Supply chain integration

    Various supply chain strategies    Push strategies Pull strategies Push-pull systems Matching products or industries with supply chain strategies Impact of the Internet on supply chain integration Effective distribution strategies  Direct shipment   Warehousing Cross-docking

Push Strategies

   Production decisions based on long-term forecasts Ordering decisions based on inventory & forecasts What are the problems with push strategies?

   Inability to meet changing demand patterns Obsolescence The bullwhip effect:  Excessive inventory   Excessive production variability Poor service levels  Hard to predict production capacity or transportation capacity

Pull Strategies

   Production is demand driven   Production and distribution coordinated with true customer demand Firms respond to specific orders Pull Strategies result in:     Reduced lead times (better anticipation) Decreased inventory levels at retailers and manufacturers Decreased system variability Better response to changing markets But:  Harder to leverage economies of scale  Doesn ’ t work in all cases

Pull strategies – a Kanban system

Inbound buffer Outbound buffer Inbound buffer Outbound buffer Move cards Production cards Move cards Production cards

Push-Pull Systems

Raw Materials Push Pull Boundary Push Strategy Pull Strategy Supply chain time line End Customer

Push-pull systems

 

A shift from a Push System...

 Production decisions are based on forecast

…to a Push-Pull System

 Initial portion of the supply chain is replenished based on long-term forecasts  For example, parts inventory may be replenished based on forecasts  Final supply chain stages based on actual customer demand.

 For example, assembly may based on actual orders.

Consider Two PC Manufacturers:

  Build to Stock    Forecast demand Buys components Assembles computers  Observes demand and meets demand if possible.

A traditional push system   Build to order    Forecast demand Buys components Observes demand   Assembles computers Meets demand A push-pull system

Push-Pull Strategies



The push-pull system takes advantage of the rules of forecasting:

 Forecasts are always wrong  The longer the forecast horizon the worse the forecast  Aggregate forecasts are more accurate  Risk Pooling impact  Delayed differentiation is another example  Consider Benetton sweater production

What is the Best Strategy?

Pull Demand uncertainty (C.V.) H I Computers II Furniture Push L IV Books & CDs L Pull III Grocery Push H Delivery cost Unit price Economies of Scale

Selecting the Best SC Strategy

    Higher demand uncertainty suggests pull Higher importance of economies of scale suggests push High uncertainty/ EOS not important such as the computer industry implies pull Low uncertainty/ EOS important such as groceries implies push  Demand is stable  Transportation cost reduction is critical  Pull would not be appropriate here.

Selecting the Best SC Strategy

 

Low uncertainty but low value of economies of scale (high volume books and CDs)

 Either push strategies or push/pull strategies might be most appropriate

High uncertainty and high value of economies of scale

   For example, the furniture industry How can production be pull but delivery push?

Is this a “pull-push” system?

Characteristics and Skills

Portion

Objective Complexity Focus Lead time Processes

Push

Minimize cost High Resource allocation Long Supply chain planning

Pull

Maximize service level Low Responsiveness Short Order fulfillment

Locating the Push-Pull Boundary

    The push section:  Uncertainty is relatively low   Economies of scale important Long lead times  Complex supply chain structures: Thus  Management based on forecasts is appropriate   Focus is on cost minimization Achieved by effective resource utilization – supply chain optimization The pull section:  High uncertainty   Simple supply chain structure Short lead times Thus  Reacting to realized demand is important   Focus on service level Flexible and responsive approaches

Locating the Push-Pull Boundary

  

The push section requires:

  Supply chain planning Long term strategies

The pull section requires:

 Order fulfillment processes  Customer relationship management

Buffer inventory at the boundaries:

  The output of the tactical planning process The input to the order fulfillment process.

Locating the Push-Pull Boundary

Demand-driven strategies

   

Demand forecast:

expected demand Using historical data to develop long-term estimates of

Demand shaping:

Determining the impact various marketing plans such as promotions, pricing discounts, rebates, new product introductions and product withdrawal on demand forecasts Inaccuracy of the forecast has a detrimental impact on supply chain performance: lost sales, obsolete inventory, inefficient resource utilization Employing supply chain strategies to reduce the impacts of forecast inaccuracy  Select the push-pull boundary so that the demand is aggregated over different dimensions: products, geography, time   Use market analysis and demographic and economic trends to improve forecast Determine the optimal assortment of products by store to reduce the impact of competing SKUs in the same market  Incorporate collaborative planning and forecasting processes with customers to better understand market demand, impact of promotions, pricing and advertising

Impact of Internet on SCM



What does internet change for a supply chain?

 Enables a whole new business model.

