Supply chain and analytics: a magic formula if you share the data

The modern supply chain is one of the starting points of analytics in companies. Integrated processes and data sharing between supply chain partners have been key in manufacturing and on-demand delivery.

However, shared data that needs to optimize processes is not always optimized. The B2B technological revolution is often as much a story of missed opportunities as a story of successful feedback.

Exactly: when are we talking about missed opportunities and why is it not obvious? There are many possibilities for data sharing because there is a myriad of processes in B2B partnerships. But when these processes are not adjacent – for example, supply and distribution – they are easy to miss. In other words, data that increases the efficiency of one part of the supply chain could feed another part.

Here are some scenarios where this can work.

From logistics to capacity planning

Traditionally, the manufacturing sector has based its production projections on demand trends. It anticipates the need to increase or reduce capacity according to the fluctuation of these trends.

It is possible, however, to have a better overview. By basing production planning on conventional ERP , manufacturing is usually based on internal historical data and external market indicators. However, shipping and delivery partners are aware of local realities and, in some cases, what defines customer demand – and who can better anticipate production needs.

The expertise in these regional criteria (fuel prices, fluctuations in the local economy, infrastructure problems) and in the behavior of the competitors resides rather with the partners involved in shipping and delivery. .

These partners themselves use this knowledge for their own planning – to prepare for disruptions in delivery, to control costs and to meet quotas and schedules. But this data would also be very useful at the beginning of the chain. The company in charge of manufacturing could also use it to refine its needs and anticipate changes.

Give yourself time by examining the key factors of demand

Having a good understanding of what customers are buying, when, where, and how they are buying can help predict how demand is going in the short term. The way this analysis is done will not change the outcome, in terms of sales.

It is rare that customers, regardless of market and demographic category, conform to a single purchasing model. Most often, there are different groups of customers with different buying habits. Using descriptive analysis to identify these groups can give more time to the manufacturing part of the supply chain by highlighting each difference. Subsequently, this makes it possible to identify and follow the indicators that determine the purchasing acts for each group. In summary, it is possible to predict the behavior of a group of customers further upstream.

Collect more data to improve results

It is difficult enough to predict the economic results of a business; it is even more difficult to predict the results of a multi-partner supply chain. In this, the collection and analysis of data surrounding the entire operation is an important point.

This additional data is external to the supply chain partners and describes the covered market. For example, they relate to the demography of the market; economic trends and changes at local, regional, national and international levels; the activity of the competitors; natural obstacles such as weather conditions; political brakes such as regional strikes; changes in the governments of other countries; social media discussions about supply chain partners and competitors; and many other factors – some ecological, some economic, some digital.

By investing in the collection of these data, adding them to the analyzes carried out by each partner and sharing them, new notification mechanisms can be put in place.

Integrate with Salesforce, Microsoft, IBM

Customer Relationship Management (CRM) may not be the first thing that comes to mind when it comes to the supply chain. But CRM systems are increasingly open to external data, able to improve the supply chain.

Salesforce CRM, Microsoft Dynamics, and other platforms deliver cloud analytics that go beyond the satisfaction and retention of original customers. They also take into account logistics. This may be sufficient to fill the gaps described above, however customization is often necessary.

It’s not about coding a feature from scratch. For example, Salesforce provides Einstein, a set of analytic functions that can be used to load historical data for descriptive and predictive analysis.

The same goes for IBM’s Watson Analytics, which offer custom analytics capabilities.

These tools can integrate historical data from different processes in a supply chain to establish correlations between events.

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