acturer of commercial and residential lighting, they recognized that in order to be more competitive they would have to reduce costs while increasing quality and service levels. For Juno, this meant adopting lean manufacturing processes, sourcing components from Asia, and optimizing their inventory beyond the capabilities of the ERP system.

Once the inventory optimization solution was implemented, Juno was capable of providing critical data and analyses across multiple SKU families. They are now performing “what-if” scenarios to examine how much inventory is required to meet a particular service level by facility, by product family

Anyone who watches or reads the news today hears about how the sourcing of materials from overseas, particularly from Asia, has impacted the amount of manufacturing jobs available in America. What manufacturing insiders worry about the most when it comes to sourcing from overseas is more closely related to inventory levels. While purchasing material overseas may bring a company advantages in terms of lower prices, the negative impact is the growth of inventory carried on the balance sheet. For large manufacturing firms with the financial scale to absorb an increase in inventory, this may not present itself as an issue. However for manufacturing firms, the stress on working capital and the inventory turn rate can be significant.

The other issue related to inventory and sourcing from overseas is the variation in lead times. While one shipment may arrive in 6-8 weeks, the next shipment may arrive in 8-10 weeks. Since manufacturers cannot afford to be without materials, they’ll increase their safety stock levels to compensate for the variability with that lead time.

Certainly when we look toward solving an inventory dilemma, we naturally focus on how inventory is being planned. In order to do so, it’s important to understand the evolution of how inventory has been planned during the past 30-40 years. In the 1970’s companies began stratifying their stock by an ABC analysis. A’s representing the fastest movers for example. In the 80’s we were exposed to a more analytical approach by adding to the ABC analysis with other variables such as cost per unit, usage rates, and perhaps even a Gross Margin contribution per unit. In the 90’s we were introduced to ERP, MRP, and other similar applications. Today, sophisticated planning involves Advanced Inventory Optimization.

Inventory Optimization takes into account all dynamic variables that impact inventory levels. Through the use of these variables, and running them through algorithmic engines, an inventory profile can be achieved which will show an optimal balance between achieving the highest possible level of service with the lowest amount of network inventory.

The results often seen include 10-40% reductions in inventory while maintaining or increasing service levels (fill rates) above 98%. These results satisfy two competing divisions within any organization: that of finance, by maintaining lower levels of inventory; and that of sales, by maintaining higher service levels.

One example is the inventory optimization effort performed at Juno Lighting Group. As a manufacturer of commercial and residential lighting, they recognized that in order to be more competitive they would have to reduce costs while increasing quality and service levels. For Juno, this meant adopting lean manufacturing processes, sourcing components from Asia, and optimizing their inventory beyond the capabilities of the ERP system.

Once the inventory optimization solution was implemented, Juno was capable of providing critical data and analyses across multiple SKU families. They are now performing “what-if” scenarios to examine how much inventory is required to meet a particular service level by facility, by product family,

cturing firms, the stress on working capital and the inventory turn rate can be significant.

The other issue related to inventory and sourcing from overseas is the variation in lead times. While one shipment may arrive in 6-8 weeks, the next shipment may arrive in 8-10 weeks. Since manufacturers cannot afford to be without materials, they’ll increase their safety stock levels to compensate for the variability with that lead time.

Certainly when we look toward solving an inventory dilemma, we naturally focus on how inventory is being planned. In order to do so, it’s important to understand the evolution of how inventory has been planned during the past 30-40 years. In the 1970’s companies began stratifying their stock by an ABC analysis. A’s representing the fastest movers for example. In the 80’s we were exposed to a more analytical approach by adding to the ABC analysis with other variables such as cost per unit, usage rates, and perhaps even a Gross Margin contribution per unit. In the 90’s we were introduced to ERP, MRP, and other similar applications. Today, sophisticated planning involves Advanced Inventory Optimization.

Inventory Optimization takes into account all dynamic variables that impact inventory levels. Through the use of these variables, and running them through algorithmic engines, an inventory profile can be achieved which will show an optimal balance between achieving the highest possible level of service with the lowest amount of network inventory.

