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.
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.
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”.
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 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.
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