"My supply chain is different and more complex"...if I had a dollar for every time that I heard this from potential clients, I would be a millionaire by now! Yes, your supply chain is unique and different but it does not mean that it is more complex than others. Every industry has its own nuances of how the supply chain can impact their business. Unless you are dealing with niche players, planning systems are designed to handle the complexity of all supply chains. However, what makes them different is the level of accuracy that they have in order to model the supply chain. With a few exceptions, all S&OP solutions treat the supply planning just like a spreadsheet does. The use bucketed capacities, static lead-times, and predefined bottleneck resources! At the level of spreadsheet modeling obviously they can handle any supply chain just like spreadsheets have been doing for a long time. If, however you are interested in a reliable plan for the supply chain then S&OP by itself is not good enough; you need S&OE to enable you to execute the plan. What good is a plan that needs to be manually changed over and over again in order to make it work? In the words of Gartner: “The No. 1 challenge among manufacturing companies is connecting sales and operations planning (S&OP) to operational plan/execution.” To learn more about how to generate reliable plans that can be executed click here: S&OE.
Like many other S&OP solutions, SAP®-IBP* is designed to perform high level “rough cut” planning. On the supply side, it uses a simplistic logic that has been used in spreadsheets, for decades, to plan production. It assumes fixed leadtimes, pre-defined bottleneck resources and bucketed capacities. This is not much different than the old MRP methodology from the 70’s, but now categorized under S&OP. Advanced planning came to play because this was not effective and plans were not accurate enough to model the capacities and coordination of material and capacity was missing. SAP-IBP, much like almost all other current S&OP solutions, is intended for very high level reporting. Once the plan is generated it can be at most 60% accurate. Hence, once given to the operations side of the business, it does not work. SAP’s answer to this problem is usually faster what-if analysis, which still adds up to lots of manual adjustments, much like working with spreadsheets to make unworkable plans to work.
This is exactly what the APS industry felt the need to change with the use of powerful optimization engines in the late 90’s followed by AI techniques in the past 15 years. Yes, SAP-IBP provides nice reports and charts, and maybe even help in collaboration, but in the final analysis, it lacks realistic plans and cannot feasibly translate generated plans into actual execution of those plans. In other words, it misses the mark when it comes to Sales and Operation Execution (S&OE). Specifically, it lacks proper modeling of resources to understand true capacities, it fails to estimate true leadtimes dynamically, it does not take into account the mix of products, it fails to estimate the impact of setup times and changeovers on the overall capacity of resources and subcontractors, it lacks pegging of orders and it cannot do attribute based planning other than at the finished goods level. Consider this, if SAP-IBP fails to do full pegging of orders, how can it accurately determine the true cause of lateness of any orders? If it does not model capacity of resources properly with dynamic leadtimes, then how can it provide accurate ATP and CTP? -- There is something to think about next time you are wondering why your inventory and service levels are never on target.
Fact: realistic operations plans require accurate modeling of the supply side and all-in-one unified planning environment (what Gartner refers to as the ultimate or stage 5 supply chain). Transformation of plans into execution requires “model integration” By model integration, I mean the ability for S&OE engine to have the same data model as the S&OP albeit at different levels of detail. Accurate plans remove the need for excessive what-if scenarios and wasting time to adjust the plans, over and over. Accurate plans ensure delivery performance levels that are required by customer, by region, by product, and so on. Accurate plans provide much better visibility into the supply chain and what is possible and what is not. Accurate plans ensure the financial projections are realistic and reliable, as you plan for the long term. As an example, one of Adexa’s clients, a global high tech manufacturer plans tens of millions of orders within minutes with accuracy of almost 100%. This is done for both long term planning, as well as short term changes in the orders. Reallocation takes place every day depending on changes in the orders and availability of resources. Visibility is provided for close to one hundred sites worldwide. The best part is that planners have no need to make any adjustments and changes once the plan is published, i.e. they can go home early!
The bottom line, unless the plans are accurate and the model of the supply side is a true reflection of your supply chain then you are not getting much more than just half-accurate, but pretty reports. Spreadsheets will do just fine at this level of planning and give you a lot more control too, not to mention they are most certainly a lot less expensive! By deploying S&OE solutions to SAP solutions, as experienced by more than 70% of our clients, you will experience enormous benefits of what true plans can do for your organization: lower cost of operations and much improvement in delivery performance across all customers and products.
