"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.
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.
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 email@example.com or visit our web site: www.adexa.com
In a few recent sessions with industry analysts, we were surprised that we were asked if our software is in-memory computing! Given the fact that for over 20 years we designed our applications to have all the data in-memory for computation, our immediate response was: Is there any other way of doing it? The response was, yes, there are others which bring in the data from the database when they need it but now they are changing and they are getting orders of magnitude improvement in speed! This improvement in speed must have caught the attention of the analysts which brings us to the core subject of this article. There is more to speed of application than just having all the data in memory. The latter is the easy part. There are also some vendors, try to improve speed by abstraction and over-simplification. I am sure you are aware of quite few who deploy “Spread-sheet” type of capacity planning in their S&OP applications. That is forming weekly or monthly buckets with fixed lead-times! This approach typically either dumbs down how to deal with capacity, or ignores it altogether. It is the old method, with NO notion of product mix and real processing time, that has been around for decades but with a new user interface which makes it slightly more attractive. Therefore, any gain in speed is offset by a very inaccurate and unrealistic plan. In addition, it has no order level information OR any order level pegging functionality. You might as well use your spreadsheets since they give you even more control!
To gain real improvement in speed with proper representation of capacity of resources and equipment, deep modeling capability is needed and the mix of products must be taken into account. In addition, to IMC, one needs to have data representations and algorithms that provide real-time answers to very complex supply chains at order level. As an example, if one material is not available, does the system go back to search all over again for a new method of making or will it just backtrack one step to find an immediate substitute pegged to that order? If a resource is a bottleneck, will it look for a whole new routing or will it look for an alternative, process or equipment. How this data is represented and how the algorithms divide and conquer in parallel processing is what makes the application fast. Just using IMC is only the beginning, there is a lot more that goes into a comprehensive planning system that can analyze tens of millions of data points from material availability to resources and tools and skill levels, to say a few, in almost real-time.
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
Los Angeles, CA, May 3, 2011—Adexa, Inc., the global provider of Supply Chain Planning and Demand Planning solutions, announced today that Rainbow Farms (Pty) Ltd, South Africa’s largest processor and marketer of chicken, has selected Adexa as its supply chain solution provider. The first module, Adexa’s Collaborative Demand Planner will be implemented at Rainbow and Vector Logistics, one of the country's major 3rd-party logistics service providers for the CPG/food industry, and also for Rainbow.
The management teams of both companies are determined to improve and automate their joint Sales and Operations planning processes. The goals of the project include reduction and optimization of inventories throughout the supply chain network, and creating a fully collaborative platform that drives the supply chain through more accurate and intelligent customer forecasts.
“We want to make better use of our infrastructure, reduce inventory and working capital, and further improve on our high standards of customer service,” said Chris Creed, Managing Director for Vector Logistics. “To that end, we needed collaborative tools to get closer to our customers, and more advanced planning systems for production and profit planning. Tools provided by our own ERP system, and the spreadsheets, did not take us nearly far enough, but Adexa will.”
“Rainbow and Vector are very important and strategic customers to Adexa,” said Cyrus Hadavi, Adexa’s CEO. “As a result of their selection, we have established new sales and support channels, in partnership with Sizwe Africa Business Consulting, to better serve them and our upcoming customers in that region.”
About Rainbow Chicken and Vector Logistics
Rainbow Farms (Pty) Ltd, is South Africa’s largest processor and marketer of chicken. Vector Logistics (Pty) Ltd, a division of Rainbow, is a specialist third-party logistics service provider (3pl) for the food and food-related industries within southern Africa across the retail, wholesale and food service sectors. Visit: www.rainbowchickens.co.za and www.vectorlog.comFor more informaiton contact:
888-300-7692 (Ext. 3)
South African partner:
Director, Sizwe Africa Business Consulting
+27 82 805 3360
Everyone is aware of potential risks in running a supply chain but what is the process by which you evaluate that risk? And, figure out the alternative solutions to lessen or eliminate it?
I bet most companies, if not all, have no real objective risk management process in their supply chains. At best, they have processes in place for different organizational pockets within which risk is mitigated. Some examples are evaluation of suppliers by the Purchasing Department to ensure supply continuity, or redundancies in capacity, and even building too much, or too little, inventory—which proves more costly.
The fundamental question: what is the damage, when something goes wrong? And, what price are you paying to avoid such inevitable risks? Payment of an insurance premium is exactly that. You pay for a service that allows you to recover from a financial disaster or lawsuit. Insurance companies do their risk assessment very well and ask for a premium that makes them profitable even though every now and then they have to cough up the cost. Are you doing the same with your supply chain? What is the premium you are currently paying (within your own supply chain) to avoid a potential disaster? What is the potential cost of that disaster? And, is it worth the premium?
A common example of this is delivery performance vs. inventory (mix) at hand. I am sure you are familiar with the exponential operational curves that imply doubling your inventory for a 5% improvement in your delivery performance, from 92% to 97%. Does this make sense? How much is really enough? And, what is the actual cost of missing delivery rather than cost of holding inventory?
