## Square Root Law – inventory in multiple locations

Got asked what would happen to inventory when the number of stocking locations change.  I thought for a minute and remembered a quick rule of thumb … The Inventory Square Root Law states that …

Average inventory increases proportionally  to the square root of the number of locations in which inventory is held.

X2 = (X1) * √ (n2/n1)

n1 = number of existing facilities
n2 = number of future facilities
X1 = existing inventory
X2 = future inventory

Here’s an example:

Current inventory is 4000 units, 2 facilities grow to 8.  Using the square root law the future inventory = (4000) * √ (8/2) = 8000 units.

* The Square Root Law was mathematically proven by D.H. Maister in his 1975, International Journal of Physical Distribution article entitled “Centralization of Inventories and The Square Root Law.”

## Table top simulation – dock operations

Simulation is the act of imitating or mimicking the behavior of some situation or some process by means of something suitably analogous.  The imitation of a process can be used for debugging, and validating process design changes or use to communicate or train associates.

Simulations can be used for:

• process design
• testing new ideas
• debugging designs
• testing understanding
• gaining commitment
• testing alternatives
• communicating and training

Sometimes the simulation is role playing theater, other times the ‘game’ has logic and is reproducible, with known inputs and expected outputs.  The photo here is of a recent workshop where we studied how the warehouse dock floor would look after changing the pick waving rules and packaging.  Here outbound goods will be switching from trailer loose stack to returnable shipping containers.

Would we need more floor space?  Do we have enough pickers and loaders?  How do we pick and load multiple deliveries nose-to-tail?

While computer modeling is certainly a consideration the use of table-top simulation has many benefits:

• Many problems are difficult or expensive to test in real life
• Many people process information visually
• A number of alternatives can be quickly tested as the team uncovers issues and finds solutions
• Simulation costs are very low; you don’t need expensive software or extensive training

Here’s the process we used to build our ‘war game’:

1. Decide what we wanted to test; i.e. the output – in this case floor loading and labor resources
2. Gather the input – shipping orders for a typical busy day, number of pickers by zone, number of packing loaders, shift schedules, picking and loading rates, floor space and equipment dimensions (carts, containers, trailers, etc)
3. Determine the constraints, rules; e.g. number of loaders per trailer, length of breaks
4. Document assumptions; e.g any trailer can be at any dock door, break and lunches can be staggered, etc.
5. Be creative and design the game pieces (entities) and determine their quantities; in this exercise carts, containers, bins, trailers
6. Scale physically (1inch=5feet), scale time (1 day of 10 hours took an hour of game time)
7. Collect metrics, such as; line per hour, wave start and end time, trailer load duration, number of floor spaces occupied, number of time floor space turned over, number of workers needed

Once the model ‘behaved’ like the current process the team began introducing rule changes which uncovered obstacles.  One of the first changes was reducing the wave batch size from 90 minutes to 30.  Next came changes to packaging and trailer loading.  By the end of the workshop new procedures were debugged and ready for full scale dry runs leading to a live implementation next month.  Stay tuned …

## Tips for warehouse sizing

Tips for warehouse sizing:

• Don’t get fooled by “averages”
• Consider using statistical tools, such as standard deviation, and P.90 probability to analyze operational data – both in and out bound
• Understand which system components can expand capacity by adding labor, and which can’t
• Design expansion capability in from the start; SKU count almost always goes up, not down over time
• Get executives to sign off on future sales projections that will serve as the basis of the design; if they won’t or can’t, then round up
• Be very leery of unrealized plans to increase inventory turns; easier said then done
• Consider ability to add overtime and additional shifts to expand initial system capacity
• Recognize more companies regret having less capacity than those that think systems were over-specified
• You can usually add labor to increase throughput in pick modules, but if a sorter is maxed out, there is not much you can usually do

## Safety Stock Optimization

Many of you are looking for a correct, comprehensive safety-stock calculation. My company’s approach is based on more than 15 years of development and testing. As we have learned through extensive experience, optimized safety stock requires more than a formula. We use complex demand–data modeling and calculations. We provide a service, not a spreadsheet. You send us your data. We send you the results.

