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BUSML 4382: Logistics Analytics Keely Croxton

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BUSML 4382: Logistics Analytics BUSML 4382

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BUSML 4382: Logistics Analytics

Table of Contents “Chapter 1: The Value of Supply Chain Network Design” by Watson, Michael; Lewis, Sara; Cacioppi, Peter; Jayaraman, Jay

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“Chapter 12: The Art of Modeling” by Watson, Michael; Lewis, Sara; Cacioppi, Peter; Jayaraman, Jay

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“Chapter 13: Data Aggregation in Network Design” by Watson, Michael; Lewis, Sara; Cacioppi, Peter; Jayaraman, Jay

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Bibliography 67

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1 THE VALUE OF SUPPLY CHAIN

NETWORK DESIGN

What Is Supply Chain Network Design and Why Is It Important? A firm’s supply chain allows it to move product from the source to the final point of consumption. Leading firms around the world, from large retailers to high-tech elec- tronics manufacturers, have learned to use their supply chain as a strategic weapon. A supply chain is defined by the suppliers, plants, warehouses, and flows of products from each product’s origin to the final customer. The number and locations of these facilities is a critical factor in the success of any supply chain. In fact, some experts suggest that 800/0 of the costs of the supply chain are locked in with the location of the facilities and the determination of optimal flows of product between them. (This is similar to the notion from manufacturing that you lock in 800/0 of the cost to make a product with its design.) The most successful companies recognize this and place significant emphasis on strategic planning by determining the best facility locations and product flows. The discipline used to determine the optimal location and size of facilities and the flow through the facilities is called supply chain network design.

This book covers the discipline of supply chain network design. Sometimes it is referred to as network modeling because you need to build a mathematical model of the supply chain. This model is then solved using optimization techniques and then analyzed to pick the best solution. Specifically, we will focus on modeling the supply chain to deter- mine the optimal location of facilities (warehouses, plants, lines within the plants, and suppliers) and the best flow of products through this facility network structure.

Here are four examples to illustrate the value of supply chain network design~

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Example #1

Often, we hear about firms acquiring or merging with another firm in the same indus- try to reduce the overall costs to operate both firms. That is, they justify the new com- bined company by determining that they can deliver the same or more products to the market at an overall lower cost. In firms that make or ship a lot of products, a large por- tion of the savings comes from the merger of the two supply chains. In such mergers, the savings often come from closing redundant plant and warehouse locations, opening new plants and warehouses, or deciding to use existing facilities to make or distribute different mixes of product. We have heard firms claim resultant supply chain savings from $40 million to $350 million over a period of a couple of years. With these kinds of savings, you can only imagine the pressure placed on the supply chain team to deter- mine the new optimal supply chain structure after an acquisition or merger is announced.

Example #2

Often, a large firm will find that its supply chain no longer serves its business needs. In situations like this, the firm will have to transform its supply chain. It may have to close many facilities, open many new ones, and use facilities in a completely different way. For example, a retail firm may have to redesign their supply chain to serve their stores as well as their new online customer base in a more integrated approach. Or a large retail- er may find that some of their product lines have grown significantly and the retailer needs new warehouses to manage this growth. If done right, this type of supply chain transformation can help reduce logistics and inventory costs, better respond to different competitive landscapes, and increase sales and profitability. We have even seen firms highlight this work in their annual reports, therefore showing the importance of this analysis to the firm as a whole.

Example #3

In the spring of 2011, we were working on a project for a global chemical company to help develop their long-term plan for their supply chain. This study was analyzing where they should locate new plants to serve a global customer base. The long-term project suddenly became extremely short-term when the CEO called the project team to inform them that within six hours they were closing their plant in Egypt due to politi- cal unrest. He also indicated there was no timeline for reopening the plant. The CEO immediately needed to know which of the existing plants should produce the products that were currently being manufactured in Egypt and how customer demand was going to be impacted. The team was quickly able to deliver the answers and minimize this sup- ply chain disruption. As seen in this example, supply chain network design models can also be a great tool for identifying risks and creating contingency plans in both the short and long term .