Management teams are encouraged to avoid risk. In other words, their incentive is to deliver rather than miss delivery. So, the employees will go out of their way to ensure delivery at company's cost! Again, does this make sense? Example: “sand-bagging” the forecast and “padding” the supply to avoid shortages are steps taken by two different organizations and processes, Sales vs. Production. Furthermore, supplier selection, cost turbulence, and delivery issues are also padded in an isolated way, adding to the overall inefficiency of risk management. How do you really assess the risk of one supplier over the other, especially as it impacts your bottom line and customer service? Consider the recent unfortunate disasters in Japan, as many of the car makers are struggling for alternate resources to get their parts delivered; earthquakes in Taiwan, SARS epidemic in China, dock worker strikes at Los Angeles ports are all other examples of supply risk.
Some element of risk avoidance is built into the jobs of each person, or department. However, if each person avoided risk in all the steps to deliver the goods, it would create too much cumulative redundancy--which would cause much higher additional cost in the final product. This can be avoided with a more holistic approach.
We believe that objective risk assessment must be part of the supply chain planning process and systems, so that objective decisions are made based on financial consequences rather than protecting select customers, or individual employees. To this end, a holistic approach offers more than just a simple spreadsheet-based S&OP process, but a Financial Sales and Operations Planning solution. The key word is “Financial”, that is integrated into the rest of the enterprise’s operations. Cost, revenue, profit, and risk become part of the supply chain equation, along with customer delivery and supplier management.
When was the last time you took a risk in your supply chain? What was the consequence? How much did the company benefit from it or lost because of it? Would you have liked to have a better optimization and analysis tool to evaluate your options? We invite you to join us in creation of the next generation of supply chain tools, integrating Financials with Sales and Operation Planning (FS&OP).
Dr. K. Cyrus Hadavi is the president and CEO of Adexa, for more information about the author please click here.
Many companies say that they want to Optimize Inventory, but they often have different things in mind when they say it.
Of course, they are all looking to make better use of the inventory on-hand, and they all have the goal of keeping customer service high and inventory low. However, what makes them different is that each company may have a dissimilar root cause as to why they are not doing better with it.
There are four main areas of supply chain planning to focus on when trying to get more from your inventory investment. From top to bottom, and with different time-horizons, each one is critical to get the whole picture right, so it’s important to target them individually:
1) Reduce forecast error with better Demand Planning
2) Establish better inventory target levels with Multi-Echelon Inventory Optimization (MEIO)
3) Further synchronize supply flow with better Sales & Operations Planning
4) Improve daily Inventory Management
Reducing Forecast Error
The two key factors that will impact the amount of inventory that is required in a supply chain are lead-times and demand uncertainty. Although, forecasts will always be wrong, there is a great deal that can be done to increase their accuracy with improvements in process and technology. Remember, you have to do everything possible to be less wrong. Forecasts and “consensus demand” (i.e. aggregation and agreement on one forecast number, by all departments) are also used to determine forecast error. So, if a company does not have a strong process in place to facilitate collaboration, they will not be able to do well in any of the other areas.
Demand Planning is a critical component of inventory management. We have a new ePaper on this topic entitled: Planning Demand for Profit Driven Supply Chains. Feel free to download it by clicking on the title.
Multi Echelon Inventory Optimization
The amount of inventory buffering should increase along with the value of a product, the amount of uncertainty in demand relative to the sales volume, and a company’s response time to deal with supply chain surprises.
Where to place inventory can be very difficult to figure out in an end-to-end supply chain with many products. There are many ways to rebalance how inventory budgets are allocated, inventory pooling and production postponement strategies can be complex and hard to execute, as planned. A Multi-echelon Inventory Optimization (MEIO) system will enable a company to consider all of these in deciding where in the supply chain and how much inventory to have. If your company is using a manual system, and pretty much guessing at how many days of coverage to have for each product, or does not have a good process in place to calculate statistical safety stock values and its “What-if” impact on customer service, then you should be looking into how an MEIO system can help your supply chain.
If you would like more information on Inventory Management and its "Optimization", I recommend Reading this paper: Demystifying MEIO
As part of the S&OP process a company needs to determine how to meet the inventory demand that comes from buffer stocks, forecasted demand, and backlog. Or it may be that capacity or material constraints, or other operating efficiency concerns, drive a company to purchase or build inventory ahead of when it’s actually needed. Regardless, the supply planning process that feeds a consensus S&OP plan is the place that these decisions are made. If a company does not have a good S&OP process in place, then it will not be able to make good decisions around inventory. Furthermore, if the S&OP system in place does not consider the effects of finite capacity, materials, and operating constraints, then control over inventory levels will not be achieved.
For more information about S&OP process, I suggest viewing this recorded webcast: S&OP 101: For all manufacturing executives
Even with a perfect plan, a company cannot keep inventory low and customer service high unless they can execute on moving inventory through the supply chain to meet customer orders. Better Inventory Management will give improved visibility of inventory through the supply chain and create the orders to move the inventory when required. Inventory Management gets the target levels from the MEIO system, and then executes as orders and forecasts are received. If a company does not have good visibility into inventory, forecasts, and orders then an improved Inventory Management system will surely help.
A last thought, there are many areas of supply chain planning that can have an impact on reducing inventory and improving customer service. Typically a company will focus on Demand Planning first, and then Inventory Management, while putting in place simple ways to set inventory target levels. They would then focus on better inventory targets with MEIO systems and better supply side S&OP planning. Each company is different, and it is important to address each area based on your needs.
About the Author: Bill Green is the Vice President of Solutions at Adexa, for more information about him please visit his profile link.