Supply Chain Optimization Journal

Cost-to-Serve and Allocation

Despite our egalitarian mindset in the U.S., when it comes to customers, let’s face it: They have never been ‘created equal.’ Certainly for decades, manufacturers and distributors have offered better pricing to some customers than others. We’re all familiar with quantity break pricing, column pricing with different discount levels for different categories of customers, and contract pricing. And who doesn’t visit the local supermarket today and notice the ‘buy 3 get 1 free’ offers to encourage us to increase our purchases?

Volume is valuable and warrants better pricing, we are in the habit of believing. And most often this is true. Not only does a high-volume customer drive our buying power with suppliers by helping us reach the next price break level on the purchasing side, but it can make each sale more profitable: The cost of servicing 10 orders that result in a sale of 100 units can be 10 times as great as the cost of servicing a single order for those 100 units.

This bias towards volume underlies traditional customer ranking methods. But many manufacturers today are taking a closer look at these policies and finding them lacking. Instead, they are engaging in a detailed cost analysis effort called ‘cost-to-serve.’ While cost-to-serve can be a very broad subject covering product costs, location costs, transportation costs and service costs, to name a few, this article will take a look primarily at customer costs.

It’s not that heretofore companies have ignored factors that shade the degree of profitability of a large client. Many firms, presented with the opportunity of doing business with, say, Wal-Mart or the federal government, may question whether it’s really worth doing. They’re thinking about the overhead of handling such a client and the cost of meeting client demands – with slim price margins.

What’s different today is that companies are trying to measure these costs precisely and to make informed, scientific decisions based upon them. Whether they engage consulting firms who have developed methods for tackling this measurement, purchase software to help them out, or devise their own internal approach, more and more manufacturers and wholesalers are gathering detailed costs and trying to apply them to decisions about their customers.

Consumer goods companies, for instance, are recording metrics such as the true cost of customer service. How much support time does this customer require of the customer service organization? How much sales time to we devote to him? Does the customer frequently return merchandise, and if so, what is the cost of processing that return? In the case of consumer goods manufacturers, we might also look at custom-branded merchandise: What is the true cost of providing private labeling for a retailer? Are we really capturing in the product cost all of the special handling required by the purchasing and distribution organizations? All of these costs are very important is assessing a customer’s true profitability.

On the other side of the equation, there may be some sales and marketing benefits that a customer brings, and these, too, should be weighed. Does the name ‘Wal-Mart’ on our client list provide positive benefit to the organization? Is another client who doesn’t seem to purchase very much an outstanding reference for us who sends other potential customers to us? If a business can establish a process and gain agreement across the organization on measuring true costs and benefits, it can define policies to more precisely control bottom-line revenue.
Certainly, one of the first decisions that can be made, once true costs are measured and accepted by an organization, is to eliminate customers who are really unprofitable. But cost-to-serve can also come into play in other ways. We may want to devise strategic programs that nurture our best clients to safeguard their business. We may hold special events for them or assign dedicated reps, for instance.

One of the situations where cost-to-serve becomes a critical tool is in inventory allocation, particularly in an inventory shortage situation. When there is insufficient inventory to meet demand, most manufacturers will want to serve the most valuable customers first.

This frequently comes into play in segments of the technology industry, such as computer peripherals, typically with the launch of a popular new consumer product. An extreme example of this might be the launch of a new Wii game player at the start of the holiday season. Armed with true cost-to-serve data, manufacturers could make allocation decisions scientifically to spread the available inventory across the order pool while maximizing profit.

You might ask whether this process can be automated today. The answer is ‘partially.’ Allocation can certainly be automated, but collecting cost-to-serve data on customers usually involves some manual steps, because most companies don’t have all the systems in place to collect this data automatically (and even with sophisticated systems, the data may not be collected in exactly the way you wish.) Some spreadsheet work may be required. Once the spreadsheet is in place, however, the process becomes straightforward.

Perhaps you want to rank customers sequentially from top to bottom, or group them into ‘profit’ segments. Once that is done, an algorithm can be designed to optimize the allocation of inventory according to the rules tied to those rankings or segments. The allocation algorithm might be designed to work directly from the spreadsheet, as well, automating even more of the process. In any case, executing the service decisions in accord with true costs ensures we are protecting our most valuable customers.

The application of cost-to-serve to inventory allocation takes on an even more interesting aspect for consumer goods manufacturers who ship to retailers. As those of us familiar with this industry are aware, most large retailers have very specific guidelines defining how suppliers must do business with them. The retailers specify how an order must arrive – shipped complete, packed by store, etc.; when it must arrive – ‘arrive by’ date; and a variety of paperwork details including design, content and placement of shipping labels and bills of lading. Associated with each of these requirements is a dollar penalty the supplier will incur, taken as a deduction from the supplier’s invoice, for violation of the guideline.

For a consumer goods manufacturer, these penalties or ‘chargebacks,’ can mean the difference between a profitable client and an unprofitable one. In this situation, the ability to allocate inventory defensively, to minimize chargebacks (or at least make an informed scientific decision to incur them) is critical. A powerful allocation engine, in an inventory shortage situation, can maximize profit by factoring potential chargeback costs for late or partial shipment into the equation. In this case, the allocation engine ensures that the cost to serve the retailer is as low as possible.

In addition to retailer penalties, another aspect of ‘allocation-according-to-true-cost’ involves inventory fulfillment location choices. If a company operates a single distribution center in Los Angeles and imports all its product from Asia, there may be only a single fulfillment option. But for the wide majority of consumer goods manufacturers who import from Asia, service clients nationwide, and operate either multiple distribution centers or a distribution center located in, for instance, the Midwest, there are several options and a variety of questions arise.

If inventory is constrained at the facility that would normally handle a particular customer’s order, should the order be fulfilled from an alternate facility? To make this decision, we need to factor in not only the additional shipping cost but also to weigh that cost against the value of the customer. There may be low profit customers, viewed from the perspective of cost-to-serve, for whom we do not want to make this investment. In the case of a retailer where a potential penalty is involved, the decision might be made dynamically based on a comparison of the chargeback incurred against the additional cost of shipping. If the chargeback fee would be higher than the additional shipping cost, it may be worthwhile to use the alternate distribution center.

This type of on-the-fly fulfillment decision is often called ‘dynamic allocation.’ Another example of dynamic allocation involves intercepting shipments in transit to, say, our hypothetical Midwest distribution center. Least cost fulfillment might dictate fulfilling west coast orders by pulling off inventory required to fulfill them at a deconsolidation facility near the port – before a shipment heads out to the distribution center in the Midwest. Under what conditions is this the least-cost choice? An inventory allocation algorithm based on cost-to-serve can make this decision mathematically, using rules the manufacturer defines.

