Patent Application
20230342676
The field relates generally to information processing systems, and more particularly to equipment manufacturing management in such information processing systems.
An original equipment manufacturer (OEM) is an entity that sells equipment to customers that typically includes components purchased from suppliers (vendors) and assembled to form the final end product delivered to the customer. In one non-limiting example, assume an OEM sells a family of computing devices such as, for example, laptops. A laptop comprises a plurality of components (i.e., motherboard, keyboard, display, etc.) typically purchased from multiple suppliers. These components, many of which are themselves assembled from multiple other components (e.g., the motherboard comprises one or more processors and one or more memory devices), are assembled to form the laptop. In some cases, the OEM partners with an original design manufacturer/contract manufacturer (ODM/CM) at whose facilities the laptop is actually manufactured, e.g., the components are assembled into a final end product.
In this illustrative context, new product introduction (NPI) is an important process for OEMs. Generally, NPI includes two scenarios: (i) a new enhancement in an existing product, i.e., a next model of a product; and (ii) a new product or product family. For the OEM, both scenarios involve a combined effort between many different entities within or otherwise associated with the OEM, e.g., a marketing team, a product engineering team, suppliers, and the ODM/CM. By way of example, the OEM product engineering team, typically in conjunction with the OEM marketing team, design the equipment and the ODM/CM manufactures the equipment. To this end, the OEM attempts to work with the ODM/CM to determine, in advance, a manufacturing cost of the new enhancement/new product. However, if the OEM does not have sufficient knowledge of what main contributing factors go into the manufacturing cost determination, they may not be able to adequately estimate the manufacturing cost of the new enhancement/new product. As such, this poses a technical issue for the OEM because they may not be in an advantageous position to negotiate with potential ODMs/CMs for the manufacture of the new enhancement/new product.
Illustrative embodiments provide intelligent prediction techniques for equipment manufacturing management.
For example, in an illustrative embodiment, a method comprises the following steps performed by a processing platform comprising at least one processor coupled to at least one memory configured to execute program code. The method comprises obtaining: (i) a structured description of at least one of components and processes associated with manufacturing of equipment in accordance with a given design; (ii) first manufacturing-related data from one or more potential manufacturing entities for the equipment; and (iii) second manufacturing-related data representing at least one of current attributes and historical attributes associated with manufacturing equipment at least similar to the equipment of the given design. The method then applies one or more prediction models based on at least portions of the obtained structured description, the first manufacturing-related data, and the second manufacturing-related data to compute a predicted attribute associated with manufacturing the equipment.
Advantageously, one or more illustrative embodiments provide an automated engineering manufacturing management system configured to predict costs associated with the manufacture of a new equipment design. The automated engineering manufacturing management system, inter alia, enables an OEM to better negotiate with potential ODMs/CMs to manufacture the equipment.
These and other illustrative embodiments include, without limitation, apparatus, systems, methods and computer program products comprising processor-readable storage media.
Referring initially to
It is realized that an OEM may have an ODM/CM manufacture their products for reasons such as, but not limited to, cost effectiveness and to avoid bottlenecks in their own manufacturing facilities. The ODM/CM not only assembles the final product (e.g., a laptop) but may also assemble different sub-assemblies or sub-products that are part of the final product using raw materials and processes, as well as commodity parts (also referred to herein as commodities, e.g., processors, memory chips), procured from suppliers. It is further realized that the ODM/CM can readily derive a manufacturing cost from their history of manufacturing similar products.
The technical problem for the OEM is they do not have this data to derive or predict the manufacturing cost of the new product/enhancement that they are planning to introduce (i.e., new product introduction or NPI). Thus, there is no systematic way for the OEM to, inter alia, challenge the ODM/CM quotation for manufacturing different components of the finished good and assembling the different components to make the final product due to lack of the cost details of the same. At best, in an existing approach, the OEM can attempt to obtain data to manually determine the manufacturing cost for an NPI. However, this is inefficient and ultimately ineffective, and thus compromises the OEM's negotiating ability with respect to the final cost of a manufactured product. If an OEM's competitors can negotiate for a better cost with an ODM/CM for the same competing products, they can release the competing product for a lower price in the market.
Illustrative embodiments overcome the above and other technical drawbacks with existing equipment manufacturing approaches by providing an automated equipment manufacturing management system with intelligent prediction functionality. As will be further explained in detail, an equipment manufacturing management system according to illustrative embodiments operates in response to a bill of material (BOM) for the new equipment being introduced (e.g., new product or enhancement to existing product) and systematically identifies the cost of the BOM with effective and unified collaboration of one or more suppliers and one or more ODMs/CMs and derives the variable cost associated with the engineering process and the labor cost.
