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 [...]]]> 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

Unlike a ‘floater’, a waterspider is assigned specific tasks, such as replenishing raw material inventories (via milk run), common area clean-up, communicate status, maintain visual metrics, etc… Waterspider duties usually don’t include tasks which take them away from the production area, or [...]]]> The term Waterspider or water beetle (mizusumashi in Japanese) comes from the behavior of the insect known in the States as a whirligig, an aquatic animal that skitters around on the top of a pond quickly changing direction as it goes.  For a lean enterprise the role of material handlers, expediters, and support staff changes. In the Toyota Production System this is the common name for a person assigned to support a production operation, so that others may focus exclusively on value-added work. The waterspider delivers parts to the other associates in the cell or on the line so that they don’t need to stop to replenish their work stations.

Unlike a ‘floater’, a waterspider is assigned specific tasks, such as replenishing raw material inventories (via milk run), common area clean-up, communicate status, maintain visual metrics, etc… Waterspider duties usually don’t include tasks which take them away from the production area, or detract from their specific, assigned duties (the waterspider is not the ’5S’ person or a ‘fill in’). Think of the waterspider as the ‘race car pit crew’ for the production team, without which it would be impossible to win or even run the race.

Waterspiders quickly become experts in the withdrawal and production kanban system. They can ‘see’ more of the up and down stream flow in real time than most others, and because of this often making it possible to identify and eliminate errors. From recent experience the waterspiders often have a better grip on reality than their managers, planners, and engineers.

Non manufacturing examples abound in restaurants, hospitals, insurance claims processing; serving the folks that add the value isn’t just for manufacturing. In product and software development the role of the program manager is sometimes something like that of the waterspider, except bringing knowledge to the various development team members instead of parts.

Here are a few references:
Single piece flow at ConMed Linvatec
Improving Workflow With Water Spiders at University of Michigan Health System
Inventory management in electronics manufacturing: The Move To Lean
Lean in the Oil Fields

Have any examples you’d like to share?

Discontinuities are big, foundational shifts which have the potential to fundamentally change the rules of the game. They occur when trends from different areas combine, resulting in game-changing, long-lasting modifications to the external landscape. If you can identify them earlier and exploit them better than your competitors, you will win. If you don’t, you will be left behind.

You need to assess how the current crisis has affected the discontinuities you were anticipating before the recession hit. Say that until recently, you thought that being “green” meant addressing your carbon footprint. But did you know that by the time we emerge from the recession, “green” will likely mean using efficiently multiple scarce resources, such as water? If your stakeholders are asking about your carbon footprint today, then tomorrow they’ll be asking about your water footprint. You need to get ahead of this opportunity – [...]]]> Dave Jones and Pierre Loewe write in Chief Executive Magazine …

Discontinuities are big, foundational shifts which have the potential to fundamentally change the rules of the game. They occur when trends from different areas combine, resulting in game-changing, long-lasting modifications to the external landscape. If you can identify them earlier and exploit them better than your competitors, you will win. If you don’t, you will be left behind.

You need to assess how the current crisis has affected the discontinuities you were anticipating before the recession hit. Say that until recently, you thought that being “green” meant addressing your carbon footprint. But did you know that by the time we emerge from the recession, “green” will likely mean using efficiently multiple scarce resources, such as water? If your stakeholders are asking about your carbon footprint today, then tomorrow they’ll be asking about your water footprint. You need to get ahead of this opportunity – IBM and Toyota already have.

Lesson: Analyze how the recent events have changed the external landscape — impacted the discontinuities you had previously identified, or created new, unexpected ones.

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 [...]]]> 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?

Reference: http://walmartstores.com/FactsNews/NewsRoom/9277.aspx

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 [...]]]> 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.

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 [...]]]> 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.

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

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 [...]]]> 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
• Adding or relaxing constraints.

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.

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 [...]]]> 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.
4. Transportation options: Less-Than-Truckload vs. Full truckload.
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.
2. Strategic planning of capacity for all regions and all products.
3. Make vs. Buy for a business unit.
4. Annual budgeting based on sourcing.
5. Supply planning for all products across all regions.
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.