Inventory Optimisation: Reducing waste, Improving availability

06/01/20 • 30 min

How do big grocery retailers maintain product availability for their customers day after day while minimising food wastage and storage costs? The answer is Inventory Optimisation, the science of maintaining sufficient stock levels of a set of products so that customers see an appropriate level of availability when they walk into your store.

The trade-off
It’s hard because it often costs money to maintain a large inventory of products, because of space that is given over to bulky stock as the cost of buying a large amount of potentially expensive items without getting a return on that investment until sale. A long lead time from stocking to use or sale means no value is extracted from those items and that can have cashflow implications for a company while obviously minimising risk of so-called stockout.

Wastage
A significant further complication comes from food and grocery retail where the items being stocked are themselves perishable with varying expiry dates. Further significant costs are incurred if the product expires while still being stocked. This leads to huge food waste problems around the world which in turn have a significant carbon and environmental impact on the direct supply chain for the retailer.

With interview guest Dr. Anna Lena-Sachs, Lecturer in Predictive Analytics at the Department of Management Sciences, Lancaster University.
https://www.lancaster.ac.uk/lums/people/anna-lena-sachs
Further Reading

Anna-Lena Sach's list of papers (via University of Lancaster)
Book: Retail Analytics: Integrated Forecasting and Inventory Management for Perishable Products in Retailing (contents via Springer)
Paper: The data-driven newsvendor with censored demand observations (summary via ScienceDirect)
Paper: A stochastic model for joint spare parts inventory and planned maintenance optimisation (summary via ScienceDirect)

Some links above may require payment or login. We are not endorsing them or receiving any payment for mentioning them. They are provided as is. Often free versions of papers are available and we would encourage you to investigate.

Recording date: 29 Jan. 2020
Interview date: 24 Jan. 2020

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Anna-Lena Sach's list of papers (via University of Lancaster)
Book: Retail Analytics: Integrated Forecasting and Inventory Management for Perishable Products in Retailing (contents via Springer)
Paper: The data-driven newsvendor with censored demand observations (summary via ScienceDirect)
Paper: A stochastic model for joint spare parts inventory and planned maintenance optimisation (summary via ScienceDirect)

Recording date: 29 Jan. 2020
Interview date: 24 Jan. 2020

Send us a text

Next Episode

Changepoint Detection: Secret Weapon of the Data Scientist

How can we spot a change in a jet engine vibration that might mean it’s about to fail catastrophically? How can a service forecast adapt to unexpected changes brought about by a pandemic? How might we spot an increase in rate of change of pollution in the atmosphere? The answer to all these questions is changepoints, or rather changepoint detection.

Common to all these systems is a set of ordered data, usually a time series of observations or measurements that may be noisy but have some underlying pattern. As the world changes, so those changes might lead to dramatic changes in the measurements and a disruption of the usual pattern. Unless these forecasts or failure-detection systems are updated quickly to take account of a change in measurement data, they will likely produce erroneous or unpredictable results.

Changepoints have many important applications in areas such as:

Climatology
Genetic sequencing
Finance
Medical imaging
Forecasting in industry

We speak to statistician Dr. Rebecca Killick from Lancaster University about her work in changepoint detection and how it is a critical part of the statistical toolkit for analysing time series and other ordered data sets. In particular:

In forecasting where most methods tend to work on the basis of extrapolating trends, it is essential to know if a changepoint has occurred so that a refreshed model calculation can be started.
If there is a change in the underlying dynamics of a system that causes a complex change in the observed output then this can often be detected with a changepoint. This might be indicative of a mechanical failure or impending change in operation or an unobserved event buried deep in a difficult-to-measure environment, like a nuclear reactor.

With interview guest Dr. Rebecca Killick, Associate Professor of Statistics at Lancaster University.

Further reading