 Online purchasing, direct shipping, auctioning, secondary markets  Improves or enables integration between different parties of the supply chain  Enables information sharing  Enables collaboration  Reduces lead times  Reduction in order processing times  Improves product availability

Impact of internet

   E-business: a collection of business models and processes motivated by Internet technology and focusing on improvement of extended enterprise performance  Business-to consumer (B2C): “direct to customer”, retail activities over the internet  Business-to-business (B2B): business conducted over the internet between businesses Impact of internet  Move from push to pull systems   Significant failures as a result Move from pull systems to push-pull systems Many

click-and-mortar

companies established. Many

brick-and mortar

companies opened online stores. Some has been successful: Dell, Cisco, Amazon. Many have failed

Grocery Industry

   Example: Peapod  Founded early 90s  Implemented a pure pull system. When an order is received, the products are picked from a nearby supermarket. Problems  Service problems: significant stock-outs  Reduced profit margins  Moved to a push-pull system. Set up a number of warehouses  Stock-out rates are lower  As compared to traditional grocers, the demand is aggregated over a larger geographical area Transportation costs versus response time  Shipments in small batches  Response time within 12 hours  No sufficient density of customers to control transportation costs Many failures so far: shoplink.com, streamline.com, etc

Book industry

  Example: Amazon.com. World’s largest bookseller  Founded in 1994  Implemented a pure pull system where it utilized Ingram Book Group to supply customer demand. Appropriate when Amazon was building its brand name. Issues became clear later when the demand increased  Ingram’s distribution capacity supports many other booksellers. Service issues for Amazon during peak demand  Amazon had to share its profit margins with Ingram   Amazon established several warehouses, where the inventory is procured using a push strategy, orders are shipped using a pull strategy  May still use a pure pull strategy for slower items World’s largest bookseller with $3.9 Billion sales in 2002. But yet to make a profit Response time is not as critical as grocers. May use parcel services.

Retail industry

   Traditional retailers added online shopping component to their offering: Wal-Mart, Kmart, Target and Barnes and Noble. Advantageous over pure Internet companies  They already have the distribution and warehousing infrastructure in place  Established brand name  Easy returns and reverse logistics Different strategies for different products  High-volume, fast-moving products stocked in stores and available online  Low-volume, slow-moving products are stocked centrally and available only online Moving from a traditional business to internet based business may require different skills that are not present in many traditional businesses

Impact of internet on fulfillment

Supply chain strategy Shipment Reverse logistics Delivery destination Lead times Traditional fulfillment Push Bulk Small part of business Small number of stores Relatively long E-fulfillment Push-pull Parcel Important and highly complex Large number of geographically dispersed customers Relatively short

Spearman

    Analyzes service level in pull systems Kanban system versus a base stock policy  Kanban would not place an order for more parts if a demand had arrived when there was no stock in the outbound stock point  Kanban has a constant WIP Stochastic ordering and Kanban systems. Two Kanban systems A and B, A has service times S A and B has service times S B .  If S A is stochastically larger than S B , then system A has worse service than system B  Two normal distributions with same variance, m

A

< m

B

 If S A is stochastically larger than S B , in the sense of increasing convex ordering, then system A has worse service than system B  Two normal distributions with same mean, s

A

< s

B

More Kanban (WIP) leads to higher service in Kanban systems

Spearman - continued

   The study argues that the superiority of Kanban systems is not the fact that material is pulled everywhere, but the fact that the WIP is constant Develops a system called a constant WIP system, or CONWIP   Pulling only at the first station Pushing on the rest of the chain using CONWIP backlog  Can utilize common setups  Consumption of item A may lead to start of the production for item B CONWIP system outperforms Kanban system   Better service May be more appropriate for large setup times, changing product mix.

Rajagopalan

  Develops a model to decide whether an item should be MTO or MTS  Items that are MTO. No inventory cost. Only setup time. Orders of a same item within a bucket still shares the setup. Ship to customer within T with probability P O  Items that are MTS. Follows an (r, q) policy. Cycle stock and safety stock. Service level (type I) of P S Decisions for items are not independent of each other. Trade-offs  Making an item MTO reduces the inventory for that item, but  Leads to more setups, thus higher capacity utilization and larger lead times   Leads to more variability in lead times Thus larger safety and cycle stocks for MTS items, poorer service for MTO items  Decreasing the lot size for MTS similar effects

Rajagopalan - continued

  Insights    MTO systems may be less costly but not always feasible Medium demand items are attracted to MTS Higher holding costs -> MTO   Higher setup costs -> MTS Larger processing times with high demands -> MTO Impact of priority queues (MTO orders are prioritized)   Leads to more MTO items However leads to more cycle stocks and safety stocks for MTS items

Stochastic ordering

0.5

0.4

0.3

0.2

0.1

0 1 0.9

0.8

0.7

0.6

0 20 40 60 80 m

=20

s

=5

m

=30

s

=5

Increasing convex stochastic ordering

1 0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0 0 20 40 60 m

=20

s

=5

m

=20

s

=8

80