The essential guide to supply chain management best practices
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The modern supply chain is the pacesetter for B2B analytics, a proving ground for new ideas and innovative technologies....
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The global success of B2B-integrated processes and the practice of sharing data freely among supply chain partners have been key factors in the advent of just-in-time manufacturing and delivery on demand.
Even so, shared data for analytics that optimizes manufacturing and delivery isn't always optimized as completely and effectively as it might be. The B2B-technology revolution is frequently as much a story of missed opportunity as it is a story of prodigious returns.
Where do those missed opportunities occur, and why aren't they obvious? Opportunities to share data exist in myriad processes in B2B partnerships, but when those processes are nonadjacent -- for instance, procurement and distribution -- they are easy to overlook. In other words, data that increases efficiency in one part of the supply chain could feed into another area, improving it as well.
Moreover, any one company in a supply chain tends to apply analytics for improvement in those processes to which it contributes. It's less common to integrate results from such a process into other supply chain partners' processes. Here are some scenarios where that can work.
From delivery logistics to capacity planning
Traditionally, manufacturing bases its output projections on patterns in product demand, anticipating the need for expansion or reduction of capacity based on fluctuation in those patterns.
Better insight is possible, however. Manufacturing, basing production planning on conventional ERP, typically relies on internal historical data and external market indicators; but the partners actually handling shipping and delivery have access to local realities, and, in some cases, the customer demand drivers that can more deeply inform production forecasting. Expertise in regional economic shifts such as fuel prices, local economy fluctuations, infrastructure issues and competitors' behaviors tends to reside with those partners doing the shipping and delivery.
Those partners use that available expertise for their own planning -- to prepare for disruptions in delivery, to control costs and to meet quotas and schedules. But the data would be also useful back at the beginning of the chain, where manufacturing can use it to refine its production requirements and anticipate changes with fewer surprises.
Buying lead time by looking at demand drivers
Having a solid grasp of what and when customers are buying and how that changes over time can help better predict shifts in near-term demand. How that analysis is done won't change the result, in terms of sales. However, a different approach can lead to additional lead time on the projections, which may not be actual revenue, but is, nonetheless, very valuable.
Seldom (if ever) do customers in all regional markets and across demographics conform to a single buying pattern. More often, there are many different groupings of customers with different buying habits. Using descriptive analytics to identify these customer groups can give the manufacturing arm of the supply chain more lead time by clarifying how these groups of customers differ and, subsequently, identifying and tracking the drivers that cause each group to buy as it does. In a nutshell, it's possible to have more advance warning when any one customer group will be buying more or less.
Gathering supplemental data to improve outcomes
Projecting business outcomes for any one company is hard enough; projecting outcomes for a complex multipartner supply chain is even more so. One important aspect of that outcome projection is the gathering and analysis of data surrounding the entire operation, beyond the fine-tuning of individual processes.
This supplemental data is external to supply chain partners -- data that describes the marketplace the chain is servicing. It includes marketplace demographics; economic trends and shifts at the local, regional, national and international levels; the activity of competitors; natural barriers such as weather; political barriers such as regional strikes; changes in other countries' governments; social media chatter about both supply chain partners and competitors; and many other factors -- some ecological, some economic, some digital.
By investing in systems that capture this data, adding it to the analytics being undertaken by each supply chain partner and sharing that data liberally, new efficiencies and notification mechanisms can be generated. A strong understanding and tracking of the marketplace within which the supply chain is functioning can improve B2B integration throughout the chain.
Real-world tools from Salesforce, Microsoft, IBM
Customer relationship management might not be the first thing you think of when supply chain analytics is mentioned, but enterprise-level CRM systems are increasingly friendly to analytical processes, capturing a broad range of data that is tailor-made for supply chain process improvement. Salesforce CRM, Microsoft Dynamics and other leading CRM platforms now provide cloud-based analytics that extend beyond their original customer satisfaction and retention functionality, accommodating logistics and forecasting. This may be enough to bridge the gaps described above; however, sometimes a canned analytics solution isn't fine-tuned enough to make a difference, or doesn't offer the flexibility to accommodate unexpected variables. Customization is required.
This doesn't mean coding something from scratch. Salesforce has recently unveiled its Einstein suite, a set of cloud-based analytics features that allow for ad hoc uploading of historical data for descriptive and predictive analysis, accessible to the nonspecialist. This new feature set mimics another similar product, IBM's Watson Analytics, which, likewise, can provide easy and inexpensive options for custom analytics. In particular, these products can take historical data from different process in a supply chain (at opposite ends of the chain, even) and mine them for correlations that reveal cause-and-effect that can be exploited for new efficiencies.
These are just a few examples of getting more than one benefit from an analytical process. Constantly asking the question, "Where can this information have an impact later or earlier in delivery and what technology can help me gather it?" can bring many more to the surface, resulting in an operation that is fine-tuned from front to back.
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