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Example #4

As consumer behavior and buying patterns change, firms often want to bring their product to the market through different channels. For example, we worked with a con- sumer products company that wanted to analyze different channels such as selling through big-box retailers, selling through smaller retailers, selling direct online, and sell- ing through distributors. This firm wanted to analyze different ways to bring their prod- uct to market and understand what the supply chain would need to look like for each of these cases. That is, they wanted to determine the optimal number and location of plants and warehouses. This would then be a key piece of information to help them determine their overall strategy.

Of course, the details of these studies can be a bit more complicated. As an example, take a minute to think through the possible supply chain for a tablet computer and compare that to the supply chain you envision for a candy bar.

The tablet supply chain faces specific challenges surrounding a time-sensitive delivery of the device for a very demanding high-tech customer market. The tablet maker must also determine how to best balance its partnerships with many contract manufacturers worldwide while still ensuring the highest quality end products. Finally, this supply chain must deal with the high costs for insurance, transport, and storage of these high- priced finished goods.

Conversely, when we shift our thoughts to the supply chain related to the candy bar, we must consider an entirely different set of challenges and objectives that the candy maker must face: government regulations that mandate different requirements for all stages of production paired with a strict shelf life of each unit produced. In addition, raw mate- rial costs, as well as costs tied to temperature control during transit and storage, add up. Major swings in demand due to seasonality or promotions also add the need for flexi- bility within their supply chain.

Despite their differences, both the tablet maker and candy bar maker must determine the best number and location of their suppliers, plants, and warehouses and how to best flow product through the facilities. And building a model using optimization is still the best way for both of them to determine their network design.

As the previous examples highlight, many different types of firms could benefit from network design and many factors go into the good design of the supply chain. Along with ever-growing complexity, the need to truly understand how all these requirements affect a company’s costs and performance is now a requirement. Using all these variables to prove out the optimal design configuration commonly saves companies millions of dollars each year.

As you would expect, a network design project can answer many types of questions such as these:

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• How many warehouses should we have, where should they be, how large should they be, what products will they distribute and how will we serve our different types of customers?

• How many plants or manufacturing sites should we have, where should they be, how large should they be, how many production lines should we have and what products should they make, and which warehouses should they service?

• Which products should we make internally and which should we source from outside firms?

• If we source from outside firms, which suppliers should we use?

• What is the trade-off between the number of facilities and overall costs?

• What is the trade-off between the number of facilities and the service level? How much does it cost to improve the service level?

• What is the impact of changes in demand, labor cost, and commodity pricing on the network?

• When should we make product to best manage and plan for seasonality in the business?

• How do we ensure the proper capacity and flexibility within the network? To meet demand growth, do we need to expand our existing plants or build new plants? When do we need to add this capacity?

• How can we reduce the overall supply chain costs?

Being able to answer these questions in the optimal manner is important to the overall efficiency and effectiveness of any firm. Companies that have not evaluated their supply chain in several years or those that have a new supply chain through acquisitions can expect to reduce long-term transportation, warehousing, and other supply chain costs from 5% to 150/0. Many of these firms also see an improvement in their service level and ability to meet the strategic direction of their company.

Although firms are happy to find 50/0 to 150/0 reduction in cost, it does highlight that your supply chain might have already missed out on significant savings you may have realized had you done the study a year ago (or two or more years ago). Some firms have realized this and now run this type of analysis on a more frequent basis (say, quarterly). This allows them to readjust their supply chain over time and keep their supply chain continually running in an optimal state while preventing costs from drifting upward.

The frequency of these studies depends on several factors. Historically, it has been cus- tomary to complete these analyses once every several years per business unit, because it was usual for business demographics and characteristics to change over this period of time. For some industries such as high-tech, the frequency was even higher because there may be higher volatility in customer demand, thereby requiring periodic

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reevaluation of the network. Any major events, such as mergers, acquisitions, or divesti- tures, should also trigger a network reevaluation study. As noted before, the savings from the optimization of the revised network typically represent a significant part of the savings that justify the merger or divestiture. A current trend we are seeing, however, is to do these studies even more frequently. Business demographics and characteristics are changing faster. In addition, the growth of the global supply chain is driving firms to cycle through studies as they go from region to region around the world. Also, firms are running the same models more frequently to stay on top of changes in their business by adjusting the supply chain. Some firms update these models several times throughout the year.