It’s important to emphasize that the decisions on exactly how to apply cost-to-serve data to inventory allocation will depend on the philosophy of the individual company. For this reason, such allocation solutions are often unique and are adjuncts to the standard capabilities of order management systems. Leading-edge firms who are structuring allocation based on true costs typically do so via point solutions that supplement their central transactional systems.

Profit Point, as the name suggest, provides these point solutions and integrates them into SAP, Oracle, and other order management systems to help clients make the best, most profitable allocation and customer decisions. Our expertise in this area can help clients drive maximal profit to the bottom line.

This article was written by Cindy Engers, a Senior Account Manager at Profit Point.

To learn more about our supply chain data integration and business optimization services, contact us here or call (866) 347-1130.

Labels: Cindy Engers, Distribution, Enterprise Resource Planning, Inventory Management, Optimization, SAP Data Integration, Supply Chain Intelligence, Supply Chain Software, Supply Chain Technology

Friday, August 07, 2009

Defining Supply Chain Performance: Nailing the Jell-O to the Wall

When I worked as a Branch Chief in Air Mobility Command’s Analysis Group my boss often talked about nailing the Jell-O to the wall. I took that to mean we had a project with some ill-defined performance measures and objectives. Our first task was going to be establishing the goals. He was a crusty old colonel who was usually dead right on these matters.

The phrase stuck with me, but its meaning has probably evolved a bit. Working with our clients we frequently create optimization models where the objective is a mixture of terms – some that are concrete and some that are ill-defined. The user has certain costs that are very real but also recognizes that a good solution has other desirable qualities. Sometimes these additional criteria are easily expressed as constraints. But just as often they have to be weighed against the concrete and known cost terms in the objective function.

As an example, we have a client that must pay to move heavy equipment around the country from one customer site to another. The model output is a schedule of the required movements for each piece of machinery. Any given solution will have values for a few performance measures. The cost per mile of moving the equipment is easily measured and is known. Although this cost might vary during the year and is subject to some uncertainty, it is still well-defined. We can get solutions for different values of this cost parameter when we do sensitivity analysis. This is the most directly and easily quantified performance measure for a solution.

On the other hand, there are some other factors that enter into the objectives and are harder to value. For instance, risk is a factor we add to the model to give some latitude in meeting deadlines for the arrival/departure of the equipment to/from locations where it will service a user contract. Clearly, we want a solution with little risk. Upgrades are another such factor. At times, equipment of higher quality than contracted for must be used to meet contract dates. Again, this is something to be avoided since there is wear and tear on the expensive machinery. Finally, there can be occasions when an appropriate configuration of equipment is just not available. Meeting the contract requires leasing equipment at substantial cost.

None of the additional factors is well-expressed in a constraint since there are no absolute limits. The model we use influences these performance measures via the cost terms in the objective function we are trying to minimize. The trick is to assign the right costs to get solutions that the user can recognize as good. In cases like these, Experimental Design applied to the optimization model is an efficient way to derive a useful set of parameters.

Experimental Design (DOE)
Design of Experiments (DOE) has long been used in industrial settings to quantify the way parameters affect product design. Taguchi methods and Robust Engineering are the most popular names for this approach. Inferior methods based on intuition or one-factor-at-a-time experimentation have been thoroughly discredited and largely driven out of practice by DOE approaches.

Applying DOE to optimization and simulation modeling is not a recent development, either. In the machinery example, the solution (a schedule with routes) is essentially a product we are designing with the help of the model. In the typical application DOE is used to summarize the behavior of the model with respect to resource allocation. Essentially, we see how much benefit is derived from the addition of resources. In this case we use DOE to see how the shape of the solution changes as costs are varied. The costs represent the relative importance of the various aspects of the solution. Some are concrete others really are not.

Returning to the machinery movement example you can probably picture what happens with some extreme values of the costs. If substitution and upgrades are free, then you get a solution with a small number of miles. But you find customers who paid for low-tech equipment getting a lot of free upgrades. This is not desirable for other reasons besides wear-and-tear. Those customers that paid for the best can feel abused and it hardly motivates the lucky customer to pay full price for the best equipment next time around.

Also, an overly conservative approach to meeting contract deadlines means that the company must buy more equipment than it really needs. So a very high penalty for taking risk, e.g. days allowed to reposition equipment, can be very expensive. The start and end dates for the contracts shift during the course of the year. A little risk is not entirely a bad thing -- especially since the allowed risk is easily controlled in the model.

Certainly what we really want to avoid is a model that recommends inferior solutions. In a two-dimensional example that would be a solution that had the same risk value but more miles associated with movement than are needed.

Numerical Examples
Realize that as time moves on the equipment example is a model that must be rerun repeatedly. New commitments are made and existing ones may have been modified. Those solutions that have attractive summary performance measures are the ones that should be examined in detail. Ones with inferior solutions can be safely ignored. In this application the cost settings seem pretty stable – produce good solutions. But there is nothing to prevent rerunning the DOE periodically to examine solutions based on their summary performance measures.

The table below (click to enlarge) shows a set of results from a point early in the year for a subset of the equipment to be scheduled for the coming year. All of the values have been changed, but patterns have been preserved for illustration purposes. In this example we chose just three of the possible six (shown in yellow) parameters that could be varied. We were able to run all eight possible combinations of the values shown. We could investigate a larger set of parameters, perhaps by using fractional experiments that look for important effects without doing all the possible runs.

Without actually estimating the effects of the parameters you can see at a glance most of the effects. High upgrade costs lead to fewer upgrades, high risk costs lead to smaller number of risk days. High costs per route lead to a set of routes with larger total miles. One could argue that run one or run three yields a schedule which is a good tradeoff among the performance measures we considered here.

So a user would be encouraged to take a look at the details of the solutions (scheduling) from Run 1 or Run 3. Finally, notice that there is an interaction between RouteParm and RiskParm on the RiskDays performance measure. That is, changing the RiskParm value has a different effect on Risk Days depending on the setting of RouteParm – much more influence when RouteParm is large than when RouteParm is small. Another benefit of DOE is the ability to spot those kinds of interactions. One-at-a-time experimentation is completely incapable of finding this sort of information.

In the heavy equipment case we had some ideas on the neighborhood to be investigated for the parameter settings. That is not always the case. Sometimes the parameters are very difficult to calibrate by reasoning from or comparison with costs that are known. For instance, we have an example from agriculture. Here the model suggests the order in which a group of farms will be harvested. Processing plants want the products in specific weight ranges and require amounts of each product for each day over the planning horizon.