More particularly, illustrative embodiments create a so-called “clean sheet” based on the BOM and a history of procured items, and generate a sequenced clean sheet. As illustratively used herein, clean sheeting refers to an analysis of a product's cost structure to assist manufacturers (e.g., OEMs) in improving product design and, inter alia, capturing cost savings. It is understood that, in order to identify cost-reduction opportunities, OEMs need to understand the main contributing factors for each product's manufacturing process. Also known as a “should-cost analysis,” clean sheeting serves as a useful OEM technique that involves, e.g., modeling commodities/raw material and conversion costs of a good, allowing for a better understanding of overhead, profit and manufacturing efficiency.
With a sequenced clean sheet, the equipment manufacturing management system predicts the cost for any time series of manufacturing dates, automatically creates engineering models (e.g., mechanical engineering (ME) models, electrical engineering (EE) models, non-recurring expenses (NRE) models, human resource (HR) models etc.), and re-calculates the predicted cost based on the engineering models to yield a final predicted cost as well as one or more negotiation analytics recommendations and/or reports.
As further shown in
Thus, after obtaining the new equipment BOM and a draft clean sheet from a product engineering team and/or a commodity management team of the OEM, initial clean sheet builder 212 of equipment manufacturing management system 210 (hereinafter system 210) obtains commodity and other component costs predicted by predictor 218 based on historical commodity cost data from data store 220. Initial clean sheet builder 212 also obtains a sequenced clean sheet from data store 214 for a previous version of the equipment (parent equipment) when the NPI is a product enhancement rather than a completely new product. If this is a new product, a sequenced clean sheet can be generated manually by the OEM and/or semi-automatically in conjunction with system 210, and then automatically adjusted as needed.
System 210 is configured to enable collaboration with one or more suppliers, via supplier collaboration engine 216, to obtain the cost from suppliers for new commodities and components over time in accordance with a standardized template. That is, supplier collaboration engine 216 comprises an interface for suppliers to provide data to system 210 as requested or otherwise agreed to with the OEM. System 210 is further configured to enable collaboration with one or more ODMs/CMs, via ODM/CM collaboration engine 222, to obtain their proposed manufacturing process for the new equipment in order to predict the future cost and build the sequence of operations. That is, ODM/CM collaboration engine 222 comprises an interface for ODMs/CMs to provide data to system 210 as requested or otherwise agreed to with the OEM.
Final sequenced clean sheet builder 224 generates the sequenced clean sheet with a predicted price for historically procured items and historical process costs. This sequenced clean sheet can also be stored in data store 214 for future use. More particularly, when a subsequent NPI occurs, the stored sequenced clean sheet can be used as a historical basis.
As further shown, system 210 comprises a third party API 226 that is configured to pull current and predicted labor cost data from one or more public information sites 228. Similarly, a web scraper 230 can be used to obtain commodity and labor cost data from one or more subscription-based information sites 232 (for example, but not limited to, HIS, Bloomberg, etc.). The external data from sites 228 and 232 can be refreshed periodically and stored in data store 234 as a knowledge base for future use.
Further, engineering model builder 240 creates engineering Models (e.g., EE, ME, NRE, HR models) which are used by labor cost predictor 236 along with the final sequenced clean sheet from engineering model builder 240 to get the predicted time series cost for engineering. A final cost model is generated by generator 242, and negotiation analytics and reports are generated in response to the final cost model. Further details of the functionalities of modules in system 210 and their interactions will be explained below in the context of subsequent figures.
Supplier collaboration engine 216 and commodity and component cost calculator and predictor 218 functionalities will be further explained in the context of
It is realized that each ODM/CM typically uses different suppliers for the same commodity or raw material in each region. Thus, in each region, system 210 can create a standard template for collecting data from the suppliers. As shown in supplier grouping process 600 of
Advantageously now that system 210 has current and future costs of all commodities and raw materials needed for a product, if a supplier is quoting excess cost for existing commodities, the OEM can learn this based on previous commodity trends and better negotiate with the supplier.
For new commodities (e.g., for which public Internet-based information may not be readily available), system 210 can use the web scraping result of web scraper 230 for determining negotiation analytics. For example, system 210 enables users to browse commodity manufacturers' websites and mark the cost to be scraped. There are a variety of existing web scraping tools and algorithms available that can be employed in illustrative embodiments. The results can be used for the supplier negotiation mainly for new commodity in the assembly.