Our safety-stock model is correct and comprehensive, providing optimal safety stock levels for your service-level and financial-performance targets. We include all the factors that affect safety stock and service level, and apply the proper statistical techniques to them. We do not utilize the usual stockout-event-based metric, but the same quantity-based fill-rate criterion that most companies use to measure actual service-level performance during a month, quarter or year. Our calculations represent the right-skewed and sporadic patterns typical of real demand data. Our model also includes past-due demand and its disruptions, probability of past-due-demand cancellation, lead time, reorder quantity (MOQ, EOQ, etc.), package size and reorder-review frequency. Finally, our results provide a high degree of confidence, typically 95%, of consistently achieving your target service levels without costly expediting.

For more details, see our white papers at www.topdownleansystems.com/white.htm. Page 16 of the “Common Safety Stock Calculations” white paper has examples of our safety-stock analysis. Also, see how you do on our Safety Stock Quiz, at www.topdownleansystems.com/quiz.htm.

To demonstrate the power of our approach, we would be happy to calculate and analyze safety-stock levels for a sample of your inventory items at no charge. Send me data on up to 30 of your items, and we will send you results – safety stock quantity and safety stock days for each item. Our analysis also includes each item’s range of expected actual performance for fill rate; average reorder and on-order quantities; average quantity on-hand, days on-hand and inventory turnover; average daily demand and demand activity percentage.

We require this data for each item: Item identifier, target fill rate, reorder quantity (MOQ, EOQ, batch size, lot size, etc.) or reorder frequency, package size (order multiple), lead time, probability of past-due-demand cancellation, days in actual service-level measurement cycle, and as much historical daily time-series demand as possible (three years is best, two is better, one is good). We perform extensive pre-screening on your input data to identify potential issues and to avoid “garbage in, garbage out.”

Send me your contact information via www.topdownleansystems.com/contact.php. I’ll provide you with a file containing input-data examples. Of course, I’ll be happy to explain our model in more detail and to answer your questions, at your request.

David McPhetrige, TopDown Lean Systems

## Things to do to improve warehouse productivity

Business may be slow now but before you know it you’ll be jammed again. Want to get more done with the folks you have?  Things to consider:

1. Keep the lifts in good repair
2. Batteries getting old?
3. Stagger shift starts – replenish forward picking before the first wave
4. Reslot often – move the A items closer to the dock
5. Not enough space, then make more – get to 10% empty forwards and 20% empty reserves
6. Qualify and prioritize the inbound freight – need the trailer now, or later?
7. Qualify the product going into reserve
8. Get the inbound current and under control before tackling pick, pack, ship
9. Fix any and all inventory inaccuracy root causes
10. Have fresh eyes look at the problem – select different supervisors or warehouse workers to look at other areas
11. Eliminate touches – live load, don’t pick and stage
12. Minimize travel – never travel empty – put one away, pick one to ship
13. Right size the forwards, so inbound doesn’t need to go into reserve
14. Align the picking method for each product with its order pattern
15. Can the WMS round up order quantities to an easily picked unit or measure?
16. Engage the troops
17. Every DC worker makes thousands of decisions each day; understand and guide discretionary decision-making
18. Solve the workforce’s boredom problem
19. Most supervisors spend less than 5% of their time on motivating employees, double that and double productivity
20. Inbound congestion means waste and extra touches
21. Housekeeping
22. Address the annoyances that demotivate
23. Keep inbound under control and putaway as timely as possible
24. Recalculate Safety Stock
26. Bust the inbound batch sizes
27. Increase inbound visibility, smooth the spikes if you can
28. Publish metrics for all to see and encourage friendly competition between zones, departments, facilities
29. Create a ‘dog pound’ and move slow movers out of the way
30. Study and fight outbound congestion
31. Adjust the number of pick zones; fewer the better
32. Synchronize order filling across all zones
33. Keep current on replenishment
34. Never run out of supplies (totes, pallets, carts, tape)
37. Reduce the number of job classifications by cross training and rotation
38. Use inbound teams and eliminate staging areas: unload, receive and put away with one touch not two or three
39. Brainstorm and then brainstorm some more
40. Be careful what you measure
41. If you are in a meltdown, get help
42. Consider postal pick location address scheme; going down an aisle picking on left and right instead of down one side and coming back the other