Besides getting within the desired weight range there are some other goals. For instance, it would be good to hit the weight targets – not simply be within the range. We also want to reduce the number of trucks that have to be dispatched and the number of miles driven. We also want to visit individual farms a limited number of times. Furthermore, if product is not harvested while in an allowed range, it is ‘wasted’ and this is one of the most costly penalties – almost an absolute requirement. Realize that as time goes on the product is always growing and so there is a limited time window on the weight range.

Not all of a given the product on a farm is of the same weight. It varies from section to another based on the time it started growing. So in a simplest of experiments you can test the tradeoffs between distance traveled and missing the weight targets. If you are willing to visit several times, you can come closer to hitting the target weight. But this will involve more visits and usually more total mileage.

Predictions
If one goes the extra step of fitting a statistical model to the DOE results, then predictions can be made for the performance measures. When multiple performance measures are involved, one can locate regions where all performance measures are in acceptable ranges. Various statistical packages, e.g. JMP or Minitab, provide the ability to create two-dimensional contour plots that show regions where both dimensions are within ranges defined by the user.

The three figures above are examples of contour plots. Given that the experiment only examined two levels of each factor, none of the plots can really show curvature. But you can see an interaction in the Miles graph.

Conclusion
Not every model requires an experimental design. But in cases where there are multiple performance measures that need to be combined using (possibly) arbitrary weights, DOE is an excellent approach. This is true for simulation and optimization models alike. Furthermore, DOE can be used as a predictive tool. If the design is carefully chosen, it can guide you to a useful operating region and reveal interactions between the various factors.

So how do you nail Jell-O to the wall? You throw a Design of Experiments net over it.

This article was written by Joe Litko, Profit Point's Business Optimization Practice Leader.

To learn more about our business optimization services, contact us here or call (866) 347-1130.

Labels: Design of Experiments, Joe Litko, Optimization, Supply Chain Intelligence

Thursday, July 09, 2009

Improving Your Odds of Winning a Stimulus Grant via Innovation, Sustainability and Partnerships

The U.S. Department of Transportation (DOT) has issued final guidance for the $1.5 billion in surface transportation grants it will award by next February, and among the top selection criteria will be environmental sustainability, innovation, and partnerships. DOT has included sustainability - improved energy efficiency, lower greenhouse gases, and/or less dependence on foreign oil - as one of five criteria it will consider in evaluating a proposed project's long-term beneficial outcomes for a metropolitan area, a region, or the country. Long-term outcomes, along with a project's impact on job creation and near-term economic stimulus, will be DOT's primary criteria for awarding grants.

DOT will also be considering two secondary criteria - innovation and partnerships. DOT is soliciting projects that use innovative technologies to achieve long-term outcomes or significantly enhance the operational performance of transportation systems, and projects that involve partnerships with non-Federal entities and the use of non-Federal funds. Priority will be given to projects for which a grant will help complete an overall financing package.

Recent estimates from DOT suggest that up to $50 Billion in grant requests may be submitted, making this a highly competitive process. It will be essential for an applicant to thoroughly meet the primary guidelines and to score well on secondary guidelines to win tiebreakers. If your project doesn't yet adequately address the three considerations of innovation, sustainability and partnership, Profit Point may be able to improve your chances of success:

Sustainability
Profit Point provides mathematics-based solutions that optimize the use of resources for maximal efficiency. Frequently this optimization results in reduced transportation mileage which minimizes greenhouse gas emissions as well as fuel consumption. It might also involve minimizing water use, minimizing output of toxic pollutants or maximizing production of beneficial byproducts.

Some examples include:

Scheduling ship berths at ports to minimize ship idle time in a harbor
Scheduling port (or canal) maritime traffic
Optimizing a port drayage schedule to minimize delays and overland carrier idle times
Optimizing local school bus or public transit system routes to minimize greenhouse gas emissions while providing optimal service
Routing your deliveries or pickups using the fewest miles traveled
Providing the algorithm to trigger variable speed limits on traffic leading to a congested area such as a city center or bridge
Optimizing deliveries for the elderly, such as "Meal on Wheels," to minimize vehicle costs and emissions
Conducting infrastructure studies to evaluate the full impact of a project, such as a port expansion with intermodal considerations

If you need to address the sustainability criterion in DOT's guidance or if you can benefit from including an optimization study as part of your application, we may be able to help you. Profit Point was recently awarded the Supply & Demand Chain Executive Green Supply Chain Award for its Green Network product. Profit Green Network can be used along with our Profit Vehicle Planner and Router Applications to create better plans and improve sustainability.

Innovation
While innovation is not a primary criterion for selection, it will be used to rank similar projects in order to break a tie. Adding leading edge technology such as mathematics-based optimization to your grant application provides one way of strengthening its innovative appeal.

Optimization is one of the hottest topics in industry today because it not only ensures operations are maximizing their current objectives, but also allows 'what if' modeling for future scenarios. "What if modeling" helps ensure continued achievement of your objectives, no matter what set of circumstances may occur.

Partnerships
After DOT considers primary criteria, priority will be given to innovative projects and those that involve State and local governments or private or nonprofit entities.

While there are certainly many partners available, adding a private, small business partner such as Profit Point, Inc. to the application may strengthen its overall appeal. Building on Profit Point's extensive sustainable logistics and mathematical optimization experience can help make your project application unique.

Presenting the Proposal
Should you need assistance preparing your application or prefer advice from a transportation expert, you may wish to work with an experienced consultant on surface transportation issues. Phillips Strategic Services, a Northern Virginia firm with strong ties to both industry and government, is one firm available to assist you. Phillips Strategic Services experience includes:

Policy leadership at the Federal Highway Administration;
Policy development and lobbying for American Trucking Associations;
Senior staff to a Senate Committee handling surface transportation issues; and
Various government affairs, marketing, and strategic planning positions with Union Pacific Railroad, Conrail and CSX

In conclusion, nearly any type of surface transportation project is eligible for funding under the discretionary program, which was authorized by the American Recovery and Reinvestment Act (ARRA) of 2009. DOT has named the program "Grants for Transportation Investment Generating Economic Recovery" or TIGER Discretionary Grants, and applications are due by September 15, 2009 with all grants to be awarded by February 17, 2010.