I encourage you to go beyond what is peddled today as S&OP, by SAP or anybody else, and look how much further you can go with more accurate and realistic plans by considering technologies that transition your supply chains into S&OE.
If I arrive on-time or early at the airport but there is a problem with the airplane and a long delay in the scheduled takeoff time, does it get me to my destination on time? Clearly the answer is No! Is it better for me to stay in the comfort of my home and go when I know the plane is ready to take off? Or even better, find another flight to my destination so that I get there on time. By going to the airport at the “wrong time” and waiting I am only increasing “WIP” or waiting time and I am also contributing to airport congestion which adds to the traffic and boarding of other flights possibly causing others to miss their flights. Such problems can be avoided by an intelligent planner or, release strategy, that can figure out exactly what the right time is for me to leave home given the traffic situation, the speed of cars, the parking time and time it takes to go through security. This kind of predictive planning is ideal for releasing lots in a semiconductor manufacturing line where depending on the mix of products, availability of resources and masks as well as WIP, it can decide which lots should be released and which ones should be held back so that we meet the following three objectives optimally:
- Cycle time
- Equipment utilization
- Delivery performance
The following diagram shows the relationship between these three parameters and how they change with WIP increase. In a high mix environment, increase in WIP does not necessarily imply additional wait times or delay in delivery because of multiple routes and balance of allocation of jobs by the system. The grey shaded area represents optimal region of operation where the desired objectives can be achieved.
In environments where there is a high mix of products, such as foundries, we can increase the number of lots released without increasing their waiting time by ensuring that they are balanced across different bottleneck equipment such as lithography equipment. Given the complexity of such environments where each process has 400-600 steps using hundreds of equipment requiring anything from 10 minutes to 10 hours with highly sensitive set up times (implanters) or batching requirements (ovens), one has to intelligently look ahead and look behind to ensure proper balance of lots re-entering the process and or entering the process with different priorities.
Unfortunately, sequencing engines with simplistic rules have been given too much attention in order to solve such a complex problem. Through years of R&D, we have concluded that unless a proper release strategy is deployed, sequencing would not be of much value. It is a reactive engine not a preventive one. But more importantly, in the presence of an adequate release strategy, sequencing can be a liability in the sense that it would try to resolve issues locally not being aware of the potential issues it might be causing 50 steps later! Can you imagine being at the gate, and the airline personnel try to sequence your entry into the plane when the plane is not even at the gate or being fixed!
One other myth is the use of simulation tools to plan fabs! Simulation tools look nice and show movement. It is like a video game, we all enjoy watching it. However, they DO NOT PROVIDE a strategy. They only show you where the problem might lie ahead without telling you how to avoid it. How could they? They do not look ahead; by definition simulation is one sequence at a time!
As in our opening example, a good release strategy is aware of the right mix of products in the fab as well as the work load of each equipment, now and future, and is constantly trying to balance what needs to go next such that the bottlenecks, as they are changing, will be fully utilized and at the same time keeping in mind which lots need to be ready and when for on-time delivery. In fact, our research shows that in the presence of a good release strategy, a simple FIFO is the best sequence for the resources. In the context of our airport example, if you left your home at the right time, as you approach your gate, without much waiting, you will show your boarding pass and get into your seat for takeoff. No need to be sequenced!
Topics: Supply Chain, Supply Chain Planning, Supply Chain Performance Management, Manufacturing Software, Manufacturing Planning, Inventory Optimization, Semiconductor, Factory Planning, Fabrication planning
Most planning systems deploy more of a reactive strategy than a predictive one. In the former category, when a problem is identified regarding a capacity shortage, material shortage, or arrival of a high priority order, the system addresses the issue by rearranging the plan. There is nothing wrong with this except that it is not optimal and it is a Band-Aid solution that could have been avoided in the first place! Let me illustrate this using a simple example. If you release too many jobs to your resources (or factory), you build a big queue in front of resources creating much WIP adding to your inventory and delaying delivery dates. In addition, you diminish your capability to address inevitable but unforeseen problems such as shortages, equipment breakdowns, and arrival of surprise orders. But the interesting part is that, most people try to resolve this issue by having better sequencing rules for each resource. As you can see a problem that could have been avoided was created and now we are trying to reactively resolve it by locally expediting, which is almost impossible. This type of strategy is prominent in many S&OP solutions which tend to operate at a high level not knowing how the plan can be executed. They assume a fixed lead-time and assume maybe ONE bottleneck resource for the entire plan for every site, and then expect to have an accurate plan! However, when it comes to executing such plans there will be a lot of expediting and adding shifts and delays to name a few. Such an approach is no different than the use of spreadsheets for planning and use of fixed lead-times, which really implies infinite capacity planning. I remember MRP systems did that really well! Are we back to the technology of 80’s?
