to find a way of moving the slowest hiker faster i.e. working on the bottleneck. An hour lost at a bottleneck, for any reason is an hour to the whole system and cannot be recovered. Don’t think you can get it back later because the way we defined a bottleneck means that you cannot. The cost for this lost hour is the total cost of running the whole factory for one hour, after all the bottleneck is governing the throughput.
Factory scheduling is at the heart of OPT and a critical factor in this is the location and elimination or management of bottlenecks, a fact which is not explicitly dealt with by JIT. The set up time reduction techniques of JIT appear again but are not formally recognised by OPT. An hour saved in the set-up time of a bottleneck is an hour saved for the whole system. OPT goes on to say that an hour saved on a non-bottleneck machine simply increases inventory and does nothing to improve throughput. It is wasted effort, so don’t do it.
In a sense OPT shares a lot of philosophy with JIT and both concentrate on quality, lead times, lot sizes and machine set-up times. A major difference is that OPT regards the ‘river and rocks’ analogy of JIT as being fundamentally flawed. In OPT terms the river is not the flat evenly flowing stream that JIT assumes but has waves on inventory moving through it depending on the order situation in the factory.
All can be fine until the inventory is at the trough of a wave. If you hit a problem then it is likely to rip the bottom out of the boat and sink the business! The OPT approach is much more like reality than the JIT approach in this situation, in other words don’t take any analogy too far. An underlying rule forgotten at your peril.
Figure 2: The OPT view of rocks in the river
In the same way OPT shares a computer based approach with MRPII and both require a large complex database of product and machine information for schedule calculation. OPT also requires information on how the product is made, the route through the factory and both set-up and run times. OPT can generally pirate a lot of this information from an existing MRPII system.
One problem with MRPII is that it ignores the in-build variation of any machine and assumes that a machine will work at capacity at all times. OPT is more realistic in accepting that the actual capacity is affected by statistical fluctuations and a dependence on previous operations to supply product for processing. In many cases this makes MRPII scheduling unrealistic and time buffers are built in to cater for this. OPT can be more realistic in scheduling than MRPII by taking this into account and also allowing for improvement in times and routing.
OPT is based on a set of rules which need to be adopted completely by management and basic statements are incorporated into these rules.
The OPT rules
Balance the flow, not the capacity.
Let bottlenecks determine usage of the non-bottlenecks and do not seek machine utilisation. If a resource is activated when output cannot get through the constraint then all it produces is inventory.
Utilisation and activation of a resource are not the same thing. Activation is when a resource is working but utilisation is when it is working and doing useful work. Producing stock for inventory is not useful work.
An hour lost at a bottleneck is an hour lost for the whole system and cannot be recovered.
An hour saved at a non-bottleneck is a mirage.
Bottlenecks govern both throughput and inventory.
A transfer batch is not necessarily equal to a process batch i.e. just because you have to cut 20 frames at a time on the optimiser saw it does not mean that you have to push them all on to the welder at one time. You can break the process batch (20 frames) down into small transfer batches (1 order).
Process batches should be variable and not fixed. Later work shows that the best results are achieved by using a drum-buffer-rope technique to control the system. You must first find the true bottlenecks that govern the factory throughput.
The bottlenecks that beat out the pace like a drum for the whole factory should be kept fully scheduled and working at all times. The bottlenecks must be protected against any interruption caused by breakdowns, quality, set-up times, labour concerns or any other variation. This protection is achieved by building in time buffers. These are a focus for process improvements. All other operations are then synchronised to the bottleneck operation and work is pulled through as if it were on a rope.
Without computers the drum-buffer-rope concept works very well for limited variety production. The introduction of variety leads to shifting bottlenecks and the need for complex computer software to run the system.
OPT requires maximising the flow of materials and rarely requires large investment in machinery or restructuring of the plant. By improving the flow of the product OPT seeks to get inventory moving and can make an immediate financial impact. OPT needs to be carried through to the whole company and encourages the view of the production area as a real profit maker for the company.
For and Against
Quickly targets areas of concern (bottlenecks, quality set up times, high inventories).
Incorporates some production and MRP.
Gives financial feedback.
Suitable for discrete, batch and process industries.
Possible to grow into via partial implementation at a practical level.
Easily understood by the shop floor.
Challenges traditional cost accounting.
Requires simulation modelling of the process.
Needs good database.
Must go via one consultancy company.
OPT is relatively new in terms of production management systems and is an overall philosophy for running the business rather than simply being about production management. OPT starts by assuming that manufacturing is all about making money and looks at optimising the complete system to achieve this rather than just optimising individual operations on a piecemeal basis.
OPT is a proprietary system in the full version (rather than just the philosophy) owned by a software and consultancy company. This does not prevent the adoption of some of the excellent ideas it contains and generates.
OPT is a trademark of the Scheduling Technology Group. The only, but excellent, book on the subject is ‘The Goal’ by E Goldratt and J Cox.
