In my new series of posts I will outline typical business problems from manufacturing companies that originate in uncertainty and explain how Analytics is an enabler for improved supply chain transparency, improved stability and – at the end of the day – improved profitability.
The main objective for manufacturing companies is to ensure that the value chain runs as smoothly and profitable as possible. The core of their business is to transform raw-materials into a product and have this product made available to fulfil a customer demand through a series of supply chain processes (production, assembly, transportation etc.).
Nonetheless, words like digitalization, IoT, big data are becoming a household in manufacturing companies, and focus is growing when it comes to utilizing data in a whole new way. The reason is not that traditional SCM theory is no longer valid, but that new technology can enhance supply chain efficiency by removing uncertainties.
The role of uncertainty
According to a Garner Survey from 2014 the main obstacles for supply chains achieving goals can be tracked back to dealing with uncertainty – e.g. demand volatility, inability to synchronize processes end-to-end, lack of collaboration, lack of visibility, complexity and cost control.
There are several ways to define uncertainty. I like two angles on uncertainty (which are not that different):
- Supply chain uncertainty refers to the decision making process in the supply chain in which the decision maker does not know definitely what to decide due to lack of transparency into the supply chain and the impact of possible actions.
- Supply chain uncertainty refers to disruption of the balance and profitability of the supply chain caused by potential and non-predictable events, which requires a response to re-establish the balance. Examples of events: a large unexpected demand, late delivery from a supplier or breakdown of a critical production equipment.
Uncertainties can be classified into three groups as upstream (supply) uncertainty, internal (process) uncertainty, and downstream (demand) uncertainty. Typical areas of uncertainty are listed in the table below.
|Upstream (Supply)||Internal (Process)||Downstream (Demand)|
Uncertainties add cost
Operational approaches for dealing with uncertainty are typically adding time and material into the manufacturing system to satisfy the customer. The more uncertainty – the bigger the surplus is needed to satisfy the customer.
The company typically tries to create a supply chain balance where inventory, transportation and supply chain / capacity costs are minimized still meeting the requested customer service level (lead-time and precision).
In general, making improvements in one of these four areas will have an impact on one or more of the other – i.e. if you lower inventory cost, you will probably lower customer service level. If you try to keep the customer service level the same – transportation or supply chain cost will increase due to more a need for higher flexibility in these areas.
Traditionally the supply chain balance is decided by gut feeling (based on years of experience) combined with some calculations on available data. Over the last years – manufacturing companies have realized that using data can take their ability to deal with uncertainty to a higher level. One way companies have started using data is in applying time series forecasting algorithms to historical sales data to provide a view of future demand. Another is using statistical methods to analyze product quality to simplify quality inspection (and reduce cost for inspection).
Many initiatives have been made to improve the balance.
- Implementing an S&OP process deals with removing uncertainty about future demand – and align the plans to a forecast.
- Preventive maintenance deals with regularly checking and repairing production equipment to avoid breakdowns.
- Postponement strategies reduce uncertainty on finished goods variants.
- Companies implement Quality management to avoid surprises due to variance in quality.
- Supplier management programs are implemented to make sure that the supply of materials used in production meets plans made.
Initiatives like these and many others have been successful in reducing uncertainty – and thus the cost related to maintaining the supply chain balance. It is however clear that the level of uncertainty in the supply chain is still increasing.
Data and Analytics will take these initiatives a step further
I admit it up front… the definition of analytics is copied from the homepage of SAS Institute – but only because I believe it is short, sweet and to the point… and much better than any of my own attempts:
Analytics is an encompassing and multidimensional field that uses mathematics, statistics, predictive modeling and machine-learning techniques to find meaningful patterns and knowledge in recorded data.
Traditional BI (reporting) only answers the question:
By applying analytics to the data that a company generates – throughout the organisation – patterns and correlations can be found which will lead answers to:
- How or why did it happen?
- What’s happening now?
- What is likely to happen next?
- What is the best I can do now?
Analytics is thus an opportunity to find a way to gain insight into what was once unknown. It can reveal the likelihood for certain events to happen – as well as guide toward the right decisions to how to solve a business question.
In the second article in this series, I will investigate how Analytics can help lower safety stock levels – just by visualising inventory data in the right way.
You can read more about SAS analytics in Manufacturing industry.