The results often seen include 10-40% reductions in inventory while maintaining or increasing service levels (fill rates) above 98%. These results satisfy two competing divisions within any organization: that of finance, by maintaining lower levels of inventory; and that of sales, by maintaining higher service levels.

One example is the inventory optimization effort performed at Juno Lighting Group. As a manufacturer of commercial and residential lighting, they recognized that in order to be more competitive they would have to reduce costs while increasing quality and service levels. For Juno, this meant adopting lean manufacturing processes, sourcing components from Asia, and optimizing their inventory beyond the capabilities of the ERP system.

Once the inventory optimization solution was implemented, Juno was capable of providing critical data and analyses across multiple SKU families. They are now performing “what-if” scenarios to examine how much inventory is required to meet a particular service level by facility, by product family

en planned during the past 30-40 years. In the 1970’s companies began stratifying their stock by an ABC analysis. A’s representing the fastest movers for example. In the 80’s we were exposed to a more analytical approach by adding to the ABC analysis with other variables such as cost per unit, usage rates, and perhaps even a Gross Margin contribution per unit. In the 90’s we were introduced to ERP, MRP, and other similar applications. Today, sophisticated planning involves Advanced Inventory Optimization.

Inventory Optimization takes into account all dynamic variables that impact inventory levels. Through the use of these variables, and running them through algorithmic engines, an inventory profile can be achieved which will show an optimal balance between achieving the highest possible level of service with the lowest amount of network inventory.

The results often seen include 10-40% reductions in inventory while maintaining or increasing service levels (fill rates) above 98%. These results satisfy two competing divisions within any organization: that of finance, by maintaining lower levels of inventory; and that of sales, by maintaining higher service levels.

One example is the inventory optimization effort performed at Juno Lighting Group. As a manufacturer of commercial and residential lighting, they recognized that in order to be more competitive they would have to reduce costs while increasing quality and service levels. For Juno, this meant adopting lean manufacturing processes, sourcing components from Asia, and optimizing their inventory beyond the capabilities of the ERP system.

Once the inventory optimization solution was implemented, Juno was capable of providing critical data and analyses across multiple SKU families. They are now performing “what-if” scenarios to examine how much inventory is required to meet a particular service level by facility, by product family

and running them through algorithmic engines, an inventory profile can be achieved which will show an optimal balance between achieving the highest possible level of service with the lowest amount of network inventory.

The results often seen include 10-40% reductions in inventory while maintaining or increasing service levels (fill rates) above 98%. These results satisfy two competing divisions within any organization: that of finance, by maintaining lower levels of inventory; and that of sales, by maintaining higher service levels.

One example is the inventory optimization effort performed at Juno Lighting Group. As a manufacturer of commercial and residential lighting, they recognized that in order to be more competitive they would have to reduce costs while increasing quality and service levels. For Juno, this meant adopting lean manufacturing processes, sourcing components from Asia, and optimizing their inventory beyond the capabilities of the ERP system.

Once the inventory optimization solution was implemented, Juno was capable of providing critical data and analyses across multiple SKU families. They are now performing “what-if” scenarios to examine how much inventory is required to meet a particular service level by facility, by product family

acturer of commercial and residential lighting, they recognized that in order to be more competitive they would have to reduce costs while increasing quality and service levels. For Juno, this meant adopting lean manufacturing processes, sourcing components from Asia, and optimizing their inventory beyond the capabilities of the ERP system.

Once the inventory optimization solution was implemented, Juno was capable of providing critical data and analyses across multiple SKU families. They are now performing “what-if” scenarios to examine how much inventory is required to meet a particular service level by facility, by product family, or even down to the individual SKU level. In doing so, their material planners are now able to play a more strategic function within their organization rather than the typical expeditor or “firefighter” role. The end result has been a reduction of safety stock by 25% while maintaining critically high service levels.