Determining the right supply chain design involves a lot of quantitative data as well as some nonquantitative considerations. We will discuss this in the rest of this chapter, as well as how we use mathematical optimization to sort through this quantitative data.

Quantitative Data: Why Does Geography Matter? It should be clear by now that the supply chain network design problem is just as much about geography as it is about business strategy. The two cannot be separated.

Take these supply chain considerations for example:

• If you have a plant in the interior of China and some of your customers are in New York, you need to physically get the product out of China, across the ocean, and to New York.

• If you make wood products (like paper or boards), you can locate plants either close to the raw materials (forest areas) or close to your customers (usually located a significant distance away from the large forest areas).

• If you have a warehouse in Indianapolis, you are close to your customers in Chicago, but far away from customers in Miami. If the warehouse is in Atlanta, you are closer to Miami, but farther from Chicago.

• If you make a critical product only in Miami, a hurricane may shut down your operation, causing a loss of revenue.

As the examples highlight, decisions about the location of your facilities impact many aspects of your business and require you to make trade-offs. Specifically, geography drives the following:

• Transportation Cost-You need to move product from its original source to its final destination. The location of your facilities determines the distance you need to move product, which directly impacts the amount you spend on trans- portation. But, also, the location of your facilities determines your access to

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transportation infrastructure such as highways, airports, railheads, and ports. Finally, because of supply and demand, different locations may have different transportation rates.

• Service Level-Where you locate relative to your customers impacts the time it takes to get product to your customers. For some products, you can negate great distances by using overnight air freight. But this usually comes at a premium cost.

• Risk-The number and location of your facilities impacts risk. If you have just one location for a critical activity, there is always the risk that a fire, flood, some other natural disaster, a strike, or legal issues will shut down your operation. There is also political risk to consider. Your facility could get confiscated or shut down for political reasons, or the borders may shut down, isolating your facility.

• Local Labor, Skills, Materials, and Utilities-The location of your facilities also determines what you pay for labor, your ability to find the needed skills, the cost of locally procured materials (which is often directly related to the local labor costs), and the cost of your utilities.

• Taxes-Your facilities may be directly taxed depending on where they are locat- ed and the type of operations being performed. In addition, you also need to consider the tax implication of shipping product to and from your locations. In some industries, taxes are more expensive than transportation costs.

• Carbon Emissions-Locating facilities to minimize the distance traveled or the transportation costs often has the side benefit of reducing carbon emissions. In addition, if your facilities consume a lot of electricity, you can reduce your emissions by locating near low-emission power plants.

As the list highlights, geography matters. What makes this challenging is that the geog- raphy often pushes the solution in different directions at the same time. For example, it would be desirable to have a facility close to all the demand. However, demand is typi- cally where people live. And it is usually very expensive, if not impossible, to locate a plant or warehouse in the middle of a major metropolitan area. So the desire to be close to customers pushes locations close to cities. The desire for cheap land and labor (and welcoming neighbors) pushes the best locations further from the city center. In global supply chains these decisions become even more extreme. In some cases it may make sense to service demand from a location on an entirely different continent.

In addition to geography, the next two sections will discuss the importance of ware- houses and multiple plants to your supply chain as well.

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Quantitative Data: Why Have Warehouses? In this book, a warehouse represents a facility where firms store product or a location where product simply passes through from one vehicle to another. It can be called a dis- tribution center, a mixing center, a cross dock, a plant-attached warehouse, a forward warehouse, a hub or central warehouse, a spoke or regional warehouse, or a host of other terms.

To understand how to optimally locate warehouses, it is important to discuss why ware- houses exist. Wouldn’t it be much cheaper for companies to load the product only once, at the manufacturing location, and ship it directly to the customer? Stopping at a ware- house adds loading, unloading, and storage costs, not to mention the cost for two legs to transportation (one leg from the plant to the warehouse and one leg from the ware- house to the customer). In cases where you can ship directly from the plant, it is usual- ly good to do so. Therefore, it is important to ask questions to see whether you can avoid warehouses altogether. But in most cases warehouses are needed in a supply chain for the following reasons:

• Consolidation of Products-Often, you will need to deliver a mix of different products to your customers and these products may come from various sources. A warehouse serves the useful function of bringing these products together so that you can then make a single shipment to a customer. This will be cheaper than having the products ship to the customers directly from each individual source of supply.