If you'd like to improve your chances of success by strengthening the sustainability and innovative appeal, or if you need a partner to help you present your application most effectively, please contact us:

Profit Point, Inc.
No. Brookfield, MA
Cindy Engers: (925) 736-6800, cengers@profitpt.com
Richard Guy: (435) 487-9141, rguy@profitpt.com

Phillips Strategic Services Ltd.
Alexandria, VA
Mary Phillips: (703) 360-3560, mphillips@phillipsstrategicservices.com

Labels: Government, Green Optimization, Optimization, Sustainability, Transportation

Friday, June 05, 2009

Understanding Your Risks with Monte Carlos

What is a Monte Carlo model and what good is it? We’re not talking a type of car produced by General Motors under the Chevy nameplate. “Monte Carlo” is the name of a type of mathematical computer model. A Monte Carlo is merely a tool for figuring out how risky some particular situation is. It is a method to answer a question like: “what are the odds that such-and-such event will happen”. Now a good statistician can calculate an answer to this kind of question when the circumstances are simple or if the system that you’re dealing with doesn’t have a lot of forces that work together to give the final result. But when you’re faced with a complicated situation that has several processes that interact with each other, and where luck or chance determines the outcome of each, then calculating the odds for how the whole system behaves can be a very difficult task.

Let’s just get some jargon out of the way. To be a little more technical, any process which has a range of possible outcomes and where luck is what ultimately determines the actual result is called “stochastic”, “random” or “probabilistic”. Flipping a coin or rolling dice are simple examples. And a “stochastic system” would be two or more of these probabilistic events that interact.

Imagine that the system you’re interested in is a chemical or pharmaceutical plant where to produce one batch of material requires a mixing and a drying step. Suppose there are 3 mixers and 5 dryers that function completely independent of one another; the department uses a ‘pool concept’ where any batch can use any available mixer and any available dryer. However, since there is not enough room in the area, if a batch completes mixing but there is no dryer available, then the material must sit in the mixer and wait. Thus the mixer can’t be used for any other production. Finally, there are 20 different materials that are produced in this department, and each of them can have a different average mixing and drying time.

Now assume that the graph of the process times for each of the 8 machines looks somewhat like what’s called a ‘bell-shaped curve’. This graph, with it’s highest point (at the average) right in the middle and the left and right sides are mirror images of each other, is known as a Normal Distribution. But because of the nature of the technology and the machines having different ages, the “bells” aren’t really centered; their average values are pulled to the left or right so the bell is actually a little skewed to one side or the other. (Therefore, these process times are really not Normally distributed.)

If you’re trying to analyze this department, the fact that the equipment is treated as a pooled resource means it’s not a straightforward calculation to determine the average length of time required to mix and dry one batch of a certain product. And complicating the effort would be the fact that the answer depends on how many other batches are then in the department and what products they are. If you’re trying to modify the configuration of the department, maybe make changes to the scheduling policies or procedures, or add/change the material handling equipment that moves supplies to and from this department, a Monte Carlo model would be the best approach to performing the analysis.

In a Monte Carlo simulation of this manufacturing operation, the model would have a clock and a ‘to-do’ list of the next events that would occur as batches are processed through the unit. The first events to go onto this list would be requests to start a batch, i.e. the paperwork that directs or initiates production. The order and timing for the appearance of these batches at the department’s front-door could either be random or might be a pre-defined production schedule that is an input to the model.

The model “knows” the rules of how material is processed from a command to produce through the various steps in manufacturing and it keeps track of the status (empty and available, busy mixing/drying, possibly blocked from emptying a finished batch, etc.) of all the equipment. And the program also follows the progress and location of each batch. The model has a simulated clock, which keeps moving ahead and as it does, batches move through the equipment according to the policies and logic that it’s been given. Each batch moves from the initial request stage to being mixed, dried and then out the back-door. At any given point in simulated time, if there is no equipment available for the next step, then the batch waits (and if it has just completed mixing it might prevent another batch from being started).

What sets a Monte Carlo model apart however is that when the program needs to make a decision or perform an action where the outcome is a matter of chance, it has the ability to essentially roll a pair of dice (or flip a coin, or “choose straws”) in order to determine the specific outcome. In fact, since rolling dice means that each number has an equal chance of “coming up”, a Monte Carlo model actually contains equations known as “probability distributions”, which will pick a result where certain outcomes have more or less likelihood of occurrence. It’s through the use of these distributions, that we can accurately reflect those skewed non-Normal process times of the equipment in the manufacturing department.

The really cool thing about these distributions is that if the Monte Carlo uses the same distribution repeatedly, it might get a different result each time simply due to the random nature of the process. Suppose that the graph below represents the range of values for the process time of material XYZ (one of the 20 products) in one of the mixers. Notice how the middle of the ‘bell’ is off-center to the right (it’s skewed to the right).

So if the model makes several repeated calls to the probability distribution equation for this graph, sometimes the result will be the 2.0-2.5 hrs, other times 3.5-4.0 hrs, and on some occasions >4hrs. But in the long run, over many repetitions of this distribution, the proportion of times for each of the time bands will be the values that are in the graph (5%, 10%, 15%, 20%, etc.) and were used to define the equation.

So to come back to the manufacturing simulation, as the model moves batches through production, when it needs to determine how much time will be required for a particular mixer or dryer, it runs the appropriate probability equation and gets back a certain process time. In the computer’s memory, the batch will continue to occupy the machine (and the machine’s status will be busy) until the simulation clock gets to the correct time when the process duration has completed. Then the model will check the next step required for the batch and it will move it to the proper equipment (if there is one available) or out of the department all together.

In this way then, the model would continue to process batches until it either ran out of batches in the production schedule that was an input, or until the simulation clock reached some pre-set stopping point. During the course of one run, the computer would have been monitoring the process and recording in memory whatever statistics were relevant to the goal of the analysis. For example, the model might have kept track of the amount of time that certain equipment was blocked from emptying XYZ to the next step. Or if the aim of the project was to calculate the average length of time to produce a batch, the model would have been following the overall duration of each batch from start to finish in the simulated department.

The results from just one run of the Monte Carlo model however are not sufficient to be used as a basis for any decisions. The reason for this is the fact that this is a stochastic system where chance determines the outcome. We can’t really rely on just one set of results, because just through the “luck of the draw” the process times that were picked by those probability distribution equations might have been generally on the high or low side. So the model is run repeatedly some pre-set number of repetitions, say 100 or 500, and results of each of these is saved.

Once all of the Monte Carlo simulations have been accumulated, it’s possible to make certain conclusions. For example, it might turn out that the overall process time through the department was 10 hrs or more on 8% of the times. Or the average length of blocked time, when batches are prevented from moving to the next stage because there was no available equipment, was 12 hrs; or that the amount of blocked time was 15hrs or more on 15% of the simulations.

With information like this, a decision maker would be able to weigh the advantages of adding/changing specific items of equipment as well as modifications to the department’s policies, procedures, or even computer systems. In a larger more complicated system, a Monte Carlo model such as the one outlined here, could help to decrease the overall plant throughput time significantly. At some pharmaceutical plants for instance, where raw materials can be extremely high valued, decreasing the overall throughput time by 30% to 40% would represent a large and very real savings in the value of the work in process inventory.

