NEXT-STEP NETWORK MODELING HOW TO CREATE AND IMPLEMENT A DECISION SUPPORT

PLATFORM FOR A LEAN AND EFFICIENT SUPPLY CHAIN

Network modeling provides a decision support platform for many critical aspects of supply chain strategy and is performed in order to design an efficient and lean distribution chain. The goal is to establish a network of facilities with efficient flows to ensure optimal service to the customers with minimal risks and disruptions. But what does the network modeling process entail? What is the process for planning, implementing and gathering meaningful outcomes from a project like this?

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NEXT-STEP NETWORK MODELING HOW TO CREATE AND IMPLEMENT A

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BEFORE YOU BEGIN: THE INTERNAL BUY-IN

Before you begin any network modeling project, a key aspect of the process is to first sell it internally. To do so, one must ask a number of key questions of your organization:

• How will this project be run?

• What data, resources and budget do I need?

• What level of modeling is best for my company?

• What are some of the outcomes I can expect from the project?

The budget is also a critical aspect of this project, and it’s important to call out the value of a successful modeling campaign. Typically, if a project like this has not been undertaken in the last few years, an organization will most certainly realize positive impacts to its bottom line. Such impact may come in the form of distribution cost reduction or improvement in service levels to the customers.

Once these questions are answered and the internal buy-in is secured, the next step is to decide which modeling strategy is the best fit for your organization. Some examples of strategy options and the questions that accompany them include:

Distribution Center (DC) Strategy1. Should I have DCs in my network?

2. If so, how many and where should they be located?

3. Which customers and regions should the DCs cover?

4. What should be the role of each DC? Should some of the DCs be a mixing center?

5. Should I implement a day of week shipping sailing schedule to balance the work load at the DC?

Manufacturing Strategy1. Based on manufacturing capacity and productivity, which SKUs should I manufacture

at my plants?

2. What products should I produce versus outsource through co-packers?

Transportation Strategy1. What should be my optimal mode mix?

2. Should I use cross-docks in my network?

3. Are my DCs located around major transportation hubs in order to ensure ample freight capacity?

Once you’ve determined which strategy best fits the needs of your organization, you’ll have to determine the key elements of this strategy.

THE BUDGET:

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NEXT-STEP NETWORK MODELING HOW TO CREATE AND IMPLEMENT A

DECISION SUPPORT PLATFORM FOR A LEAN AND EFFICIENT SUPPLY CHAIN

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CRITICAL DATA ELEMENTS FOR THE MODELING EXERCISE

These are some of the key data elements that will factor in to your network modeling strategy. It’s important to have the capability to accurately gather data for each of these elements in order to build the necessary scenarios for the modeling exercise.

Entities:Elements of supply chain that are physical locations, organizations or facilities. Some of the entities that are modeled during network optimization are:

1. Manufacturing plants

2. Warehouses

3. Distribution centers

4. Supplier locations

5. Customer ship to locations

6. Transit facilities

7. Third Party facilities such as co-packers

8. Import locations / De-consolidation points

For a network optimization project, information related to cost, capacity and role is collected for each entity as part of the data modeling. For example, how much capacity a DC can handle every day in terms of total pallets or pounds shipped and received. Is the DC owned or managed by a third party? What is the labor rate and per square feet rate at the DC? All these questions and more must be considered and reflected in the modeling process.

Flows:Represent transfer of product from one entity to the other. Commonly modeled flows include customer deliveries, vendor (supplier) inbound, inter facility stock transfers and customer returns.

Transactions:Key modeling elements that drives decisions is the transactions and information. The availability of product, the delivery expectation, the cost of freight, handling, inventory and more are all carefully modeled.

For each of these data elements, your organization will have to gather the necessary data from your existing supply chain and transportation management network. It will also be important to gather a wealth of data from your customers, which ensures visibility into their needs and will help you tailor scenarios for maximum positive impact to your customers.