We believe that predictive planning avoids the problems in the first place and diminishes the need for reactive solutions. We also believe that any plan generated by the system has to be accurate enough so that it is executable. Spreadsheet type of planning (pre-defined bottlenecks, fixed lead-times and bucketed capacity) deployed by most S&OP solutions are simply NOT accurate enough! They give you a false sense of hope and control as well as poor visibility into what can be accomplished; resulting in erroneous delivery dates. By performing predictive planning, you can account for potential issues of shortages and breakdowns or even quality issues in advance. Furthermore, one can “release” the orders (virtually in subcontractor facilities or actually in your own) in a way that they do not have to wait for a long time in front of resources, reducing WIP and at the same time maximizing utilization. By producing realistic plans based on “shifting bottlenecks,” one can also ensure realistic due dates maximizing on-time delivery. Predictive planning is performed by having an accurate model of your supply chain (yours and your suppliers’) and understanding the mix of products that need to be built and how they compete for resources in a dynamic manner. In addition, making sure that we account for probability of breakdowns, demand variations, maintenance schedules, supplier lead-time variations and so on. By taking into account all such variations, one has a realistic model of the supply chain and can precisely predict the behavior and how each order can be delivered through different choices of the Supply, Make and Distribution. The key is to take all such combinations into account and optimize the use of resources and inventory using an optimal order release strategy that maximizes delivery performance, minimizes inventory and optimizes the utilization of resources. For additional information, please refer to Adexa Whitepapers on this topic by clicking HERE.
Planning is the world’s second oldest profession! Why? Because no matter what you want to do, you need to plan for it. You may argue that there are things that you do not plan for: accidents, Acts of God, inventions. However even these are somewhat planned for in case they happen. Buying insurance is a good example of such planning. Perhaps emotions and feelings are exceptions! But even these were planned by nature for our survival. In the world of manufacturing and supply chain, planning is probably the most important aspect of the business since it has to do with getting the right product in the hands of the consumer on time in the right place. In the absence of proper planning the cost can be very high leading to the demise of the business. Despite, the importance of planning, it is not enough to ensure on-time delivery of goods at the lowest cost. This is due to the stochastic nature of events that can change the demand or supply. Examples are a breakdown of an equipment or plant due to earthquake. Or, sudden increase in demand or shortage of supply from a supplier can lend the original plan somewhat under-optimized. Hence the title of this piece which is a quote from General Eisenhower.
We conclude that S&OP is good for aggregate level decisions for things like expected total production required in a month, but not good enough to figure out how much to produce of each individual product along with the capacity needed of each machine type. You need to have accurate plans that can be executed; and while they are being executed the plan is constantly adjusted to account for the changes that were not foreseen by the plan. To be able to do this, one needs S&OE (Sales & Operation Execution) system that can translate the original plan into more and more detail. A typical S&OP plan can only be as accurate as 40-65%. When combined with S&OE, the accuracy can be as high as 98% or better. We have actually performed simulation studies that confirm the above results.
What S&OE does is simply take the constraints defined by S&OP and apply additional detailed constraints in order to make the plan work and more accurate. In this process, it enables the users to see the details of what will be delivered and where and what potential issues need to be addressed, if any. For example, the S&OP Plan may say the forecast for the month of Product group A is 100, while S&OE says how much for A1 and A2 per day. S&OP says 80 hours of drilling is required, while S&OE says how much of each type of drill is required each week.
Adexa solutions deploy attributes to define the characteristics of machines, processes customers and suppliers, in order to mold the solution to a particular environment. Furthermore, as the supply chain changes and business or business priorities change then attributes are used to adjust to the new conditions. This has been extensively covered in a number of white papers that can be referenced by clicking HERE. In this paper, we discuss the use of attributes for a greener supply chain and how they may be deployed to improve on the carbon footprint and use of hazardous materials.