In the previous pages we have looked at 3 different methods of production management and have reviewed the significant areas for improvement and change. As an overall summary MRPII does not seek to change anything whereas JIT actually forces a fundamental but painful search for excellence. OPT is probably even more powerful because it uses many of the JIT ideas and also follows through into the overall system. The current strategy of many companies uses a blend of these three main methods at various points in the company to achieve the right blend of success.
“The Manufacturing Strategy” Series
“The Manufacturing Strategy” series is designed to give production managers and their staff some insights into new manufacturing methods and to prompt the industry into considering the benefits of alternative approaches to manufacturing. The series is:
Part 1: Setting the strategy
Part 2: The systems and MRP II
Part 3: Just in time (1)
Part 4: Just in time (2)
Part 5: Just in time (3)
Part 6: Optimised Production Technology (OPT) (This section)
Part 7: A fundamental quality
Part 8: Quality management techniques & tools
Part 9: ‘There’s no accounting for manufacturing strategy’
Part 10: Performance measurement
Part 11: Changing roles and things to do NOW!
Last edited: 29/03/04
© Tangram Technology Ltd. 2001
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dvanced planning and scheduling
Clients in the consumer products manufacturing and process industry have revealed that many are interested in knowing more about how advanced planning and scheduling (APS) systems can support them in making the right decisions to drive supply chain benefits. More and more companies are in the process of implementing such a system to further optimize their planning processes. To learn more, read the following book exerpt:
“How to Get the Most Out of Your Supply Chain”
An overview of Advanced Planning and Scheduling (APS) systems in the consumer products manufacturing and process industry by Deloitte’s Rhiannon Davies, Nadine Diepeveen, Erik Diks and Vincent Vloemans. Published December 2002.
Requirements driving APS
Pressure on performance has been steadily increasing over the last decade, and it does not appear to be abating. But where can a company still squeeze out performance improvement? According to Gartner, a leading technology research and advisory firm, supply chain management is one of the key differentiators for the future. Gartner researchers predict that:
“By 2004, 90 per cent of companies that fail to apply supply chain management technology and processes to increase their flexibility will lose their status as preferred suppliers (0.8 probability).”
“Through 2005, organizations that implement supply chain planning applications with a continuous improvement program will increase ROI by 40% during a 5 year lifecycle (0.7 probability).”
Changing business requirements and markets are making effective supply chain management and the resulting competitiveness and flexibility more important. Customers are demanding more flexibility, more visibility of availability, more speed and highly customized products. To provide this information, more and more emphasis is put on the supply chain planning capabilities to allow the visibility along the supply chain to react quickly to changing customer demand in a cost competitive way.
The alternative to reliable planning is a fully flexible supply chain. Some companies have made good headway in this area, but for many, the cost of this full flexibility is till too high. At Deloitte, we recommend balancing planning improvements with increasing flexibility in the supply chain and continuous development of supply chain professionals.
Discussions with our clients in the consumer products manufacturing and process industry have revealed that many are interested in knowing more about how APS systems can support them in making the right decisions to drive supply chain benefits. More and more companies are in the process of implementing such a system to further optimize their planning processes. This book will help our clients to answer the following questions:
What is an APS system and what role do they play in supply chain optimization?
How APS is different from ERP (Enterprise Resource Planning)?
To what businesses could it apply? Will it apply to my business? How?
What are the benefits that could be gained from such a system?
Who are the key vendors of APS systems?
How can I choose which vendor to use for my organization?
How are APS packages implemented and how can I apply this methodology to my own organization?
The book is written in cooperation with the 10 major APS vendors in consumer products manufacturing and process industry. As sort of an APS primer, the book includes profiles of each vendor, listing the company, its strategy, product technology, functionality and other important information. But the book also is appropriate for readers with a strong knowledge of APS, providing them with up-to-date knowledge of technology and vendors.
The APS vendor assessment
APS applications cover various domains in supply chain management. To structure the discussion on APS, we introduce our APS reference model. This model serves as a guideline for the general building blocks of an APS system, and allows for a level comparison of the functionality of the various vendors. Furthermore, we discuss the most common models and techniques used by APS packages and assess the level of sophistication of the functionality provided by each vendor in each of the areas outlined in the APS reference model.
Each industry has its own specific business issues and these must be addressed during the definition of the requirements that need to be met by an APS package. For example, in the process industry tank planning and dealing with shelf life are usually required, whereas in the high tech industry, complex capacity planning with many constraints. We concentrate on APS packages that cover most requirements in the consumer business and process industry. We provide a classification of consumer business manufacturers to explain the different requirements that need to be addressed by an APS package. We also describe how major changes in the business environment, as well as process and technology innovations impact the requirements on APS software.
To provide an up-to-date overview of the software capabilities offered by major APS vendors in the consumer products manufacturing and process industry arena, we performed a large survey among the largest APS vendors within the consumer business and process arena, including Adexa, Agilisys, Aspen Tech, Baan, i2, Logility, Manugistics, Oracle and SAP. We took a two-stage approach to the survey process. First, each vendor filled out a questionnaire that gave insight into the company, its strategy, its product and footprint, product strategy and development plans to allow us to better understand and position their capabilities. Second, we visited each vendor to discuss in more detail their answers to the survey, to view demonstrations of how their product functionality really answers supply chain planning requirements, and to get an impression of their product and company.