Hopefully, this discussion has helped to clarify just what a Monte Carlo model is, and how it is built. This kind of model accounts for the fundamental variability that is present is almost all decision making. It does not eliminate risk or prevent a worst-case scenario from actually occurring. Nor does it guarantee a best-case outcome either. But it does give the business manager added insight into what can go wrong or right and the best ways to handle the inherent variability of a process.

This article was written by John Hughes, Profit Point's Production Scheduling Practice Leader.

To learn more about our supply chain optimization services, contact us here.

Labels: Enterprise Resource Planning, Infrastructure Planning, John Hughes, Operations Research, Optimization, Risk Management, SC Operations Planning, Scheduling

Thursday, March 19, 2009

Why Profit Point can provide good value with fixed price projects

Uncertainty can be a tremendous challenge when it comes to managing a project. How much will a project cost? How long will it take to complete it? As a client, wouldn't you like to know exactly what a project will cost and when it will be completed to be able to determine whether a project will achieve what you intend with a satisfactory return before you commit to it?

For most of our projects, we work with our clients to develop a project scope and commit to delivering that scope at a fixed price with a fixed timeline. Our customers appreciate this approach because it gives them a good understanding of what the project will entail and they know the cost up front. Our approach takes time and effort before the project starts but in the end our clients and Profit Point benefit.

How does this happen?

We rigorously develop a scope to the point that we are comfortable in understanding what the client wants and the client is comfortable with what we will deliver. The scope is detailed enough such that we are both confident of a successful project but not so detailed that we can't change course somewhat as the project enfolds. Our customers appreciate our collaborative approach and being flexible when assumptions that we were made up front don't pan out exactly the way we both expected.

Then we very carefully manage the scope of the project to ensure that the deliverables are met. For minor changes we typically just take care of them without any additional cost to the client. For more major scope changes we will develop a separate scope for them with a separate fixed price.

Our average 20 years experience allows us to operate this way. Our supply chain consultants have been completing successful projects for many years. We know not only the technical details of our consulting practice but how to deliver those technical details that ultimately delivers what the client wants in a way where both the client and Profit Point benefit. How can we do this for you today?

This article was written by Mark Rockey, Profit Point's Production Scheduling Practice Leader.

To learn more about our supply chain optimization services, contact us.

Labels: About Profit Point, Optimization

Friday, March 06, 2009

Rohm and Haas Picks Profit Point to Improve Production Scheduling

Profit Point's data integration and scheduling optimization services deliver reliable results with reduced operations costs.

North Brookfield, MA

Profit Point today announced that its Profit Data Interface^TM software has been selected by Rohm and Haas Company (NYSE: ROH) to integrate its scheduling processes with the company's ERP data warehouse. The company, which last reported nearly $9 billion in annual sales, produces innovative products for nine industries worldwide through a network of more than 100 manufacturing, technical research and customer service sites. Optimizing and supporting the production and distribution scheduling across this network is a complex and ever-changing process.

"Rohm and Haas has a history of improving our operations to enhance customer service levels and reduce cost," said Dave Shaw, the company's Business Process Manager for MFG and Supply Chain. "Production scheduling, which entails constant change to meet demand, is one of the toughest challenges in the supply chain. In the past, the lack of a reliable data interface has limited our ability to react quickly and with a high degree of confidence in our results. Profit Point's Data Interface software has given us near real-time access to highly reliable data, so we can respond quickly and know that our plan is right."

Profit Data Interface is a robust application that helps decision makers boost the effectiveness of their ERP data by extending its usefulness with optimization applications. By leveraging existing ERP systems, the software provides a robust and proven method that supply chain managers can rely upon to optimize their critical business processes and improve profitability.

"Rohm and Haas is a recognized leader in the chemicals industry with a reputation for supply chain excellence," said Jim Piermarini, Profit Point's CEO. "We have supported their scheduling processes for years. So, it was clear that the next evolution was to directly connect their optimization software to the date store using our Data Interface product."

Profit Data Interface, which integrates with SAP® and Oracle® data stores, can be used to optimize the entire supply chain including network planning, production and inventory planning, distribution scheduling, sales planning and vehicle routing.

To learn more about Profit Point's supply chain software and services, visit www.profitpt.com.

About Profit Point:
Profit Point Inc. was founded in 1995 and is now a global leader in supply chain optimization. The company's team of supply chain consultants includes industry leaders in the fields infrastructure planning, green operations, supply chain planning, distribution, scheduling, transportation, warehouse improvement and business optimization. Profit Point's has combined software and service solutions that have been successfully applied across a breadth of industries and by a diverse set of companies, including General Electric, Dole Foods, Logitech and Toyota.

About Rohm and Haas Company:
Leading the way since 1909, Rohm and Haas is a global pioneer in the creation and development of innovative technologies and solutions for the specialty materials industry. The company’s technologies are found in a wide range of industries including: Building and Construction, Electronics and Electronic Devices, Household Goods and Personal Care, Packaging and Paper, Transportation, Pharmaceutical and Medical, Water, Food and Food Related, and Industrial Process. Innovative Rohm and Haas technologies and solutions help to improve life every day, around the world. Visit www.rohmhaas.com for more information.

Contact:
Richard Guy
Profit Point
(866) 347-1130
http://www.profitpt.com

Labels: Distribution, Enterprise Resource Planning, Optimization, Press Releases, Rohm and Haas, SAP Data Integration, Scheduling

Thursday, February 12, 2009

Doing It Better

People ask... What do you do? My response: "We help companies do things better." Specifically we help supply chain and manufacturing executives "do things better". It might be better planning, better scheduling, better processes, or several other areas of better. We receive calls and emails on a daily basis from executives, managers, analysts, and other consultants that want to share their business pain with us. Many want to improve their supply chain operations by reducing costs. Many executives have a fairly good idea of what keeps them up at night, and they want to share their challenges with us to see if we can offer any help. Almost all have an idea on how to make things better, but they are unclear on how to start the process or how to make the magic happen.

Sometimes we cannot help. Many times we can.

We listen. We ask questions. We ask for clarification. Then, we restate the "thing that they want to do better" in our vocabulary to get validation that we understand. They might be searching for software. They might be searching for services. Many times it's a combination of both, but in all cases, people are searching for solutions.

Deliverables can take the form of improved technology with the tools that a company already owns, or with new tools. A good consultant does not promise the big "solve everything button." A good consultant will understand the goal and match results with expectations.

