Classifications

• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
  • G06Q10/00—Administration; Management
  • G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
  • G06Q10/087—Inventory or stock management, e.g. order filling, procurement or balancing against orders

Definitions

• the present invention relates to a method and system for managing manufacturing, and, more particularly, to a method and system for determining demand and output variability analysis.
• BRP/MRP/DRP type systems are typically architected as hierarchical to pass data between independent nodes, rather than to solve planning and scheduling problems, as they exist throughout a supply chain.
• Dependencies for multiple enterprises between demand, material, capacity, logistics, customer allocations, supplier allocations, and related business constraints are not solved by these conventional systems.
• each enterprise When using conventional transactional execution systems, each enterprise generates its own plan from its point of view.
• Business Resource Planning (BRP) has been used in manufacturing businesses in orders to increase the speed of new products to market, to provide sufficient products to customers by carrying sufficient inventory, and to reduce production costs.
• BRP processes which directly impact inventory are demand forecasting, inventory planning, master production planning, material requirements planning, and distribution requirements planning.
• Material Requirements Planning is concerned with the ability to assemble and make available all the materials, parts, and supplies needed for a production run.
• Material Requirements Planning uses the projected inventory from the master production schedule to communicate the need for various materials throughout the manufacturing process and to inform suppliers of quantities and delivery dates. Inaccurate and out-dated projected inventory causes the manufacturing facility to order too much or too little material, leading to frequent orders changes to suppliers.
• Distribution Requirements Planning is concerned with the ability to maintain adequate inventory at distribution points outside the manufacturing facility. Such distribution points might be warehouses, terminals, or consignment stock at a distributor or customer. Distribution Requirements Planning systems calculate and set restock trigger points so that products can be shipped in time from the manufacturing facility to the distribution points. Distribution Requirements Planning depends on the accuracy of the demand forecast. Traditional ways to generate Distribution Requirements Planning use only the lead-time needed to manufacture and transport products from the manufacturing facility to the distribution point. No consideration is given to the customer orders lead-time available when a customer requests products from the distribution facility.
• EDI electronic data interchange
• the Design for Six Sigma can be applied to any process such as a business, manufacturing, service, etc.
• the sigma value is a metric that indicates how well that process is performing. The higher the sigma value, the better the output. Sigma measures the capability of the process to perform defect-free- work, where a defect is synonymous with customer dissatisfaction. With six sigma the common measurement index is defects-per-unit where a unit can be virtually anything - a component, a piece part of a jet engine, an administrative procedure, etc. The sigma value indicates how often defects are likely to occur. As sigma increases, customer satisfaction goes up along with improvement of other metrics (e.g., cost and cycle time).
• the six sigma methodology has been used by a number of companies such as Motorola Semiconductors, Texas Instruments, Allied Signal and Digital Corporation. All of these companies use this process for a specific application such as semiconductor manufacturing in the case of Motorola and Texas Instruments.
• a drawback to specific applications of the six sigma process is that there is a lack of flexibility to allow for the existing implementation to be applied to other business processes. ⁇
• An exemplary embodiment of the method and system disclosed herein is a method for determining demand and output variability analysis.
• the method comprises inputting a plurality of demand data for a product.
• the plurality of demand data for the product is analyzed.
• a demand pattern for the product is predicted.
• a supply trend for the product is predicted.
• a minimum order quantity of the product is predicted.
• the demand data is analyzed according to a grade container of the product and/or by machine. Demand fluctuations are identified when analyzing the demand data. A stable supply trend is predicted to establish a lead time. A product stocking policy to meet the demand pattern is determined based upon the predicted minimum order quantity.
• Figure 1 is a flow diagram illustrating an exemplary embodiment of the method and system for managing manufacturing of the present invention
• Figure 2 is a histogram plot of demand variability data of the present invention
• Figure 3 is a histogram plot of demand/output stability and relationship to Product-Sales-Inventory of the present invention
• Figure 4 is an exemplary embodiment of the statistical forecasting module of the present invention.
• Figure 5 is an exemplary embodiment of a statistical forecast of a demand pattern for products using the statistical forecasting module of the present invention
• Figure 6 is a Gaussian curve indicating a normal distribution using a Six Sigma statistical analysis
• Figure 7 is an exemplary embodiment of a statistical forecast after applying Six Sigma analysis
• Figure 8 is an exemplary embodiment of a MTO/MTS Classification Data Matrix of the present invention.
• Figure 9 is an exemplary embodiment of a Carrier Ramp Up Forecast using the capture and analysis of products delivery dates of the present invention.
• Figure 10 is an exemplary embodiment of the tracking of carrier's actual delivery dates using the capture and analysis of products delivery dates of the present invention.
• the sales branch and the manufacturing branch of a company do not communicate with one another.
• the sales branch of the company monitors the respective dollar amounts for each products ordered while tracking future forecasts.
• the manufacturing branch of the company oversees the manufacturing scheme for products.
• a method and system for managing manufacturing a supply chain comprises a supply chain process 10 for responding to a customer demand by supplying quality products operating within a Business Planning System.
• the Business Planning System provides business rules and financial targets to the Supply Chain Process so that it can initiate manufacturing or shipping in response to demand.
