EXECUTIVE SUMMARY

Dynamics partners have a huge opportunity to reinvent supply chain scenarios and deliver a data-driven application that uses machine learning, optimization solvers and other advanced analytics to embed intelligence and surround the traditional rule-based automated business process enabled by Dynamics. They could do this by dynamically predicting supply chain input. Instead of asking a supply chain planner to enter the ‘temperature’ of a healthy supply chain and then assume that the supply chain runs on that ‘temperature’ all the time, new solutions and services should derive insights and predictions from ambient data, predict what ‘temperature’ is likely to be and provide alternate execution paths to the supply chain planner. An application like this that embeds Cortana analytics suite components into Dynamics business process and is delivered on Azure platform can be built using existing technology - will provide the ecosystem players with most bang for the buck, short time-to-market, will appeal to existing Dynamics customers, will showcase power of Microsoft platform and will provide unmatched differentiation.

SUPPLY CHAINS 2020

Just like service sector is abuzz with talk of how Big data has potential to revolutionize various industries like financial services, healthcare, tourism, similarly in the supply chain field there is no dearth of “experts” claiming how Big data and related technologies will make and break business models. Just like in late 1990s there was a big wave of investments in e-commerce, today coupled with Big data, digital commerce trends are shaping the thinking of supply chain executives who want to build a supply chain that an listen and learn and sense and shape demand. If “every company is a software company” is true, there is truth in “every trader wants to be an Amazon” and “every manufacturer wants to sell an experience and not just a product”. Just like in 1990s financial services firms particularly traders and brokers moved to a high speed digital system reliant on advanced analytics delivered in the back office by data scientists, today supply chain executives are looking to use similar technologies. Supply chain planning will become much more automated and will be dependent on scientific models rather than being based on manual transaction processing systems. Just like in late 1990s supply chain executives in large global firms bought every software that came with a promise of improving supply chain, today again there is an appetite. However, there are key differences and those who understand the differences can capitalize on this opportunity:

1. Supply chain executives want solutions that help their supply chains become resilient and/or agile, not just reliable. And in some cases for supply chain leaders they are looking for demand-driven approaches.
2. Larger companies are still digesting all the software from 1990s, real appetite is with SMB companies who are looking towards cloud computing vendors to provide them such services at costs they can afford.
3. SMB executives want point solutions so they can quickly try them and they also want end-to-end solutions in the cloud
4. SMB executives are more happy to try such solutions in a subscription model
5. CIO maybe a Chief Integration Officer in larger enterprises but SMB segment is mindful of integration cost and measured about what IT or LOB executives buy

INTELLIGENT BUSINESS PROCESS

It is true that cloud offers more choice to customers to switch between vendors, however it is also true that this new found convenience will become the norm and IT will find the task of moving to another cloud as onerous as in on premise world. Microsoft provides one of the best and easiest to use full lifecycle data management tools in Azure and some will consider this enough incentive for customers to stick to Azure. However, economics will force large platform vendors to move beyond tools. Just like with the advent of power utilities Edison Electricity Company moved to become GE to provide not just electricity grid platforms and components but also industrial and household appliances, Microsoft together with its partners will reinvent and provide higher value adding applications on its’ data platform. More data-driven applications will get more data which will spur demand for more applications. With MS strength in the enterprise, and GTM geared towards enterprise buyers’, business applications are a natural area to showcase such advanced analytics applications. Within business applications - sales, marketing and supply chain applications are hottest from a VC investment point of view and Microsoft Dynamics applications provide the necessary beachhead in the growing SMB market – and therefore this is the time for Dynamics partners to lead the way.

Microsoft partners should reinvent scenarios in their vertical and deliver a data-driven e2e application that uses machine learning, optimization solvers and other advanced analytics to embed intelligence and surround the traditional rule-based automated business process enabled by Dynamics.

