Push or Pull

Push or Pull

There are two conceptual models for inventory management – push and pull. The push inventory model is so called because the emphasis is on “pushing” speculative inventory, made-to-forecast (MTF) in response to forecasted demand, out the door to customers. The push model financially outperforms the pull model when manufacturing utilization is critical and the cost of production is high relative to inventory carrying cost and to the risk of obsolescence. We have recently helped successfully convert a variety of clients in the CPG, food, beverage, and confectioners industries to push models resulting in much higher profits, return on invested capital, market share, and customer satisfaction.

 

Push

Definition: Sell what you make.

When: When keeping manufacturing utilization high is critical and the cost/risk of obsolescence is low.

Examples: Cigarettes, Dog Food, Candy

 

The pull inventory model is so called because true demand is said to pulling made-to-order inventory to customers on a just-in-time basis. The pull model financially outperforms the push model when the cost of inventory carrying cost and risk of obsolescence are high relative to production and postponementcosts. Products such as high-end, highly configurable electronics and pharmaceuticals are examples of products that work best financially and operationally in pull-based systems.

 

Pull

Definition: Make/ship what you sell.

When: When inventory is very expensive, postponement is feasible, and there is high cost and risk of obsolescence.

Examples: Retail Apparel, Personal Computers

 

Over the years since the advent of pull-based systems like the Toyota Production System (TPS), Just-in-Time (JIT), and Lean – many demonstrative proponents of pull-based inventory and supply chain management have published and soap-boxed to the point where any other approach to inventory or supply chain management is considered second-class, immature, or old fashioned. Yet, when we work with our clients to compute true return on invested capital, profitability, and customer satisfaction for each of their SKUs moving through each node and link in their unique supply chains we are finding that an optimal mix of push and pull depending upon the product characteristics and transition point within the supply chain yields dramatically superior financial and service performance. That optimal mix is based on a wide variety of item characteristics including demand variability, item value, shelf life, and risk of obsolescence AND logistics characteristics including setup/PO costs and inventory carrying rates. A qualitative presentation of those factors and their impact on push-pull models is presented in the figure.

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Push-Pull Decision Factors

On-Going Customer Satisfaction Monitoring

On-Going Customer Satisfaction Monitoring

Once the customer service policy has been established, monitoring the performance to it and overall customer satisfaction are keys to maintaining customer intimacy – keeping the pulse on the customer.  (The greatest business failures can be traced to companies losing step with customer requirements.)   Customer satisfaction monitoring is a key discipline of customer response organizations and can be used to prioritize logistics initiatives and to maintain constructive customer communications.  Customer satisfaction surveys can be implemented over the Internet, over the telephone, and/or in person.  In fact some element of customer satisfaction should be monitored during each customer interaction.  The survey process should begin by having the customers decide and rank the factors that define customer satisfaction for them.  The survey should permit the customer to then rank our performance relative to expectations and relative to the competition with respect to the key factors identified by the customer.

 

Lot Size

Lot Size

The lot size (LS) (also known as the replenishment quantity (RQ) or the cycle stock (CS)) is the number of units that arrive in a replenishment lot or are produced in a manufacturing lot (Points 1, 2, and 3 in the figure). The average replenishment quantity (ARQ) is the average size of lot size replenishments derived by dividing the total replenishment quantity over a particular period of time by the number of replenishments received during that time.

 

Economic Order Quantity

Economic Order Quantity

The economic order quantity (EOQ) is the lot size that minimizes the sum of ordering cost and inventory carrying cost associated with the size of the order (see figure). The higher the order quantity, the greater the inventory level.  However, the higher the order quantity the fewer the number of orders and the lower the resulting ordering cost.

The economic run quantity (ERQ) is the production lot size (or run quantity) that minimizes the total of setup/changeover costs and the inventory carrying costs associated with the inventory produced by the run length. The tradeoffs between manufacturing setup cost and inventory carrying costs for determining optimal production run sizes for a large textiles client are illustrated in the figure below. Note in the example that the optimal run length is 3 or 4 rolls per setup for that particular SKU. As is often the case with EOQ modeling, the total cost curve is fairly flat near the optimal solution. The key, as is often the key, is to make decisions that are at least in the “ballpark of optimal”. Unfortunately we often find that lot sizing is off by 200% or 300%.