• Figure 1 illustrates an exemplary embodiment of a flow diagram off the Supply Chain Process 20.
• the Supply Chain Process 20 can include a Planning Feedback Process 22 and a Production Feedback Process 24.
• a plurality of customer orders 26 indicates a desire for products processed by the supply chain process 20 at a Customer Orders site 28.
• the supply chain process 20 can initiated manufacturing of products if the products are not currently in inventory by submitting a demand signal 30 to a Demand data site 32. If the ordered products are available, the supply chain process 20 can initiate transferring the products from an Inventory site 62 by sending a ship signal 26 to Orders-Ship-Bill site 64.
• Supply chain process 10 operates within a Business Planning System that provides business rules and financial targets so that the supply chain process 10 can initiate manufacturing or shipping in response to these inputs.
• the manufacturing process begins when The customer demand is processed at Demand Data site 32.
• Demand Data site 32 compares the demand signal 30 to the historical demand pattern of the customer and products, and transmits the information to a Scheduler Tools site 38 and a Finished Goods Services site 4436.
• the Planning Feedback Process 22 begins at Finished Goods Services site
• the Finished Goods Services site 44 analyzes the products and customer information using a statistical forecasting module. The analysis is independently examined at a ProdMgr Roll site 46 and a Sales Track site 48 and, if necessary, adjusted at a Product-Sales-Inventory adjustment site 50 and Marketing Intelligence site 52. The adjusted information is correlated at a Finished Goods Interface site 54 and transmitted to a Master Production Schedule site 56.
• the Production Feedback Process 24 can begin at the Master Production Schedule site 56.
• the Master Production Schedule site 56 coordinates the production schedule with Scheduler Tools site 38 and Purchasing System site 58 before transmitting the production schedule to a Production site 60.
• Purchasing System 58 can transmit material purchase and consumption information to an Inventory site 62.
• Production site 60 forwards the finished products to Inventory site 62 for stocking and recording.
• Inventory Site 62 oversees transporting the products to Orders-Ship-Bill site 64 where the products are prepared for shipment at a Shipping site 66 for Customer Inventory 68.
• FIG. 1 illustrates a flow diagram outlining one exemplary embodiment of a method and system for proactively managing manufacturing a supply chain.
• the supply chain process 20 generally serves as an exemplary transactional execution system, whereby demand is acknowledged by the system and the system employs a method for proactively managing the manufacture the products in demand.
• Demand for products is expressed as a plurality of customer orders 26.
• the plurality of customer orders 26 are transacted by placing, forwarding, or receiving the customer orders 26 at to a Customer Orders site 28.
• a customer, a distributor, or a point of sale entity places the customer order, or even the business planning system, or supply chain process 20 itself, can internally generate the order by using a forecasted orders based upon monitoring the customer's inventory.
• a single enterprise, branch of an enterprise, distributor, factory, manufacturing entity, and the like, can typically receive at least approximately one thousand customer orders 26 or more, in a day.
• an overseas branch of the manufacturing entity can receive the customer orders and forward it to a central location, e.g. a Customer Orders site 28 in the United States, that can coordinate or supply the demand.
• the customer orders comprise information such as the customer name, products types, products stockkeeping unit (hereinafter referred to as "SKU”), number of units ordered, invoice number, grade of container for shipment purposes, request date, delivery information such as date, time, and location, and top level critical-to-quality (hereinafter referred to as "CTQ") characteristics.
• Top Level CTQs are key Critical-To-Quality characteristics are set by customers. Based on those CTQs, internal measurements and specifications are developed in orders to quantify quality performance. Quality improvement programs, such as Six Sigma, are developed whenever there is a gap between the customer CTQs and the current performance level. Typically, the first step in a quality improvement program is defining the real problem by identifying the CTQs and related measurable performance that is not meeting customer's expectations. In the instant application, the top level CTQs are identified through an initial Six Sigma analysis of the customer's expectations, or by referring to previous orders placed by the customer, or even by previously identified top level CTQs being implemented by the supply chain process 20.
• Transmitting data, information, and the like, throughout the supply chain process 20, and the entire business planning system as well, can be accomplished using an electronic data interchange (hereinafter referred to as "EDI"), or other software that performs the same or similar function, which provides a data specification format and external communication interface for the transactional execution system.
• EDI electronic data interchange
• a computer system having a plurality of various platforms, desktop terminals, or personal computers, or terminals connected by a network to a central server, and combinations thereof, and the like.
• the central server can comprise an internet server, intranet server or private internet server, a data transfer connection, as well as other software, hardware, peripherals, and combinations thereof, and the like.
• the terminals are equipped with software permitting each terminal to communicate with the server via the Internet.
• the terminals or personal computers require readily available Internet browser software such as Microsoft Internet Explorer or Netscape Navigator.
• the local terminals also have a microprocessor for executing common software programs used by the computer system and mass memory for storing data obtained from within or outside the computer system. Data is imported/exported between the local terminals and computer system via a data transfer connection, which can be a WAN or LAN network, an e-mail or file transfer connection, or physical exchange of data storage media.
• a data transfer connection which can be a WAN or LAN network, an e-mail or file transfer connection, or physical exchange of data storage media.