## Walmart Sustainable Product Index

Why do you think Walmart is asking these questions of their 100,000 suppliers? Can you answer these questions by October 1, 2009? How much staff time will it take to gather this data? If you aren’t a Walmart supplier don’t think for a second you are off the hook; when will your key customers start asking similar questions?

Energy and Climate: Reducing Energy Costs and Greenhouse Gas Emissions
1. Have you measured your corporate greenhouse gas emissions?
2. Have you opted to report your greenhouse gas emissions to the Carbon Disclosure Project (CDP)?
3. What is your total annual greenhouse gas emissions reported in the most recent year measured?
4. Have you set publicly available greenhouse gas reduction targets? If yes, what are those targets?

Material Efficiency: Reducing Waste and Enhancing Quality
1. If measured, please report the total amount of solid waste generated from the facilities that produce your product(s) for Walmart for the most recent year measured.
2. Have you set publicly available solid waste reduction targets? If yes, what are those targets?
3. If measured, please report total water use from facilities that produce your product(s) for Walmart for the most recent year measured.
4. Have you set publicly available water use reduction targets? If yes, what are those targets?

Natural Resources: Producing High Quality, Responsibly Sourced Raw Materials
1. Have you established publicly available sustainability purchasing guidelines for your direct suppliers that address issues such as environmental compliance, employment practices and product/ingredient safety?
2. Have you obtained 3rd party certifications for any of the products that you sell to Walmart?

People and Community: Ensuring Responsible and Ethical Production
1. Do you know the location of 100 percent of the facilities that produce your product(s)?
2. Before beginning a business relationship with a manufacturing facility, do you evaluate the quality of, and capacity for, production?
3. Do you have a process for managing social compliance at the manufacturing level?
4. Do you work with your supply base to resolve issues found during social compliance evaluations and also document specific corrections and improvements?
5. Do you invest in community development activities in the markets you source from and/or operate within?

## Simplified Systematic Network Planning – step 6

STEP 6: DETAIL AND DO
Step 6 details and implements the network plan selected in Step 5. If the purpose of the network planning project is simply to conduct and analysis and make a presentation, no actual changes will be planned. When actual changes will be made, the planner first prepares a Gantt chart of the implementation schedule in the Detail and Do worksheet

The Gantt chart serves as a communication tool, outlining the tasks needed to change the network, the person(s) responsible for each task and the scheduled time for the task to be undertaken. Actual implementation is done by professionals in the field. But it is always good for the network planner to be involved in this process to track the changes, build credibility, and confirm the effectiveness of the recommendation.

Post implementation audits capture actual saving from changes to the network. The Detail and Do worksheet provides a section for the planner to measure the variances between the projected and actual savings and to explain them. This is especially important in understanding why the model did or did not result in the expected savings and provides useful lessons for future modeling efforts. In our example, fuel price increases eliminated half the projected savings. Given this impact the planners should probably include fuel price projections in future models of this type.

## Simplified Systematic Network Planning – step 5

STEP 5: EVALUATE ALTERNATIVES
In Step 5, the planner evaluates the network plans developed in Step 4 by running several alternative scenarios.

Evaluation takes two forms:

• Cost analysis – comparing relevant costs among scenarios and their network plans.
• Intangible analysis – for factors or considerations that cannot be easily modeled or measured in economic terms.

Cost analysis is generally straightforward.

Modeling software typically computes each alternative’s difference from the baseline on each element of total cost. But when comparing alternatives, planners must decide whether to show all costs or only those that are affected by the proposed alternatives.

In the MTT example, as shown in the Alternatives Analysis Worksheet, the four alternative plans compare what the company’s historical costs would have been if 32 oz capacity had been added in one of four existing plants.