Supply chain
specific Discussion

Let’s look a level deeper by considering an example of what could be done. Economic order quantity (EOQ) is the number of widgets you order so ordering costs and inventory holing costs are in balance. We should ask ourselves how someone can really know EOQ when purchasing costs are so hard to nail-down and inventory carrying costs vary depending on geography, distribution center, maintenance, insurance, taxation and change with time. Could it be that EOQ is just one of those highly-averaged broad-brush measures that was good enough to give a scientific “feel” to punch-card era accountants of 60s and 70s? Isn’t it obvious that measures like these were invented by statisticians for accountants who both lived in some data dearth decade? We should ask ourselves – is an individual click on internet really more value-adding than an average product stored in a warehouse. My opinion is perhaps not. Then why is it that it is possible to tag, track & monitor clickstream data and use it to offer a very personalized advertisement for each one of us say, valued for hundredth of a penny but we are happy to use one gross overall EOQ average and keep ordering same quantity of product for years to come with complete disregard to actual demand at the time, cost of product being ordered etc. and destroy millions of dollars of value in the process? Is that because of some grand plan somewhere or is it simply because data science hasn’t reached the supply chain domain yet? No one really knows EOQ but it has been the centrepiece of inventory management ever since such solutions were first implemented in 70s. Millions of products are ordered, stocked, sold based on such unscientific static measures and millions of dollars of value is lost in the process. Many best in class companies know this and are reinventing such systems like Amazon.

Same applies to reorder level. Every company who uses reorder levels sets them statically once for a fairly long time. If they plan to revise this number they will probably look at ABC classification and do so for select high-value fast-moving A class items. They would never get to do more than that and will never get to long-tail products. It’s not an exaggeration to say that the mathematical ingenuity of the whole supply chain discipline is contained within the bounds of Pareto principle! Most software solutions will only allow possibility to set one value, some more recent solutions do dynamic calculation of this metric. Safety stock as the name applies is used to compensate for forecast error. Again most traditional vendors offer a possibility to setup static value per product-location. Same thing can be said about minimum quantities, maximum quantities and standard order quantities in both distribution and production.

Above are just examples. We can ask same questions for every metric, every process. Every time we make a decision based on space-time bound metric we can ask - will capturing more data make the metric more fluid in space-time, more continuous instead of discrete? Will it improve quality of decisions? Is airline pricing more fluid than concert tickets than the price of that polo you want to buy? Probably, Yes. Does a hotel reservation system know its’ inventory (hence availability) at any time? Probably yes, but does a retailer know? How can we reduce data latency between two nodes in a supply chain?

Delivering point solutions like say predicting reorder level of inventory or predicting willingness to pay for retailers or distributors of consumer goods or simply, showing customer segmentation could be one of the options to show the potential hidden in troves of data. Another option could be to build vertical specific apps focused in a particular domain/problems space that combine many such prediction and optimization services together in a chain of decisions which could answer higher level questions like “which is the best distribution center to service an incoming customer order from?” or “what is the best price for a product given customer and channel”, “how best to price a service contract” or “which are most valuable customers”? Cortana Analytics suite has some apps that illustrate this point. Recommendation service is in instance where the cross-sell and up-sell problem has been rethought and a traditional rule-based system has been upended. Similarly, Dynamics partners could reinvent several other sales, marketing and supply chain problems.

However, it’s important to realize that while advanced analytics provides the most needed deep insight, the number of users it touches is fairly limited in most scenarios. Customers are willing to pay top dollar if such insights are integrated into a business process and create a sort of decision management system – that reduces variability and improves reliability and repeatability of the process that most of the organization is engaged in. In the supply chains of future all benefits will be directly related to the speed of flow of materials and information. Therefore, to derive greatest long-term value and stickiness Dynamics partners should integrate insights deep into a business process so a business user like a (1) customer service rep can automatically get the suggested price from Cortana analytics suite while she is in the process of creating a sales order for a customer order within the context of order management process or (2) so that the system automatically choses the right distribution center to ship from while consumer entered order is being logged into Dynamics.  

Since most companies even in SMB and mid-market space have multiple business solutions, to provide customer with maximum value at the outset, Dynamics partners could use Azure data catalogue (part of Cortana analytics suite) to publish the result set of such advanced analytics apps into the company data catalogue and provide custom visualizations in Power BI so that the data latency is kept to minimum and business can quickly utilize the results where they want and IT can quickly build the required integrations to in-house or other 3^rdparty solutions which may lie outside of Microsoft ecosystem.