A 5-Minute Guide
to Supply Chain
Analysis for Discrete
Manufacturing
One way of looking at the supply chain is as a necessary evil – something that cost should be driven from. Efficiency is all, so analysis should be about how to lower the costs of sourcing goods, transporting them, holding them in warehouses, pick, pack and ship. The primary purpose of “supply chain
optimization” was overall cost leadership against the competition.1
But in a survey by the Aberdeen Group of 149 supply chain professionals in March and April 2011, the main concerns unearthed appeared to be very different. Indeed the “cost” model of supply chain operations can be assigned
as being of the 20th rather than the 21st century. The term “supply chain”
was only coined some 30 years ago2, and the change in emphasis can be
appreciated by pausing for a brief moment and thinking about manufacturing back in 1982: back then around one-fifth of the US workforce were employed making things; today that percentage has halved. Globalization, omni-channel and demand volatility analysis are today’s major influences in supply chain optimization and, while they all have a cost element, strategy and risks to that strategy are of primary concern.
The Aberdeen report, “Business Intelligence Command and Control Center
for the Chief Supply Chain Officer,”3 looked at the challenges enterprises face
identified the top strategic actions that companies are taking to operate more efficiently through business intelligence.
If we define supply chain management as looking at the movement and storage of raw materials, work-in-process inventory, and finished goods from point of origin to point of consumption then the first thing that has to be emphasized is the word “movement.” Movement is important because there is just a whole lot more of it going on.
Supply Chain - A Moving Experience
As manufacturing has relocated, supply chains have become stretched and this has had a profound change on the role of analytics. “Where” is often the most important question being asked, and it is interesting to note that around 80% of all business data contains reference to a location of some sort.
The configuration of the network of manufacturing facilities, suppliers and distributors is vital not just from a cost perspective. Two other considerations must be taken into account. The first of these could be called “constituencies” – for example, what is the constituency of suppliers that can feed our manufacturing if primary suppliers fail? What is the constituency of retail establishments being fed by our distribution hubs? What is the constituency of alternative transportation infrastructure in the case of physical disruption?
Analytics here becomes much more about risk – business executives want to see their business critical data literally mapped onto a representation of their supply chain. That applies one level of reassurance, but then they need to go beyond this and produce a detailed geospatial analysis of their facilities, suppliers, distribution centers, cross-docks and, ultimately, end-user customers.
Let us take a plausible business question – the road route from one of your manufacturing plants to the distribution point is blocked by heavy rains. How much does that a) add to shipping and fuel costs; b) create orders at risk of late delivery, with perhaps incumbent penalties attached and c) affect likely future orders from affected customers? Further, do we have the visibility into excess capacity at other manufacturing plants to make up on these orders?
Our stretched supply chain has created a series of questions that can only be answered by mashing together data from lots of different sources into a sophisticated model that will predict the outcomes. And this scenario is simple – add into that changing demand patterns, the price of oil, price breaks at 3PL providers not achieved, and we can quickly see that we need a high-horsepower risk calculator.
And, ultimately, we can take a strategic view of this problem. Maybe it’s OK for the road to get blocked 3 or 4 times a year, for a maximum of 6 weeks. If we are looking at “optimizing” of supply chain nodes, then downtime can be built into the model and scenarios properly planned for.
It’s All About Demand
Most large manufacturers are forced to “make-to-inventory” rather than “to order” by sheer economies of scale. However, when we make to inventory there is the financial imperative to reduce cycle times – which can cost the company in several different ways. Predicting demand for products and using that prediction as the driver for production and procurement has become the norm for most companies.
But, again, the 20th century model has started to fall apart at the seams.
Talk today in many industries is about greater demand volatility4 that makes
demand forecasting less and less accurate. Here we can use an analytics approach to not just predict demand accurately but, first, find the root cause of the volatility. When conducting a root cause analysis one of the most essential components is to have all the relevant data sources – these can be then brought together and regression analysis used to find causal effects. Without all the data sources, however, significance cannot be trusted and the volatility we see in markets goes unexplained.
have just one data source – past sales. Their algorithms can take past order data and predict both seasonality and other usage patterns, and even automate the production of works and purchase orders – but if the predictions are not correct, then the attendant event processing is redundant.
In the era of analytics we can mash together all relevant contributors to demand and start to see the true causal relationships. Seasonality may well exist for many types of goods, but should be separated from underlying trends of growth or decline (as below) if consistent predictions are to be made. What if the prediction we are looking at is not seasonality, per se, but weather-related? The two are not the same: a bad summer need not adversely affect the demand forecasted.