Labor cost prediction according to illustrative embodiments will now be further explained. It is to be understood that while the cost of commodities can bee predicted by prices of the components, some assemblies require purchase of raw materials that then need to be processed (i.e., by human and/or machines) as part of the overall equipment manufacturing process However, even with commodities as well as other components, they typically have to be stored, inspected, installed, tested, etc., also incur labor costs.
It is assumed that, for an OEM, its product engineering team has high level engineering models that can be used for determining processing and other labor that incur costs such as, for example, mechanical costs (e.g., machine cost, lab cost, warehouse cost, etc.), electrical costs (e.g., switchboard, electric charges, etc.), human resource costs (e.g., resources needed for design and execution), non-recurring costs (e.g., new machines and new processes to setup for the new assemblies). ODM/CM collaboration engine 222 enables the product engineering team to validate assumptions and come up with the cost model for each engineering model. ODMs/CMs can login to ODM/CM collaboration engine 222 and verify the data provided by the product engineering team.
Once collected, there can be additional commodities/raw material required for the new process. This will go back to supplier collaboration engine 216 to obtain the cost from the supplier. With this information, system 210 re-builds the sequenced clean sheet for the assembly in the current clean sheet.
Once an engineering model is obtained from engineering model builder 240, system 210 determines labor costs needed for the engineering model. In one or more embodiments, system 210 leverages labor cost statistical providers (sites 228 and/or 232), such as Bloomberg or another source, to get the current and historical labor cost in different regions.
As mentioned above, once system 210 has the initial engineering models as a result of ODM/CM collaboration etc., system 210 builds each engineering final model for the new assembly and engineering process (note that business can override) with commodity and labor costs. System 210 predicts the engineering process cost using a linear regression model with seasonal autoregressive integrated moving average (SARIMA) time series. Since system 210 already has the time series predicted commodity/raw material cost, labor cost and engineering process, system 210 builds the time series predicted cost for each engineering model and human resources cost.
This is shown in forecasted labor cost model generation process 900 of
Accordingly, with the final sequenced clean sheet (with current and time series forecasted cost), current and forecasted engineering models, system 210 can plot the current and should-be cost with all details (e.g., commodity cost, raw material cost, labor cost, engineering process cost, HR cost, NRE cost) and build analytics (e.g., 244 in
Still further, it is realized that there can be some overhead cost for the ODM/OC that cannot always be accounted for by the OEM. Overhead for each ODM/OC will be different (e.g., due to salary difference costs, regional-unique costs, etc.). In accordance with illustrative embodiments, once system 210 matures (i.e., executes for a length of time and develops a deep knowledge base), the overhead (so-called padding) over a period of time can be determined by system 210.
A manufacturing overhead prediction table 1000 generated by system 210 is shown in
As shown, step 1102 obtains a structured description of at least one of components and processes associated with manufacturing of equipment in accordance with a given design. In one or more illustrative embodiments, the structured description comprises a clean sheet as illustratively described herein.
Step 1104 obtains first manufacturing-related data from one or more potential manufacturing entities for the equipment. In one or more illustrative embodiments, first manufacturing-related data comprises cost data, and one or more potential manufacturing entities comprise one or more ODMs/CMs as illustratively described herein.
Step 1106 obtains second manufacturing-related data representing at least one of current attributes and historical attributes associated with manufacturing equipment at least similar to the equipment of the given design. In one or more illustrative embodiments, second manufacturing-related data comprises cost data, such that the current attributes comprise current costs and the historical attributes comprise historical costs associated with manufacturing equipment at least similar to the equipment of the given design (e.g., parent equipment and the like) as illustratively described herein. The term “similar to” with respect to the equipment illustratively means equipment that is related to the newly designed equipment or that has several common components and/or manufacturing processes associated therewith.
Step 1108 applies one or more prediction models based on at least portions of the obtained structured description, the first manufacturing-related data, and the second manufacturing-related data to compute a predicted attribute associated with manufacturing the equipment.
Advantageously, as described herein, illustrative embodiments provide a system and method to predict the cost of an enhanced product or next model of a hardware product based on commodity cost, raw material cost, labor cost, and other costs, using a SARIMA-based time series prediction model. Systems and methods also source the cost from different suppliers and corelate with existing cost to predict the time series cost variation in the future for negotiation. Systems and method also build automated engineering models for new products/assemblies and derive costs for each model for current and future uses. Systems and methods also relearn to understand overhead cost for each ODM/CM and refine the cost prediction for each ODM/CM. One or more of the prediction models used herein may comprise one or more artificial intelligence/machine learning (AI/ML) algorithms or other intelligent prediction functionalities.
