1 April 2019

How you can put your Supply Chain data to work

If you are a Procurement Manager, Sales Planner or Supply Chain Director, you are confronted daily with a strategic subject, the use of data. Due to the scale and impact of such a project, we observe many companies being held back by the legitimate fear of change.

Where to start? How to measure your data performance? Allow us to guide you through the steps so the implementation and use of data in your Supply Chain is a success!

 

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Why use your Supply Chain data?

You already know your customers have become more volatile than ever before, and they want faster service and lower prices, with greater choice for distributors and manufacturers. To meet these requirements and to know whether you’ve done so, you have access to different data sources (both internal and external). This collected data will provide a more clear vision of your business and help you to manage it with accuracy.

By making the best use of your Supply Chain data, you’ll be able to:

Monitor your activity: This is the first level of information provided to you by quality data. The purpose is to continuously diagnose your Supply Chain and monitor the indicators that are important for your S&OP processes.

Detect anomalies: Receive alerts to outlier data points that hinder the best performance of your Supply Chain and resolve them quickly to mitigate consequences.

Improve forecasting:  With a qualitative data system, you will be able to forecast your future activity with greater accuracy and certainty, and gain in responsiveness, and performance.

 

The prerequisites for making good use of your Supply Chain data

Have accessible data

The first condition for a good use of data is to have data. Good news, you made it! The different departments of your company already use their own data systems.

The bad news is that it is scattered throughout your organization and you’ll have to aggregate it. This is a tedious first step because nothing is standardized. Each team has its own way of structuring its data, and sometimes it varies with each person on the same team!

With regards to the new data that you do not yet have and that you are going to start collecting, make sure that you start off on the right foot and that your data meet our prerequisites below!

 

Provide a solid technical foundation

To support all this information, it is essential to provide a solid technical foundation. On the collection side, it is important that practices and tools are consistent throughout the organization, or that differences are known and voluntary. On the other hand, hosting must be carried out on servers and data warehouses that are sufficiently powerful and equipped with sufficient capacity. As for the data, it must be redundant (stored and updated in several places).

Not providing a sufficient technical base means limiting the capacities of your project from the outset and multiplying the risks of malfunctions. With limited storage capacity, you will need to restrict the history of your data or forget about information that might have been useful. With cheap servers, you are exposed to regular slowness or crashes that will cause information loss.

In short, before you think about collecting and using your Supply Chain data, think about providing it with a stable and powerful environment!

 

Evaluate your data quality

Do you have a lot of data at your disposal? That’s good! Do you have the technical means to support this volume in good conditions? Great! But are they qualitative? This is the most important condition, because it is better to act with little knowledge of true information than on the basis of much false information.

Several essential criteria define quality data. It must be consistent with reality, unique (not having the same information defined in several ways), understandable, structured and documented. If your data does not meet these criteria, you can use them, but the results will not be available. Imagine your doctor prescribed a treatment with knowledge of bad symptoms- you would not get better and it could even have undesirable side effects!

Beyond a prerequisite, quality control is a continuous task to perpetuate the value of your database, which is enhanced every day with new data generated automatically or manually.

 

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Boost your data security

Another requirement is that you must heighten your data security. You handle confidential information about you and your customers, including payment information. It is your responsibility to ensure that this information is not available to anyone.

While companies have long chosen to keep their data warm at home, the norm today is the use of the cloud. Today, cloud players allow you to put your entire database on a remote server (at Vekia, we chose Microsoft Azure). In addition to the security provided by an actor like Microsoft, this process frees you from server maintenance constraints, instability risks, and allows your data to be accessible by your various central offices around the world. In addition, the cloud offers a geo-redundancy service that allows data to be duplicated in several centres around the world to protect against technical or natural incidents.

 

Brace for change

The last prerequisite is not operational, but your mindset. Starting a data valuation project thinking that all you need is a budget to invest would be idyllic. Your teams’ jobs will evolve, leaving behind them tasks that have become technologically obsolete (such as filling excel files on hundreds of lines) to replace them with more automation, supervision, analysis and decision-making.
These changes only happen if you are prepared for them and if you have prepared your teams for them, who will see their daily lives change.

 

Which data should you collect?

The basic data of your Supply Chain

As a Supply Chain expert, it is essential for you to collect certain standard indicators of your Supply Chain. At the same time, having a quality data history is a substantial plus that can accelerate the ROI of your project.

You must at least have access to article (volume, brand, price, lifespan, etc.) and supplier repositories (name, purchase conditions, types of discounts negotiated, cadencier, catalogue, etc.), information from your logistics network (warehouses, hubs, stores/agencies, etc.), your movements (sales, stocks, orders, receipts, inventories) and your customers (contracts, loyalty, etc.).

Depending on your sector, other information will be added or replaced. If you manage field services for example, you will need to have the data of your vans (location, distance travelled, maintenance costs, etc.). Beyond the basic data mentioned above, it is up to you, therefore, to find out which data is specifically relevant for your company.