Madison’s costs (Alt IV) are highest. Vicksburg’s costs (Alt III) are the lowest, and would have saved about \$739,000 per year over the current or baseline network, and saved about \$1 million more per year than Madison. These savings easily justify the upgrade of a production line.

Note that the planners have dropped purchasing costs from the previous Step 4 cost summary since these costs are unaffected and the same for all plans. This action helps to accentuate the cost differences between the alternatives. (See line 5 of the Cost Summary section in the Alternatives Analysis Worksheet.)

Naturally we would like to implement the network plan with the lowest total cost. But intangible factors or considerations may also play a role. For MTT, annual costs differ by only four percent among the four alternatives. With such a small difference, costs alone should not decide which plan is best. When two or more plans yield similar costs, the best one is typically found by comparing such intangibles as:

• Ease of implementation
• Exposure to various risks
• Fit with organization structure
• Labor-related considerations
• Facilities-related considerations

To evaluate intangibles, SSNP uses the weighted-factor approach shown in the lower portion of the Alternatives Analysis Worksheet. The planners list relevant factors and management assigns weights reflecting their relative importance. By convention, SSNP assigns a weight of 10 to the most important factor. Next, those who will implement and operate the network discuss and rate the effectiveness of each alternative on each factor. SSNP uses the vowel-code convention of A, E, I, O, U and X, in descending order of effectiveness, where A=4, E=3, I=2, O=1, and U=0. A rating of “X” disqualifies a plan unless the objectionable feature can be fixed.

After all plans have been rated on all factors, the ratings’ numerical values are multiplied by the factor weights to arrive at total scores for each plan. The highest score indicates the best network plan from an intangibles perspective. Hopefully the highest scoring plan will also have the lowest total cost. But if not, this procedure will reveal the intangible benefits of the more costly network plans. When cost comparison results in a stand-off and does not indicate a clear winner, the weighted factor approach will help discover which plan is best and why.

In the example, Alternative II (Briansville) scores roughly 50 percent (84/54) better than the lowest cost Alternative III, Vicksburg. Briansville offers more capacity relief, easier implementation, and a better fit with the current organizational structure. For these reasons, MTT management selected Briansville for the 32 oz bottling line upgrade.

## Simplified Systematic Network Planning – step 4

STEP 4: CREATE SCENARIOS
In Step 4, the planner develops scenarios to model various elements of the problem. Each scenario is generated by making one or more of the following changes to the baseline model set-up:

• Adding or deleting products, locations, resources or lanes
• Changing demand allocations

The Scenario Summary Sheet (overleaf) records these changes to the baseline model and the results of the scenario model runs. Changes and model results are summarized in terms of demand, resource utilization, lanes, and flow. A diagram visualizes the scenario network. In this way, each scenario represents an alternative network plan.

Often, those managing the network and its resources will be skeptical and reluctant to accept an initial scenario run that predicts significant cost savings from current conditions. These results may be characterized as “too optimistic”. The assumptions, parameters or constraints may be challenged and adjusted until the scenario run yields a more modest improvement – one that the line organization is willing to be accountable for obtaining.

But such a “pessimistic” outcome may be resisted by the planners who rightfully have faith in their validated model. The wise planner anticipates this give and take, and budgets time for optimistic, pessimistic, and most-likely cases. These can then be presented to management to show the likely range of scenario outcomes.

For MTT, several scenarios place the 32 oz upgrade at different plants. Each scenario was run with optimistic, pessimistic, and most-likely cases. Scenario I upgrades a production line in Jonesville (P1). Lanes to branch distribution centers are set up to receive products from Jonesville or Sommersville, with an added constraint that each DC order must be for a full truckload.

When the model was run, the optimizer moved 300,000 cases of 32 oz production from Sommersville to Jonesville. Of these cases, 200,000 were formerly cross-docked through Jonesville to its branch DCs. In terms of resources, Sommersville was relieved of 450 hours of production, while 300 hours were added to Jonesville. The difference is due to the greater efficiency of the newly upgraded line in Jonesville.