Consider a process or an equipment that has more CO2 emission than other alternatives. The value, or relative value of the CO2 emission becomes one of the attributes of that process. On the other hand, cost might be another attribute of the process in a way that the lower cost process may contribute more to CO2 emission. Attributes, once defined, form dynamic constraints that are used by the system’s search strategy to find the optimal solution. The optimal solution can vary depending on the region, government regulations, carbon use quotas and even customer. Within the framework of such restrictions as defined for the attributes, the system can recommend the highest use of lower cost process without going over the CO2 emission limits as decided by the company policy. As alternatives, the system may recommend higher cost processes, use of substitute materials or manufacturing in a different region.
Another example, is the use of different transportation means in order to deliver gas or oil to different regions in Europe. The choices are by sea or land as well as pipelines. Each has attributes of cost, carbon footprint (depending on the distance travelled) and of course time to destination as well as associated risks. For example, it is imperative that oil gets from A to B if it is to be used for heating in the middle of North European winter. Land delivery has a much higher risk of road closures in certain regions.
As a final example, some customers or retailers may have a preference to sourcing products that are eco-friendlier. This kind of requirement is sent to the suppliers as an attribute that is then built into the BOM of the product. On the manufacturing side, the use of such embedded materials in the product is treated as an attribute of the material as well as the product. This attribute is then taken into account and honored as a constraint when plans are made by the system for making and delivery into those customers.
As it can be seen, attributes can be used in order to define local and global constraints on the operations of the entire supply chain including tariffs, carbon usage, supplier types, transportation means and material properties depending on the regions, government/international regulations, and company policy. For instance, a company can reduce the global value of an attribute (say, CO2 emission) by 10% per year by region and product. The system would then plan production of the entire supply chain by making sure that the local attributes used in every region does not exceed the maximum defined by the management of the company. We welcome your feedback and sharing innovative uses of attributes in your supply chain operations.
Where is the “E” in S&OP?
According to Gartner, there can be no effective S&OP process without an S&OE—Sales and Operations Execution, process. In other words, why make a plan if cannot be executed accurately or cannot be translated into execution? Here is what Gartner says:
• The No. 1 challenge among manufacturing companies is connecting sales and operations planning (S&OP) to operational plan/execution.
• Value-adding, effective S&OP process cannot exist without S&OE, as it provides the planning interface to execution.
There has been a recent surge of interest in S&OP. The attraction is primarily in integrating sale and operation as well as better visibility into potential issues. However, the major problem with most, if not all, S&OP systems is that they are far from accurate especially when it comes to capacity and mix of products as well as order level pegging. The inaccuracy of plans leads to all kinds chaotic manual adjustments and tweaking just to get it to work. S&OP shows you the direction but cannot take you there! In order to arrive at your destination at the right time and place you need to be able to execute the plan not just get a high level idea of which direction to go. There may be traffic jams, there may be road closings, there may be flat tires; and there may be bad weather and bad road conditions. In all such instances your S&OP plan is inadequate to deal with such inevitable but unpredictable issues and cannot give you any help as to what your best course of action should be. Their best suggestion is “keep simulating and perform What-if” all possible conditions that might occur! This is not a feasible approach!
To be able to perform execution, you need to have:
1- Accurate plans (not just rate-based planning like spreadsheets)
2- Have a unified data model between plan and execution engines
3- Ability to adjust the plan as needed
Furthermore, execution systems, just like vehicles, can handle all the bumps on the road to make the ride a lot easier. Shock absorbers in your vehicles are precisely a method of execution of plans. In their absence, the ride can be extremely uncomfortable. This is often the case when execution systems are missing and people often end up performing all kinds of expediting and manual changes, trying to absorb the “shocks.” Even more importantly, the main issue is that the financial predictions that they had made regarding the plan at the S&OP level is no longer valid and totally false. For example, use of more expensive substitute materials or more expensive freight methods, because of late deliveries, can substantially add to the real cost of products. To this end, the S&OE system needs to have the capability to monitor the financials, as such changes are made, on an on-going basis. Costs based on S&OP alone is wishful thinking and have no resemblance to the actual.