• Buffer Lead Time-In many cases, you will need to ship to your customers with lead time that is shorter than that which can be offered by shipping direct- ly from the plant or supplier location. For example, you may promise to ship products to your customers the next day but your plants or suppliers may have a lead time of several weeks before they are able to make the product available to the customer. In this case, the warehouse holds product at a location closer to the customers in order to provide the next day transport promised each time an order is placed.

• Service Levels-Where you store the product and its proximity to the market where it will be consumed is also a measure of the service level the company can provide. The need to be close to customers can create the need for multiple warehouses. Overall cost versus service level is one of the most classic trade-offs in supply chain network design.

• Production Lot Sizes-Setting up and starting the production of a single prod- uct or group of similar products on a line can have a significant fixed cost asso- ciated with it. Therefore, production plans attempt to maximize the number of units of product made during each run. (This production amount is called a lot

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size.) Understandably, these lot sizes normally do not match the exact demand from the market at the time. This requires the extra units to be “stored” in warehouses until future demand requires them. Production lot sizes versus inventory storage costs is also a common supply chain design trade-off.

• Inventory Pre-Build-Some industries see huge spikes in the supply of raw materials (seasonal food harvests) or in the demand of finished goods (holiday retail shopping). In the case of raw material supply spikes, some firms must store these abundant raw materials until the time they will be needed for steady monthly production cycles. Other firms must immediately use these raw mate- rials to produce finished goods that are not yet demanded. These additional fin- ished goods must then be stored until demand in future time periods requires them. In the case of demand spikes, companies find themselves with insufficient production capacity to fulfill all orders during peak periods of demand. As a result, they must use their additional capacity during off-peak time periods to make finished-good units to be stored awaiting their use to fulfill the upcoming spikes in demand. The use of costly overtime production versus inventory stor- age costs is another common supply chain design trade-off.

• Transportation Mode Trade-offs-Having warehouses often allows you to take advantage of economies of scale in transportation. A warehouse can help reduce costs by allowing the shipment of products a long distance with an efficient (and lower cost) mode of transportation and then facilitating the changeover to a less efficient (and usually more expensive) mode of transportation for a shorter trip to the final destination (as opposed to shipping the entire distance on the less efficient mode).

It is also important to match up the preceding list of reasons for warehouses with the types of warehouses in the supply chain. A supply chain may have many types of ware- houses to meet many different needs. Here are some common types of warehouses:

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• Distribution Center-Typically refers to a warehouse where product is stored and from which customer orders are fulfilled. This is the most common and traditional definition of a warehouse. When a customer places an order, the dis- tribution center will pick the items from their inventory and ship them to the customers. These types of facilities are also called mixing centers because they «mix’) products from many locations so that your customers can place and receive an order from a single location. If a manufacturing company does not have this type of warehouse in the supply chain, customers may have to place several orders or receive several shipments from different locations depending on where each product they want is made.

• Cross-Dock-Usually refers to a warehouse that is simply a meeting place for products to move from inbound trucks to outbound trucks. The term simply

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means that products pass (or cross) from one loading dock (for inbound trucks) to another loading dock (for the outbound trucks). For example, in the case of a produce retailer with 50 stores, they may have a full truck of fresh peaches arriving at the inbound docks from a single supplier. The peaches are then removed from the truck and some are placed in each of the 50 waiting trucks on the outbound side, according to the relevant store demand. This hap- pens for peaches as well as a host of other produce items. Basically full trucks arrive from a single supplier on the inbound side of the facility, and then trans- ferred to multiple trucks on the outbound side of the facility resulting in fully loaded truckloads with a mix of product from each of the suppliers quickly sent on their way. The best-run cross-dock systems have all the inbound trucks arriving at approximately the same time so that product stays at the cross-dock for only a short period of time.

• Plant-Attached Warehouse-Refers to a warehouse that is attached to a manu- facturing plant. Almost all plants have some sort of product storage as part of their operations. For some, it may simply be a small space at the end of the line where product is staged prior to being loaded onto a truck for shipment. In other cases, the warehouse can act as a storage point for product made at the plant or for products made at other plants. In this case, this warehouse acts like a distribution center co-located with the manufacturing facility. A major benefit of a plant-attached warehouse is the reduction of transportation costs because a product does not have to be shipped to another location immediately after it comes off the end of the line. When you have plant -attached warehouses, some- times the standalone warehouses are called forward warehouses, meaning they are placed “forward” or out closer to customers .