Illustrative embodiments are described herein with reference to exemplary information processing systems and associated computers, servers, storage devices and other processing devices. It is to be appreciated, however, that embodiments are not restricted to use with the particular illustrative system and device configurations shown. Accordingly, the term “information processing system” as used herein is intended to be broadly construed, so as to encompass, for example, processing systems comprising cloud computing and storage systems, as well as other types of processing systems comprising various combinations of physical and virtual processing resources. An information processing system may therefore comprise, for example, at least one data center or other type of cloud-based system that includes one or more clouds hosting tenants that access cloud resources. Cloud infrastructure can include private clouds, public clouds, and/or combinations of private/public clouds (hybrid clouds).
The processing platform 1200 in this embodiment comprises a plurality of processing devices, denoted 1202-1, 1202-2, 1202-3, . . . 1202-K, which communicate with one another over network(s) 1204. It is to be appreciated that the methodologies described herein may be executed in one such processing device 1202, or executed in a distributed manner across two or more such processing devices 1202. It is to be further appreciated that a server, a client device, a computing device or any other processing platform element may be viewed as an example of what is more generally referred to herein as a “processing device.” As illustrated in
The processing device 1202-1 in the processing platform 1200 comprises a processor 1210 coupled to a memory 1212. The processor 1210 may comprise a microprocessor, a microcontroller, an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other type of processing circuitry, as well as portions or combinations of such circuitry elements. Components of systems as disclosed herein can be implemented at least in part in the form of one or more software programs stored in memory and executed by a processor of a processing device such as processor 1210. Memory 1212 (or other storage device) having such program code embodied therein is an example of what is more generally referred to herein as a processor-readable storage medium. Articles of manufacture comprising such computer-readable or processor-readable storage media are considered embodiments of the invention. A given such article of manufacture may comprise, for example, a storage device such as a storage disk, a storage array or an integrated circuit containing memory. The term “article of manufacture” as used herein should be understood to exclude transitory, propagating signals.
Furthermore, memory 1212 may comprise electronic memory such as random-access memory (RAM), read-only memory (ROM) or other types of memory, in any combination. The one or more software programs when executed by a processing device such as the processing device 1202-1 causes the device to perform functions associated with one or more of the components/steps of system/methodologies in
Processing device 1202-1 also includes network interface circuitry 1214, which is used to interface the device with the networks 1204 and other system components. Such circuitry may comprise conventional transceivers of a type well known in the art.
The other processing devices 1202 (1202-2, 1202-3, . . . 1202-K) of the processing platform 1200 are assumed to be configured in a manner similar to that shown for computing device 1202-1 in the figure.
The processing platform 1200 shown in
Also, numerous other arrangements of servers, clients, computers, storage devices or other components are possible in processing platform 1200. Such components can communicate with other elements of the processing platform 1200 over any type of network, such as a wide area network (WAN), a local area network (LAN), a satellite network, a telephone or cable network, or various portions or combinations of these and other types of networks.
Furthermore, it is to be appreciated that the processing platform 1200 of
As is known, virtual machines are logical processing elements that may be instantiated on one or more physical processing elements (e.g., servers, computers, processing devices). That is, a “virtual machine” generally refers to a software implementation of a machine (i.e., a computer) that executes programs like a physical machine. Thus, different virtual machines can run different operating systems and multiple applications on the same physical computer. Virtualization is implemented by the hypervisor which is directly inserted on top of the computer hardware in order to allocate hardware resources of the physical computer dynamically and transparently. The hypervisor affords the ability for multiple operating systems to run concurrently on a single physical computer and share hardware resources with each other.
It was noted above that portions of the computing environment may be implemented using one or more processing platforms. A given such processing platform comprises at least one processing device comprising a processor coupled to a memory, and the processing device may be implemented at least in part utilizing one or more virtual machines, containers or other virtualization infrastructure. By way of example, such containers may be Docker containers or other types of containers.
The particular processing operations and other system functionality described in conjunction with
It should again be emphasized that the above-described embodiments of the invention are presented for purposes of illustration only. Many variations may be made in the particular arrangements shown. For example, although described in the context of particular system and device configurations, the techniques are applicable to a wide variety of other types of data processing systems, processing devices and distributed virtual infrastructure arrangements. In addition, any simplifying assumptions made above in the course of describing the illustrative embodiments should also be viewed as exemplary rather than as requirements or limitations of the invention.