Another important point that was made earlier above was having the ability to translate plans into execution. In other words, a seamless transition between planning into execution. A unified data model in plan and execution systems is a requirement for this to happen. Furthermore, the planning engine must have a realistic model of the actual environment so that the plans are produced in an accurate manner. Current methods of capacity planning which are based on bucketed rate-per-period, is very inaccurate and insensitive to the mix of products. Even if you had two products A and B, if B takes twice as long as A to make, one cannot plan this unless you know how many B’s and how many A’s are needed. An average of X per day or week is very misleading which is exactly what almost all S&OP systems do. In many cases, such as asset-based industries, the actual models of equipment are a must in order to produce accurate plans. Capacity depends on set-up times, batching, availability of tools to be used on the equipment, processing times and so on.
Lastly, almost all S&OP systems cannot trace orders to the components and supplies that are to be used for every order. Thus, the ability to peg orders, to qualify suppliers, and to find root causes of issues and latenesses are totally impossible for these systems! They can only say what is short and which orders will get what is available. In high tech, pharmaceutical and many other industries, pegging to the right supply, to the right supplier and to the right processes are requirements. S&OP systems must be able to demonstrate such capabilities or else the actual execution of the orders would be a nightmare.
S&OP and S&OE are two essential processes that go hand in hand and in the absence of one or the other the whole purpose of having the system is defeated.
For further information on how Adexa’s S&OP and S&OE work to avoid the aforementioned issues, please send an email to firstname.lastname@example.org or visit our web site: www.adexa.com
Given that more than 90% of the enterprises in the world use spreadsheets in one form or another, one may conclude that spreadsheets are the most desirable and successful enterprise software in the world! So, why would you want tospend so much money and effort to invest in planning software? The justifications to use spreadsheets are that they are simple, easy to manipulate and they “do the job!” There are a number of reasons, discussed below, that make spreadsheets inadequate for planning purposes. Mostly the fact that just an ad hoc plan can be far inferior to other more optimized plans; and in the absence of suitable systems and algorithms, one cannot tell one from the other. I am sure you have heard of the expression: good is the enemy of great! In case of spreadsheets, it is merely the perception of good that is preventing companies to do something exceptional and distance themselves from their competition! It is amazing that companies invest hundreds of millions of dollars in people and equipment and then rely on a simple spreadsheet to run their business and make use of the resources that they have so heavily invested in. Every one percent improvement in plan can translate into millions, if not tens of millions, of dollars in inventory savings and higher utilization of resources. In fact, it is more than just savings that need to be considered; it is more relevant to know that there are opportunities for increasing revenue and market share, by deploying adequate planning systems. More recently companies have been investing more heavily in supply chain execution systems such as warehouse management and logistics or even shop floor sequencing. The problem is that executing without a good plan results in a more efficient way of doing the wrong thing! What is the point of building and delivering the wrong goods to the wrong place in an “efficient” manner? Planning prevents making costly mistakes, it makes companies more responsive, it shows where to spend money before it is spent and it creates opportunities to expand market share by having the right product at the right place at the right time. A multinational CPG customer of Adexa with over 100distribution centers reduced inventory by 33%, reduced material cost by 5% and improved delivery performance by deploying planning systems that enable optimization and improve visibility of the entire supply chain. ROI for the project was over 2100% realized within half a month! The point is that before deploying Adexa, they were running a successful and profitable business but could not see the hidden potential of their supply chain and opportunities that could be exploited using a more sophisticated system.
There are many reasons that make spreadsheets less than ideal for planning purposes. Spreadsheets cannot account for mix of products (different mix results in different capacity needs and different lead-times). They assume fixed lead-time whereas in reality lead-times are variable depending on the mix. In addition, they do not take into account availability and synchronization of material and capacity at the same time. Furthermore, there are myriads of other constraints such as tool availability, setup times, batching possibility, process and product attributes etc. that all need to be accounted for that spreadsheets cannot model. Many users are fond of spreadsheets because they can manually manipulate the plan. The question is why is there a need for manual interaction? The answer lies in the fact that the plan that is being created is not accurate enough to execute therefore requires manual adjustments. The planning systems create an accurate and near-optimal plans such that little manual effort is needed. Finally, spreadsheets cannot perform incremental planning, dynamic allocation and ATP/CTP, and the underlying models are static, deviating from reality the more they are used. As an example, one of our clients used to take up to two weeks to figure out delivery dates of orders to respond to its customers. It would take about a week of spreadsheet planning in their HQ in US and another week with their subcontractors in Asia. After they started using Adexa’s planning engine, the commitment dates to their customers have been practically instantaneous and more importantly accurate and reliable.