• An individual at a local terminal, or by several individuals or groups of persons can access the computer system across a global array of web-enabled platforms.
• a demand and output variability analysis is a macro of a database program such as Access ® , Oracle ® , or any Windows ® based database program.
• the demand and output variability analysis macro is programmed to formulate calculations and chart transferred, downloaded, or keyed-in data. The macro is opened when needed or placed in an Access ® start directory, Oracle ® start directory, or any Windows ® based database program directory so that it will read each time the program is started.
• the demand and output variability analysis macro is used on any Windows ® based PC or any instrumentation or hardware the user may use, such as a network, intranet, or internet, to perform the demand and output variability analysis.
• the demand and output variability analysis identifies potential minimum orders quantities, examines historical demand patterns, and reviews appropriate stocking policies. The analysis is conducted according to the grade container and/or by machine. All pertinent information with regard to the customer orders is coordinated and organized at Demand data site 32. Such data includes, but is not limited too, fiscal periods, scheduling protocols, orders acceptance policies and selling days, and the like.
• the demand and output analysis analyzes the above- mentioned data and identify orders patterns and predict demand trends for products, family of products, etc. At the same time the analysis examines the trends to determine which are more or less stable so that an accurate lead-time is established.
• Figure 2 is an exemplary embodiment of a plot of demand variability data shown in histogram form.
• the x-axis represents the year/fiscal week when the customer orders were placed.
• the left hand y-axis represents the quantity of products ordered.
• the right hand y-axis represents the customer lead time in calendar days.
• the customer lead time is generally the amount of notice the customer gave the enterprise, branch of the enterprise, or manufacturing entity, and the like, to supply the product, or the difference between the date the customer order 18 was received and the date the customer wants the product delivered.
• PSI Product-Sales-Inventory
• Figure 3 is an exemplary embodiment of a plot of demand/output stability and relationship to PSI.
• the x-axis represents the fiscal week.
• the y-axis represents the weekly demand for products.
• the y-axis can also represent a product family or all products for a demand/output stability analysis on a larger scale.
• the output of products and/or product families can be adjusted quarterly using PSI to generate new potential output or establish minimum order quantities.
• the new potential output or minimum order quantities can typically be consumed on a weekly basis while As stable demand patterns and supply trends are generated on a monthly basis.
• the weekly output of a product or product family can be adjusted using the Product-Sales-Inventory adjustment 42 to generate a new potential output or minimum order quantity for products and/or product families.
• the demand and output variability analysis analyzes the demand trend and predict new potential output or minimum orders quantities using historical demand data.
• a potential minimum orders quantity can be suggested to meet the apparent demand fluctuations so that the customer maintains an appropriate stocking policy.
• the demand data may indicate that a customer orders five units of a specific product once every two weeks.
• the demand and output variability analysis can examine the products and determine whether it costs less to manufacture ten units of products once a month or five units of products every two weeks. If cost savings are generated by manufacturing ten units of products a month rather than five units every two weeks, then the suggestion will be made to the customer and the orders will be adjusted. As a result, the company can reduce carrying costs associated with maintaining an inventory surplus.
• Demand Data site 32 in Figure 1 transmits the demand and output variability analysis data, historical demand data, and the like to a Scheduler Tools site 38.
• the method disclosed herein can structure a multi-site system can be structured to for meeting the demand and supplying the products. Multi-site structuring permits the setup of a network whereby any branch can supply any other branch for any and all items. In that situation, no hierarchical branch supply relationship is required so demand can flow in all directions. Applying this concept to the instant application, Demand Data site 3224 can be located at a
• Demand Data site 3224 can identify top-level CTQs by accessing prior customer orders received by the Pacific Rim.
• the method and system disclosed herein can facilitate the supply/demand relationships between different branches within a locale, city, county, state, country, and global community at the item level, planning family or plant level to optimally determine which branch should best supply specific data, items, commodities, or services to another branch and the customer.
• Demand Data site 3224 also transmits the demand and output variability analysis data, etc., to a Finished Goods Services site 44 (hereinafter referred to as
• FGS 44 FGS 44
• ProdMgr Roll site 46 a Sales Track site 48
• PSI Adjustment site 50 a PSI Adjustment site 50
• Finished Goods Services Interface site 54 comprise the Planning Feedback Process 22.
• the Planning Feedback Process 22 facilitates communication and interaction between the sales and manufacturing departments.
• Those skilled in the art who are familiar with forecast development and modeling, and multi- dimensional forecast planning are aware that reports are generated on a weekly, monthly, quarterly, and annual basis to accurately predict supply/demand can become unmanageable. More importantly, each report must be examined to determine future supply/demand of a customer(s), or particular product(s), or raw material(s).
• Figure 4 illustrates an exemplary embodiment of the statistical forecasting module 70 of FGS 44.
• Statistical forecasting module is a macro of a database program such as Access ® , Oracle ® , or any Windows ® based database program.
• the statistical forecasting module includes any database program that can be programmed to employ Six Sigma statistical analysis in formulating calculations and charting transferred, downloaded, or keyed-in data. The module is opened when needed or placed in an Access ® start directory, Oracle ® start directory, or any Windows ® based database program directory so that it will read each time the program is started.
