1、附录一 Supply chain optimization Supply Chain Optimization is the application of processes and tools to ensure the optimal operation of a manufacturing and distribution supply chain. This includes the optimal placement of inventory within the supply chain, minimizing operating costs (including manufact
2、uring costs, transportation costs, and distribution costs). This often involves the application of mathematical modelling techniques using computer software. What need is being addressed? Typically, supply chain managers are trying to maximize the profitable operation of their manufacturing and dist
3、ribution supply chain. This could include measures like maximizing gross margin return on inventory invested (GMROII)( balancing the cost of inventory at all points in the supply chain with availability to the customer ), minimizing total operating expenses (transportation, inventory and manufacturi
4、ng), or maximizing gross profit of products distributed through the supply chain. Supply chain optimization addresses the general supply chain problem of delivering products to customers at the lowest total cost and highest profit. This includes trading off the costs of inventory, transportation, di
5、stributing and manufacturing. Supply chain optimization has applications in all industries manufacturing and/or distributing goods, including retail, industrial products, and consumer packaged goods (CPG). What approaches and solutions exist? The classic supply chain approach has been to try to fore
6、cast future inventory demand as accurately as possible, by applying statistical trending and best fit techniques based on historic demand and predicted future events. The advantage of this approach is that it can be applied to data aggregated at a fairly high level (e.g. category of merchandise, wee
7、kly, by group of customers), requiring modest database sizes and small amounts of manipulation. Unpredictability in demand is then managed by setting safety stock levels, so that for example a distributor might hold two weeks of supply of an article with steady demand but twice that amount for an ar
8、ticle where the demand is more erratic. Then, using this forecast demand, a supply chain manufacturing and distribution plan is created to manufacture and distribute products to meet this forecast demand at lowest cost (or highest profitability). This plan typically addresses the following business
9、concerns: - How much of each product should be manufactured each day? - How much of each product should be made at each manufacturing plant? - Which manufacturing plants should re-stock which warehouses with which products? - What transportation modes should be used for warehouse replenishment and c
10、ustomer deliveries? The technical ability to record and manipulate larger databases more quickly has now enabled a new breed of supply chain optimization solutions to emerge, which are capable of forecasting at a much more granular level (for example, per article per customer per day). Some vendors
11、are applying best fit models to this data, to which safety stock rules are applied, while other vendors have started to apply stochastic techniques to the optimization problem. They calculate the most desirable inventory level per article for each individual store for their retail customers, trading
12、 off cost of inventory against expectation of sale. The resulting optimized inventory level is known as a model stock. Meeting the model stock level is also an area requiring optimization. Because the movement of product to meet the model stock, called the stock transfer, needs to be in economic shi
13、pping units such as complete unit loads or a full truckload, there are a series of decisions that must be made. Many existing distribution requirements planning systems round the quantity up to the nearest full shipping unit. The creation of for example, truckloads as economic shipment units require
14、s optimization systems to ensure that axle constraints and space constraints are met while loading can be achieved in a damage-free way. This is generally achieved by continuing to add time-phased requirements until the loads meet some minimum weight or cube. More sophisticated optimization algorith
15、ms (ORTEC) take into account stackability constraints, load and unloading rules, palletizing logic, warehouse efficiency and load stability with an objective to reduce transportation spend (minimize shipping air). Optimization solutions are typically part of, or linked to, the companys replenishment
16、 systems distribution requirements planning, so that orders can be automatically generated to maintain the model stock profile. The algorithms used are similar to those used in making financial investment decisions； the analogy is quite precise, as inventory can be considered to be an investment in
17、prospective return on sales. Supply chain optimization may include refinements at various stages of the product lifecycle, so that new, ongoing and obsolete items are optimised in different ways: and adaptations for different classes of products, for example seasonal merchandise. Whilst most softwar
18、e vendors are offering supply chain optimization as a packaged solution and integrated in ERP software, some vendors are running the software on behalf of their clients as application service providers. What are the claims for supply chain optimization? Firstly, the techniques being applied to suppl
19、y chain optimization are claimed to be academically credible. Most of the specialist companies have been created as a result of research projects in academic institutions or consulting firms: and they point to research articles, white papers, academic advisors and industry reviews to support their c
20、redibility. Secondly, the techniques are claimed to be commercially effective. The companies publish case studies that show how clients have achieved reductions in inventory whilst maintaining or improving availability. There is limited published data outside of these case studies, and a reluctance
21、for some practitioners to publish details of their successes (which may be commercially sensitive), therefore hard evidence is difficult to come by. Flowcasting Collaborative Flowcasting is a business process that connects real time daily consumer demand to trading partners in the retail supply chai
22、n to create an integrated and comprehensive Model of the Business. While the concept is simple and intuitive, technology has historically limited the ability of software applications to scale economically a complete retail supply chain (in a time-phased manner and one year into the future) to the vo
23、lumes required by the largest retailers and their suppliers The Collaborative Flowcasting process starts at the head of the retail supply chain (the retail store) and creates a unique sales forecast for every product in every store and calculates time phased requirements one year into the future all
24、 the way from the store shelf to the factory. This process enables retail trading partners to manage their entire retail supply chain inside a single system and driven by A Single Set of Numbers. The Collaborative Flowcasting business process described in Andre Martin, Mike Doherty and Jeff Harrops book “Flowcasting the Retail Supply Chain.” Actual results to date demonstrate that deploying the Collaborative Flowcasting business process will enable retailers and their trading partners to increase store in- stock availability into the 98-99+% range resulting in sales increases of 3%-4% at no
