Got asked what would happen to inventory when the number of stocking locations change. I thought for a minute and remembered a quick estimate. The Square Root Law states that total safety stock can be approximated by multiplying the total inventory by the square root of the number of future warehouse locations divided by the current number.
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.
Determining an appropriate production model starts with Demand Profile and Demand Segmentation. High volume low variability items, and low volume high variability items behave very differently. How to decide if a particular product is a candidate for a one piece flow cell or a craftsmen job bench? Look to the coefficient of variation for a clue.
Type 1 – Rate-base or Just-in-time
- forecasting of the flow rate or takt time
- RCCP – rough cut capacity planning to monitor impact of mix and volume on pace maker operation
- produce to rate (or TAKT) vs discrete order or customer pull
- demand flow vs time-phased requirements planning
- maintain flow priority and timing
- no detailed Capacity Requirements Planning required
- no or minimal shop order launch or inventory transactions
- highly visual and standardized shop floor control
- “one-piece” flow, zero inventory, standard WIP – work-in-process
- seamless flow/pull of material
- Dynamic cycle time (Little’s Law)
Type 2 – Pull
- combination of discrete forecasting and/or demand rate-based forecasting
- MRP planning — pull Kanban, Heijunka visual shop floor control
- RCCP, but no detailed CRP
- flat Bills Of Materials
- more cellular manufacturing
- point-of-use vs. central stores
- inventory is strategic: standard inventory, time-based replenishment, pull based on consumption vs. push based on demand
- based on statistically balanced rate, build to level-loaded demand with calculated standard inventory buffers
Type 3 – Push or Job Shop Discrete
- discrete requirements planning (firm orders and long range forecast)
- Rough Cut Capacity Plan
- time phasing of requirements
- application of order policies: lead time, safety stock & time
- Capacity Requirements Planning
- MRP shop order launch & order maintenance (message filters and “noise management”)
- ability to aggregate disparate requirements across multiple products by work center, supplier, product
- central stores of inventory
- multi-level inventory: stores, pick, kit, move, queue
- batch processing
- demand leveling difficult and uneconomical
- Determine Total Cycle Time
- Determine Queue Times between steps
- Create Step segments proportional to the task times
- Place steps, queue’s along the line segment in the order that they happen
> Place Value Adding steps above the line
> Place Non-value Adding steps below the line
- Draw in feedback loops & label Yield percentages
- Sum Activity / Non-activity times
- Sum Value / Non-value Times
Oil Production
- Issue: Oil producer was experiencing long and unpredictable plant maintenance turnarounds (also known as a shutdown or an outage). Downtime durations consistently exceeded plans and budgets, and this resulted in lost production and excessive maintenance costs.
- Solution: Applied lean techniques including:
- SMED (task separation, conversion, and simplification to complete outage tasks outside the outage window),
- Constraint Busting (e.g., dramatically improved critical path management),
- Visual Controls (e.g., optimized Outage Command Center),
- Streamlined Collaborative Planning (e.g., earlier and greater involvement of contractors and cross functional personnel), and
- High impact metric capture and utilization (e.g., shift in focus from “Cost only” to Cost and Duration).
- Result: Greater Profits – More stable outage planning and execution process with a reduction in outage durations of 10-15% resulting in increased production valued at $25 million increase in annual profit. Reduced Costs – Decrease in outage costs valued at $2-3 million annualized. Improved Collaboration – Significantly improved collaboration among all groups, greater performance reporting transparency, and improved continuous improvement through capture and application of lessons from one outage to another.
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