• the statistical forecasting module is used on any W
• the line item detail is summarized by SKU and fiscal month, and/or prioritized by information indicated by the enumerated headings such as orders numbers, item number, invoice number, request date, and shipping date (See Figure
• the statistical forecasting analyzes the demand history and the demand and output variability analysis data, to predict future demand patterns for a specific customer based on conventional forecasting algorithms.
• the forecasting algorithms generate forecasting information dealing with levels, trends, seasonality and end of quarter pushes for specific products types. Any regular demand pattern can be and is forecasted (See Figure 5).
• the statistical forecasting lays the baseline for determining future demand;
• Six Sigma statistical analysis identifies where non-random patterns occur, that is, demand fluctuations, and develop that invalidate the statistics. For instance, a normal distribution, often characterized by a bell-shaped Gaussian curve (See Figure 6), indicates which data points fall within and outside the 6 ⁇ range, i.e., ⁇ -3 ⁇ to ⁇ +3 ⁇ , that is predicated upon the top level CTQs mentioned earlier. Those data points falling within the acceptable 6 ⁇ range comprise a random pattern which is consistent with the 6 ⁇ standard of defect free products. Those data points falling outside the acceptable 6 ⁇ range indicate a non-random pattern which signals a "red flag" to those interpreting the data.
• Six Sigma analysis indicates when a demand fluctuation occurs in a demand trend for a particular products, for a particular customer's orders of a particular products, etc. (See Figure 7).
• Integrating Six Sigma statistical analysis with conventional statistical forecasting algorithms creates a proactive method for managing by exception.
• Both the sales department and products managers at a Sales Track site 48 and ProdMgr Roll site 46, respectively, can independently review the statistical analysis data to find the demand fluctuations and then meet to discuss their findings (See Figure 1).
• the exception arises in that typical supply chain processes fail to integrate the sales department's activities with the overall manufacturing process. To overcome this obstacle the method and system disclosed herein proactively manages by exception.
• the analyzed data is examined at a products level.
• the products level indicates all sales for specific products.
• the sales figures for those specific products can be divided amongst each specific customer account.
• the customer accounts are divided amongst the sales department.
• the sales department reviews the statistical data generated by the six sigma statistical forecasting module, and locates non- random patterns in the supply/demand patterns for a products, a family of products, a customer, a group of customers, and the like. Based upon the forecasted supply/demand patterns, the sales department and products managers determine whether to classify products as Make-To-Orders or Make-To-Stock using a method and system for classifying products types.
• the method and system for classifying a products types comprises applying an Make-To-Orders/Make-To-Stock Classification Data Matrix 72 to products information reviewed by both the sales department and products managers (See Figure 8).
• the Make-To-Orders (hereinafter referred to as "MTO") classification indicates products that are manufactured on demand. For example, the products may be ordered infrequently or once a fiscal period, i.e. a quarter. Rather than manufacturing excess products and drive up inventory costs, the products receives a Make-To-Orders classification, thus indicating the products is manufactured per customer orders.
• MTO Make-To-Stock
• MTS Make-to-Stock classification indicates a specific product that is manufactured in excess and held in inventory. For example, a products ordered on a regular basis can be held in inventory. The high turnover rate resulting from frequent sales can prevent high inventory costs so that it becomes affordable to stock those Make-To-Stock products.
• the MTO/MTS Classification Data Matrix 72 comprises a macro spreadsheet program that organizes information according to products data, statistical forecasting data, and historical demand data (See Figure 8).
• the MTO/MTS Classification Data Matrix 72 is a macro of a spreadsheet program such as Excel ® , Lotus ® , or any Windows ® based spreadsheet program.
• the spreadsheet program such as Excel ® , Lotus ® , or any Windows ® based spreadsheet program.
• MTO/MTS Classification Data Matrix 72 includes any spreadsheet program that can be programmed to formulate calculations and chart transferred, downloaded, or keyed-in data.
• the macro for the MTO/MTS Classification Data Matrix 72 is opened when needed or placed in an Excel ® start directory, Lotus ® start directory, or any Windows ® based spreadsheet program directory so that it will read each time the program is started.
• the macro for the MTO/MTS Classification Data Matrix 72 is used on any Windows ® based PC or any instrumentation or hardware the user may use, such as a network, intranet, or internet.
• the exemplary product information is correlated according to Group (“GRP”), Grade Container ("Grade Container”), ABC Code (“ABC”), Make-To-Batch (“MTB”), Recommended ABC Code (“Suggestion”), Reason Code (“Reason”), Number of Customers ordering the grade within the last year (“Customers”), and Number of Orders (“n”).
• GRP represents a particular area of products that can be organized according to a number of exemplary characteristics. The importance of the product(s) within the group assists in determining whether the product is classified as MTO or MTS.
• the Grade Container is the basic stock-keeping-unit.
• the ABC is the current MTO/MTS Classification being considered for potential change.
• MTB represents a previous customer sensitive classification scheme.
• the Suggestion represents the suggested ABC codes A, B, C, X, which stand for the recommendation criteria that influence a MTO or MTS product classification.