Simpsons (DC 23) could not satisfy the full truckload constraint from Jonesville and was reassigned to Sommersville. DCs 11 & 12 – Harrystown and Clinton, along with all the Virginia and West Virginia DCs, received all of their 32 oz products from Jonesville.

Often, some cost categories of concern at the outset of modeling prove to be insignificant once results are in. Or, an important stakeholder may have a fixation on some minor element of network cost. The Cost Summary section of the Scenario Summary Sheet includes these minor or occasionally irrelevant costs in order to remove any concerns or doubts. In our model, crossdocking costs and reduced overtime are insignificant and do not matter to the choice of network plan. Yet the planner includes these to satisfy the concerns and interests of key decision-makers.

## Simplified Systematic Network Planning – step 3

STEP 3: ANALYZE SENSITIVITIES
In Step 3, the planner runs the model using optimization software; identifies any infeasibilities, and then troubleshoots.

Once free from infeasibilities, the planner runs and fine tunes the model, establishing a baseline that replicates current network performance.

Model results for demand, costs and constraints are summarized on the Baseline Validation Worksheet. They are compared to the to the actual performance of the current network for the same time period. Notes and explanations address any changes made to the model and reasons for the variances between model results and actual performance. This exercise builds credibility. The smaller the variance, the more accurate the model and the greater the acceptance of model results

While appropriate optimization software must be used, the Simplified SNP procedure is not dependent on any specific algorithm or software, and there are many products that could be used. Most software conforms to the general structure of a user interface to view the data and results; a mid-layer wherein mathematical equations are generated; and an optimization engine which solves the problem.

Examples of simple modeling problems include:

1. Change in inventory policy decision.
2. Adding capacity to existing production for a specific product line.
3. Adding or closing branch in a region.
5. Impact of changing lot sizes for a product line.
6. New product introductions.
7. Make vs. Buy for one or few product lines.
8. Dealing with seasonality of demands (Overtime vs. Pre-building)

Examples of complex modeling problems include:

1. Greenfield analysis/new location analysis.
4. Annual budgeting based on sourcing.
6. Long range operations strategy

While modeling software generates lots of statistics, the key to successful projects is to present them at an appropriate level of detail, and to only present the most crucial results needed for the decisions at hand.

For MTT, as shown on the Baseline Validation Worksheet, the model results for demand and cost are almost identical to the actual results of the current network. And relevant constraints were respected. Demand and cost variances are less than one percent. Even so, explanations should be sought and presented. Any additional constraints that may have been discovered during fine tuning are noted as lessons learned for future modeling.

## Simplified Systematic Network Planning – step 2

STEP 2: DEFINE THE VARIABLES
In Step 2 of Simplified SNP, the planner adapts an existing model to the needs of the problem at hand. The planner summarizes these tasks on the Variables Summary Sheet.

This step involves:

• Choosing or specifying design characteristics of the model and its parameters, including a network diagram to visualize the model’s scope
• Identifying data elements and their sources
• Defining relevant constraints
• Documenting assumptions
• Writing any formulas or algebraic expressions that will be used and formulating the model

For MTT, the network will consist of:

• One raw material supplier, six other bottlers, six MTT plants, and 42 branch distribution centers. These can each be seen in the network diagram (overleaf) using an adaptation of industry-standard operation process charting symbols.
• All transportation lanes between locations are potentially active. Certain products are produced in certain plants and cross docked through other plants. In this way, a branch can receive product from any plant (also shown on the diagram).

The model will include all juice products, both purchased and manufactured, measured in cases. Results will be based on 12 months of historical demand in weekly buckets. Resources to be modeled include: manufacturing lines, transportation, and storage. Data about products will come from the Demand Planning information system. Data about resources will be manually entered. Manufacturing cost data will come from records in the ERP system. Other cost data will come from various sources.