The vendor analysis in the book is structured to allow easy comparison of vendor capabilities. First, we inventarize the capabilities of each of the vendors per functional area as outlined in the aforementioned APS reference model. Second, we compare the vendors by their industry focus and their technology capabilities. Finally, per vendor we give an overview of their user interface functionality and development areas.
Selection and implementation methodology
One of the objectives of the book is to serve as a guide to support the initial selection of the APS application/vendor that best meets the business needs of our clients. As such, in addition to providing a vendor selection methodology, we explain Our proven five step methodology to implement an APS system. We explain each step in detail, as well as our methods and the tools we use during an APS implementation. Finally, we provide some lessons learned (secrets of our success) based on our practical implementation experience at numerous customers.
Advanced Planning & Scheduling (APS)
For complex planning & scheduling activities – especially those that are heavily constrained or require multi-stage scheduling and frequent re-scheduling – our experience is that off-the-shelf software packages just don’t work. Because of the many differences between problem types and industries, you often end up with a rigid system with preset objectives, logic, and scope, which doesn’t quite fit your core operation. Because our Advanced Planning & Scheduling (APS) system is tailored to your unique business rules, constraints, and processes, it can be used to optimise a wide variety of planning & scheduling activities, including
Production-line planning, scheduling, and sequencing
Labour planning and timetabling
Maintenance planning & scheduling
Equipment planning & scheduling
Features and benefits of our APS system include:
Creating schedules that are optimised for cost, profit, or client-defined objectives (service levels, utilization, etc.)
Increased delivery on time and in full (DIFOT)
Reduced work-in-progress and finished goods inventory
Reduced planning time
Dynamically re-optimising around unexpected changes in demand and other events
Conducting financial what-if analysis and scenario comparison
Setting more than one objective/goal and analysing the trade-offs
Centralising the planning and scheduling function across multiple plants
Optimising across multiple production stages or steps
Evaluating the impact of your business rules, processes, and constraints
Seamless connection with your existing databases, MRP/ERP systems, and other enterprise software
March 2010 » ORDINA signs partner agreement with ICRON. …
Advanced Planning and Scheduling
Planning and scheduling has never been easy, but today it is far more challenging than it was a decade ago. Planners are feeling more and more pressure to generate accurate and timely plans by considering complex production and supply chain environment, ever changing demand, heavy constraints, conflicting business objectives and processes. And to make things worse, traditional tools at hand are becoming obsolete: spreadsheet based manual planning and scheduling cannot cope with the complexity, your ERP system hardly helps and there is no off the shelf product which can address your unique production environment and supply chain network. What you need is a flexible and reliable Advanced Planning and Scheduling solutions which is tailored for your unique requirements.
With 15+ years of experience in Supply Chain Optimization, ICRON Technologies provides ICRON Advanced Planning and Scheduling (ICRON APS) solution to revolutionize your planning and scheduling activities by its mature, cutting-edge technology, and innovative modeling and implementation practices. ICRON APS provides optimized, automatically generated plans and schedules while simultaneously considering demand, resource and material constraints and business objectives.
Benefits and features of ICRON APS are:
ICRON APS provides significant cost and waste reduction by optimization based on user defined objectives (reduced cost of early/late job completion, inventory, overtime, transportation, reduced WIP times, etc.).
ICRON APS greatly improves available-to-promise and capable-to-promise capabilities by generating realistic completion times for individual operations and jobs on entire supply chain network. This quickly translates into increased customer satisfaction.
ICRON APS provides feasible, finite capacity schedules which can be readily published to the shop floor.
ICRON APS significantly reduces the planning time. ICRON automatically performs most of the schedule generation activities and produces schedules in minutes rather than hours or days.
ICRON APS provides tremendous what-if analysis capability. With its speed, accuracy easily generates as many scenarios as user requires and provides user friendly, efficiently tools for planner and management to selects the best scenario to be used as the official plan.
ICRON APS maximizes the resource utilization by reducing the setup times by better sequencing, especially when sequence dependent setups exist.
ICRON APS provides you fast rescheduling capability to respond to frequent changes. With ICRON, the production planning shifts from reactive, fire fighting planning to proactive, strategic planning.
ICRON APS integrates and centralizes the planning and scheduling along the entire supply chain network.
Advanced Planning and Scheduling (APS) -Techniques that deal with analysis and planning of logistics and manufacturing during short, intermediate and long-term time periods. APS describes any computer program that uses advanced mathematical algorithms or logic to perform optimization or simulation on finite capacity scheduling, sourcing, capital planning, resource planning, forecasting, demand management, and others. These techniques simultaneously consider a range of constraints and business rules to provide real-time planning and scheduling, decision support, available-to-promise, and capable-to-promise capabilities. APS often generates and evaluates multiple scenarios. Management then selects one scenario to use as the “official plan.” The five main components of APS systems are
Distribution Planning, and
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