• the Reason indicates which three recommendation criteria lead to the MTO MTS classification determination.
• the Customers indicates the number of customers ordering the product.
• the "n” represents the number of orders for the product.
• the exemplary statistical forecast data is incorporated into the MTO/MTS Classification Data Matrix 72 as the Average Customer Requested Lead Time ("Ave CLT"), Standard Deviation of Customer Lead Time (“StDev CLT”), Average Quantity Ordered Over The Past Year (“Avgas Qty”), and Standard Deviation of Quantity Ordered (“StDev Qty”).
• the Ave CLT indicates how much notice the customer typically gives when in need of the product.
• the StDev CLT represents a measurement of how consistently the customers give more notice than the manufacturing lead time. If the measurement is or becomes variable, then an incentive exists to classify the product as MTS.
• the Avgas Qty and StDev Qty indicate the average quantity of product ordered, and its statistical quantification to the typical batch size of product. If the average quantity significantly differentiates from the typical batch size, then an incentive exists to classify the product as MTS.
• the exemplary historical demand data is also correlated and incorporated into the MTO/MTS Classification Data Matrix 72 as the Total Quantity Ordered Over The Last Year ("Quantity”), Total Pounds Orders Over The Last Year (“Pounds”), Total Dollar Value of Orders Over the Last Year (“Dollars”), Percent That The Grade Container Orders Contribute To Total Orders Dollars In The Past Year For That Group (“Cumulative %”), Most Recent Stock Orders Request Date ("Latest”)
• Safety Stock Cost Amount of Safety Stock Required If The Grade Container Is Classified As Make-To-Stock ("Safety Stock Cost"), Promise Lead Time Quoted To The Customer (“PLT”), Gap Between PLT and Ave CLT (“LT Gap”), and Sum of 1/Customer Rank, Based On The Customers Who Purchase That Grade Container (“USA Rank Wt”).
• the Quantity indicates how much product, e.g. units of a particular product according to a particular SKU, has been ordered over the previous year. If the particular product is shipped using a container, such as a drum, then multiplying the total quantity of product by the weight of the container equals the pound volume of the product ordered over the previous year. A large number provides incentive to classify the product as MTS. The Pounds indicates the pound volume of product ordered. A large volume provides incentive to classify the product as MTS. An exception can arise when the product is shipped in bulk, in which case; the container is filled on order and the volume reflects this difference. The Dollars represents the total dollar value of orders placed over the last year. A high dollar amount provides incentive to classify the product as MTS.
• a container such as a drum
• the Cumulative % represents a measure of how important the product is within the product group.
• the Latest Request indicates the general importance of the product to the company. If there are no recent orders within about, e.g, the last three to six months, then there exists an incentive to classify the product as MTO, or even classifying the product as obsolete.
• the Safety Stock Cost indicates the cost to the company to stock this product as MTS.
• the PLT represents the manufacturing lead time against which the customer lead time is compared.
• the LT Gap represents a comparison of the customer lead time and the manufacturing lead time.
• the USA Rank Wt represents a method for normalizing the measure of the customer base. The largest customer typically receives a customer rank of 1, while the lowest ranked customer, for instance, receives a rank of 500.
• Utilizing the USA Rank Wt allows the company to identify smaller products that can be important in aggregate but not obvious individually.
• the exemplary embodiment of the MTO/MTS Classification Data Matrix 72 filters products through certain recommendation criteria to determine whether a Product-Sales-Inventory Adjustment 50 (hereinafter referred to as a "PSI
• the sales department and products managers can determine the necessary PSI adjustment in response to this classification. If the products fall under one of the three criteria, then the products are classified as MTO products. In this exemplary embodiment of the method and described herein, the products are classified as an MTO products when (1) the Revenues for the Grade Container are under fifty-thousand dollars ($50,000.00) for one year; and/or (2) there are less than 13 orders placed a year (or one orders placed per month); and/or (3) the Average Requested Lead Time is greater than Promise Lead Time. Other criteria can be evaluated to determine whether an MTO or MTS classification is appropriate.
• a significant customer lead time variation can mean that the average requested lead time may not be an accurate indicator for classifying that particular products.
• Customer rank can also influence the MTO classification.
• a customer rank closer to "1" can indicate a customer that places a few orders for that product in higher volumes.
• the Cumulative % can identify the highest volume grade containers for a specific group.
• the latest request, or customer orders, made for that product can identify the potential obsolescence of the products and/or the statistical forecast data. For example, a product can be new, or a replacement, or even a one-customer product that may be ordered several time a week or only once a year. As marketing intelligence and demand data for products is updated, the sales team and products managers can observe which products are becoming obsolete or "dead" products.
• Products typically become dead products when the products are not ordered for a period of six months or longer. In that situation, the latest request date can indicate this fact. The sales department and products managers can then adjust the product's forecast accordingly so that erroneous data is omitted from consideration.
• the analyzed products data is also transmitted from FGS site 44 to ProdMgr Roll site 46.
• the products managers independently review independently the data generated by the statistical forecasting module 70 at ProdMgrRoll site 46.
• the Business Planning System compiles all of the accumulated information and formats it in an understandable format for the products managers.