Many times this takes the form of delivering services and software that is customized to individual clients needs. It could take the form of optimizing your supply chain. It's not reinventing the wheel. It's inflating the tire to the correct air pressure depending on the road conditions. When the economy get's rocky, you might need better tires. So, when your problems become challenging, maybe it's time to get a better approach.

So what do supply chain consultant's offer? They help supply chain and manufacturing executives do things better.

If you would like to learn more about our Optimization services, please contact us. And if you would like to receive future updates on the supply chain optimization industry, subscribe to our SCO Journal and our SCO Newsletter.

Contributed by Richard Guy, Profit Point's Director of Sales

Labels: About Profit Point, Optimization

Wednesday, January 28, 2009

Where is Optimization Technology Headed in 2009?

By Dr. Alan Kosansky, President of Profit Point

There is certainly a lot of change in the business optimization world today. From the significant changes at optimization software companies, to the growing areas of business optimization implementation, the world of optimization is a fast changing and exciting place.

In 2008, two of the leading software providers of mathematical optimization engines - both Profit Point partners - were acquired by much larger companies: ILOG, and their CPLEX optimization software, was acquired by IBM. Dash Optimization, and their XPRESS optimization software, was acquired by Fair Isaac. In addition, 2008 saw the launch of a new significant player into the market: Gurobi Optimization, with a management team that was involved with the initial launch of CPLEX.

Each of these players has the potential to lead the market over the long term, yet each presents their own uncertainty looking forward. How optimization technology fits into the long term strategies of acquiring companies IBM and Fair Isaac is the topic of much speculation. How quickly Gurobi is able to release competitive products and how well they are able to compete for customers is the topic of much anticipation. We will be keeping a close eye on these situations as they unfold, and will be back to share with you our insights in the future.

From the business and application perspective, business optimization and the software applications that act as enablers for better business decision making continues to explode in the marketplace. Leveraging the fact that more and more data is available to businesses and the key decision makers in their organization, those companies that are incorporating advanced decision technologies are realizing significant competitive advantages.

We have had recent success with a number of these industry leaders. They include a retailer using optimization to determine how best to manage their backlog of orders and fulfill customer needs, a production equipment leasing company managing the a delivery of capital intensive assets to maximize utilization and throughput, and a transportation delivery company minimizing customer wait times and maximizing the satisfaction of their customers' experience.

Each of these success stories were driven by data, elevated by optimization, and guided by thoughtful and forward thinking management. Are you the next success waiting to happen?

If you would like to learn more about our Optimization services, please contact us. And if you would like to receive future updates on the supply chain optimization industry, subscribe to our SCO Journal and our SCO Newsletter.

Labels: Dash, Gurobi, ILOG, Optimization, Supply Chain Intelligence, Supply Chain Software, Supply Chain Technology

Thursday, November 13, 2008

System Dynamics Modeling to Improve Your Supply Chain

Profit Point has recently added another tool for analyzing and managing businesses and supply chains - system dynamics modeling. System dynamics focuses on feedback effects in complex systems. That is what distinguishes it from other simulation and modeling techniques. Feedback means that although X influences Y, it is also true that Y influences X even if this influence is mediated by a string of causal relations.

Here is a typical System Dynamics drawing from Wikipedia. In this diagram there is a flow of individuals from a category called potential adopters into a category called adopters. The rate of adoption is controlled by the 'valve' called 'new adopters'. Many simulation modeling approaches would treat this as a 'one-way street' and attempt to model the system by controlling the valve with some external data in the hope of reproducing behaviors observed in the real system.

The point of System Dynamics is to explicitly include the feedback which causes the observed behavior. The model above could be a portion of a model of product adoption for the pharmaceutical industry. Another classic example is a predator - prey population model. When prey are plentiful, the predator population increases, which reduces the prey, which in turn reduces the predators. The extension of this classic model to a system of a business and its resources is fairly natural.

The System Dynamics approach has also been widely applied in typical supply chain scenarios. The effects of feedback can be studied in the context of inventory management and shipping policies. The figure below is of a classic System Dynamics approach to modeling the supply chain from raw materials production through shipment of the finished goods. Feedback is represented by the directed arrows from upstream processes back to the downstream processes. In many cases these represent control mechanisms meant to keep the system in balance or within limits. Successful feedback usually dampens oscillatory behavior - stock-outs, long supply, or obsolete inventory items, for instance.

System dynamics models are classified as continuous simulation models and are typically built in languages specifically designed for these types of models. They are quite different from the usual simulation models that are based on discrete entities and systems that jump from event to event in time. System dynamics models are based on equations that describe (usually) continuous flows of material, people, money, influence, etc. The models are highly useful as policy analysis tools - revealing the often unintended consequences of business rules. The models can examine periods of years or decades, while including external effects, e.g. the economy, and all the significant interactions within a business and with the outside world.

We look forward to bringing this exciting, powerful, and well-established analysis tool to bear on problems and opportunities of our clients.

This article was written by Joe Litko, Profit Point's Business Optimization Practice Leader.

To learn more about our Business Optimization services, contact us.

Labels: Operations Research, Optimization, Supply Chain Intelligence, System Dynamics Modeling

Tuesday, October 21, 2008

What Decision Makers Should Know About Infrastructure Planning

"The structure of your supply chain network
determines 75-80% of your total supply chain costs.
Therefore, it is the biggest opportunity
to reduce those costs."

The opportunity to improve your infrastructure and design your supply chain network only comes along once in a while. But, our supply chain consultants are optimizing supply chain networks every day. So, we've seen the pitfalls and the opportunities that face decision makers when they make critical infrastructure investments.

What else should you know before your begin designing your network? We've compiled a list of the top 10 things that supply chain and manufacturing executives should consider before undertaking any significant infrastructure investments. Complete the the short form below to receive the complete list of things you should know about supply chain planning.

Download a copy of 10 Things to Know About Infrastructure Planning.

Labels: Infrastructure Planning, Optimization, Supply Chain Intelligence, White Papers

Tuesday, October 07, 2008

Profit Point Improves Toyota's North American Part Center California's Supply Chain Processes

Leveraging Profit Point's supply chain optimization methodologies, Toyota North American Part Center California improves efficiency and quality of their workload planning sequencing process to receive containers from Japan.

North Brookfield, MA (PRWEB) October 6, 2008

Profit Point today announced that Toyota Motor Sales (TMS), U.S.A., Inc.'s North American Part Center California (NAPCC) has improved its receiving sequencing processes using advanced mathematical optimization techniques. NAPCC is one of the parts distribution centers among TMS' North American Parts Operations network, which was established to improve local parts sourcing and manage a parts distribution network that supplies all North American Toyota distributors, U.S. Toyota, Lexus and Scion dealers as well as export to parts centers in Japan. NAPCC turned to Profit Point to apply mathematical optimization techniques to further improve their supply chain operations.