Constraints specify limitations on the various resources being modeled. For example, manufacturing lines cannot run more than 140 hours per week and must run at least 80 hours. Assumptions clarify model scope, simplifications, and the manner in which some variable will be treated. For example, raw material has infinite capacity, i.e. no limitations. Where formulas can be used to express costs or resource performance, they are given. Thus, if X1, X2, X3, X4 are trip frequencies for each lane, the transportation cost is defined as:

Transportation cost = X1*(2-way private fleet cost) +X2*(One way incentive for common carrier use) +X3*(Backhaul Factor) +X4*(Reverse logistics factor, i.e. container and damaged goods return)

This formula shows that the planner is using a weighted average approach to estimate a single transportation cost for each lane, rather than modeling each kind of transportation as a separate resource on each lane. The latter approach would significantly increase the complexity of the model without significantly increasing the precision of the results.

Key parameters for each manufacturing plant are summarized in a table. In the MTT example, the parameters include number of lines, maximum and minimum capacities expressed in maximum and minimum hours of operation, and pallets of storage capacity. For instance, P1 (Jonesville) has six manufacturing lines and their line speeds are their demonstrated speeds, meaning that the modeler will use the line speeds in cases per hour normally used by the production planners when scheduling each line.

## Simplified Systematic Network Planning – step 1

STEP 1: ORIENT THE PROJECT
Step 1 organizes the project and assures that it is well-defined, understood and realistically scheduled. SNP uses a standard, one-page Orientation and Issues Worksheet to capture project objectives, scope, issues, and schedule. The project’s schedule is developed around the six steps of Simplified SNP. Additional steps are added to give extra attention to key tasks. The worksheet uses the Gantt chart format, but any project scheduling software output can be used.

In the MTT example, only one facility produces 32 ounce plastic bottles. With increasing volumes and product proliferation, this facility cannot meet expected demand. MTT’s network planner has been asked to cost justify a second 32 oz “big bottle” manufacturing line. This line will be achieved by upgrading an existing line. The planner must also determine which of six existing plants will be the best location for the upgrade, and he will use an existing sourcing model and software to find the location with the lowest total cost. In addition, the decision must also consider other intangible or “non-cost” factors.

In the center of the worksheet, the planner lists the issues that must be resolved in order to reconfigure the existing sourcing model for use on the Big Bottle Analysis. The planner also lists the actions needed to resolve open issues and any proposed resolution. For example, the new capacity will be achieved by upgrading an existing line, but not every existing line can be upgraded and some are easier to upgrade than others. To assure that the project resolves critical network planning issues, it is good practice to list and rate their significance, importance or dominance as follows:

A – Abnormally high
E – Especially high
I – Important
O – Ordinary
U – Unimportant

Issues or factors beyond the planners’ control or outside of the project’s scope are flagged with an “X”.

## Simplified Systematic Network Planning

THE NETWORK IN SIX STEPS

CHANDRA NATARAJAN and LEE HALES describe a systematic six-step procedure for effective logistics network planning.

Supply chain networks comprise locations – suppliers, plants, warehouses, and customers – and transportation routes between them. Planning such networks requires a hierarchy of decisions, the implications of which can be worth millions of dollars. Typical decisions include:

• Which customers will be served and in which locations?
• What products will be supplied?
• Which products will be made internally and which sourced from outside?
• Which products will be made or distributed at which locations, and in what quantities?
• How much capacity will be provided at each producing or distributing location?
• Which suppliers will be used?
• Which customers will be served from which locations?
• How much inventory will be held at which locations?
• What will be the hours and days of operation?
• What modes of transportation will be used between locations?

The field of network optimization has evolved to improve such decisions, and in many companies, network modeling and planning are now daily activities. But in spite of powerful mathematical algorithms and software, a number of challenges make effective planning difficult:

1. Understanding the theory of optimization does not assure a well managed project or effective network planning. And while there are a number of excellent texts on optimization theory and tools, there are very few publications on how to manage their application on everyday business projects.
2. No formal methodology exists for planning and managing network planning projects.
3. Network analysis and planning become tedious when problems are not appropriately defined at the outset.
4. Lack of attention to pre-planning of projects leads to much rework and waste.
5. Network planning lacks standard outputs and documentation.