• the term "product manager” refers to an exemplary employee of a company, enterprise, branch of an enterprise, distributor, factory, manufacturing entity, who is responsible for overseeing and/or having knowledge associated with a product, a family of products, a product line, including the research and development of such product, and other information associated with such product.
• Each products manager typically oversees a product, a specific family of products or several families of products, and reviews the statistical forecast data and search for non-random patterns.
• the products manager makes an individual determination based upon his/her knowledge of the products and/or product families, family and customer(s), to assess whether or not the non-random pattern requires an adjustment.
• the products managers examine or "roll" the information from a customer level to a products level, then back to the customer level. At this point the products managers can modify the information at the customer level. Afterwards, the products manager rolls the information back to the products level where further modifications and adjustments can be made to the information at either the products level or family level. For instance, the products manager may personally know that a particular customer will begin ordering a lesser or greater quantity of products within his/her family.
• the statistical forecast data, and non- random pattern as well may overlook this specific customer need because it comprises information not found in the historical data. Consequently, the product manager assesses the statistical forecast data and determine whether the demand fluctuations present reasons for making adjustments.
• the analysis performed by the MTO MTS Classification Data Matrix 7232 is compiled by the sales department at Sales Track site 48 and presented to the product manager at the PSI Adjustment site 50 (See Figure 1). Likewise, the product managers compile their respective analysis of the statistical forecast data and present it to the sales team personnel. The product managers and sales department present to each other their interpretations of the statistical forecast data to each other, and share additional other knowledge as well. Each demand fluctuation discovered by the products managers and sales team personnel are discussed and evaluated. Although the products managers typically make the final determination, e.g., as to whether a product is classified as MTO or MTS, the products managers can reach that decision by a consensus with the sales department.
• the Marketing Intelligence site 52 of Figure 1 further enables forecast adjustments by providing of the forecasts with information that will affect future demand but that is not found in the historical data.
• the Marketing Intelligence site 52 updates its information on a weekly basis within the Business Planning System. New data from market intelligence or about planned products promotions, or events, as well as information on new products, for example, is implemented be created for to W
• This piece of the proactive performance feedback loop Planning Feedback Process 22 disclosed herein also permits incorporating marketing intelligence into a forecast to determine its effect on demand. Information can be organized by product lines, geographic areas, units of measure, or other groupings. Marketing Intelligence site 52 also informs both the product manager and sales team personnel when promotional products shipments are scheduled. Special promotions can often times create fluctuations in the demand/supply pattern for a product, a products family, a customer, or a group of customers, and the like. The method and system disclosed herein can adjust the forecast in recognition of a promotional event that is considered unusual for a given products, without impacting the statistical forecast for future analysis.
• the proactive performance feedback loop Planning Feedback Process 22 of the method and system disclosed herein further includes compiling the adjustments made to the products information and statistical forecast data by the marketing intelligence data at a Finished Goods Services
• FGS Interface site 54 represents the convergence of the sales and manufacturing branches, which typically does not take place in conventional supply chain processes.
• MPS site 56 a Master Production Schedule Site 56
• the Production Feedback Process 24 comprises MPS site 56, Scheduling Tools site 38, and Purchasing System site 58, which create a second performance feedback process within supply chain process 20.
• MPS site 56 represents the planning stage for the manufacturing scheme.
• Master production scheduling concerns the ability to schedule production while maintaining inventory levels between the maximum and minimum levels determined by inventory planning. Based on current inventory and the demand forecast, master production scheduling projects the level of inventory into the future. Whenever inventory is projected to decrease to the minimum level, the manufacturing facility is advised to produce more products. Whenever inventory is projected to exceed the maximum level, the manufacturing facility is advised to produce fewer products or even no products.
• Supply chain process 20 provides a more accurate master production schedule implementation due to the integrated proactive performance feedback process, which ensures an accurate an inventory count so that production MPS site 56 can determine a cost effective schedule.
• MPS site 56 of Figure 1 can generate scheduling scenarios, provide optimum solutions based on existing constraints and apply least cost analysis. Several scheduling scenarios can be generated and compared based on factors such as total cost and net profit of the various options. In addition, existing business constraints can be identified and optimized around through advanced planning. Likewise, schedules can be automatically adjusted based on new exceptions to the existing constraints. Those constructing the schedules can exercise control over the relative priority of the optimization objectives for different constraints. Prioritizing optimization objects can result in simultaneously satisfying all of the constraints identified.
• the MPS site 56 disclosed herein can also incorporate and consider high level objectives indicated by the Business Planning System.
• Specific business objectives can include improving inventory turnover, increasing manufacturing efficiencies, and improving customer service.
• the relative priority of business objectives can be adjusted by, for instance, scaling the importance of the business objectives from zero to critical during each optimization run.
• MPS site 56 can also generate schedules within a holistic view of all plants and facilities involved with the manufacturing and production.
• the MPS site 56 can determine which facilities in a town, city, county, state, country, or global community can be best equipped to supply raw materials, pre-fabricated components, or even implement the entire master planning schedule from start to finish.
• MPS site 56 maintains direct communication with Scheduler Tools site 38 and a Purchasing System site 58.