"We turned to Profit Point to apply mathematical optimization techniques to further improve our supply chain operations," Johnnie Garlington, NAPCCs warehouse operations manager. The program supported the increase in daily offload by 16% resulting in labor savings, off-site storage costs and detention expenses.

Profit Point, the leading supply chain optimization company, combines proprietary software with proven optimization techniques to help business managers improve their operations. Profit Point supported NAPCC's objective to redesign their workload planning process to improve the efficiency and quality of their sequencing processes. Profit Point carried this out by designing and building custom supply chain software to optimize their sequencing processes.

"We were asked to investigate a mathematical approach to solving Toyota NAPCC's container receiving sequencing process," said Joe Litko, Profit Point's Business Optimization Practice Leader. "This was an interesting challenge for several reasons. We needed a cost-effective solution using legacy tools, the model needed to run quickly, be flexible, and give robust solutions that consider several performance measures simultaneously."

NAPCC had been using a traditional spreadsheet to manually achieve an hourly workload plan. Profit Point reviewed the sequencing process and designed a stand-alone application to smooth out the flow of containers to maximize the daily unload capacity.

"Like most businesses, Toyota NAPCC was using good, traditional operations practices," said Dr. Alan Kosansky, Profit Point's President. "But, by combining the right mathematical optimization methods with a clear understanding of the business requirements, we were able to achieve a superior supply chain process for Toyota."

To learn more about Profit Point's supply chain optimization software and services, visit www.profitpt.com.

About Profit Point:

Profit Point Inc. was founded in 1995 and is now a global leader in supply chain optimization. The company's team of supply chain consultants includes industry leaders in the fields infrastructure planning, green operations, supply chain planning, distribution, scheduling, transportation, warehouse improvement and business optimization. Profit Point's has combined software and service solutions that have been successfully applied across a breadth of industries and by a diverse set of companies, including The Coca-Cola Company, General Electric, Rohm and Haas and Toyota.

Contact:
Richard Guy
Profit Point
(866) 347-1130
http://www.profitpt.com

Labels: Case Study, Optimization, Press Releases, Scheduling, Software Optimization

Monday, September 29, 2008

Can you be green and profitable?

This month's cover story in the CSCMP's Supply Chain Quarterly magazine feature's an excellent article written by Profit Point's Green Optimization Practice Leader, Ted Schaefer, and the firm's President, Dr. Alan Kosansky.

The article, Can you be green and profitable?, deals with two competing, yet critical issues that face supply chain managers across the globe. As the authors point out, "profitability and sustainability don't have to be mutually exclusive. By considering environmental issues when setting financial objectives for a supply chain network analysis, companies can successfully balance the trade-offs between them."

You can read the complete article here.

If you would like to learn more about our Green Supply Chain Optimization services please contact us.

Labels: Green Network, Green Optimization, Infrastructure Planning, Optimization, Publications, SC Operations Planning, Sustainability

Tuesday, January 22, 2008

Congratulations to Profit Point partner Dash Optimization!

Earlier today, Fair Isaac Corporation (NYSE:FIC), the leading provider of analytics and decision management technology, announced that it has acquired U.K.-based Dash Optimization, makers of Xpress-MP, the world's leading software product for decision modeling and optimization. The move augments Fair Isaac's decision management solutions, which automate, improve and connect decisions to enhance business performance.

Read the complete press release here.

The acquisition is considered a strategic move by Fair Isaac that will greatly benefit both Dash and Fair Isaac customers. According to Dash, all of the Dash employees are very excited about this new direction and will continue working as Fair Isaac employees. Fair Isaac views Dash as a key business which it is looking to grow and leverage while making a significant investment in the product suite.

We look forward working with the new combined company and expect that it will provide new resources and opportunities for all Profit Point clients.

Labels: Optimization, Press Releases

Friday, December 21, 2007

Smart Software Applications

Look for our new web-based release of Profit Network in spring 2008. Profit Network is a stand-alone optimization planning software package that is used to design better supply chain networks. Profit Network can be used to analyze the placement and location of production facilities, distribution centers and warehouses over a multi-period planning horizon. Profit Network helps firms restructure their supply chains after mergers, periods of rapid growth or in anticipation of geographic or product preference shifts in the market. Savings of 10% of supply chain costs and 25% of supply chain cycle time are typical.

Profit Vehicle Router (PVR) helps distributors save money by cutting the time needed to develop sales/distribution territories and schedules, as well as reducing delivery miles and the number of sales/delivery vehicles and drivers needed. PVR helps you plan optimal delivery or sales territories, cycle-day territories (what days each site will receive deliveries), and daily routes from a distribution center or office, improving customer service, employee productivity and ultimately increasing profits.

Labels: Optimization, Profit Network, Profit Vehicle Router, Software Optimization

Wednesday, December 19, 2007

Re-writing the Rules with Optimization

Machinery Link is "Re-writing the Rules" about owning farm equipment. Today, machinery operating and ownership expenses account for nearly 40 percent of a producer's total annual production costs. And the cost of owning agricultural equipment increases each year.

The MachineryLink Innovation Ag Equipment Program provides producers with popular name-brand combines. MachineryLink's fleet of machines allows them to provide their customers with the size, model, and equipment features that best fit their farming operation.

MachineryLink manages transportation, scheduled delivery, maintenance, and parts, supported by professional operations, transportation, logistics, and field service teams. Combines move efficiently and dependably across the country between producers as harvest seasons progress.

Profit Point supports this process with a unique optimization approach for the logistics and delivery of the combines. The solution integrates world-class optimization, with state-of-the-art visualization and mapping software, allowing MachineryLink to rebalance their combine to customer assignments during the season. The system accommodates new customers and weather related delays, both issues that had upset their schedules in the past. Jim Bramlett, VP of Operations says: "This system is great; it allows us to increase our asset utilization, while maintaining excellent customer service." Machinery Link is helping farmers lower their costs, and Profit Point is pleased to help them do it.

Labels: Asset Utilization, Optimization, Routing, Transportation

Tuesday, November 15, 2005

Improved Process and Optimization Tool

Business Problem: Dole Food Company, Inc. is a producer and marketer of high-quality fresh fruit, fresh vegetables and fresh-cut flowers, and markets several lines of packaged foods. Dole globally purchases containerboard from several paper companies to manufacture containers to transport and inventory their products.

Dole uses an MS Excel spreadsheet to optimize the variables and constraints to develop an annual strategic purchasing plan and on-going monthly tactical purchase plans for the year to minimize the total costs of buying paper products.