With the recent innovations in processor speed of computers and advances in programming and Artificial Intelligence, we are now in a position to accurately predict inventory requirements at every level of the supply chain by considering the probability of usage of every part# from raw material to WIP to finished goods. This allows companies to keep the right amount and mix of inventory at different stages of supply chain to maximize responsiveness at lowest cost of inventory. Such disruptive technologies help to save tens of millions of dollars in inventory cost and improving responsiveness dramatically.
When it comes to efficiency, use of spreadsheets to perform planning function is probably as good and efficient as using a bicycle to travel from Los Angeles to New York city! It gets the job done but …
Topics: Supply Chain, Supply Chain Planning, Inventory Planning, Excel, Spreadsheets, WIP, Manufacturing Planning, Inventory Optimization, CPG, Factory Planning, Material Planning, Scheduling, Business Planning, MRP
Bullwhip or Forrester effect is result of uncertainty and changes in demand that magnify as we move upstream in the supply chain. The farther upstream the supplier is, in the supply chain, the more variations in inventory levels. Unfortunately this behavior is taken for granted for most industries. Some advocates of Kanban and JIT believe that using these techniques would eliminate such behavior and makes the supply chain more predictable to the extent that large variations are avoided. This is not a true assumption for the following reason. Kanban and JIT are not planning tools, they are execution methods. Hence they cannot be used to dynamically plan ahead of time when there are inevitable variations in demand. When you design your supply chains with a certain demand in mind, then as the demand goes down, Kanban would react accordingly unless your buffers are too large such that much of the inventory will remain unused between stages resulting in excess inventory. If the buffers are NOT big enough to avoid the excess inventory problem, then it is likely that shortages will occur when there is a surge in demand? The buffers are all used up and the pipeline will sit empty resulting in shortages and loss in revenue etc.
Here are some observations and reasons why we no longer have to run our supply chain under the assumption of bullwhip phenomena. We all know that plans are not perfect however re-planning is the key and doing it fast and in parallel is the reason why we can avoid BW effect. This is explained in more detail below.
From Serial to Parallel
Bullwhip happens because of the serial behavior of the supply chains. In other words each downstream stage tells the stage before it until it gets to the first stage. This delay is one of the reasons for the rise in the amplitude of the inventory. However this behavior can be changed by providing multiple levels of visibility upstream using collaboration tools. Such tools can be set up to send signals to suppliers as far back as needed in order to share with them the trends in demand that are observed in the consumer behavior. Using point of sale information as well as demand signaling and demand planning technologies, the information shared can save suppliers much cost as well as make them a better and more reliable supplier.
Whole vs Segments
Another notion related to parallel analysis of the supply chain has to do with how the buffers are set up at various stages of the supply chain. In contrast to the traditional techniques of each stage deciding on their own inventory levels before, during and after that stage, Multi Echelon Inventory Optimization (MEIO) technology looks at the entire supply chain and each layer thereof in parallel, not in an isolated and serial manner. Using probability and queuing theory it can make fairly accurate predictions as to how much inventory of each item should be at every stage of the supply chain to avoid shortages and/or excesses yielding unprecedented delivery performance while minimizing cost. Such a parallel treatment of the supply chain would eliminate the BW effect and change it to a “stick effect.” MEIO takes into account both the cost and service levels at every stage given the lead-times and interactions between stages to produce a holistic solution not an isolated serial solution.
Responsive vs Predictive
The more responsive we are the less predictive we need to be. Widespread use of cell phones have made all of us a lot more responsive. As a result we do a lot less planning. How many times have you heard someone saying “I will call you when I get there.” In the past you had to specify exact time and location to meet up with someone! Today’s S&OP technology allows real-time planning to be more responsive. In other words within hours a new plan can be generated if and when there is a change in demand or supply. Obviously faster planning does not eliminate the time it take to physically build and transfer goods, however it does significantly shorten the cycle time to delivery. Hence it can reduce the potential amount of inventory quite considerably resulting in a more stable supply chain rather than a BW supply chain. This is more of a responsive planning in contrast to predictive planning.