• MPS site 56 structures production schedules for MTO and MTS products while linking multiple work orders, schedules and purchase orders together for rework, rush orders, etc.
• the production schedule considers demands, statistical forecast data, products family schedules, and other production schedules.
• production schedule can process work orders in groups, like families or products groups. Production schedules can also be consolidated to pull in or push out and/or combine schedules. MPS site 4856 can make these and other scheduling determinations related to formulating a production schedule by working cooperatively with Scheduler Tools site 38 and Purchasing System 5058.
• Scheduler Tools site 38 can receive information from both MPS site 56 and Demand Data site 32.
• Scheduler Tools site 38 generates scheduling scenarios using a macro of a database program such as Access ® , Oracle ® , or any Windows ® based database program.
• the macro is opened when needed or placed in an Access ® start directory, Oracle ® start directory, or any Windows ® based database program directory so that it will read each time the program is started.
• the macro for Scheduling Tools site 38 is used on any Windows ® based PC or any instrumentation or hardware the user may use, such as a network, intranet, or internet.
• Scheduling Tools site 38 acts as an exception based reporting mechanism that can provide a line of sight measurement and reporting for a production department within Supply Chain process 20.
• the Production department manufactures product having little knowledge behind the decision making process for manufacturing one product type over another.
• the scheduling tools macro of Scheduling Tools site 38 provides that knowledge.
• the scheduling tools concurrently examine the production schedule transmitted by MPS site 56, and also examine the safety stock levels, and the like, at Inventory site 66.
• the scheduling tools indicates if a product is being manufactured, for example, too early or too later, or if the proposed quantity for product is insufficient or too much.
• the Scheduling Tools site 38 can also be implemented within a holistic view of all plants and facilities involved with the Production Feedback Process 24. For example, when considering the Production Feedback Process 24 in a global perspective, the Scheduling Tools site 38 can determine which facilities in a town, city, county, state, country, or global community can be best equipped to supply raw materials, pre-fabricated components, or even implement the entire master planning schedule from start to finish.
• Scheduling Tools site 38 reports the identified exceptions to MPS site 56 where it is reviewed and adjusted if necessary. Once the proposed production schedule is approved, the Scheduler Tools site 38 transmits the schedule to Purchasing System site 58. The Purchasing System site 58 confirms the availability of raw materials and pre-fabricated components as well as indicate which materials must be purchased. Estimates for the purchase of raw materials and consumption of existing inventory are then made according to the scheduling scenario. Purchasing System site 58 transmits this information to MPS site 56 for review, adjustment and approval, before transmitting the final raw material and inventory consumption data to Inventory site 62.
• MPS site 56 transmits the final production schedule to Production site 60.
• Production site 60 oversees the actual manufacture of the MTO/MTS products.
• the finished products are stored at Inventory site 62.
• Inventory site 62 monitors existing stock quantities for all products so that safety stock quantities are maintained to meet existing and forecasted demand.
• Inventory site 62 reviews the information transmitted by Purchasing System site 58 and MPS site 56 to assure the finished products from Production site 60 meet the customer's specifications. At that time Inventory site 62 forwards the finished products to an Orders-Ship-Bill site 64 (hereinafter referred to as "OSB site 64") to fulfill the customer orders.
• OSB site 64 Orders-Ship-Bill site 64
• OSB site 64 oversees the fulfillment of the customer orders.
• OSB site 64 generates a shipment's bill of lading, including, but not limited too, itemized shipping instructions, notes, quantities, item descriptions, and dates, and the like.
• OSB site 64 reviews the customer's requirements and CTQs such as shipping instructions, quantities, delivery dates, delivery locations, etc., that are transmitted from Customer Orders site 28.
• a typical selection process selects a carrier based on, for example, cost, load capacities, service level, transportation mode, etc., and track the carrier's performance based on, for example, the number of loads accepted, volume given, number of loads rejected and costs of each rejected.
• OSB site 64 implements a method and system for the capture and analysis of products delivery dates.
• the capture and analysis of products delivery dates method and system disclosed herein selects a carrier best suited to meet the customer's CTQs based upon a six sigma statistical analysis of the carrier's historical performance.
• the capture and analysis of products delivery dates is implemented by linking all carriers with OSB site 64.
• the carriers are linked to the OSB site 64 using EDI, or other software, or digital medium to exchange a information via a telephone, email, WAN, LAN, internet or intranet connection so that direct bi-directional communication takes place between each carrier and OSB site 64.
• OSB site 64 transmits the shipping and implementation guidelines to the carriers.
• OSB site 64 follows up with the carrier within a time frame such as, for example, one week to confirm receipt and revisit the implementation guidelines.
• OSB site 64 receives daily updates with regard to scheduled shipments and reports such updates to the Business Planning System.
• the reporting and tracking is accomplished using a ramp-up forecast (See Figures 9-1010) that is generated using a macro of a database program such as Access ® , Oracle ® , or any Windows ® based database program.
• the macro is opened when needed or placed in an Access ® start directory, Oracle ® start directory, or any Windows ® based database program directory so that it will read each time the program is started.
• the macro for the capture and analysis of product delivery date data is used on any Windows ® based PC or any instrumentation or hardware the user may use, such as a network, intranet, or internet.