The formula to compute the EOQ for a purchased item is as follows:

EOQ = {(2 x FAD x POC) / (UIV x ICR)}1/2

For example, if an item has an annual demand of 3,000 units per year; a purchase order cost of $300 per purchase order; a purchase price of $2,100 per unit; and an inventory carrying rate of 30% per year then its EOQ is

EOQ = [(2 x 3,000 x $300)/($2,100 x 30%)] ½ = [(1,800,000)/(630)] ½ = [2,857]1/2 = 53 units

The formula to compute the EOQ for a manufactured item, sometimes referred to as the economic run quantity (ERQ) is as follows:

ERQ = {(2 x FAD x SUC) / (UIV x ICR)}1/2

For example, if an item has an annual demand of 5,000 units per year; a setup cost of $3,200 per setup; a standard cost of $85.00 per unit; and an inventory carrying rate of 25% per year then its EOQ is

EOQ = [(2 x 5,000 x $3,200)/($85 x 25%)] ½ = [(32,00,000)/(21.25)]½ = [1,505,882]1/2 = 1,227 units

EOQ is considered passé, outdated, and nearly pre-historic in many inventory circles. Yet, in our work with the most advanced supply chain organizations around the world we are finding great profit, service, and operational improvements with EOQ.

Unit Fill Rate (UFR)

Unit Fill Rate (UFR)

The unit fill rate (UFR) for an item is the portion of the total number of units requested with inventory available to fill the request. It is distinct from and higher than line fill rate (% of lines shipped complete) and order fill rate (% of orders shipped complete). The target unit fill rate is a decision, not an outcome. It is perhaps the most important inventory planning decision of all.

As discussed previously, the higher the unit fill rate, the lower the lost sales cost.  However, the higher the unit fill rate, the greater the inventory required to provide it, and the greater the resulting inventory carrying cost.  There are many ways to determine optimal target unit fill rates. One method is to choose the unit fill rate that minimizes expected inventory policy cost. Another method is to choose the unit fill rate that maximizes expected GMROI. Still another method is to choose the unit fill rate that maximizes IVA. What do we do? It depends on the financial, service, and operational goals. The ability to visualize and simulate those relationships as demonstrated in the figure from the RightStock™ Inventory Optimization System is the key and often missing piece in the inventory strategy puzzle.

As explained earlier, fill rate requirements go a long way toward determining overall inventory requirements.  Simply put, all things being equal, the higher the fill rate requirement, the higher the inventory level required to support it. The higher inventory levels are the result of additional safety stock inventory.

An example inventory and fill rate analysis from a recent engagement in the health and beauty industry is provided in Figure 2. Note that as fill rate increases (from 50% to 99.95%) the required inventory investment increases accordingly from $4,646,094 to $8,644,548. At the same time, lost sales cost declines from a high of $17,953,234 at a 50% fill rate to a low of $17,953 at a 99.95% fill rate.

The current inventory investment in the example was $8,300,000 and the lost sales cost was $3,949,712. The inventory investment that should have yielded a 99.9% fill rate only yielded an 87% fill rate. The discrepancy turned out to be a major mis-deployment of inventory.

Customer Service Policy

Customer Service Policy

I have heard it said, ”Either manage the customers or they will manage you.”  The customer service policy (CSP) is the first step in proactive customer and demand management.  The customer service policy (CSP) is the contract between the logistics organization and the customer.  It defines the service targets and objectives for logistics.  The customer service policy sets the service requirements for each logistics (1) the flow of material, information, and money between consumers and suppliers including the processes of customer service, inventory management, supply, transportation, and warehousing (2) the activiies of customer service, inventory management, supply, transportation, and warehousing process including inventory management, supply, transportation, and warehousing.  The customer service policy is the foundation for logistics master planning.