The value of an item at the most downstream point in the supply chain is several times higher than the cost of an item at the most upstream location! So if you look at the weighted variation of inventory taking into account cost factors, then the variation in value is fairly constant and not as variable as the quantity depicted in the BW. This is a key issue in balancing the supply chain and risk management. In order to ensure the availability of parts, the upstream locations can take higher risks than the downstream locations. However, the way the supply chains are set up today, the reward/risk ratio is a lot higher for the downstream companies than the upstream suppliers. By making this ratio more equitable, much better and more efficient supply chains can result in terms of adaptability and responsiveness. One way to do this is a commitment to buy a minimum amount within a defined window of time. With this level of confidence, suppliers can assess their own risk and not only ensure delivery of what is needed but take additional risk knowing that they have some level of downside protection.
Although BW effect may not be completely eliminated however the size of the waves can be significantly reduced resulting in a much more stable and predictable supply chain.
Topics: Multi Echelon Inventory Optimization, Supply Chain, Supply Chain Planning, Supply Chain Performance Management, MEIO, Inventory Management Software, Inventory Management, Sales & Operations Planning, S&OP
How do the Oakland A’s achieve results like this at a fraction of cost of a team like the Yankee’s?
2002 New York Yankees: Team Salary $126 million; 103 Wins 59 Losses; Division Winner
2002 Oakland Athletics: Team Salary $ 40 million; 103 Wins, 59 Losses; Division Winner
2012 New York Yankees: Team Salary $198 million; 94 Wins 68 Losses; Division Winner
2012 Oakland Athletics: Team Salary $ 55 million; 93 Wins, 69 Losses; Division Winner
You have most likely seen or heard of the story behind the movie “Moneyball”. In 2002 the Oakland Athletics had a very limited budget to “carry” their team roster through the season, and they still had to compete with topnotch teams in their league. Some of the teams they had to compete with, like the Yankees, spent up to four times (4x) as much as they did on their “inventory” of ball players (i.e. “products” in baseball). The A’s turned away from traditional thinking on how to allocate their budget to field a team, which meant relying on the gut feel of managers and buying the highest priced players. Instead, they started to rely on “Sabermetrics”, the use of statistical analysis to determine the most cost-efficient baseball players based on measure of in-game activity/history. Hence, based on mathematical models, the A’s figured out how to best optimize the team at every position on the field. The result was that Oakland won 103 games in 2002, made it to the playoffs, and tied with the Yankees for most wins that season. Again, Yankees spent more than three times (3x) of what Oakland paid for its team, in the same year.
Coming back to the manufacturing world, in the same manner that Sabermetrics can help optimize the baseball players on a team, Multi Echelon Inventory Optimization (MEIO) can optimize your inventory that is deployed throughout your supply chain, in order to achieve target customer service levels, and maximize profit. There are obvious parallels in taking the Moneyball philosophy to the optimization of inventories. Instead of the General Manager in baseball using statistics to determine the best players to have on a baseball team, the Supply Chain Manager can use statistics and mathematical models in a MEIO system in order come up with the highest profitable scenarios. By examining these scenarios, the Supply Chain Manager can decide how to right-size the inventory levels at different locations, and achieve targeted customer service levels, at the highest margins.
Of course, instead of baseball metrics (e.g. RBI’s, on base%, ERA, salary), there are statistical supply chain metrics (e.g. Demand variability, supply variability, BOM, Inventory value, etc.) that can be used to objectively calculate the value of each unit of inventory that you plan to place at a given “position” in your supply chain (e.g. Raw Materials, WIP, Finished Goods, etc.). This would make it possible to optimize inventory deployment for meeting certain customer service objectives, and squeeze the most profit out of your supply chain, while not exceeding the budget allocated for working capital.
The Oakland A’s are back in the playoffs again this year, with a budget that is one-third of the Bronx Bombers. Not surprisingly, the use of statistics (i.e. the right system) is helping them get the most out of their small budget.
Adexa has the equivalent of Moneyball’s Sabermetrics for your Supply Chain, it’s called the Inventory Optimizer to ensure each dollar of inventory is spent in the best possible way.
About the Author: Bill Green is the Vice President of Solutions at Adexa, for more information about him please visit William Green profile link.