To address these challenges, the authors have developed a simple six-step procedure aimed at improving planners’ effectiveness on “simple” projects, i.e. those using existing models to address problems of relatively limited scope, such as the best existing location at which to add capacity, or the impact of a change in inventory policy, or of adding or closing a branch warehouse.

Simplified SNP uses the High Performance Planning model developed by Richard Muther and used in the well-known Muther methods of systematic facilities planning. It can be mastered in less than one day without formal training in network optimization. However, the planner will still need training in the optimization software necessary for network planning.

Natarajan and Hales illustrate the use of Simplified SNP at Mountain Trail Tonic (MTT), a fictitious manufacturer and distributor of organic juices. A key document is presented for each step. These and other worksheets are available free from Richard Muther & Associates.

## Supply Chain Optimization Benefits

Supply chain and logistic networks consist of locations – suppliers, plants, warehouses, and customers – and the product transportation between them. Network optimization seeks to maximize the company’s profits or minimize costs while providing the desired level of customer service subject to relevant constraints, policies, and intangible considerations.
Typical questions answered by supply chain network planning include:

• How many plants and warehouses should we have, how big, and when?
• We currently have one distribution facility, should we open up a west coast operation?
• Which customers should be served from which locations?
• Which products should be made internally or sourced from outside, and where?
• How much inventory to hold at which locations?
• What modes of transportation to use between locations?
• How much capacity will be needed at each plant or distribution location?
• Customers are asking about our extended supply chain carbon footprint (Green Supply Chain).  How do we figure that out, and is there anything we can do in our network design and operations to reduce GHG?

Benefits from good supply chain planning:

• Facility Locations – Historically, people considered network planning solutions to be facility location studies on the distribution side of the business. Where should the warehouse or plants be located to minimize total supply chain costs?
• Total Profit Optimization – The impact of supply chain performance on the bottom line and shareholder value are increasingly well understood. As a result, companies are looking at supply chains not just from a “cost minimization” perspective, but in terms of maximizing profitability – and return on capital or assets employed.
• Tactical Issues –  Use smaller models to answer more focused and near-term questions. An example: managing “end of life” scenarios for a specific product in a way that maximizes profitability (e.g., when does it make the greatest sense to stop production of the product in one of the two plants where it is manufactured?).
• New Product Introduction – Companies with rapid product lifecycles often a lack an integration between the product/demand side of the business and the supply side regarding such issues as the optimal production and storage points, optimal inventory targets through the product life cycle, etc.

On-going Network Monitoring is important.  Supply Chain managers typically do a good job of initially balancing their material flows. Over time, however, customer demand changes, products and suppliers come and go, and before too long freight costs are way up, order fulfillment rates are way down and response times are negatively impacting customer service and profitability.  It is important to continually reevaluate the distribution footprint to keep it operating at maximum efficiency.  Some do this on an annual basis leading up to a supply chain strategic review, others monitor key metrics on a monthly or quarterly basis along side their master scheduling and Sales & Operations Planning process reviews.

## Paper Modeling for Supply Chain Optimization

Whenever doing any kind of analysis its tempting to start loading up the tables or spreadsheets and start slicing and dicing only to find out that somethings foggy, missing, or corrupted.  John Trestrail of Next Wave Agency introduced me to an intermediate step between data collection and building a distribution network model, a step he calls Paper Modeling.  Paper Models are visual summaries of material flows and key model parameters on a single sheet of paper.

Depending on the scope and nature of the network modeling objectives we create a detailed list of data requirements.   Keeping track of the data can be tricky.  As we start gathering up the source files we need to validate and verify, checking for formatting, date ranges, duplicate and missing data, units of measure, etc. To do this we’ll summarize, sort, sub total, and graph and then compare our results to other independent quantifiers for a sanity check. For example when looking at the customer address book and customer orders we’ll filter for duplicates, sort and search for missing zip codes, filter out customers with zero sales, then check with Shipping and Accounts Receivables to see how many customers they think we have.  Another example is matching shipments to ship-to addresses and not bill-to.   The number of ship-to’s should be less than or equal to the number of bill-to’s, right?  Total shipments at cost should tie out to Cost Of Goods Sold, and so we cross check our shipment grand totals with Finance.