Next we may want to look at factors such as market sentiment. Existing often as unstructured data in tweets, blog posts and the like, we now have the ability to bring this into the structured world to see if the growth of sentiment expressed on these forms (often exponential in nature) is deepening our demand troughs and heightening our peaks.
Lastly, we mentioned the weather – these types of external data sources are now easily combined into an analytics solution and can often be seen graphically alongside our corporate data as well as being component parts of a statistical model. Weather, demographics, commercial retail data and a host of other sources may help us find what is truly driving demand for our products.
As a footnote to this short section on demand forecasting, we should state that this is just the start toward an enterprise analytics platform that will help drive our
corporate KPI’s. Having this foundation will allow us to build out into areas such as: • The true effect of marketing campaigns and price promotions
• The effects of competitor actions such as price promotions, new product releases etc.
• The effect of replacement products (of increasing importance as product lifecycles shorten)
• Line extension cannibalization of demand
• Changes to product specification, naming, retail outlet location, packaging etc.
All of these effects can now be seen with much greater clarity once the demand prediction engine is honed.
Omni-Channel
The 20th century was about building products to deliver, as efficiently as possible,
to a store or another business. And that’s pretty much all you had to do. Once the goods got to the destination (usually through a distributor or your own hub) then the receiver did the rest. They either placed the product in a retail establishment, or, in the case of B2B your reseller added value then. The concept of “channel” was confined to VAR’s, distributors, retailers and OEM.
In the 21st century consumers routinely investigate and select products in
non-store environments, even if they eventually purchase in a non-store. Similarly, in B2B there is a huge amount of online product information that makes distribution an increasingly low-margin business: value added activities are exposed to massively open competition.
The challenge for the supply chain executive is building an efficient structure for this new paradigm: one where the ultimate consumer doesn’t care about the carefully constructed physical channel with all of its price and margin rules and other demarcations.
Some manufacturers have tried to separate their online businesses from bricks and mortar, but price transparency, the lack of a seamless customer experience and the need to integrate order and inventory management for competitiveness has largely blunted these attempts to bridle the newly-powerful consumer.
So instead the 21st century supply chain will enable anyone to buy from any store
of inventory, anywhere and anytime. In this scenario the value-add activities re-focus around fulfillment and shipping: making and keeping firm promises; tight integration with 3PL; consolidating multiple orders; and predicting how late supplier replenishment shipments may jeopardize order promises you have made. Lastly, the omni-channel world creates a greater feedback channel from the ultimate consumer to the manufacturer – one that poses the threat of adverse sentiment around a product, service or delivery issue, though can also help
manufacturers understand the consumer as never before.5 If not analyzed and
managed these issues can become problematic for large brands and those selling highly diversified products when consumers actively broadcast dissatisfaction or nonconforming product features: the effect on future sales will also need to be predicted.
More than this, however, the steps taken to mitigate low customer satisfaction become key drivers in the supply chain. Corrective action requires being able to anticipate missing ASN’s, identify bottlenecks in the fulfillment process and opportunities to utilize slack resources.
Summary
Across the areas of location intelligence, demand planning and omni-channel
management the supply chain of the 21st century is far more about organizational
efficiency to minimize risk (lost customers, geo-political and competitive pressures) than it is about taking an existing chain and “rationalizing” it on the basis of cost alone.
As such we need to bring far wider and diverse data sets to bear on our analysis and this analysis needs to be more rigorous in its scope and predictive ability. The sheer complexity of today’s supply chains, stretched across the globe and subject to high demand variability means that finding the trends to follow cannot be a ‘yearly planning’ or ‘quarterly reporting’ task. Analytics needs to happen all of the
TIBCO Spotfire North America: +1-866-240-0491
1 Michael Porter, Competitive Strategy: techniques for analyzing Industries and Competitors, New York, The Free Press, 1980.
2 Supply chain optimization theories though were being built in the 1970s by McKinsey & Co.
3 Aberdeen Group, Business Intelligence Command and Control Center for the Chief Supply Chain Officer, 2011
4 Industry Week, Responding to Demand Volatility, http://www.industryweek.com articles/responding_to_demand_volatility_15902.aspx
5 Anderson, JC, Relationships in Business Markets: Exchange Episodes, Value Creation, and their Empirical Assessment, Journal of the Academy of Marketing Science 23, 1995, p349
time, with every action and event in the business (and outside the business) captured and assimilated into an ever-improving analytic model of the supply chain.