 

The data you already have

This step is in line with the first prerequisite we mentioned. To know what data your organization already has, you will need a data project manager. Its role will be simple: it will have to be responsible for collecting and making available to employees all the organization’s data.

To do this, it will create a used data inventory based on what state they are in (see the quality prerequisite), by means of a mapping of the data held by the various services for example.

 

Exogenous data goes further

In the introduction, we mentioned the possibility of using, internal, external or exogenous data. By observing the environment in which you operate and detecting synergies with your business, you will be able to better predict a movement in demand, or better identify opportunities that are available to you. For example, for a personal service company, an aging population in a defined area may represent an opportunity. The development of an epidemic will lead to a jump in the urgent demand, and therefore in the human or material resources to be mobilized. In the case of a textile manufacturer, we can estimate the impact that the virality of a part on social networks will have on the production and supply to be expected.

To collect this data, you can simply subscribe to services that provide you with the desired information. At the same time, INSEE data is easy to integrate and sometimes free. You will be able to implement school holidays, weather, economic or demographic data directly in your tools.

 

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Build a quality database

Analyze problematic data sets

As we have seen, it is likely that your database is punctuated by errors. Most often, these are problems that can be classified into two categories:

Unrealistic data: You may be confronted with data that simply cannot reflect reality. A negative stock or sales higher than the mobilized stock are obvious examples of a lack of realism in the data. Information of this type clearly indicates a lack of quality and becomes unusable.

Unstructured data: In addition, your data may be accurate, but not properly linked to other data. For example, the system does not know which supplier to order a given part from. This prevents you from analyzing certain synergies in your Supply Chain and hinders the automation of certain operations (here, we will not be able to automatically replenish the part because the tool will not know where to order it).

Data analysis work will make it possible to identify errors like these.

 

Data cleaning

Once you have detected a problem, you will first have to locate the cause. Does the error occur when sharing the data? About his treatment? Or directly to the collection?
At Vekia, data-scientists have observed that a data problem often comes from a stage of human manipulation. In this case, you can set up precise and detailed processes that will guide the participants and limit errors.

The objective of data cleaning is to emerge with reliable data that accurately reflects reality and is as detailed as possible to avoid structural problems. Unfortunately, some errors are still difficult or even impossible to detect today because they do not seem unrealistic. We cannot be fully certain that a base is totally healthy and we are all faced with a margin of error.

 

Generate performance indicators

Collaborate with data experts

Beyond being able to generate KPIs, you need to be able to interpret them! This is why you will have to establish a collaboration between data experts, and Supply Chain experts (you). The Data team will provide you with KPIs but may not be able to translate them into concrete facts. Your role will be to understand what these indicators of your Supply Chain performance show.

 

Refresh your KPI analysis tools

You are well placed to know that the efficiency of your Supply Chain is defined by many different factors. To be able to monitor your organization’s performance, you will need analysis tools that allow you to explore your data and indicators via a specially designed interface. The real added value of your data lies in the way you can visualize it and interact with it.

The more powerful the tool you use, the more you will be able to inspect your data in depth, make decisions based on proven facts, and monitor the impact of your actions.

 

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Diagnose your Supply Chain

Pinpoint cause-and-effect relationships

That’s it, the time has come… You can finally use your data! Here, your Supply Chain expert cap is your best ally. Thanks to your data work and your analysis tool, your Supply Chain has gone from an immaterial and frightening colossus to a playground that you can explore from top to bottom to discover its secrets.

You are in the front row to observe your Supply Chain, whether at the macro or micro level. Above all, you can more easily investigate the causalities in your Supply Chain system. Is the non-sale of a product due to a lack of demand? To a breakup? Is there incorrect information in your database from you or your supplier? You can explore these different paths quickly.

Recently, we were able to meet a similar case with our client Engie Home Services. By working together on the data, we detected too much stock coverage on some parts, which represented a cash flow mobilized for items that are unlikely to sell for several weeks. We were then able to work on reducing stock coverage on these references, while increasing their availability rate.

Without data analysis, we would have missed this crucial improvement in the inventory optimization process.

 

Conclusion

The aim of this forum was to have provided answers to your questions, and to allow you to approach your project more calmly.

With the variety of collection sources and the growth of the IOT, the data will only become more relevant and volumetric in the coming years. There is no doubt that suppliers and other Supply Chain functions will be assigned tasks previously reserved for data scientists.

Among the companies we meet that wish to integrate artificial intelligence into their Supply Chain Management, many aren’t  ready from a data point of view. We are convinced that this is a turn to be taken as soon as possible to limit the delay against those who have already passed it.

Using good data correctly has become a prerequisite for the efficiency of your Supply Chain, you possess the tools to be an active part of this change and no longer simply react.