Dole had a desire to improve the optimization tool by using Profit Point's supply chain consultants to:

Validate the current optimization methodology and algorithms
Investigate if there might be a better approach or tool to solve this problem

One of Dole's purchase challenges was developing dynamic monthly plans that were consistent with the annual plan as they move through the year. The terms and conditions offered by the containerboard manufactures include variable costs and constraints that were non-linear. The monthly plan needed to consider these conditions to produce a purchasing plan that provided Dole an optimal cost minimization solution as they reach year-end.

Profit Point's Solution: Validation: Profit Point reviewed Dole's data inputs, assumptions, optimization process and validated that the current spreadsheet model was operating correctly and that the spreadsheet was providing Dole an optimal answer. A few modifications were made to the model which allowed Dole's purchase managers to quickly update and run the tool, and review the output reports. This provided them the ability to confidently make a purchase decision by using the model output or a variant of the output or change the input and re-run the Optimization Tool.

Improved Process and Tool: Profit Point provided Dole with an improved Containerboard Optimization Tool using Frontline Systems' solvers that gave management the ability to:

Dynamically solve the optimization problem on a monthly basis and consider all the contractual terms associated with optimizing annual tonnage purchase levels.
Easily develop strategic plans that include multiple prices and programs offered by suppliers.
Improve the current optimization performance.

A better model and process created value to Dole by:

Reducing management's time to analyze multiple scenarios each month
Improving management's confidence in making purchase recommendations
Reducing total paper purchase costs through use of better management practices and model resources

And creating the opportunity for Dole to:

Decrease paper purchase costs
Increase management productivity
Provide Dole's container production plants with improved purchase plans

To learn more about how Profit Point can help improve your Excel Solver optimization , contact us here:

(866) 347-1130 or
(435) 487-9141

Send us an Email

Labels: Case Study, Excel Solver, Optimization, SC Operations Planning, Solver Optimization, Supply Chain Intelligence

Wednesday, October 20, 2004

Solver Consulting Partner

Frontline Systems develops mathematical solvers and optimizers and has customers in over 50 countries. Frontline's flagship product, the Premium Solver Platform, greatly extends the power of the Solver in Microsoft Excel, which Frontline developed for Microsoft. This powerful product offers built-in linear, quadratic, conic, nonlinear, interval global and evolutionary Solvers.

Profit Point is Frontline Systems' consulting partner. Profit Point can quickly assist Microsoft Excel users with their modeling needs and projects, reducing model development and programming effort and time. Profit Point's consultants can assist with development of models "from scratch", or help companies that are starting a project that requires more in-depth technical consulting support.

Frontline's Solver technology spans the full range of linear and nonlinear, convex and non-convex, discrete and continuous optimization. The Premium Solver Platform offers the world's best platform for convex optimization - the natural extension of linear programming - and the world's best platform for global optimization.

For more capacity and speed, users can choose from eight plug-in large scale Solver Engines that offer a range from traditional linear, quadratic and mixed-integer programming to powerful convex and conic optimization, nonlinear optimization, global optimization, and optimization of arbitrary Excel models using genetic algorithms, tabu search and scatter search.

To learn more about how Profit Point can help you get the most out of Excel Solver optimization, contact us here:

(866) 347-1130 or
(435) 487-9141

Send us an Email

Labels: Excel Solver, Optimization, Press Releases, Solver Optimization

Carlisle SynTec's Infrastructure Planning Study

The construction materials division of Carlisle Companies wanted to evaluate their longstanding practice of co-locating their major warehouses next to their manufacturing operations. Carlisle was looking to reduce their transportation and warehousing costs and questioned if there was a particular set of new warehouse locations that would result in a more desirable distribution network resulting in lower costs and better customer service.

Profit Point developed an optimization model to trade off transportation and warehousing costs while meeting product demand. The model included the top 100 distribution locations in the US along with Carlisle's existing warehouse sites as potential locations for the model to consider.

Profit Point was able to identify achievable annual savings of $1 million by showing them how to use their existing network more efficiently and by adding one new warehouse location next to the new manufacturing plant being built. The model showed that Carlisle's existing warehouses were located in desirable areas regarding operating cost and proximity to vendor, manufacturing and customer locations, but also identified business changes to the way Carlisle manages their inventory at several warehouses, allowing them to realize larger savings. The study also confirmed which new manufacturing plant location out of several candidates was the most cost efficient in regards to transportation costs.

"Profit Point did not come to Carlisle with a pre-determined answer to our logistics issues. They did an excellent job of listening to our needs, working with our personnel to extract the necessary information, and formulating recommendations to reduce our costs." said Bob Stout, Vice President in charge of Purchasing and Logistics at Carlisle SynTec Inc.

To learn more about how Profit Point can help you get the most out of your Supply Chain Infrastructure Planning, call us at (866) 347-1130 or send us an email.

Labels: Distribution, Infrastructure Planning, Inventory Management, Optimization, Press Releases, Transportation, Warehouse Optimization

Saturday, April 03, 2004

Profit Point selected as ILOG Service Alliance Partner

ILOG has selected Profit Point Inc. as a preferred ILOG SP Service Provider due to our strong technology expertise and proven ability to integrate software components into custom solutions.
ILOG products are used by tens of thousands of end users in telecommunications, manufacturing, transportation, defense, and other industries.

Labels: ILOG, Optimization, Press Releases, Software Optimization

Thursday, April 01, 2004

Infrastructure Planning with Profit Network

Our Profit Network optimization tool is a stand-alone software package that is used to solve supply chain network design problems. Profit Network can be used to analyze alternative placements of production facilities, distribution centers and warehouses over a multi-period planning horizon. Profit Network helps firms restructure their supply chains after mergers, periods of rapid growth and in anticipation of geographic or product preference shifts in the market. Savings of 10% of supply chain costs and 25% of supply chain cycle time are typical when implementing results from models such as Profit Network.

Profit Network allows the user to model their existing or proposed supply chain for a geographic

area, with its locations, flow limits and costs. Input data include raw material sources and costs, plant locations, plant production rates and costs, warehouse and distribution center locations and costs and customer locations and anticipated demand. You will be able to solve detailed supply chain network design problems in a few moments with optimal results.

Profit Point has both delivered this product to clients and used it on

Supply Chain consulting engagements. This proprietary tool is now available for delivery and use at your company.

To learn more about Profit Network, go to: http://www.profitpt.com/profit_network.asp

To learn more about how Profit Point can help you get the most out of your Supply Chain Infrastructure Planning, call us at (866) 347-1130 or send us an email.

Labels: Asset Utilization, Distribution, Infrastructure Planning, Optimization, Press Releases, Profit Network, Transportation, Warehouse Optimization

Wednesday, August 19, 2009