Learn how to properly use the data in your supply chain
20 de octubre de 2021
20 de octubre de 2021
The current consumption model is based on a digital model, generating more and more data that needs to be processed. This data is a real goldmine for companies: it transforms business and supply chain models from transactional, where the transaction was triggered only at the moment of demand, to predictive models, allowing them to better diagnose anomalies through information exchange.
In our consumer society, customers are more volatile than ever, and it is important and necessary to use the data that is at your disposal. The competition is getting stronger and stronger and your customers are more and more demanding. You must therefore master your data in order to be able to analyze the response you offer to your customers' demands in the face of competing offers.
3 main objectives should guide you in the use of your data:
Thanks to the use and analysis of your data, you will be able to ensure a continuous diagnosis and a complete follow-up of the important indicators of your S&OP (Sales & Operation Planning) operations. You will be able to better coordinate your purchasing and sales decisions according to your company's internal capabilities.
By analyzing the data of your Supply Chain, you will be able to be alerted quickly on the problems that prevent it from working optimally, so that you can solve them as quickly as possible and thus limit the impact of failures.
When you master the analysis of your data, you will have enough historical data to predict with accuracy and certainty future inventory movements (products with high seasonality, etc.) and thus gain accuracy and performance in the management of your Supply Chain.
Before you can use your data in an optimal way, you will have to prepare your data and your working environment in order to be able to carry out all your analyses using reliable and secure data.
Each department in your company has different kinds of data, which can be useful in your analysis. You should therefore start by collecting data from your various employees. Once this data has been collected, you will have to standardize it: in fact, each department does not have the same data treatment process. You will then have to create and distribute standardization processes to your employees so that the data you collect later is already in the right format, thus saving you time.
Once you have identified the data to be collected, you will quickly realize the extent of the data to be stored. You will then have to choose a storage method within structures that are powerful enough to accommodate all your data, but also capable of making frequent updates to your various databases. For example, you may want to consider storing your data in multiple locations to avoid problems in the event of a technical or natural disaster that destroys the storage equipment.
The possession of high capacity storage servers will not be the only criterion for the choice of the storage method. The security of your data should be the primary criterion. You will indeed have to handle confidential data about your company and your customers (contact details or even payment information) which will have to be totally secured so that they cannot be accessed by other people. For optimal security, you can use remote servers, available in the cloud. Choosing not to store the data within your company is also choosing not to have to manage server maintenance operations or instability risks, by calling on professionals. They will duplicate the data on several servers to mitigate the natural and technological risks to ensure the continuity of your data processing.
Once you have collected all the data to be processed, chosen your storage method and secured the data, you must then work on evaluating the quality of your data. Indeed, quality data is data that is consistent with reality, unique, understandable, structured and documented. You will have to carry out this evaluation work in a continuous way in order to apply it to the new data collected thereafter manually or automatically.
Among all the data at your disposal, you will have to learn how to choose the most relevant information to collect in order to optimize your analysis and your predictions to adapt your Supply Chain more easily and quickly.
The first data to collect are the data concerning your entire supply chain. You will have to use the data providing typical indicators to build a quality data history, a criterion to accelerate your ROI. To constitute your Supply Chain database, you will have to select the following information:
o Item repository: brands, prices, lifetimes, volume, etc,
o Supplier repository: names, purchase conditions, catalogs, order frequency, etc,
o Customer repository: contact information, purchase history, etc,
o Information on your logistics network: warehouses, hubs, stores, etc,
o Information on your stock operations: sales, orders, receipts, inventories, etc.
This information will be adapted according to your company's typology: depending on your sector of activity, it may be different (for example, you may have to collect data from your mobile intervention units if you have them).
You already have a lot of data at your disposal, even before the implementation of your data management. This data, distributed among your various departments, is precious: it will help you build a data history that will reinforce your ability to forecast the various risks linked to your supply chain. The best way to collect and process this data is to appoint a data project manager, in charge of mapping, gathering and making available all the data that your company may have generated in the past.
In addition to processing your company's internal data, you will also need to integrate exogenous data, i.e. data that is not generated by your company but in your environment. They will help you make your analysis more relevant. Depending on your field of practice, you may want to aggregate different types of economic and demographic data that influence your business.
Once you've built your database, you'll need to constantly maintain it to keep it relevant.
You'll need to start by fully analyzing your database to ensure you don't encounter the 2 most common data errors:
o Unrealistic data: Unrealistic data is data that cannot accurately reflect reality. In the context of inventory management, a data unrealistic data can, for example, correspond to a negative inventory or a number of sales that are much higher than the inventory level.
o Unstructured data: Unstructured data is data that is not correctly linked to the rest of the data. For example, it could be a product that is not correctly linked to its supplier. Unstructured data can not only be problematic during data analysis, but also during automated processes (in this case, product replenishment).
Once you have identified the errors that have crept into your data, you will need to clean up your database to remove the errors in order to get reliable, real and detailed data. You will need to prevent these errors from recurring: it will be important to identify their source: do they come from the sharing of data, from its processing or from its collection? In most cases, it will come from a human error: it will therefore be necessary to set up new precise and detailed data treatment processes and to transmit them to all your collaborators.
Once all your data has been collected and prepared for processing, you can finally move on to the analysis stage in order to diagnose your Supply Chain.
A KPI (Key Performance Indicator) is a criterion for analyzing the overall efficiency of a system. It is important to set up KPIs to carry out your diagnosis according to precise criteria. These criteria can be multiple, and will have to be adapted to the various stages of your Supply Chain: stock rotation rate, service rate, satisfaction percentage, lead times, etc. It is the monitoring of the evolution of your KPIs that will allow you to be alerted in case of failure in your Supply Chain.
For an optimal diagnosis, you will have to call upon your Supply Chain expert: he knows your entire Supply Chain from an internal point of view. He will be able to use the powerful tool that data represents to analyze and find more easily the origin of the various problems that could disturb your Supply Chain.
The multiplication of data collection sources and the development of IoT (Internet of Things) technologies will generate more and more data in the coming years. This data, more and more relevant, will be necessary for the continuous improvement of the Supply Chain, and especially for the installation of new technologies such as artificial intelligence.
The use of data will thus become an essential criterion to ensure the efficiency of your Supply Chain, in the detection of anomalies and the prediction of activities, in order to always better satisfy your customers.
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