• Hub Warehouse or Central Warehouse-Refers to a warehouse that consoli- dates products to be shipped to other warehouses in the system before moving on to customers. Different from cross-docks, the products are normally stored in these locations for longer periods of time before being used to fulfill demand. The other warehouses in the network are then typically called spokes or regional warehouses.

In practice, you will find many different names for warehouses. These names are most likely just different terms for what is described in the preceding list. In addition to the types of facilities, there are also needs for different temperature classes (frozen, refriger- ated, or ambient), different levels of safety (hazardous or nonhazardous), and different levels of ownership (company owned, company leased, or the company uses a third- party facility).

As an interesting side note and to further illustrate the wide range of warehouse types we have experienced, we have even seen caves used as warehouses. Caves have the nice advantage of maintaining the same (relatively low) temperature and have prebuilt roofs .

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If you can get trucks into and out of them and have room to store products, caves also make great warehouses. Kansas City is probably the best-known location with cave warehouses.

Quantitative Data: Why Have Multiple Plants? Similar to our use of the word “warehouse;’ we are also going to use the term “plant” to broadly refer to locations where product is made or where it comes from. So a plant could be a manufacturing plant that produces raw material, components, or finished goods, or just does assembly. A plant could be owned by the firm, it could be a supplier, or it could be a third-party plant that makes products on behalf of the firm. These third- party plants are often called co-packers, co-manufacturers, or toll manufacturers (a term used for third-party manufacturers common in the chemical industry).

A plant can also contain multiple production lines. So, often we are determining not only the location of plants, but also the number and location of the production lines.

For companies that make products, the number and location of plants are important. For retailers and wholesalers however, the location of the suppliers is often beyond their scope of control.

Even for a firm that has only one product, the location of the plant can impact trans- portation costs and the ability to service customers. In some cases, the location of the plant is primarily driven by the need to have skills in the right place or the need to be next to the corporate headquarters. However, in most cases, there are choices for plant locations. An even more interesting choice is to determine whether you should locate multiple plants to make the same product-even when a single plant could easily han- dle all the demand.

When you have choices for where to put your plants or the option to have several plants, you must consider some of the same questions we did when locating warehouses. For example, factors that would drive you to have multiple plants making the same product include:

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• Service Levels-If you need your plants to be close to customers, this will drive the need for multiple plants making the same product. This becomes especially important if your business does not use warehouses. In this case, your plants face the customer and their location can drive service levels.

• Transportation Costs-For producers of heavy or bulky products that easily fill up truckload capacities, you will want to be as close to your markets as possible. This may also drive the need for multiple warehouses.

• Economies of Scale-As a counterbalance to the benefits of transportation, you also want to factor in the economies of scale within production. As mentioned

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previously, the more you make of a given product at a single location, the lower the production cost per unit. This is driven by a reduction in production line setup time and costs and the benefit of being able to create a more focused manufacturing process. So while it may be ideal to have many producti9n loca- tions to minimize transportation cost, economies of scale in manufacturing suggest that fewer plants will be better.

• Taxes-In a global supply chain, it is often important to consider the tax implications of producing and distributing product from multiple or different locations.

• Steps in the Production Process-In a production process with multiple steps, you may need to decide where you should do various activities. For example, it can often be a good strategy to make product in bulk at a low-cost plant, ship it in bulk to another plant closer to the market to complete the conversion to a finished good.

As with the warehouses, a plant can also represent many types of facilities. A plant may represent any of the following:

• A Manufacturing or Assembly Site-This is a site that is owned by the firm that makes products. The products coming out of the plant could be raw mate- rials, semifinished goods, or finished goods.

• A Supplier-This is a location that is not owned by the firm but supplies prod- uct to the firm. The products could again be raw material, semi finished goods, or finished goods. In the majority of cases, you have no control over where your suppliers are located. You may, however, be able to pick which supplier you will purchase from.