• Figure 9 is an exemplary embodiment of a ramp up carrier forecast, or actual delivery date data for the carriers A-NN, in histogram form.
• the x-axis represents the carrier used by the enterprise.
• the left hand y-axis represents the Fiscal Week of the calendar year.
• the right hand y-axis represents the actual delivery date percent completion.
• the ramp-up forecast indicates the top performing carriers according to the percentage of shipments completed on time within the implementation guidelines.
• a delivery made on the date specified by the implementation guidelines receives a score of 0 ⁇ (See Figure 6). Deliveries made a day before or a day after the specified date can fall within the ⁇ -l ⁇ to ⁇ +l ⁇ range of the 6 ⁇ curve, or within a wider range depending upon the implementation guidelines' specifications.
• Figure 10 is an exemplary embodiment of a plot of the EDI implementation percentage by each carrier.
• the x-axis represents the carrier, such as carriers A-S from Figure 9.
• the left hand y-axis represents the Fiscal Week of the calendar year.
• the right hand y-axis represents the EDI implementation percentage of the respective carrier week by week.
• Each EDI implemented carrier reports their actual delivery progress on a daily basis.
• OSB site 64 therefore immediately determines which carriers are performing at a Six Sigma efficiency standard rather than a typical optimum level or best solution level associated with conventional methods such as BRP, DRP, and MRP.
• the OSB site 64 monitors the carrier's performance so that customer orders are shipped from Shipping site 66.
• the Supply Chain Process Overview of the Business Planning System also monitor inventory held by the customer at Customer Inventory site 68. As orders are placed and fulfilled, the products types and quantities can be monitored to determine when the customer may require additional products. An order can be confirmed with the customer and automatically placed internally within the method and system disclosed herein.
• the method and system disclosed herein provides several advantages over the existing methods and systems fordetermining demand and output variability analysis.
• the method and system disclosed herein can analyze the demand trend and predict new potential output or minimum orders quantities using historical demand data.
• the manufacturing entity can in turn suggest to the customer a potential minimum order quantity to meet apparent demand fluctuations and maintain a safety stock.
• the company can reduce carrying costs associated with maintaining an inventory surplus.
• the analysis can examine the demand trends to determine which are more or less stable so accurate lead-times can be established for each customer.
• the method and system disclosed herein also incorporates a proactive performance feedback loop that promotes communication and cooperation between Product Managers and Sales personnel within a manufacturing scheme and supply chain process.
• Existing management schemes allow the Sales department and Product Managers to operate independently from one another. As a result, Product Managers operate unaware that potential customer inventory shortages and/or inventory surplus, and the like, may be approaching which will spur a dramatic changes in existing production schedules.
• the Sales Department will forecast demand patterns and supply trends unaware what safety stock levels are for specific products or products families due to inconsistencies in the manufacturing scheme.
• the method and system disclosed herein can allow Product Managers and Sales personnel to independently review the statistical forecast data and then meet to determine by a consensus the appropriate steps to take in orders to meet demand/supply.
• Six Sigma can facilitate the "managing by exception" principle within the proactive performance feedback loop. Managing by exception can allow the Products Managers and Sales personnel to proactively evaluate a potential fluctuation in the demand/supply pattern for a products, family of products, or series customer orders, etc. These demand/supply fluctuations can readily appear as non-random patterns in the Six Sigma
• the Products Managers and Sales personnel can evaluate the potential obstacle indicated by the non-random pattern and determine an appropriate course of action. The obstacle can be avoided prior to expending time, energy, and labor for manufacturing, or consuming existing inventory and endangering safety stock levels.
• Another recognized advantage of the method and system disclosed herein involves inventory planning. Carrying too much inventory increases costs through the inventory carrying charge, while carrying too little inventory causes orders to be missed or placed in backlog, both decreasing the service level to the customer. Since the time available between when a customer places an orders and when the customer expects to receive the products is never considered, the traditional techniques tend to advise carrying higher than necessary inventory levels.
• the proactive integrated performance feedback loop ensures the sales and manufacturing branches communicate so that inventory, including safety stock, remains at cost effective levels.
• the method and system for demand and output variability analysis can generate cost effective strategies for restructuring customer orders in response to customer demand patterns and manufacturing costs. The manufacturer can reduce inventory carrying charges while improving business relations with customers by responding to their needs rather than their complaints.
• the present invention can be embodied in the form of computer-implemented processes and apparatuses for practicing those processes.
• the present invention can also be embodied in the form of computer program code containing instructions, embodied in tangible media, such as floppy diskettes, CD-ROMs, hard drives, or any other computer-readable storage medium, wherein, when the computer program code loaded into and executed by a computer, the computer becomes an apparatus for practicing the invention.
• the present invention can also be embodied in the form of computer program code, for example, whether stored in a storage medium, loaded into and/or executed by a computer, or transmitted over some transmission medium, such as over electrical wiring or cabling, through fiber optics, or via electromagnetic radiation, wherein, when the computer program code is loaded into and executed by a computer, the computer becomes an apparatus for practicing the invention.
• the computer program code segments configure the microprocessor to create specific logic circuits.

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