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NEXT-STEP NETWORK MODELING HOW TO CREATE AND IMPLEMENT A

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DETERMINE YOUR NETWORK MODELING APPROACH

In order to begin the network modeling process, it is critical to decide the level of data required and assumptions behind the future of the business. You have to select the appropriate modeling level going into the project. It’s important to consider your timeline, necessary amount of effort, and all the baseline information that you need. At times, the availability and accessibility of data dictates the level of modeling effort. A list of assumptions must be weighted by the project teams and the internal stakeholders to maintain their buy-in for the network study.

For example, if your manufacturing plants and related footprint of warehouses is going to remain fixed, certain flows will be rigid and the model needs to account for those assumptions.

The following are options for different approaches to network modeling:

SKU Level Modeling:If your company has specialized manufacturing, then SKU level information should be used for modeling. An extreme example of modeling without SKU level is assuming all products can be manufactured at all qualified plants. In that option, the network modeling output will recommend the optimal manufacturing location for each product based on the total distribution cost. However, where plant capacity is tight and production lines are dedicated for specialized SKUs, the manufacturing function becomes a constraint in the modeling process. In such cases customer demand should be modeled at SKU level and as many SKUs should be consolidated into product families or groups.

Flows:Represent transfer of product from one entity to the other. Commonly modeled flows include customer deliveries, vendor (supplier) inbound, inter facility stock transfers and customer returns.

P R O S C O N S

This approach is suited for both network and inventory optimization. Supports most of the project objectives for supply chain strategy formulations

Strategy formulation is at avery granular level

Most suited for manufacturing,fast food supply chains wherestock is a big risk

Risk of delays in turnaroundtime is high

Higher turnaround time16+ weeks

Data modeling is extremelyintensive. You may needhigher number of supplychain analysis along withSMEs to be part of the modeling

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NEXT-STEP NETWORK MODELING HOW TO CREATE AND IMPLEMENT A

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Order Level Modeling with Product Aggregation:An order level modeling typically involves modeling at individual customer delivery level. Typically, such modeling involves SKU data rolled up at product family level. For example, if you are manufacturing electric motors versus light bulbs, these can be treated as product families based on freight classification. Such modeling is helpful for deciding optimal role of DCs in shipping to customers.

An ideal candidate for such modeling would be those companies who have customers that receive a diverse set of products that are manufactured in multiple plants.

Shipment/Lane Level Modeling:These are projects where network is modeled at a very high level assuming consistent levels of replenishment from plants to DCs. Such modeling does not include product or product family information. This information is captured at an aggregate flow level based, such as plant to DC lane, DC to customer regions, DC to DC etc.

P R O S C O N S

Multiple project objectivesare supported for broaderdecision making acrossmultiple functions

Easy to formulate strategy andsell internally. Typically buy-inis achieved

Direction on implementationis more clearly articulatedand diverse choice in available output reports

Output from network modeling will need to beassessed due to the processof rolling multiple SKUs intoproduct families

Higher turnaround time -10-16 weeks depending onnumber of unique businessunits

Data modeling is fairlyinvolved. Need SMEs to beinvolved throughout the project. High level of internal coordination andalignment required

P R O S C O N S

Least data intensive. Typicallymost of the companies haveaccessible to shipment leveldata

Quick project turnaroundtime. Usually 6-8 weeks

Provides quick and directionaloutcomes for urgent decisionmaking situations

Outcomes are directional, soan investment in phase 2 maybe required

Hard to sell the outcomes of these projects due to lack of consideration tomanufacturing

Limited in decision supportbecause of higher scopeand high number of assumptions

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NEXT-STEP NETWORK MODELING HOW TO CREATE AND IMPLEMENT A

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Network Entities (facilities): • Location

• Role

• Capacity

• Costs (through put, fixed and variable operations)

• Level of flexibility

• Supply/ demand

Product/SKU: • Item number

• Aggregate Product family

• Unit of measures

• Stackability

• Sourcing options

• Inventory/ unit

• Revenue/ unit

• Freight class

Customer Demand: • Shipment / lane level

• Product level

• Time periods

Flows: • Replenishments and/or stock transfers

• Customer flows

• Returns

• Vendor/ supplier inbound

Transactions with Costs: • Inbound freight

• Outbound freight

• Transfer freight

• Storage and handling

Facility Costs • Lease/ rent

• Fixed & variable costs

DATA ELEMENTSOne of the most intensive phases of network modeling is data collection and baseline. Following are broad categories within the supply chain network required for modeling and analysis:

IMPLEMENTAT ION

PROJECTK ICKOFF

▪ Introduce the network optimization team▪ Finalize scope of the project▪ Data selection criteria and elements

DATAVAL IDAT ION

▪ Review statistics /network maps▪ Finalize assumptions for any missing data

BASEL INE &AD JUS TMENT S

▪ Create baseline and validate▪ Finalize scenarios▪ Finalize parameters for scenario analysis

SCEN AR IOAN ALY S IS

▪ Execute network▪ Optimization scenarios▪ Review output with the team for each scenario▪ Review output with the team for each scenario

F IN AL IZESCEN AR IO

▪ Provide most optimal scenario as recommendation for implementation

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TACKLING NETWORK MODELING: KEY STEPS

It’s important to keep in mind that a critical part of network optimization is scenario analysis. Multiple scenarios are designed to help answer key questions that come across while formulating supply chain strategy. Such questions form the base objective of the project, and will help determine a blueprint for your enhanced and more efficient transportation strategy.

The chart above outlines the key steps involved in network modeling projects. Starting with the kick off and gradually progressing through data collection, baseline, and scenario analysis towards making a recommendation for developing strategy. The outcome of any network modeling project is always going to be important data and KPIs that may potentially drive your future supply chain strategy. Since you’ll be running several scenarios, for example, asking “What if I had 6 DCs?” versus “What if I had 8 DCs?” or “What if I ship everything from my plants and not utilize DCs?” will ensure that your data will be varied.

Typically after running 5 to 6 scenarios, the project team should get together and determine, based on the outcomes seen from those test scenarios, which scenario to choose for implementation. Once a scenario is chosen, an implementation team is assembled to review and validate the feasibility of the outcomes and identify any operational challenges. The team will devise a plan of action with key milestones identified. For example, if the scenario includes eliminating any DCs, you need to know where those customers would be receiving their freight from. What are the SKUs that are needed in each DC? Is one DC going to be a mixing center? These questions and many more will need to be answered.

For all scenarios, you’ll want to develop an output report on how everything changes for each scenario and compare against the base-line. Factor in the costs and benefits for each alternative and select the most optimal scenar-io for implementation. Typically, you will have to select the option that balances both cost to serve and service impacts to the customers.

NEXT-STEP NETWORK MODELING HOW TO CREATE AND IMPLEMENT A

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NEXT-STEP NETWORK MODELING HOW TO CREATE AND IMPLEMENT A

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EXPECTED OUTCOMES

CONCLUSIONS

As discussed above, a key step in the network project is scenario analysis. Typically, scenario analysis provides detailed reports for the future supply chain. Such reports help formulate the implementation process.

Some examples of key outcomes from network optimization include:

1. List of SKUs manufactured at each plant in annual quantities.

2. Number, size and location of optimal DCs required along with the regions/ customers serviced.

3. Distribution strategy by customer: Plant direct vs. through DC and at what frequency.

4. Transportation Strategy: Mode split and use of cross docks. Identify tactical opportunities as next phase of the project.

This last outcome can also provide the blueprint for a TMS implementation, which can offer myriad benefits in any company’s core transportation strategy. For many companies, one of the biggest benefits a TMS provides is visibility, which can help lead to streamline supply chain operations including improvements in safety, service and costs.

More so than ever, companies understand the tremendous impact logistics and transportation can have on their overall business performance and are taking meaningful strides to optimize their supply chain operations. Any network modeling solution should be analyzed for variations and uncertainty in key modeling variables. Some of these variables are fluctuations in customer demand, changes in fuel prices, impact of labor and capacity etc.

We encourage you to conduct sensitivity analysis on key scenarios prior to presenting the final solution to your management. This will provide a range of possible outcomes from worst case to best case scenario that will enable more robust decision making. The final implementation road map should weigh in risks and contingency planning and financial impact under each scenario. Conducting sensitivity analysis will help anticipate risks in the supply chain and plan to minimize disruptions. Seen in the last few projects, people are wanting to model not just what is happening today, but what may happen in the next 5 years.