Many business leaders fail to see the opportunities that an economic downturn provides. To take advantage of opportunities you first need to make a rapid assessment of your vulnerabilities and then move quickly to minimize them. Advantage can be gained by using process improvement tools to increase profitability and competitiveness:
- Drive out waste through Lean Sigma operations
Tools: value stream mapping, line balancing, kaizen to reduce direct and overhead labor
- Customer Collaboration
Tools: customer segmentation, goal alignment, cycle time reduction, product line rationalization
- Supplier Optimization
Tools: supplier & material consolidation, lead time reduction, product design-to-cost
- Accelerate cash flow and aggressively manage working capital
Tools: inventory reduction, excess/slow moving/obsolete inventory analysis, safety stock tuning, cycle time reduction to reduce WIP, receivables and payables management
- Increase Capacity
Tools: overall equipment effectiveness, theory of constraints, quick changeover, product family turnover analysis
- Product Redesign
Tools: component substitution, design for six sigma, design for manufacturability, value engineering
Johann Carl Friedrich Gauss (1777-1855) seen here on a German 10 Deutsche Mark banknote was a mathematician, astronomer, scientist noted for least squares, the normal distribution, and number theory. Also known as the Gaussian distribution, the normal distribution is defined by the mean ("average", μ) and variance (standard deviation squared, σ2). The standard normal distribution is the normal distribution with a mean of zero and a variance of one. Common applications include process modeling, queuing theory, safety stock and service level calculations, as was as statistical process control.
1. Ownership Rule – the person who first detects the problem is responsible for finding the root cause of the problem.
2. Quickly Rule – the problem must be dealt with and solved within 30 minutes, not put on a list or in a report for action at another time.
3. Actually Rule – if possible play back or recreate the process that occurred before the defect.
4. Support Rule – the person who detects the problem has primary responsibility for solving it, but supervisor and fellow workers can stop working and lend problem solving support.
5. Shut Up Rule – the discoverer is expected to solve the problem and be allowed time to dicsuss the problem and attempt to solve it. Others can help but the supervisor or manager must keep quiet and give the person a chance to solve the problem.
Recently read Edward R. Tufte’s
The Cognitive Style of PowerPoint: Pitching Out Corrupts Within and initially dismissed his thesis as troglodyte. Now sensitized, I’ve been watching for evidence of PowerPoint Abuse. Found an unfortunate example with two parallel teams during a strategic capital equipment review. Both teams were given the same mission and access to data: scrutinize the new capital equipment plans, challenge assumptions, collect new data and define cost reduction and risk mitigation plans. Both teams were staffed with bright industrial, process, manufacturing, quality engineers who pulled on other subject matter experts in their data gathering. Leadership effectively facilitated and guided both teams through the current state to future state diagnostic journey. Significant productivity, utilization, overall equipment effectiveness opportunities were identified and tested over the two week full-time exercise.
One team plastered their “war room” with all of their data, continuously rearranging their wall, retelling their story. The other team began typing their findings and abandoned their wall after a couple of days. Individual leaders would visit with the teams randomly throughout the study period but never “walked the wall”, instead expected PowerPoint slides for the daily out briefs. Attempts were made to reconcile the two teams leading up to a joint presentation to senior management.
Bottom line – what’s the new equipment price tag to support the new 5 year strategic operating plan?
One team argued for showing both the prior and new estimates as side by side stacked bar charts, the other team just a table listing the $9.6 million delta.
Despite coaching challenges the delta display won out. Too bad because the Executive VP had remembered “the number” and misinterpreted the table. Had the first team taken the EVP on a tour of their wall the message would have been clearer.
When I first learned inventory planning the math was rather simple. On top of the cycle stock (expected demand during lead time) I would add a percentage or a number of days (or more likely weeks). If the lead time was 2 weeks I might carry 3 or 4 weeks. I soon learned that demand for some inventory items is more volatile than for others, and some suppliers less reliable than others. I’d rather have too much then not enough, and I’d never gotten in trouble for having a little too much.
So since all items and situations are different I started using some statistics; (average demand * lead time) + (one sided Z factor * demand standard deviation) for the target inventory level; a little better approach.
Here’s another formula from Inventory Management Review;
Safety Stock:
{Z * SQRT (Avg. Lead Time * Standard Deviation of Demand ^2 + Avg. Demand ^2 * Standard Deviation of Lead Time ^2}.
Over at QuickMBA ; To calculate the safety stock, first calculate the standard loss function, designated as L(z). This function is dependent on the values of the desired fill rate f, the demand μ and its standard deviation σ , the time between orders p, and the replenishment lead time l : L(z) = ( 1 – f ) µ p / σ ( p + l )1/2. Once L(z) is known, z can be found in a look-up table and the safety stock can be calculated by:
Safety Stock = z σ ( p + l )1/2
Here’s a new one recently published by Kent Linford in the APICS Magazine Nov/Dec 2006.
SS = √ [( σFE)2 x (LTI/FI)beta + ( σLT)2 x D2] x Z x (FI/OCI)beta
Where:
SS = safety stock
FE = forecast error
LT = lead time interval
FI = forecast interval (pick a beta between 0.5 and 0.7)
D = average demand during lead time
Z = normal distribution service factor based on desired service level
OCI = order cycle interval
Dave Piasecki at InventoryOps.com uses;
safety stock = (standard deviation)*(service factor)*(lead-time factor)*(order cycle factor)*(forecast-to-mean-demand factor)
Jon Schreibfeder has another approach.
Any questions? Got any other versions? What formula do you use?