• Third-Party Manufacturing Site-This is a location similar to supplier plants, but these sites make product on behalf of the firm and are therefore treated more like the firms’ own plants than a raw material supplier. Many firms use third-party manufacturing sites because manufacturing may not be their core competency. Their competitive advantage comes from all other activities includ- ing the design, marketing, and/or final sale of the product. These third-party firms, typically called contract manufacturers, are widely used in the electronics industry. Other third-party firms, termed co-manufacturers, co-packers, or toll manufacturers, are used to simply supplement the capacity of the firm itself. These are quite common with consumer packaged goods like food and bever- ages or chemical companies.

As with warehouses, in practice, you will find many variations in terminology for plants and different types of manufacturing sites and suppliers serving roles similar to those mentioned previously.

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Solving the Quantitative Aspects of the Problem Using Optimization Because of the supply chain complexities and rich set of quantitative data we have dis- cussed, mathematical optimization technology is the best way to sort through the various options, balance the trade-offs, determine the best locations for facilities, and support better decision making. The mathematical optimization relies on linear and integer programming.

One common misconception we see is that managers sometimes confuse having good data and a good reporting system with having an optimal supply chain. That is, they think that their investment in good data and reporting systems should equip them with the ability to complete network design analysis. (These systems are often called business intel- ligence systems.) But in reality, if your warehouses or factories are located in the wrong places, these reporting systems will not correct the situation or suggest new locations.

So although these systems do, in fact, generate good reports and allow managers to gain good insights, they do not lead to better designs for the supply chain. That is, they are not built to construct models of your supply chains to which you can apply mathemat- ical optimization. At best, they may allow you to evaluate a small handful of alternatives, but in doing so, you have to define all the details of each of the alternatives. Optimization is a complementary, not competitive, technology that allows you to actu- ally determine the best locations for your facilities. And you can let optimization do the heavy number crunching to determine the details of the alternatives (where to locate, what is made where, how product flows, which customer is served by which warehouse). And, in many cases, if the optimization is set up correctly, it will uncover ideas that you never thought about.

Of course, because these problems are of great strategic value to an organization and touch on many aspects of the business, there will be non quantitative aspects you must consider. These non quantifiable aspects are important and are discussed later in this chapter and throughout the book.

With all that said, solving the quantitative aspects of this problem with mathematical optimization is the key to coming up with the best answers. So, let’s start there.

To formulate a logical supply chain network model, you need to think about the follow- ing four elements:

• Objective

• Constraints

• Decisions

• Data

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The objective is the goal of the optimization and the criteria we’ll use to compare differ- ent solutions. For example, the most common objective in strategic network design is to minimize cost. If our objective is to minimize cost, we can now compare two solutions and judge which one is better based on the cost. When the mathematical optimization engine is running, it searches for the solution with the lowest cost. So with this common example you can see that an optimization problem needs to have a quantifiable objec- tive. It is important to point this out because we have encountered many situations in which supply chain managers say their objective is to “optimize their supply chain.” The appropriate response is to ask what exactly they want to optimize, or what criteria they will use to determine which of two solutions is better. For example, is the key criteria transportation cost, is it service, is it facilities costs, or something else? Later, we’ll dis- cuss optimizing multiple objectives (as long as you can quantify them). And we will show methods for analyzing nonquantifiable factors such as risk and robustness, because these are also very important. But for now, we’ll start with one quantifiable objective. When we understand this one, we’ll be able to understand more detailed analysis later .

. The constraints define the rules of a legitimate solution. For example, if you want to just minimize cost, it is probably best to not make any products, not ship anything, or have no facilities. A cost of zero is indeed the minimum; however, that’s clearly not realistic. So there are some logical constraints we must include such as the fact that you want to meet all the demand. There are also constraints that specify which products may be made where, how much production capacity is available, how close your warehouses must be to customers, and a variety of other details. In this step, you also want to be careful not to specify so many constraints that you prohibit the optimization from find- ing new and creative strategies.

The decisions (sometimes called decision variables) define what you allow the optimiza- tion to choose from. So in the optimization of the physical supply chain, the main deci- sions include how much product moves from one location to another, how many sites are picked, where those sites are, and what product is made in which location. But, cer- tainly, there are other decisions as well. The allowable decisions cannot be separated from the constraints. For example, if you have existing warehouses, you mayor may not be able to close some of these sites.