Shulou(Shulou.com)06/02 Report--

Mobile Productivity and traditional Enterprise Dilemma

Throughout the previous productivity revolutions, they all started from the technological innovation of producers, and finally spread to consumers. In several industrial revolutions, the upgrading of energy and manufacturing technology has greatly improved production efficiency, and created a large number of new jobs, and finally improved the quality of life of consumers, promoting round after round of consumption upgrading. In the recent information technology revolution, the Internet was first used in large-scale industries abroad, and then spread to social life, bringing countless opportunities and challenges to productivity improvement.

However, the emergence and development of mobile Internet productivity is different, it is produced in the consumer side rather than the production side. Mobile Internet productivity brings two core competencies:

Distributed, decentralized

Shorten the path of information dissemination

On the consumer side, the mobile Internet connects a large number of consumer entities and realizes decentralization through a spontaneous organization. It eliminates the previous single point bottleneck of productivity and makes the scale of consumption grow exponentially. The distributed organization form also provides a very short path for message delivery, which enables sales activities to be organized very efficiently, increases the transparency of consumption links, and greatly promotes consumption power.

Affected by the development of the Internet, the demand of the consumer side is gradually increasing, which brings more and more production capacity pressure to the production side. The upgrading of production capacity at the production end is urgently needed, and the rebalance of productivity is on the brink of an arrow, and it has to be issued. It has become a more and more important proposition that how to make use of the technical advantages of mobile Internet and more and more rich artificial intelligence algorithm capabilities to improve production efficiency at the production end. At the same time, with the upgrading of the efficiency of consumers and producers, production relations also need to be reconstructed, while the complex systems built by many traditional enterprises offline for many years are facing great challenges.

Based on this proposition, singularity cloud provides a digital intelligent solution. In many industries, we continue to polish and precipitate, deeply try mobile Internet technology and AI algorithm, and form a cutting-edge innovative algorithm model, which greatly improves the efficiency of offline productivity and reconstructs production relations. The new business model, everything for efficiency.

The quality of data is so poor, how can enterprises upgrade digitally?

In the past 10 years, mobile productivity has greatly improved the efficiency of online consumption scenarios through the transformation of the information flow of people and goods yards. But offline, there is no full coverage of the information flow. A large number of traditional industries still stay in the original manual operation, and a few have established isolated information systems. In the era of big data, traditional enterprises still can not get rid of the curse of lack of data. The lack of such data is mainly manifested in two aspects:

1. The data of offline operation and management is stored in an independent MIS system, and the business is deeply coupled, so it is difficult to get through the data isolated island.

two。 Offline user behavior is single, there is no complete behavior link to collect and track users' offline transactions, and there is convincing data to support users' preferences and dislikes.

Based on such thin and sparse data, it is difficult to greatly improve the efficiency in any scene in the cargo yard, and it can only automate the most basic work, which is far from the online intelligence and data.

To solve these problems, Singularity Cloud data platform service through the integration of data islands, and combined with industry knowledge, the design of industry data model, through the data of different departments, but also through the business of different departments. Combined with our capabilities, enterprises can fully achieve: from the absence of data to the ability to produce data independently. Production is dynamic and continuous, which is also one of the data bases for the future digital transformation of enterprises.

However, the sparse and single behavior data of offline users is still a major obstacle for AI to land in traditional industries.

In order to solve the problem of data, there are two main directions: to enrich the offline end and to improve the efficiency of data utilization:

1. The intelligent magic cabinet, magic mirror and customer recognition system we developed are good collection terminals for users' offline behavior. At the same time, with the advent of the AI era, more powerful algorithm models are used to deal with offline sparse data more efficiently.

two。 The data of the enterprise are mainly sales data and membership data and so on. These commodities with single original data dimension and low turnover still have the problem of sparsity. After practice in a large number of projects, we have summed up a reliable methodology for these two problems:

(1) single dimension: we can supplement the features of more dimensions by increasing the model's derivative mining of related features.

(2) sparse data: we can improve the model effect by decoupling and establishing sub-model, through simple model fusion and cross-task fusion. Combined with the advantages of structured data mining of deep neural network, many problems of offline data have been weakened or even basically solved.

Actual combat polishing, AI helps enterprises upgrade their intelligence

At present, we have accumulated a large number of practical projects to help traditional enterprises make better use of their own data and tap the value of offline data.

Sales data is the main source of offline user behavior, but the analogy online data: click, purchase, collection, purchase, appears single and sparse. And offline data lacks the support of negative samples, which makes it particularly difficult for traditional supervised learning to use offline data scenarios.

Singularity Cloud Big data algorithm team currently has two sharp knives, which have achieved good results in real offline data scenarios.

Unsupervised portrait: through the establishment of empirical probability model, combined with the entity relationship in the industry, the entity probability model is modeled and solved. Multi-dimensional portraits of people, goods and fields are excavated from the POS list. Through cluster analysis and manual interpretation, we gain insight into the hidden interpretable relationship between the portraits of each dimension. On the one hand, it can provide businesses with more abundant and flexible means of operation; on the other hand, it can be added to the model as a feature to solve the problem of generalization of sparse data, in intelligent replenishment, intelligent scheduling, intelligent pricing model, the accuracy of demand forecasting is significantly improved.

Demand forecasting and analysis: an important application of offline scenarios is user demand forecasting, which can improve supply chain efficiency, enterprise management efficiency and operation quality. The demand forecast based on data center platform can assist the decision-makers of enterprises to make more reasonable business decisions. Therefore, the accuracy of demand forecasting is very important. Leading foreign manufacturing companies have invested a lot of costs in demand forecasting. 1% of the demand forecast accuracy will be improved, which will lead to a 10% increase in net profit of 20%.

Through the in-depth analysis of the sales data of different industries, the singularity cloud algorithm team studies many methods, such as time series, boosting, depth model and so on, and comprehensively compares the effects of different methods in different scenarios. Found that in fact, the sales data is only the appearance, the fundamental is the business logic behind, different sales attributes of products, the distribution of data is very different, the relationship between the data is very different, the applicable algorithm is also completely different. We have put forward a solution from "division" to "cooperation":

1. From the perspective of "points", we decompose the demand forecasting problem according to the different situations of the industry, and decompose the demand forecasting again according to the different behaviors in the same industry.

two。 From the perspective of "integration", we merge common problems between industries and common solutions to merge problems. It has formed a demand forecasting solution in many areas, such as retail, shoes and clothing, complex and so on.

Demand forecasting is not a simple forecasting problem, but also involves business interaction and understanding. Singularity cloud demand forecasting platform: support business understandable demand decoupling and demand reshaping. Provide operators and executives with more in-depth insight into the business, and accurate attack weapons for making business plans.

Commercial battlefield, rapidly changing, our algorithm tools, can not call the wind and rain, but can predict the future. Between success and failure, details are king. With the help of data center, data analysis and mobile Internet algorithm technology, the singularity cloud algorithm platform hopes to become the core trump card of enterprises and help enterprises that focus on offline scenarios to have the last laugh.

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