Starting with the network sites or facilities and our understanding of how the goods flow we build a network schematic, like so …

With this visualization we can make sure we understand the source and destination of goods moving through the network. In this example Plant 2 is a satellite facility that gets almost all of its raw materials from Plant 1 rather than directly from the suppliers. Changing this arrangement might be a what-if scenario, but for now we are only trying to describe the current state.

The software model will need to have the address, zip code, or longitude and latitude for each of these facilities.  We’ll also need to confirm that there are no other facilities in the network, other warehouses folks ‘forgot’ to talk about.  We’ll also want to be sure we have all the shipping transactions represented here, especially the transfers between the two plants, something often overlooked in the initial data collection.  In Excel we might build a From-To pivot table to sum up all the dollars, pieces, pounds moving between each site.  Often we find nontrivial volume of activity outside of the initial standard flows.  Are these transactions erroneous, miscoded, new or old?  What’s the story?

Adding the end customers to this schematic would make it unnecessarily cluttered.  For now we can assume each sales warehouse serves its own ‘region’, but maybe not, so we’ll use the modeling software show us that later.

Now we come to the Paper Model where we add data and summary tables to the flow schematic and to capture total spend, fixed and variable costs, etc. In very complex networks all of the sites may not fit on a single page, even when using A3 or 11×17 size paper. Then it may be necessary to prepare paper models for each of the major activity centers, such as the production facilities and distribution centers. With the paper models complete we now shop them around and get feedback on completeness and reasonableness. First drafts are almost always done by hand and in pencil. Here’s a mock up, sorry can’t show you real data – proprietary non disclosure agreements and all of that…

## What is Supply Chain Optimization? part 2

Gather up all of your:

• Sites – customers, suppliers, company facility addresses
• Customer Orders and out-bound shipment transactions
• Purchase Orders and in-bound shipment transactions
• Freight rates and any inter-company shipments
• Product definitions: part numbers, group codes, packaging codes, costs, weights, cube, etc
• Document your transportation, inventory, customer service policies

Enter this information into anyone of several supply chain optimization software packages and start modeling your current state in and outbound logistics.

As-Is Baseline

Optimized for Service

What if optimized for cost?

Often out of a lack of visibility or the absence of business rules we ship from or store product in the wrong places.  Over time product mix and customer markets shift, suppliers come and go and chaos and randomness increases.  SCO is a analytical approach to aligning and balancing the tradeoffs in customer service, inventory levels, production and stocking locations, freight and facility costs.

Once we have a model that closely reflects the actual network costs and performance we can run the ‘solver’ or ‘optimizer’ and see where we should have shipped our product from given the current facilities and policies. With What-if scenarios we can start exploring possible future sates.

• What if we opened up a new west coast facility?
• We’re growing, where are we going to put our next plant?
• With the recent acquisitions we’ve made how many warehouses do we need and where?
• Given two equally capable suppliers which one will have the lowest transportation cost?
• Should we ship product from warehouse A or warehouse B to optimize lead time and freight?
• What’s our carbon footprint? What changes can we make in our network to reduce it?
• Is it better to keep all of the new product in one DC or push them out to the branches?

Example graphics from ILOG Logicnet

## Warehouse Photos

While warehouses rarely make a contribution to architecture, they fill an important role in the supply chain. Here’s a collection of warehouses photographs.

## Trucks and Trucking Photos

“The little four-wheelers live on risk. They endanger themselves. They endangered us. If you’re in a big truck, they’re around you like gnats.”, wrote John McPhee in the February 17, 2003 issue of The New Yorker.  Have always loved the visual variety, powerful sounds that 18 wheelers make.  Here’s a great collection of photographs.

## Shipping Container Photos

Containers, cargo, ports, ships, industry… all that gray, steamy, rusty, stinky but at the same time fascinating stuff.  If you are a visual thinker and  fascinated, as I am, by how goods are moved then take a browse through this collection of photographs.